I like using Catalyst Cloud to host some of my personal sites. In the past I used to use CAcert for my TLS certificates, but more recently I've been using Let's Encrypt for my TLS certificates as they're trusted in all browsers. Currently the LoadBalancer as a Service (LBaaS) in Catalyst Cloud doesn't have built in support for Let's Encrypt. I could use an apache2/nginx proxy and handle the TLS termination there and have that manage the Let's Encrypt lifecycle, but really, I'd rather use LBaaS.

So I thought I'd set about working out how to get Dehydrated (the Let's Encrypt client I've been using) to drive LBaaS (known as Octavia). I figured this would be of interest to other people using Octavia with OpenStack in general, not just Catalyst Cloud.

There's a few things you need to do. These instructions are specific to Debian:

1. Install and configure Dehydrated to create the certificates for the domain(s) you want.
   • apt install barbican
2. Create the LoadBalancer (use the API, ClickOps, whatever), just forward port 80 for now (see sample Apache configs below).
3. Save the sample hook.sh below to /etc/dehydrated/hook.sh, you'll probably need to customise it, mine is a bit more complicated!
4. Insert the UUID of your LoadBalancer in hook.sh where LB_LISTENER is set.
5. Create /etc/dehydrated/catalystcloud/password as described in hook.sh
6. Save OpenRC file from the Catalyst Cloud dashboard as /etc/dehydrated/catalystcloud/openrc.sh
7. Install jq, openssl and the openstack tools, on Debian this is:
   • apt install jq openssl python3-openstackclient python3-barbicanclient python3-octaviaclient
8. Add TLS termination to your LoadBalancer
9. You should be able to rename the latest certs /var/lib/dehydrated/certs/$DOMAIN and then run dehydrated -c to have it reissue and then deploy a cert.

As we're using HTTP-01 Challenge Type here, you need to have the LoadBalancer forwarding port 80 to your website to allow for the challenge response. It is good practice to have a redirect to HTTPS, here's an example virtual host for Apache:

<VirtualHost *:80>
    ServerName www.example.com
    ServerAlias example.com

    RewriteEngine On
    RewriteRule ^/.well-known/ - [L]
    RewriteRule ^/(.*)$ https://www.example.com/$1 [R=301,L]

    <Location />
        Require all granted
    </Location>
</VirtualHost>

Alias /.well-known/acme-challenge /var/lib/dehydrated/acme-challenges

<Directory /var/lib/dehydrated/acme-challenges>
        Options None
        AllowOverride None

        # Apache 2.x
        <IfModule !mod_authz_core.c>
                Order allow,deny
                Allow from all
        </IfModule>

        # Apache 2.4
        <IfModule mod_authz_core.c>
                Require all granted
        </IfModule>
</Directory>

And that should be all that you need to do. Now, when Dehydrated updates your certificate, it should update your LoadBalancer as well!

deploy_cert() {
    local DOMAIN="${1}" KEYFILE="${2}" CERTFILE="${3}" FULLCHAINFILE="${4}" \
          CHAINFILE="${5}" TIMESTAMP="${6}"
    shift 6

    # File contents should be:
    #   export OS_PASSWORD='your password in here'
    . /etc/dehydrated/catalystcloud/password

    # OpenRC file from the Catalyst Cloud dashboard
    . /etc/dehydrated/catalystcloud/openrc.sh --no-token

    # UUID of the LoadBalancer to be managed
    LB_LISTENER='xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx'

    # Barbican uses P12 files, we need to make one.
    P12=$(readlink -f $KEYFILE \
        | sed -E 's/privkey-([0-9]+)\.pem/barbican-\1.p12/')
    openssl pkcs12 -export -inkey $KEYFILE -in $CERTFILE -certfile \
        $FULLCHAINFILE -passout pass: -out $P12

    # Keep track of existing certs for this domain (hopefully no more than 100)
    EXISTING_URIS=$(openstack secret list --limit 100 \
        -c Name -c 'Secret href' -f json \
        | jq -r ".[]|select(.Name | startswith(\"$DOMAIN\"))|.\"Secret href\"")

    # Upload the new cert
    NOW=$(date +"%s")
    openstack secret store --name $DOMAIN-$TIMESTAMP-$NOW -e base64 \
        -t "application/octet-stream" --payload="$(base64 < $P12)"

    NEW_URI=$(openstack secret list --name $DOMAIN-$TIMESTAMP-$NOW \
        -c 'Secret href' -f value) \
        || unset NEW_URI

    # Change LoadBalancer to use new cert - if the old one was the default,
    # change the default. If the old one was in the SNI list, update the
    # SNI list.
    if [ -n "$EXISTING_URIS" ]; then
        DEFAULT_CONTAINER=$(openstack loadbalancer listener show $LB_LISTENER \
            -c default_tls_container_ref -f value)

        for URI in $EXISTING_URIS; do
            if [ "x$URI" = "x$DEFAULT_CONTAINER" ]; then
                openstack loadbalancer listener set $LB_LISTENER \
                    --default-tls-container-ref $NEW_URI
            fi
        done

        SNI_CONTAINERS=$(openstack loadbalancer listener show $LB_LISTENER \
            -c sni_container_refs -f value | sed "s/'//g" | sed 's/^\[//' \
            | sed 's/\]$//' | sed "s/,//g")

        for URI in $EXISTING_URIS; do
            if echo $SNI_CONTAINERS | grep -q $URI; then
                SNI_CONTAINERS=$(echo $SNI_CONTAINERS | sed "s,$URI,$NEW_URI,")
                openstack loadbalancer listener set $LB_LISTENER \
                    --sni-container-refs $SNI_CONTAINERS
            fi
        done

        # Remove old certs
        for URI in $EXISTING_URIS; do
            openstack secret delete $URI
        done
    fi
}

HANDLER="$1"; shift
#if [[ "${HANDLER}" =~ ^(deploy_challenge|clean_challenge|sync_cert|deploy_cert|deploy_ocsp|unchanged_cert|invalid_challenge|request_failure|generate_csr|startup_hook|exit_hook)$ ]]; then
if [[ "${HANDLER}" =~ ^(deploy_cert)$ ]]; then
    "$HANDLER" "$@"
fi

Hello there, friends! This is going to be a short update from me because I’m deep in the throes of Hanami 2.0 release preparation right now. Even still, I didn’t want to let September pass without an update, so let’s take a look.

A story about Hanami::Action memory usage

September started and ended with me looking at the r10k memory usage charts for hanami-controller versus Rails. The results were surprising!

We’d been running some of these checks as part of our 2.0 release prep, the idea being that it’d help us shake out any obvious performance improvements we’d need to make. And it certainly did in this case! Hanami (just like its dry-rb underpinnings) is meant to be the smaller and lighter framework; why were we being outperformced by Rails?

To address this I wrote a simple memory profile script for Hanami::Action inheritance (now checked in here) and started digging.

Here were there initial results:

Total allocated: 184912288 bytes (1360036 objects)
Total retained:  104910880 bytes (780031 objects)

allocated memory by gem
-----------------------------------
  56242240  concurrent-ruby-1.1.10
  53282480  dry-configurable-0.15.0
  34120000  utils-8585be837309
  30547488  other
  10720080  controller/lib

That’s 185MB allocated for 10k subclasses, with concurrent-ruby, dry-configurable and hanami-utils being the top three gems allocating memory.

This led me straight to dry-configurable, and after a couple of weeks of work, I arrived at this PR, separating our storage of setting definitions from their configured values, among other things. This change allows us to copy less data at the moment of class inheritance, and in the case of a dry-configurable-focused memory profile, cut the allocated memory by more than half.

From there, I moved back into hanami-controller and updated it to use dry-configurable for all of its inheritable attributes (some were handled separately), also taking advantage the support for custom config classes that Piotr added so we could preserve Hanami::Action’s existing configuration API.

This considerably improved our benchmark! Behold:

Total allocated: 32766232 bytes (90004 objects)
Total retained:  32766232 bytes (90004 objects)

allocated memory by gem
-----------------------------------
  21486072  other
  10880120  dry-configurable-0.16.1
    400040  3.1.2/lib

Yes, we brought 185MB allocated memory down to 33MB! This also brought us on par with Rails in the extreme end of the r10k memory usage benchmark:

Here’s a thing though: the way r10k generates actions for its Rails benchmark is to create a single controller class with a method per action. So for the point on the far right of that chart, that’s a single class with 10k methods. Hardly realistic.

So I made a quick tweak to see how things would look if the r10k Rails benchmark generated a class per endpoint like we do with Hanami::Action:

That’s more like it. This is another extreme, however: more realistically, we’d see Rails apps with somewhere between 5-10 actions per controller class, which would lower its dot a little in that graph. In my opinion this would be a useful thing to upstream into r10k. It’s already a contrived benchmark, yes, but it’d be more useful if it at least mimicked realistic application structures.

Either way, we finished the month much more confident that we’ll be delivering on our promise of Hanami as the lighter, faster framework alternative. A good outcome!

Along the way, however, things did feel bleak at times. I wasn’t confident that I’d be able to make things right, and it didn’t feel great to think we might’ve spent years putting somethign together that wasn’t going to be able to deliver on some of those core promises. Luckily, I found all the wins we needed, and learnt a few things along the way.

Hanami 2.0, here we come

What else happened in September? Possibly the biggst thing is that we organised ourselves for the runway towards the final Hanami 2.0.0 release.

We want to do everything possible to make sure the release happens this year, so I spent some time organising the remaining tasks on our Trello board into date-based lists, aiming for a release towards the end of November. It looked achievable! The three of us in the core team re-committed ourselves to doing everything we could to complete these tasks in our estimated timeframes.

So far, things have gone very well!

We’ve all been working tremendously hard, and so far, this has let us keep everything to the schedule. I’ll have a lot to share about our work across October, but that’s all for next month’s update. So in the meantime, I have to put my head back down and get back to shipping a framework. See you all again soon!

It’s been a little over a year since our Redflow ZCell battery and Victron Energy inverter/charger kit were installed on our existing 5.94kW solar array. Now that we’re past the Southern Hemisphere spring equinox it seems like an opportune time to review the numbers and try to see exactly how the system has performed over its first full year. For background information on what all the pieces are and what they do, see my earlier post, Go With The Flow.

As we look at the figures for the year, it’s worth keeping in mind what we’re using the battery for, and how we’re doing it. Naturally we’re using it to store PV generated electricity for later use when the sun’s not shining. We are also charging the battery from the grid at certain times so it can be drawn down if necessary during peak times, for example I set up a small overnight charge to ensure there was power for the weekday morning peak, when the sun isn’t really happening yet, but grid power is more than twice as expensive. More recently in the winter months, I experimented with keeping the battery full with scheduled charges during most non-peak times. This involved quite a bit more grid charging, but got us through a couple of three hour grid outages without a hitch during some severe weather in August.

I spent some time going through data from the VRM portal for the last year, and correlating that with current bills from Aurora energy, and then I tried to compare our last year of usage with a battery, to the previous three years of usage without a battery. For reasons that will become apparent later, this turned out to be a massive pain in the ass, so I’m going to start by looking only at what we can see in the VRM portal for the past year.

The VRM portal has three summary views: System Overview, Consumption and Solar. System Overview tells us overall how much total power was pulled from the grid, how much was exported to the grid, how much was produced locally, and how much was consumed by our loads. The Consumption view (which I wish they’d named “Loads”, because I think that would be clearer) gives us the same consumption figure, but tells us how much of that came from the grid, vs. what came from the battery vs. what came from solar. The Solar view tells us how much PV generation went to the grid, how much went to the battery, and how much was used directly. There is some overlap in the figures from these three views, but there are also some interesting discrepancies, notably: the “From Grid” and “To Grid” figures shown under System Overview are higher than what’s shown in the Consumption and Solar views. But, let’s start by looking at the Consumption and Solar views, because those tell us what the system gives us, and what we’re using. I’ll come back after that to the System Overview, which is where things start to get weird and we discover what the system costs to run.

The VRM portal lets you chose any date range you like to get historical figures and bar charts. It also gives you pie charts of the last 24 hours, 7 days, 30 days and 365 days. To make the figures and bar charts match the pie charts, the year we’re analysing starts at 4pm on September 25, 2021 and ends at 4pm on September 25, 2022, because that’s exactly when I took the following screenshots. This means we get a partial September at each end of the bar chart. I’m sorry about that.

Here’s the Consumption view:

This shows us that in the last 12 months, our loads consumed 10,849kWh of electricity. Of that, 54% (5,848kWh) came from the grid, 23% (2,506kWh) came direct from solar PV and the final 23% (2,494kWh) came from the battery.

From the rough curve of the bar chart we can see that our consumption is lower in the summer months and higher in the winter months. I can’t say for certain, but I have to assume that’s largely due to heating. The low in February was 638kWh (an average of 22.8kWh/day). The high in July was 1,118kWh (average 36kWh/day).

Now let’s look at the Solar view:

In that same time period we generated 5,640kWh with our solar array, of which 44% (2,506kWh) was used directly by our loads, 43% (2,418kWh) went into the battery and 13% (716kWh) was exported to the grid.

Unsurprisingly our generation is significantly higher in summer than in winter. We got 956kWh (average 30kWh/day) in December but only 161kWh (5.3kWh/day) in June. Peak summer figures like that mean we’ll theoretically be able to do without grid power at all during that period once we get a second ZCell (note that we’re still exporting to the grid in December – that’s because we’ve got more generation capacity than storage). The winter figures clearly indicate that there’s no way we can provide anywhere near all our own power at that time of year with our current generation capacity and loads.

Now look closely at the summer months (December, January and February). There should be a nice curve evident there from December to March, but instead January and February form a weird dip. This is because we were without solar generation for three weeks from January 30 – February 11 due to replacing a faulty MPPT. Based on figures from previous years, I suspect we lost 500-600kWh of potential generation in that period.

Another interesting thing is that if we compare “To Battery” on the Solar view (2,418kWh) with “From Battery” on the Consumption view (2,494kWh), we see that our loads consumed 76kWh more from the battery than we actually put into it with solar generation. This discrepancy is due to the fact that in addition to charging the battery from solar, we’ve also been charging it from the grid at certain times, but the amount of power sent to the battery from the grid isn’t broken out explicitly anywhere in the VRM portal.

Now let’s look at the System Overview:

Here we see the same figures for “Production” (5,640kWh) and “Consumption” (10,849kWh) as were in the Consumption and Solar views, and the bar chart shows the same consumption and generation curves (ignore the blue overlay and line which indicate battery minimum/maximum and average state of charge – that information is largely meaningless at this scale, given we cycle the battery completely every day).

Now look at “To Grid” and “From Grid”. “To Grid” is 754 kWh, i.e. we somehow sent 38kWh more to the grid than came from solar. “From Grid”, at 8,531kWh, is a whopping 2,683kWh more than the 5,848kWh grid power consumed by our loads (i.e. close to half as much again).

So, what’s going on here?

One factor is that we’re charging the battery from the grid at certain times. Initially that was a few hours overnight and a few hours in the afternoon on weekdays, although the afternoon charge is obviously also provided by the solar if the sun is shining. For all of July, August and most of September though I was using a charge schedule to keep the battery full except for peak times and maintenance cycle nights, which meant quite a bit more grid charging overnight than earlier in the year, as well as grid charging most of the day during days with no or minimal sunshine. Grid power sent to the battery isn’t visible in the “From Grid” figure on the Consumption view – that view shows only our loads, i.e. the equipment the system is powering – but it is part of the “From Grid” figure in the System Overview.

Similarly, some of the power we export to the grid is actually exported from the battery, as opposed to being exported from solar generation. That usually only happens during maintenance cycles when our loads aren’t enough to draw the battery down at the desired discharge rate. But again, same thing, that figure is present here on the system overview page as part of “To Grid”, but of course is not part of the “To Grid” figure on the Solar view.

Another factor is that the system itself needs some amount of power to operate. The Victron kit (the MultiPlus II Inverter/Chargers, the Cerbo GX, the MPPT) use some small amount of power themselves. The ZCell battery also requires power to operate its pumps and fans. When the sun is out this power can of course come from solar. When solar power is not available, power to run the system needs to come from some combination of the remaining charge in the battery, and the grid.

On that note, I did a little experiment to see how much power the system uses just to operate. On July 9 (which happened to be a maintenance cycle day), I disabled all scheduled battery charges, and I shut off the DC isolators for the solar PV, so the battery would remain online (pumps and fans running) but empty for all of July 10. The following day I went and checked the figures on the System Overview, which showed we drew 35kWh, but that our consumption was 33kWh. So, together, the battery doing nothing other than running its pumps and fans, plus the Multis doing nothing other than passing grid power through, used 2kWh of power in 24 hours. Over a year, that’s 730kWh. As mentioned above, ordinarily some of that will be sourced from mains and some from solar, but if we look at the total power that came into the system as a whole (5,640kWh from solar + 8,531kWh from the grid = 14,171kWh), 730kWh is just slightly over 5% of that.

The final factor in play is that a certain amount of power is naturally lost due to conversion at various points. The ZCell has a maximum 80% DC-DC stack efficiency, meaning in the absolute best case if you want to get 10kW out of it, you have to put 12.5kW in. In reality you’ll never hit the best case: the lifetime charge and discharge figures the BMS currenly shows for our ZCell are 4,423 and 3,336kWh respectively, which is a bit over 75%. The Multis have a maximum efficiency of 96% when doing their invert/charge dance, so if we grid charge the battery, we lose at least 4% on the way in, and at least 4% on the way out as well, going to and from AC/DC. Again, in reality that loss will be higher than 4% each way, because 96% is the maximum efficiency.

A bunch of the stuff above just doesn’t apply to the previous system with the ABB inverter and no battery. I also don’t have anything like as much detailed data to go on for the old system, which makes comparing performance with the new system fiendishly difficult. The best comparison I’ve been able to come up with so far involves looking at total power input to the system (power from grid plus solar generation), total consumption by loads (i.e. actual locally usable power), and total power exported.

Prior to the Victron gear and Redflow battery installation, I had grid import and export figures from my Aurora Energy bills, and I had total generation figures from the ABB inverter. From this I can synthesise what are hopefully reasonably accurate load consumption figures by adding adding grid input to total PV generation minus grid export.

I had hoped to do this analysis on a quarterly basis to line up with Aurora bills, because then I would also be able to see how seasonal solar generation and usage went up and down. Unfortunately the billing for 2020 and 2021 was totally screwed up by the COVID-19 pandemic, because there were two quarters during which nobody was coming out to read the electricity meter. The bills for those quarters stated estimated usage (i.e. were wrong, especially given they estimated grid export as zero), with subsequent quarters correcting the figures. I have no way to reliably correlate that mess with my PV generation figures, except on an annual basis. Also, using billing periods from pre-battery years, the closest I can get to the September 25 based 2021-2022 year I’m looking at now is billing periods starting and ending in mid-August. But, that’s close enough. We’ve still got four pretty much back-to-back 12 month periods to look at.

Year	Grid In	Solar In	Total In	Loads	Export
2018-2019	9,031	6,682	15,713	11,827	3,886
2019-2020	9,324	6,468	15,792	12,255	3,537
2020-2021	7,582	6,347	13,929	10,358	3,571
2021-2022	8,531	5,640	14,171	10,849	754

One thing of note here is that in the 2018-2019 and 2019-2020 years, our annual consumption was pretty close to 12MWh, whereas in 2020-2021 and 2021-2022 it was closer to 10.5MWh. If I had to guess, I’d say that ~1.5MWh/year drop is due to a couple of pieces of computer equipment that were previously always on, now mostly running in standby mode except when actually needed. A couple of hundred watts constant draw is a fair whack of power over the course of a year. Another thing to note is the big drop in power exported in 2021-2022, because most of our solar generation is now used locally.

The thing that freaked me out when looking at these figures is that in the battery year, while our loads consumed 491kWh more than in the previous non-battery year, we pulled 949kWh more power in from the grid! This is the opposite of what I had expected to see, especially having previously written:

In the eight months the system has been running we’ve generated 4631kWh of electricity and “only” sent 588kWh to the grid, which means we’ve used 87% of what we generated locally – much better than the pre-battery figure of 45%. I suspect we’ve reduced the amount of power we pull from the grid by about 30% too, but I’ll have to wait until we have a full year’s worth of data to be sure.

– by me at the end of Go With The Flow

When I wrote that, I was looking at August 31, 2021 through April 27, 2022, and comparing that to the August 2020 to May 2021 grid power figures from my old Aurora bills. The mistake I must have made back then was to look at “From Grid” on the Consumption view, rather than “From Grid” on the System Overview. I’ve just done this exercise again, and the total grid draw from our Aurora bills from August 2020 to May 2021 is 4,980kWh. “From Grid” on the Consumption view for August 2021 to May 2022 is 3,575kWh, which is about 30% less, but “From Grid” on the System Overview is 4,754kWh, which is only about 5% less. So our loads pulled about 30% less from the grid than the same time the year before, but our system as a whole didn’t.

Now let’s break our ridiculous September-based year down further into months, to see if we can see more detail. I’ve highlighted some interesting periods in bold.

Month	Grid In	Solar In	Total In	Loads	Export
Sep 21 (part)	153	101	254	213	6
Oct 21	636	629	1,265	988	55
Nov 21	430	747	1,177	866	97
Dec 21	232	956	1,188	767	176
Jan 22	652	450	1,102	822	74
Feb 22	470	430	900	638	83
Mar 22	498	568	1,066	813	64
Apr 22	609	377	986	775	27
May 22	910	238	1,148	953	3
Jun 22	1,114	161	1,275	1073	2
Jul 22	1,163	223	1,386	1118	11
Aug 22	910	375	1,285	966	64
Sep 22 (part)	754	385	1,139	857	92
Total	8,531	5,640	14,171	10,849	754

December is great. We generated about 25% more power than our loads use (956/767=1.25), and our grid input was only about 30% of the total of our loads (232/767=0.30).

January and February show the effects of missing three weeks of potential generation. I mean, just look at December through February 2021-2022 versus the previous three summers.

	2018-2019	2019-2020	2020-2021	2021-2022
December	919	882	767	956
January	936	797	818	450
February	699	656	711	430

June and July are terrible. They’re our highest load months, with the lowest solar generation and we pulled 3-4% more power from the grid than our loads actually consumed. I’m going to attribute the latter largely to grid charging the battery.

If I dig a couple of interesting figures out for June and July I see “To Battery” on the Solar view shows 205kWh, and “From Battery” on the Consumption view shows 558kWh. Total consumption in that period was 2,191kWh, with the total “From Grid” reported in System Overview of 2,277kWh. Let’s mess with that a bit.

Bearing in mind the efficiency numbers mentioned earlier, if 205kWh went to the battery from PV, that means no more than 154kWh of what we got out of the battery was from PV generation (remember: real world DC-DC stack efficiency of about 75%). The remaining 404kWh out of the battery is power that went into it from the grid. And that means at least 538kWh in (404/0.75). Note that total from grid for these two months was 86kWh more than the 2,191kWh used by our loads. If I hadn’t been keeping the battery topped up from the grid, I’d’ve saved at least 134kWh of grid power, which would have brought our grid input figure back down below our consumption figure. Note also that this number will actually be higher in reality because I haven’t factored in AC/DC conversion losses from the Multis.

Now let’s look at some costs. When I started trying to compare the new system to the previous system, I went in thinking to look at in in terms of total power input to the system, total consumption by loads, and total power exported. There’s one piece missing there, so let’s add another couple of columns to an earlier table:

Year	Grid In	Solar In	Total In	Loads	Export	Total Out	what?
2021-2022	8,531	5,640	14,171	10,849	754	11,603	2,568

The total usable output of the system was 11,603kWh for 14,171kWh input. The difference between these two figures – 2,568kWh, or about 18% – went somewhere else. Per my earlier experiment, 5% is power that went to actually operate the system components, including the battery. That means about 13% of the power input to the system over the course of the year must have gone to some combination of charge/discharge and AC/DC conversion (in)efficiencies. We can consider this the energy cost of the system. To have the ability to time-shift expensive peak grid electricity, and to run the house without the grid if the sun is out, or from the battery when it has charge, costs us 18% of the total available energy input.

Grid power has energy costs too, but we’re not usually aware of this because it happens somewhere else. I haven’t yet found Tasmanian figures, but this 2021 Transmission Annual Planning Report PDF from Powerlink in Queensland has historical figures showing that about 7% of generation there went to auxiliaries, i.e. fans and pumps and things running at the power stations. And according to the Australian Energy Market Operator (AEMO), 10% of grid power generated is lost during transmission and distribution. Stanwell (a power company in Queensland) have a neat explainer of all this on their What’s Watt site.

Finally, speaking of expensive grid electricity, let’s look at how much we paid Aurora Energy over the past four years for our power. The bills are broken out into different tariffs, for which you’re charged different amounts per kilowatt hour and then there’s an additional daily supply charge, and also credits for power exported. We can simplify that by just taking the total dollar value of all the power bills and dividing that by the total power drawn from the grid to arrive at an effective cost per kilowatt hour for the entire year. Here it is:

Year	From Grid	Total Bill	Cost/kWh
2018-2019	9,031	$2,278.33	$0.25
2019-2020	9,324	$2,384.79	$0.26
2020-2021	7,582	$1,921.77	$0.25
2021-2022	8,531	$1,731.40	$0.20

So, the combination of the battery plus the switch from Flat Rate to Peak & Off-Peak billing has reduced the cost of our grid power by about 20%. I call that a win.

Going forwards it will be interesting to see how the next twelve months go, and, in particular, what we can do to reduce our power consumption. A significant portion of our power is used by a bunch of always-on computer equipment. Some of that I need for my work, and some of that provides internet access, file storage and email for us personally. Altogether, according to the UPSes, this kit pulls 200-250 watts continuously, but will pull more than that during the day when it’s being used interactively. If we call it 250W continuous, that’s a minimum of 6kWh/day, which is 2,190kWh/year, or about 20% of the 2021-2022 consumption. Some of that equipment should be replaced with newer, more power efficient kit. Some of it could possibly even be turned off or put into standby mode some of the time.

We still need to get a heat pump to replace the 2400W panel heater in our bedroom. That should save a huge amount of power in winter. We’re also slowly working our way through the house installing excellent double glazed windows from Elite Double Glazing, which will save on power for heating and cooling year round.

And of course, we still need to get that second ZCell.

August’s OSS work landed one of the last big Hanami features, saw another Hanami release out the door, began some thinking about memory usage, and kicked off a fun little personal initiative. Let’s dive in!

Conditional slice loading in Hanami

At the beginning of the month I merged support for conditional slice loading in Hanami. I’d wanted this feature for a long time, and in fact I’d hacked in workarounds to achieve the same more than 2 years ago, so I was very pleased to finally get this done, and for the implementation work to be as smooth as it was.

The feature provides a new config.slices setting on your app class, which you can configure like so:

module MyApp
  class App < Hanami::App
    config.slices = %w[admin]
  end
end

For an app consisting of both Admin and Main slices and for the config above, when the app is booted, only the Admin slice will be loaded:

require "hanami/prepare"

Hanami.app.slices.keys # => [:admin]

Admin::Slice # exists, as expected
Main         # raises NameError, since it was never loaded

As we see from Main above, slices absent from this list will not have their namespace defined, nor their slice class loaded, nor any of their Ruby source files. Within that Ruby process, they effectively do not exist.

Specifying slices to load can be very helpful to improve boot time and minimize memory usage for specific deployed workloads of your app.

Imagine you have a subset of background jobs that run via a dedicated job runner, but whose logic is otherwise unneeded for the rest of your app to function. In this case, you could organize those jobs into their own slice, and then load only that slice for the job runner’s process. This arrangement would see the job runner boot as quickly as possible (no extraneous code to load) as well as save all the memory otherwise needed by all those classes. You could also do the invserse for your main deployed process: specify all slices except this jobs slice, and you gain savings there too.

Organising code into slices to promote operational efficiency like this also gives you the benefit of greater clarity in the separation of responsibilities between those slices: when a single slice of code is loaded and the rest of your app is made to disappear, that will quickly surface any insidious dependencies from that slice to the rest of your code (they’ll be raised as exceptions!). Cleaning these up will help ensure your slices remain useful as abstractions for reasoning about and maintaining your app.

To make it easy to tune the list of slices to load, I also introduced a new HANAMI_SLICES env var that sets this config without you having to write code inside your app class. In this way, you could use them in your Procfile or other similar deployment code:

web: HANAMI_SLICES=main,admin bundle exec puma -C config/puma.rb
feed_worker: HANAMI_SLICES=feed bundle exec rake jobs:work

This effort was also another example of why I’m so happy to be working alongside the Hanami core team. After initially proposing a more complex arrangement including separate lists for including or excluding slices, Luca jumped in and help me dial this back to the much simpler arrangement of the single list only. For an Hanami release in which we’re going to be introducing so many new ideas, the more we can keep simple around them, the better, and I’m glad to have people who can remind me of this.

Fixed how slice config is applied to component classes

Our action and view integration code relies on their classes detecting when they’re defined inside a slice’s namespace, then applying relevant config from the slice to their own class-level config object. It turned out our code for doing this broke a little when we adjusted our default class hierarchies. Thanks to some of our wonderful early adopters, we picked this up quickly and I fixed it. Now things just work like you expect however you choose to configure your action classes, whether through the app-level config.actions object, or by directly updating config in a base action class.

In doing this work, I became convinced we need an API on dry-configurable to determine whether any config value has been assigned or mutated by the user, since it would help so much in reliably detecting whether or not we should ignore config values at particular levels. For now, we could work around it, but I hope to bring this to dry-configurable at some point in the future.

Released Hanami 2.0.0.beta2

Another month passed, so it was time for another release! With my European colleagues mostly enjoying some breaks over their summer, I hunkered down in chilly Canberra and took care of the 2.0.0.beta2 release. Along with the improvements above, this release also included slice and action generators (hanami generate slice and hanami generate action, thank you Luca!), plus a very handle CLI middlewares inspector (thank you Marc!):

$ hanami middlewares

/    Dry::Monitor::Rack::Middleware (instance)
/    Rack::Session::Cookie

The list of things to do over the beta phase is getting smaller. I don’t expect we’ll need too many more of these releases!

Created memory usage benchmarks for dry-configurable

As the final 2.0 release gets closer, we’ve been doing various performance tests just to make sure the house is in order. One thing we discovered is that Hanami::Action is not as memory efficient as we’d like it to be. One of the biggest opportunities to improve this looked to be in dry-configurable, since that’s what is used to manage the per-class action configuration.

I suspected any effort here would turn out to be involved (and no surprise, it turned out to be involved 😆), so I thought it would be useful as a first step to establish a memory benchmark to revisit over the course of any work. This was also a great way to get my head in this space, which turned out to take over most of my September (but more on that next month).

Quietly relaunched Decaf Sucks

Decaf Sucks was once a thriving little independent online café review community, with its own web site (starting from humble beginnings as a Rails Rumble entry in 2009) and even native iOS app (two iterations, in fact).

I was immensitely proud of what Decaf Sucks became, and for the collaboration with Max Wheeler in building it.

Unfortunately, as various internet APIs changed, the site atrophied, eventually became disfunctional, and we had to take it down. I still have the database, however, and I want to bring it back!

This time around, my plan is to do it as a fully open source Hanami 2 example application. Max is even on board to bring back all the UI goodness. For now, you can follow along with the early steps on GitHub. Right now the app is little more than the basic Hanami skeleton with added database integration and a CI setup (Hello Buildkite!), but I plan to grow it bit by bit. Perhaps I’ll try to have something small that I can share with each of these monthly OSS updates.

After Hanami 2 ships, hopefully this will serve as a useful resource for people wanting to see how it plays out in a real working app. And beyond that, I look forward to it serving once again as a place for me to commemorate my coffee travels!

My team at SUSE is working on a new S3-compatible storage solution for Kubernetes, based on Ceph’s RADOS Gateway (RGW), except without any of the RADOS bits. The idea is that you can deploy our s3gw container on top of Longhorn (which provides the underlying replicated storage), and all this is running in your Kubernetes cluster, along with your applications which thus have convenient access to a local S3-compatible object store.

We’ve done this by adding a new storage backend to RGW. The approach we’ve taken is to use SQLite for metadata, with object data stored as files in a regular filesystem. This works quite neatly in a Kubernetes cluster with Longhorn, because Longhorn can provide a persistent volume (think: an ext4 filesystem), on which s3gw can store its SQLite database and object data files. If you’d like to kick the tyres, check out Giuseppe’s deployment tutorial for the 0.2.0 release, but bear in mind that as I’m writing this we’re all the way up to 0.4.0 so some details may have changed.

While s3gw on Longhorn on Kubernetes remains our primary focus for this project, the fact that this thing only needs a filesystem for backing storage means it can be run on top of just about anything. Given “just about anything” includes an old school two node Pacemaker cluster with DRBD for replicated storage, why not give that a try? I kinda like the idea of a good solid highly available S3-compatible storage solution that you could shove into the bottom of a rack somewhere without too much difficulty.

# crm configure show
[...]
primitive drbd_s3 ocf:linbit:drbd \
        params drbd_resource=s3 drbdconf="/etc/drbd.conf" \
        op monitor interval=29s role=Master \
        op monitor interval=31s role=Slave
primitive fs_s3 Filesystem \
        params device="/dev/drbd0" directory="/data" fstype=ext4 \
        meta target-role=Started \
        op start timeout=60s interval=0 \
        op stop timeout=60s interval=0 \
        op monitor interval=20s timeout=40s
primitive https nginx \
        op start timeout=40s interval=0 \
        op stop timeout=60s interval=0 \
        op monitor timeout=30s interval=10s \
        op monitor timeout=30s interval=30s \
        op monitor timeout=60s interval=20s
primitive s3-ip IPaddr2 \
        params ip=192.168.100.50 \
        op monitor interval=10 timeout=20
primitive s3gw podman \
        params image="ghcr.io/aquarist-labs/s3gw:latest" run_opts="-p 7480:7480 -v/data:/data" \
        op start interval=0 timeout=90s \
        op stop interval=0 timeout=90s \
        op monitor interval=30s timeout=30s
primitive s3gw-ui podman \
        params image="ghcr.io/aquarist-labs/s3gw-ui:latest" run_opts="-p 8080:8080 -e RGW_SERVICE_URL=https://s3gw.sleha.test" \
        op start interval=0 timeout=90s \
        op stop interval=0 timeout=90s \
        op monitor interval=30s timeout=30s
group g-s3 fs_s3 s3gw s3gw-ui https s3-ip
ms ms-drbd_s3 drbd_s3 \
        meta master-max=1 master-node-max=1 clone-max=2 clone-node-max=1 notify=true
colocation col-s3_on_drbd inf: g-s3 ms-drbd_s3:Promoted
order o-drbd_before_fs Mandatory: ms-drbd_s3:promote g-s3:start
[...]

worker_processes  1;

events {
    worker_connections  1024;
    use epoll;
}

http {
    include       mime.types;
    default_type  application/octet-stream;

    sendfile        on;
    keepalive_timeout  65;

    server {
        listen       80;
        return       301 https://$host$request_uri; 
    }

    server {
        listen       443 ssl;
        server_name  s3gw.sleha.test;

        access_log /var/log/nginx/s3gw.access.log;

        location / {
            proxy_set_header        Host $host;
            proxy_set_header        X-Real-IP $remote_addr;
            proxy_set_header        X-Forwarded-For $proxy_add_x_forwarded_for;
            proxy_set_header        X-Forwarded-Proto $scheme;

            add_header Access-Control-Allow-Origin 'https://s3gw-ui.sleha.test';
            add_header Access-Control-Allow-Methods 'GET,HEAD,PUT,POST,DELETE';
            add_header Access-Control-Allow-Headers '*';
            add_header 'Access-Control-Allow-Credentials' 'true';

            if ($request_method = 'OPTIONS') {
                add_header Access-Control-Allow-Origin 'https://s3gw-ui.sleha.test';
                add_header Access-Control-Allow-Methods 'GET,HEAD,PUT,POST,DELETE';
                add_header Access-Control-Allow-Headers '*';
                add_header 'Access-Control-Allow-Credentials' 'true';
                add_header 'Content-Type' 'text/plain charset=UTF-8';
                add_header 'Content-Length' 0;
                return 204;
            }

            proxy_pass          http://localhost:7480;
            proxy_read_timeout  90;
            proxy_redirect      http://localhost:7480 https://s3gw.sleha.test;
        }

        ssl_certificate      cert.pem;
        ssl_certificate_key  cert.key;
        ssl_protocols        TLSv1.2;
        ssl_session_cache    shared:SSL:1m;
        ssl_session_timeout  5m;
        ssl_ciphers  HIGH:!aNULL:!MD5;
        ssl_prefer_server_ciphers  on;
    }

    server {
        listen       443 ssl;
        server_name  s3gw-ui.sleha.test;

        access_log /var/log/nginx/s3gw-ui.access.log;

        location / {
            proxy_set_header        Host $host;
            proxy_set_header        X-Real-IP $remote_addr;
            proxy_set_header        X-Forwarded-For $proxy_add_x_forwarded_for;
            proxy_set_header        X-Forwarded-Proto $scheme;

            proxy_pass          http://localhost:8080;
            proxy_read_timeout  90;

            proxy_redirect      http://localhost:8080 https://s3gw-ui.sleha.test;
        }

        ssl_certificate      cert-ui.pem;
        ssl_certificate_key  cert-ui.key;
        ssl_protocols        TLSv1.2;
        ssl_session_cache    shared:SSL:1m;
        ssl_session_timeout  5m;
        ssl_ciphers  HIGH:!aNULL:!MD5;
        ssl_prefer_server_ciphers  on;
    }
}

access_key = test
secret_key = test
host_base = https://s3gw.sleha.test/
host_bucket = htts://s3gw.sleha.test/%(bucket)

Hi there friends, it’s certainly been a while, and a lot has happened across May, June and July: I left my job, took some time off, and started a new job. I also managed to get a good deal of open source work done, so let’s take a look at that!

Released Hanami 2.0.0.alpha8

Since we’d skipped a month in our releases, I helped get Hanami 2.0.0.alpha8 out the door in May. The biggest change here was that we’d finished relocating the action and view integration code into the hanami gem itself, wrapped up in distinct “applicationâ€� classes, like Hanami::Application::Action. In the end, this particular naming scheme turned out to be somewhat short lived! Read on for more :)

Resurrected work using dry-effects within hanami-view

As part of an effort to make it easy to use our conventional view “helpers� in all parts of our view layer, I resurrected my work from September 2020(!) on using dry-effects within hanami-view. The idea here was to achieve two things:

1. To ensure we keep only a single context object for the entire view rendering, allowing its state to be preserved and accessed by all view components (i.e. allowing both templates, partials and parts all to access the very same context object)
2. To enable access to the current template/partial’s #locals from within the context, which might help make our helpers feel a little more streamlined through implicit access to those locals

I got both of those working (here’s my work in progress), but I discovered the performance had worsened due to the cost of using an effect to access the locals. I took a few extra passes at this, reducing the number of effects to one, and memoziing it, leaving us with improved performance over the main branch, but with a slightly different stance: the single effect is for accessing the context object only, so any helpers, instead of expecting access to locals, will instead only have access to that context. The job from here will be to make sure that the context object we build for Hanami’s views has everything we need for an ergonomic experience working with our helpers. I’m feeling positive about the direction here, but it’ll be a little while before I get back to it. Read on for more on this (again!).

Unified application and slice

The biggest thing I did over this period was to unify Hanami’s Application and Slice. This one took some doing, and I was glad that I had a solid stretch of time to work on it between jobs.

I already wrote about this back in April’s update, noting that I’d settled on the approach of having a composed slice inside the Hanami::Application class to providing slice-like functionality at the application level. This was the approach I continued with, and as I went, I was able to move more and more functionality out of Hanami::Application and into Hanami::Slice, with that composed “application sliceâ€� being the thing that preserved the existing application behaviour. At some point, a pattern emerged: the application is a slice, and we could achieve everything we wanted (and more) by turning class Hanami::Application into class Hanami::Application < Hanami::Slice.

Turning the application into a slice sublcass is indeed how I finished the work, and I’m extremely pleased with how it turned out. It’s made slices so much more powerful. Now, each slice can have its own config, its own dedicated settings and routes, can be run on its own as a Rack application, and can even have its own set of child slices.

As a user of Hanami you won’t be required to use all of this per-slice power features, but they’ll be there if or when you want them. This is a great example of progressive disclosure, a principle I follow as much as possible when designing Hanami’s features: a user should be able to work with Hanami in a simple, straightforward way, and then as their needs grow, they can then find additional capabilities waiting to serve them.

Let’s explore this with a concrete example. If you’re building a simple Hanami app, you can start with a single top-level config/settings.rb that defines all of the app’s own settings. This settings object is made available as a "settings" component registration in both the app as well as all its slices. As the app grows and you add a slice or two, you start to add more slice-specific settings to this component. At this point you start to feel a little uncomfortable that settings specific to SliceA are also available inside SliceB and elsewhere. So you wonder, could you go into slices/slice_a/ and drop a dedicated config/settings.rb there? The answer to that is now yes! Create a config/settings.rb inside any slice directory and it will now become a dedicated settings component for that slice alone. This isn’t a detail you had to burden yourself with in order to get started, but it was ready for you when you needed it.

Another big benefit of this code reorganisation is that the particular responsibilities of Hanami::Application are much clearer: its job is to provide the single entrypoint to the app and coordinate the overall boot process; everything else comes as part of it also being a slice. This distinction is made clear through the number of public methods that exist across the two classes: Application now has only 2 distinct public methods, whereas Slice currently brings 27.

There’s plenty more detail over in the pull request: go check it out!

The work here also led to changes across the ecosystem:

• Because we’re now importing components from the app into other slices in the root key namespace (rather than prefixed by "application." as before), we needed to make that possible in dry-system.
• But with it being possible for each slice to have its own dedicated version of certain components (like "settings" in the example above), we also needed to make dry-system prefer local components when importing.
• This itself needed a tweak to dry-container such that we could reverse the order of its Container#merge via a block parameter.
• To make certain components conditional within the slice containers, we’re using dry-system’s class-based provider sources, which needing tweaking to support deeper class hierarchies.

This is one the reasons I’m excited about Hanami’s use of the dry-rb gems: it’s pushing them in directions no one has had to take them before. The result is not only the streamlined experience we want for Hanami, but also vastly more powerful underpinnings.

Devised a slimmed down core app structure

While I had my head down working on internal changes like the above, Luca had been thinking about Hanami 2 adoption and the first run user experience. As we had opted for a slices-only approach for the duration of our alpha releases, it meant a fairly bulky overall app structure: every slice came with multiple deeply nested files. This might be overwhelming to new users, as well as feeling like overkill for apps that are intended to start small and stay small.

To this end, we agreed upon a stripped back starter structure. Here’s how it looks at its core (ignoring tests and other general Ruby files):

├── app/
│   ├── action.rb
│   └── actions/
├── config/
│   ├── app.rb
│   ├── routes.rb
│   └── settings.rb
├── config.ru
└── lib/
    ├── my_app/
    │   └── types.rb
    └── tasks/

That’s it! Much more lightweight. This approach takes advantage of the Hanami app itself becoming a fully-featured slice, with app/ now as its source directory.

In fact, I took this opportunity to unify the code loading rules for both the app and slices, which makes for a much more intuitive experience. You can now drop any ruby source file into app/ or a slices/[slice_name]/ slice dir and it will be loaded in the same way: starting at the root of each directory, classes defined therein are expected to inhabit the namespace that the app or slice represents, so app/some_class.rb would be MyApp::SomeClass and slices/my_slice/some_class would be MySlice::SomeClass. Hat tip to me of September 2021 for implementing the dry-system namespaces feature that enabled this! 😜

(Yet another little dry-system tweak came out of preparing this too, with Component#file_name now exposed for auto-registration rules).

This new initial structure for starter Hanami 2.0 apps is another example of progressive disclosure in our design. You can start with a simple all-in-one approach, everything inside an app/ directory, and then as various distinct concerns present themselves, you can extract them into dedicated slices as required.

Along with this, some of our names have become shorter! Yes, “applicationâ€� has become “appâ€� (and Hanami::Application has become Hanami::App, and so on). These shorter names are easier to type, as well as more reflective of the words we tend to use when verbally describing these structures.

We also tweaked our actions and views integration code so that it is automatically available when you inherit directly from Hanami::Action, so it will no longer be necessary to have the verbose Hanami::Application::Action as the superclass for the app’s actions. We also ditched that namespace for both routes and settings too, so now you can just inherit from Hanami::Settings and the like.

Devised a slimmed down release strategy

Any of you following my updates would know by now that the Hanami 2.0 release has been a long time coming. We have ambitious goals, we’re doing our best, and everything is slowly coming together. But as hard as it might’ve been for folks who’re waiting, it’s been doubly so for us, feeling the weight of both the work along with everyone’s expectations.

So to make sure we can focus our efforts and get something out the door sooner rather than later, we decided to stagger our 2.0 release. We’ll start off with an initial 2.0 release centred around hanami, hanami-cli, hanami-controller, and hanami-router (enough to write some very useful API applications, for example), then follow up with a “full stack� 2.1 release including database persistence, views, helpers, assets and everything else.

I’m already feeling empowered by this strategy: 2.0 feels actually achievable now! And all of the other release-related work like updated docs and a migration guide will become correspondingly easier too.

Released Hanami 2.0.0.beta1!

With greater release clarity as well as all the above improvements under our belt, it was time to usher in a new phase of Hanami 2.0 development, so we released 2.0.0.beta1 in July! This new version suffix represents just how close we feel we are to our final vision for 2.0. This is an exciting moment!

And a bunch more

This update is getting rather long, so let me list a bunch of other Hanami improvements I managed to get done:

• You can now autoload your app’s constants from within your settings file, which is useful if you’re referring to a types module to type check your settings. This turned out to be a larger change than expected, but it’s further bolstered our slice capabilities, because it led to each slice getting its own Zeitwerk autoloader instance, meaning each slice fully owns its code loading lifecycle.
• .env file loading is now a much clearer step upon subclassing Hanami::App, rather than it being a side effect of loading the settings. This is useful in case you want to directly access the ENV for any other purpose.
• The .prepare_container escape hatch allowing for last-minute manual adjustments to a slice’s internal dry-system container is now run after all possible framework setup has been applied
• In the few places we rescue from LoadError, we now ensure we don’t swallow any unintended errors
• Hanami now respects RACK_ENV if HANAMI_ENV is not set, which is useful for Rack-first apps that happen to be migrating to Hanami 2.0 (these do exist!)
• I removed the settings allowing for customisation of the routes and settings file paths. The further I go with batteries included framework development, the better sense I think I’m developing for what should’t be configurable. This is one such case: we can’t provide reasonable documentation or support if these files can live in arbitrary locations.
• Last but not least, I built conditional slice loading! But since this isn’t merged yet, I’ll go into details on this in next month’s update ;)

Outside my Hanami development, a new job and a new computer meant I also took the change to reboot my dotfiles, which are now powered by chezmoi. I can’t speak highly enough of chezmoi, it’s an extremely powerful tool and I’m loving the flexibility it affords!

That’s it from me for now. I’ll come back to you all in another month!

As described in some detail in my last post, we have a single 10kWh Redflow ZCell zinc bromine flow battery hooked up to our solar PV via Victron inverter/chargers. This gives us the ability to:

• Store almost all the excess energy we generate locally for later use.
• When the sun isn’t shining, grid charge the battery at off-peak times then draw it down at peak times to save on our electricity bill (peak grid power is slightly more than twice as expensive as off-peak grid power).
• Opportunistically survive grid outages, provided they don’t happen at the wrong time (i.e. when the sun is down and the battery is at 0% state of charge).

By their nature, ZCell flow batteries needs to undergo a maintenance cycle at least every three days, where they are discharged completely for a few hours. That’s why the last point above reads “opportunistically survive grid outages”. With a single ZCell, we can’t use the “minimum state of charge” feature of the Victron kit to always keep some charge in the battery in case of outages, because doing so conflicts with the ZCell maintenance cycles. Once we eventually get a second battery, this problem will go away because the maintenance cycles automatically interleave. In the meantime though, as my project for Hack Week 21, I decided to see if I could somehow automate the Victron scheduled charge configuration based on the ZCell maintenance cycle timing, to always keep the battery as full as possible for as long as possible.

Last week I finished up at Culture Amp, and I’m excited to announce that I’ll be joining Buildkite as an engineer!

My time at Culture Amp was special. It was my first role after a decade of running Icelab with Max and Michael. Culture Amp hired everyone at Icelab after we decided to close the business, providing both a smooth transition and new opportunities to a singular group. I built a great working relationship with my manager, I was trusted to do big things, and I relished the chance to work with and learn from a large group of engineers. I’m deeply thankful for all of this.

Towards the end, I was serving as Culture Amp’s Director of Back End Engineering, and moving into engineering management. However, as any astute reader of this blog might attest, I am deeply motivated by hands on programming work, and all the learning and collaboration opportunities that go with it. I realised it was not the time to draw that chapter to a close (it might never!), and through that consideration I connected with Buildkite.

I’m excited to join Buildkite for many reasons! It’s a great Australian company with heart and personality. It brims with people I’ve long dreamt of working with. Developer tooling is an area close to my heart. And they’re growing a (majestic) Ruby app at the core of their tech. I can’t wait to dig in.

For me, this is also an intentional decision to stick with Ruby. The work I’m doing in Ruby OSS right now might be one of the biggest “dents in the universe” I get to make. I want to see this effort through, to complete our vision for Hanami 2.0, then learn from how it’s adopted by our community.

I have some time off between jobs, which I’ll use to give our Hanami work a real boost: I’ll be commiting nearly 6 weeks of full-time work to Hanami! Based on previous experience, this should see me get through what otherwise might have taken 6 months of part-time effort. I’m hoping this will get us significantly closer to 2.0. I’ll likely start another tweet thread of my efforts, so find me on Twitter if you’d like to follow along!

April was a pretty decent month for my OSS work! Got some things wrapped up, kept a few things moving, and opened up a promising thing for investigation. What are these things, you say? Let’s take a look!

Finished centralisation of Hanami action and view integrations

I wrote about the need to centralise these integrations last month, and in April, I finally got the work done!

This was a relief to get out. As a task, while necessary, it felt like drudge work – I’d been on it since early March, after all! I was also conscious that this was also blocking Luca’s work on helpers all the while.

My prolonged work on this (in part, among other things like Easter holidays and other such Real Life matters) contributed to us missing April’s Hanami release. The good thing is that it’s done now, and I’m hopeful we can have this released via another Hanami alpha sometime very soon.

In terms of the change to Hanami apps, the biggest change from this is that your apps should use a new superclass for actions and views:

require "hanami/application/action"

module Main
  module Action
    # Used to inherit from Hanami::Action
    class Base < Hanami::Application::Action
    end
  end
end

Aside from the benefit to us as maintainers of having this integration code kept together, this distinct superclass should also help make it clearer where to look when learning about how actions and views work within full Hanami apps.

Enabled proper access to full locals in view templates

I wound up doing a little more work in actions and views this month. The first was a quickie to unblock some more of Luca’s helpers work: making access to the locals hash within templates work like we always expected it would.

This turned out to be a fun one. For a bit of background, the context for every template rendering in hanami-view (i.e. what self is for any given template) is an Hanami::View::Scope instance. This instance contains the template’s locals, makes the full locals hash available as #locals (and #_locals, for various reasons), and uses #method_missing to make also make each local directly available via its own name.

Luca found, however, that calling locals within the template didn’t work at all! After I took a look, it seemed that while locals didn’t work, self.locals or just plain _locals would work. Strange!

Turns out, this all came down to implementation details in Tilt, which we use as our low-level template renderer. The way Tilt works is that it will compile a template down into a single Ruby method that receives a locals param:

def compile_template_method(local_keys, scope_class=nil)
  source, offset = precompiled(local_keys)
  local_code = local_extraction(local_keys)

  # <...snip...>

  method_source << <<-RUBY
    TOPOBJECT.class_eval do
      def #{method_name}(locals)
        #{local_code}
  RUBY

Because of this, locals is actually a local variable in the context of that method execution, which will override any other methods also available on the scope object that Tilt turns into self for the rendering.

Here is how we were originally rendering with Tilt:

tilt(path).render(scope, &block)

My first instinct was simply to pass our locals hash as the (optional) second argument to Tilt’s #render:

tilt(path).render(scope, scope._locals)

But even that didn’t work! Because in generating that local_code above, Tilt will actually take the locals and explode it out into individual variable assignments:

def local_extraction(local_keys)
  local_keys.map do |k|
    if k.to_s =~ /\A[a-z_][a-zA-Z_0-9]*\z/
      "#{k} = locals[#{k.inspect}]"
    else
      raise "invalid locals key: #{k.inspect} (keys must be variable names)"
    end
  end.join("\n")
end

But we don’t need this at all, since hanami-view’s scope object is already making those locals available individually, and we want to ensure access to those locals continues to run through the scope object.

So the ultimate fix is to make locals of our locals. Yo dawg:

tilt(path).render(scope, {locals: scope._locals}, &block)

This gives us our desired access to the locals hash in templates (because that locals key is itself turned into a solitary local variable), while preserving the rest of our existing scope-based functionality.

It also shows me that I probably should’ve written an integration test back when I introduced access to a scope’s locals back in January 2019. 😬

Either way, I’m excited this came up and I could fix it, because it’s an encouraging sign of just how much of this view system we’ll be able to put to use in creating a streamlined and powerful view layer for our future Hanami users!

Merged a fix to stop unwanted view rendering of halted requests

Thanks to our extensive use of Hanami at Culture Amp, my friend and colleague Andrew discovered and fixed a bug with our automatic rendering of views within actions, which I was happy to merge in.

Shipped some long awaited dry-configurable features

After keeping poor ojab waiting way too long, I also merged a couple of nice enhancements he made to dry-configurable:

• Make Config#finalize! finalize itself recurively and (optionally) allow freezing of setting values
• Accept hashes as possible setting values when using #update, which I followed up with support for anything implicitly convertible to a hash.

I then released these as dry-configurable 0.15.0.

Started work on unifying Hanami slices and application

Last but definitely not least, I started work on one of the last big efforts we need in place before 2.0: making Hanami slices act as much as possible like complete, miniature Hanami applications. I’m going to talk about this a lot more in future posts, but for now, I can point you to a few PRs:

• Introducing Hanami::SliceName (a preliminary, minor refactoring to fix some slice and application name determination responsibilities that had somehow found their way into our configuration class).
• A first, abandoned attempt at combining slices and applications, using a mixin for shared behaviour.
• A much more promising attempt using a composed slice object within the application class, which is currently the base of my further work in this area.

Apart from opening up some really interesting possibilities around making slices fully a portable, mountable abstraction (imagine bringing in slices from gems!), even for our shorter-term needs, this work looks valuable, since I think it should provide a pathway for having application-wide settings kept on the application class, while still allowing per-slice customisation of those settings in whichever slices require them.

The overall slice structure is also something that’s barely changed since I put it in place way back in late 2019. Now it’s going to get the spit and polish it deserves. Hopefully I’ll be able to share more progress on this next month :) See you then!

Back in March of 2020, I decided to take up the habit of writing monthly status updates for my open source software development. 22 updates and 25k words later, I’m happy to be celebrating two years of status updates!

As each month ticks around, I can find it hard to break away from cutting code and write my updates, but every time I get to publishing, I’m really happy to have captured my progress and thinking.

After all, these posts now help remind me I managed to do all of the following over the last two years (and these were just the highlights!):

• Renamed dry-view to hanami-view and kicked off view/application integration (Mar 2020)
• Received my first GitHub sponsor (Apr 2020), thank you Benny Klotz (who still sponsors me today!)
• Shared my Hanami 2 application template (May 2020)
• Achieved seamless view/action/application integration (May 2020)
• Brought class-level configuration to Hanami::Action (Jun 2020)
• Introduced application-level configuration for actions and views (Jul 2020)
• Added automatic inference for an action’s paired view, along with automatic rendering (Jul 2020)
• Introduced application integration for view context classes (Jul 2020)
• Supported multiple boot file dirs in dry-system, allowing user-replacement of standard bootable components in Hanami (Aug 2020)
• Rebuilt the Hanami Flash class (Aug 2020)
• Resumed restoring hanami-controller features through automatic enabling of CSRF protection (Sep 2020)
• Added automatic configuration to views (inflector, template, part namespace) (Oct 2020)
• Released a non-web Hanami application template (Oct 2020)
• Started the long road to Hanami/Zeitwerk integration with an autoloading loader for dry-system (Nov 2020)
• Introduced dedicated “component dirâ€� abstraction to dry-system, along with major cleanups and consistency wins (Dec 2020/Jan 2021)
• Added support for dry-system component dirs with mixed namespaces (Feb/Mar/Apr 2021)
• Released dry-system with all these changes, along with Hanami with working Zeitwerk integration (Mar/Apr 2021)
• Ported Hanami’s app configuration to dry-configurable (May 2021),
• Laid the way for dry-configurable 1.0 with some API changes (May/Jul 2021)
• Returned to dry-system and added configurable constant namespaces (Jun/Jul/Aug/Sep/Oct 2021)
• Introduced compact slice source dirs to Hanami, using dry-systems constant namespaces (Sep/Oct 2021)
• Added fully configurable source dirs to Hanami (Nov/Dec 2021)
• Shipped a huge amount of dry-system improvements over two weeks of dedicated OSS time in Jan 2022, including the overhaul of bootable components as part of their rename to providers, as well as partial container imports and exports, plus much more
• Introduced concrete slice classes and other slice registration improvements to Hanami (Feb 2022)
• Refactored and relocated action and view integration into the hanami gem itself, and introduced Hanami::SliceConfigurable to make it possible for similar components to integrate (Mar 2022)

This is a lot! To add some extra colour here, a big difference betwen now and pre-2020 is that I’ve been working on OSS exclusively in my personal time (nights and weekends), and I’ve also been slugging away at a single large goal (Hanami 2.0, if you hadn’t heard!), and the combination of this can make the whole thing feel a little thankless. These monthly updates are timely punctuation and a valuable reminder that I am moving forward.

They also capture a lot of in-the-moment thinking that’d otherwise be lost to the sands of time. What I’ve grown to realise with my OSS work is that it’s as much about the process as anything else. For community-driven projects like dry-rb and Hanami, the work will be done when it’s done, and there’s not particularly much we can do to hurry it. However, what we should never forget is to make that work-in-progress readily accessible to our community, to bring people along for the ride, and to share whatever lessons we discover along the way. The passing of each month is a wonderful opportunity for me to do this 😀

Finally, a huge thank you from me to anyone who reads these updates. Hearing from folks and knowing there are people out there following along is a huge encouragement to me.

So, let’s keep this going. I’m looking forward to another year of updates, and—checks calendar–writing April’s post in the next week or so!

When we’re writing a method in Ruby and receiving objects as arguments, a helpful principle to follow is “don’t mutate what you don’t own.”

Why is this? Those arguments come from places that we as the method authors can’t know, and a well-behaved method shouldn’t alter the external environment unexpectedly.

Consider the following method, which takes an array of numbers and appends a new, incremented number:

def append_number(arr)
  last_number = arr.last || 0
  arr << last_num + 1
end

If we pass in an array, we’ll get a new number appended in the returned array:

my_arr = [1, 2]
my_new_arr = append_number(my_arr) # => [1, 2, 3]

But we’ll also quickly discover that this has been achieved by mutating our original array:

my_arr = [1, 2]
my_new_arr = append_number(arr) # => [1, 2, 3]
my_arr # => [1, 2, 3]

We can confirm by an object equality check that this is still the one same array:

my_new_arr.eql?(my_arr) # => true

This behavior is courtesy of Ruby’s Array#<< method (aka #append or #push), which appends the given object to the receiver (that is, self), before then returning that same self. This kind of self-mutating behaviour is common across both the Array and Hash classes, and while it can provide some conveniences in local use (such as a chain of #<< calls to append multiple items to the same array), it can lead to surprising results when that array or hash comes from anywhere non-local.

Imagine our example above is part of a much larger application. In this case, my_arr will most likely come from somewhere far removed and well outside the purview of append_number or whatever class contains it. As the authors of append_number, we have no idea how that original array might otherwise be used! For this reason, the courteous approach to take is not to mutate the array, but instead create and return a new copy:

def append_number(arr)
  last_number = arr.last || 0

  # There are many ways we can achieve the copy; here's just one
  arr + [last_number]
end

This way, the caller of our method can trust their original values to go unchanged, which is what they would likely expect, especially if our method doesn’t give any hint that it will mutate.

my_arr = [1, 2]
my_new_arr = append_number(arr) # => [1, 2, 3]
my_arr # => [1, 2]

This is a very simple example, but the same princple applies for all kinds of mutable objects passed to your methods.

A more telling story here comes from earlier days of Ruby, around how we handled options hashes passed to methods. We used to do things like this:

def my_method(options = {})
  some_opt = options.delete(:some_opt)
  # Do something with some_opt...
end

my_method(some_opt: "some value")

Using trailing options hashes like this was how we provided “keyword arguments” before Ruby had them as a language feature. Now the trouble with the method above is that we’re mutating that options hash by deleting the :some_opt key. So if the user of our method had code like this:

common_options = {some_opt: "some value"}

first_result = my_method(common_options)
second_result = my_method(common_options)

We’d find ourselves in trouble by the time we call my_method the second time, because at that point the common_options hash will no longer have some_opt:, since the first invocation of my_method deleted it — oops!

This is a great illustration of why modern Ruby’s keyword arguments work the way they do. When we accept a splatted keyword hash argument like **options, Ruby ensures it comes into the method as a new hash, which means that operations like options.delete(:some_opt) do in fact become local in scope, and therefore safe to use.

So now that we’ve covered both arrays and hashes now as Ruby’s most common “container” objects, what about the other kinds of application-specific structures that we might encounter in real world codebases? Objects representing domain models of various kinds, an instance of an ActiveRecord::Base subclass, even? Even in those cases, this principle still holds true. Our code is easier to understand and test when we can reduce the number of dimenstions to its behaviour, and mutating passed-in objects is a big factor in this, especially if you think about methods calling other methods and so on. There are ways we can design our applications to make this a natural approach to take, even for rich domain objects, but that is a topic for another day!

Until then, hopefully this walkthrough here can serve as a reminder to keep our methods courteous, to respect mutable values provided from outside, and wherever possible, leave them undisturbed and unmutated. Salubrious!

Bonus content! In preparing this post, I thought about whether it might be helpful to note that Ruby is a “pass by reference” language, since that’s the key underlying behavior that can result in these accidental mutations. However, my intuition here was actually back to front! Thanks to this wonderful stackoverflow answer, I was reminded that Ruby is in fact a “pass by value” language, but that all values are references.

We installed 5.94kW of solar PV in late 2017, with an ABB PVI-6000TL-OUTD inverter, and also a nice energy efficient Sanden heat pump hot water service to replace our crusty old conventional electric hot water system. In the four years since then we’ve generated something like 24MWh of electricity, but were actually only able to directly use maybe 45% of that – the rest was exported to the grid.

The plan had always been to get batteries once we are able to afford to do so, and this actually happened in August 2021, when we finally got a single 10kWh Redflow ZCell zinc bromine flow battery installed. We went with Redflow for several reasons:

• Unlike every other type of battery, they’re not a horrible fire hazard (in fact, the electrolyte, while corrosive, is actually fire retardant – a good thing when you live in a bushfire prone area).
• They’re modular, so you can just keep adding more of them.
• 100% depth of discharge (i.e. they’re happy to keep being cycled, and can also be left discharged/idle for extended periods).
• All the battery components are able to be recycled at end of life.
• They’re Australian designed and developed, with manufacturing in Thailand.

Our primary reasons for wanting battery storage were to ensure we’re using renewable energy rather than fossil fuels, to try to actually use all our local power generation locally, and to attain some degree of disaster resilience.

Being in Tasmania, most of our grid power is from renewable sources anyway (hydro), so the renewable energy argument may seem a little weak at first, unless you cast your mind back to the time some idiot decided to deplete our dams by selling a whole lot of hydro-generated power to Victoria in an El Niño year, then the Basslink cable broke and Tasmania had to fire up a bunch of diesel generators to get through winter. Good times.

On the local generation and use front, as I mentioned at the start of this post, we’ve previously exported more than half the energy we generated, but the feed-in tariff we get from Aurora (the power company) is only $0.06501 per kWh. Compare that to the rate we pay for grid energy ($0.29852/kWh peak or $0.139/kWh off-peak). Say we exported 13.2MWh in the last four years, we would have received about $858 worth of credit… But then when we drew power back from the grid at night, or on cloudy days, we would have paid somewhere between $1834-$3940 for that same amount of power. Treating the grid as a proxy for battery storage does not make any sort of financial sense.

As for disaster resilience, we don’t often have grid outages, but we do have them, and that can be a problem. Notably, all our potable water comes from rainwater tanks attached to the house and shed, and the pumps that push that water to the taps are electric, so if the grid is down we don’t have running water. Sure, I can get water out of the tanks with a bucket or a jug, and that’s fine for a little drinking or handwashing, but it’s not good long term. Then there’s our fridges and freezer – at any given time we’re likely to have a lot of stored frozen meat from animals we farm. We don’t want to lose that in a potential extended grid outage, as could happen in bushfire season or during a severe weather event. Also, it’s nice to still have power for our local network and NBN kit, so we can check the TasNetworks Power Outages page to find out WTF is going on.

Complete grid independence would be nice, and with our current power utilisation and a single Redflow battery we could almost do it in the height of summer, or even on some good days in spring or autumn, where we can generate all we need to run the house during the day and charge the battery to 100%, then draw it down overnight. The kink is that Redflow batteries need to undergo a maintenance cycle at least every three days where they are completely discharged for a few hours. If you’re grid connected you don’t really notice this, because the maintenance cycle commences at sunset and once the battery is drained you’re just using grid power again until the sun comes up, but it does mean we can’t be grid independent even if we theoretically have enough PV generation to do so, until we get a second battery (with more than one ZCell, the maintenance cycles are interleaved so at least one battery will always have some power in it).

The other problem with grid independence is that as much as Tasmania is excellent for solar PV generation in summer, it sucks in winter. Looking at our generation and usage figures for 2019, from mid-May to mid-August we were only able to generate 17% of the power we used, and I’ve seen days where we only generated 1-2kW in the entire day. Compare that with summer when we’ve peaked above 40kW some days in December.

Still, if the grid went away for a long time in the warmer half of the year with our current setup, it’d be irritating every few nights, but I reckon we’d manage OK. Of course, there would be some adjustments required to minimise our utilisation: I’d set the blockout timer on the Sanden so the hot water only heated during daylight hours, I’d turn most of our computer equipment off overnight, we’d try to avoid using the microwave at the same time as any other chunky electrical appliance so as not to pull more than 3kW continuously from the ZCell, there’s some lights we usually leave on that we’d just turn off, and so forth. In the colder half of the year, well, I guess we’d try to eat all the frozen food quickly then limp along as best as possible. We would still have some power, some of the time.

When we originally had the PV installed, it was AC coupled, i.e. the solar panels were connected to the inverter, and the inverter was connected to the grid, along with our loads. Our choice of inverter (ABB) was partly because it was Selectronic certified, and at the time we knew Redflow to be working with Selectronic battery inverters. Once we finally got to contacting Murray Roberts from Lifestyle Electrical Services in order to get a quote and talk about installation, circumstances had changed. It turns out that the Selectronic kit just doesn’t like flow batteries with their need to be completely discharged periodically. Victron Energy gear on the other hand works really well with – and is fully supported for use with – Redflow’s ZCell batteries. Lesson learned: with changing technology it doesn’t always pay to plan too far in advance.

Murray initially proposed a setup which would have hooked straightforwardly into our AC coupled solar, i.e. the ABB inverter and PV cells would remain connected as is, then there’d be a Victron energy meter to measure what was coming from the PV and from the grid, a Victron MultiPlus II inverter/charger to charge the battery and pull from the battery to run some loads, plus a Victron Cerbo GX and GX Touch to provide monitoring and control. Some of our power circuits to the house would be hooked up as Essential House Loads (i.e. to be supported by the battery during a grid outage), and some would be Non-Essential House Loads, i.e. powered by the grid and/or PV, but without battery backup. The Victron Cerbo GX and the Redflow ZCell Battery Management System (BMS) are internet connected to hook up with Victron’s VRM portal, which provides handy monitoring tools and graphs, and to allow remote support and assistance and firmware updates. The initial proposal looked something like this:

That configuration would have involved the least messing around, and met our goals of:

• Utilising as much of your own energy as possible locally.
• Dealing with occasional/unexpected grid outages (modulo the ZCell maintenance cycle).

But, it still fundamentally relied on the grid. With AC coupled solar, if the grid goes down your inverter automatically goes into anti-islanding mode, and won’t give you any power from your PV array even if the grid is down during the day and the sun is shining on your panels. Your first thought here may be “wait, if the grid is down but I still have sunlight, surely I should still have power”, and that’s an understandable reaction, but anti-islanding is actually a safety feature. If the juice goes from the PV cells to the inverter to the grid and your loads, and you’re exporting power while a power company employee is doing maintenance works on the grid side, you could electrocute them. This would not be a good thing.

A perhaps less obvious problem with this setup is that you can’t black start the site during an extended grid outage. If the grid is down and the inverter is thus in anti-islanding mode, you have no way to get power to restart the system and recharge the battery from empty (unless you’ve also got a generator). “But the grid never goes down for that long…” you might say, until you look at the outages really severe bushfires can cause (think: East Gippsland in the 2019-2020 Black Summer bushfires).

After some further discussion, Murray proposed getting rid of the ABB inverter, and doing DC coupled solar instead, with a Victron MPPT RS hooked up to the PV, two MultiPlus IIs, so that we could handle up to 10kW of power (that’s the maximum limit of all loads and grid export), with all house loads hooked up as Essential, so they can be supplied by whatever combination of grid, solar and battery power is available at any given time. Voilà:

One thing that’s missing in the above diagram is a manual changeover switch we had installed later, so that if there’s ever a fault in the Victron cluster that requires major works, but the grid is still up, we can manually switch all our loads back over to the grid side for the duration. Not that I expect to need that functionality, but better to have it than not just in case.

The 10kW maximum has proven to be fine, by the way – we just don’t ever put anything like that much power through the system at once. During the day we might pull 400-700W continuously with spikes from 1-3kW or occasionally 4-5kW when multiple heavier loads come on. I think the highest we’ve managed was 7kW very briefly one time with a panel heater and the microwave and the hot water and the clothes dryer and gods only know what else was on at the time, the point is it’s not easy to get the load that high. Note that we currently have a gas stove and oven – if we switched that to electric we might want to be a bit cautious about running lots of other heavy loads while cooking, but I suspect we’d still be fine in general.

Rhys, one of Murray’s crew, did a fantastic job of installing all the kit over several days, then Murray came out to do the final commissioning and bring the system online on August 31, 2021. Here’s a couple of pictures:

The transparent box in the above photo contains the Cerbo GX and the ZCell BMS, along with a little 12V backup power supply so that those things keep running if the grid fails and the ZCell is at 0% State of Charge (SoC). The ethernet switch above the sub board hooks up to the network point I installed previously when I was using a Raspberry Pi to monitor the ABB inverter’s generation. It’s Power-over-Ethernet, run from a UPS in my office which also keeps the NBN box and router alive, so the whole system still has internet access for about an hour even if all other power sources are dead (handy if there were some sort of fault and we needed remote assistance).

Here’s what the main switchboard looks like now. The leftmost switch (“Main switch (grid supply)”) turns grid power on/off to the sub board under the house, which goes from there to the Multis’ AC input. The AC out comes back up here to the right-hand switch (“Victron Sub Board Backup Supply”) and thence to all the loads (the various switches in the middle).

Inside the house there’s a neat little touchscreen console (the Victron GX Touch), which connects via an HDMI cable in the wall to the Cerbo under the house. This shows you what everything is doing at any given time, provides notifications of alarms (e.g.: “Grid Lost”) and has a series of menus for configuring the system. The exact same console is also accessible via a web browser or mobile phone, either over the local network, or remotely via the VRM portal.

Three days after everything was up and running, we went out to run some errands and came back home in the afternoon to discover the Cable PI device in our kitchen beeping like mad. All the power was still on in the house. I called Murray in a bit of a panic thinking something was broken, but it turned out we were experiencing an actual grid outage over a large area – all the way from Grove to Leslie Vale, 802 properties were without power due to wires down in strong wind, and the PV was powering our house. We didn’t feel a thing. The UPS in my office noticed a small dip for a second or two when the grid failed, but the microwave clock was still on, and other computer gear not hooked up to a UPS remained up during the cutover. And the battery kept on charging – it was at 65% SoC when the grid went out and up to 83% by the time the grid came back.

This was just awesome.

A few days after that, suddenly, at 19:39 on September 6, we were without power. The grid was up, but the Victron kit had shut down. This was not awesome. It happened during the ZCell’s regular maintenance cycle, and at the time we got a warning in the BMS logs, and a battery high voltage warning from the MPPT. It was unclear then exactly what the problem was though – our MPPT RS was a newer model so maybe it was different somehow from what had been previously tested? Also, we discovered the DVCC setting still needed to be turned on, so maybe that was the issue. Anyway, I power cycled the Victron kit and everything was fine again until a couple of weeks later on September 19, when the system shut down again during a maintenance cycle, and again coinciding with battery high voltage warnings.

Because of the previous shutdown, Murray had been in contact with Simon Hackett from Redflow, and Simon subsequently enabled the “DC-coupled PV – feed in excess” setting. The assumption here was that the extra power being delivered from the ZCell during the maintenance discharge wasn’t absorbed by our house loads, i.e. it was trying to discharge at 1kW, but our loads were utilising less than that, and there was nowhere for the excess to go, hence the shutdown. Enabling “DC-coupled PV – feed in excess” allows power from the DC bus to be sent to the grid if necessary, which turns out to be the case at our site. A second ZCell would mitigate this because the one under maintenance would simply be able to dump to the other (assuming it wasn’t already full), but we only have one battery so far.

At this point, after those two final settings changes (and a firmware update) the system was operating exactly as it should. Everything was configured correctly, and we could survive grid outages if there was charge in the battery and/or the sun was up. Our electricity meter was replaced on September 21 so we could switch to Tariff 93 Peak & Off-Peak billing. There were no further unexpected shutdowns. Everything was totally fine, except we were still seeing these weird battery high voltage warnings during the ZCell maintenance cycle, reported by both the BMS and the MPPT.

This is something neither Murray nor Simon had seen before. What followed was several weeks of troubleshooting and analysis, which I found absolutely fascinating.

I was keeping detailed notes of what happened during each maintenance cycle, and what I saw in the BMS logs, and on the Cerbo console. Several maintenance cycles later I discovered a correlation between the battery high voltage warnings, and sudden large changes in AC loads, notably when a 2400W panel heater in our bedroom turned itself on and off overnight. Also, the high voltage warnings seemed to be more likely to occur if we went into maintenance with a high state of charge, versus a low state of charge.

Simon, who knows and understands how the ZCell behaves during maintenance, explained about the Energy Extraction Device, which is part of the unit whose purpose is to deliberately drain the battery down to zero for maintenance in a timely fashion. He wondered if there was some issue where very high power demands at short notice, while the the EED was active, were causing the DC bus voltage to fluctuate and in turn cause the MPPT to respond in an unusual manner. We experimented with changing settings on the BMS to activate the EED later than usual in the maintenance cycle (“Start maintenance when SoC below x%”), and also experimented with limiting inverter output on the Multis, and tweaking the maximum charge voltage.

After a few more cycles of observation, the suspicion became that the MPPT itself wasn’t at fault, rather it was just being the messenger. Maybe these odd voltage spikes had always happened at other sites too, but the new MPPT RS units were doing a better job of noticing them?

Later in October, Simon noticed that we were seeing spikes very close to when the battery was nearly completely empty. At that point in time, the EED was telling the Multis that it had a 10 amp output capacity, but if the Multis tried to draw on that to handle a sudden rise in load (as from our panel heater for example), the battery voltage would collapse, and the system would oscillate a bit between those two states. That behaviour was fixed with a small firmware change which I believe later landed in the BMS 1.1.11 release. Unfortunately, that change by itself didn’t make the high voltage warnings go away.

A few days after that, we suddenly had a slew of #201 – Internal DC voltage errors from the MPPT, so we were back to being concerned that maybe there was something wrong with that piece of equipment, especially given those errors began to crop up more often as time passed.

Victron’s documentation ominously stated that this error meant “a measurement circuit inside the unit is broken” and the unit “is really broken, not safe for use, and if it hadn’t stopped working already then it would have stopped working soon”. Clearly a replacement or repair was in order, but I’ll get to that later.

On November 4, looking at the logs I’d collected from November 2, Simon noticed that one of the high battery voltage warnings happened at quite a high state of charge (72%), which meant it wasn’t really about the battery running out of energy and having the voltage collapse. It looked like it was the EED being over-drawn, regardless of how much energy was still in the battery. It turns out there’s a thing that the ZCell does to handle surge demand when the EED is on, called an “EED switchback”. ZCells internally have three contactors, for Charge, Discharge and EED (also known as Strip). In normal operation, the C and D contactors are on, and E is off, so the battery can be charged or discharged at will, and the EED is doing nothing. During maintenance, the EED comes on, but it can’t deliver more than 20 amps. If the site pulls more than the EED can supply, the battery goes back to normal operation (C and D on, E off) while the high demand is present. Once the high demand goes away, it switches the EED back on, to keep discharging at the normal rate of 1kW. But, by default, that switchback process only happens five times per battery maintenance cycle so as to avoid the potential for excessive cycling of the contactors in weird edge cases.

Looking at our overnight load with the panel heater on, there’s loads of spikes from below 1kW to above 1kW, so we were getting through that switchback limit very quickly. After that, with the high load from the panel heater, it was entirely possible that the Multi cluster would try to pull more power than the EED could provide, and the EED would shut down in response, resulting in weirdness on the DC bus. Simon’s suspicion for why this hadn’t been seen before was that it needed at least four casual factors to be present at once:

1. Site demand is spiky for the entire night.
2. The spikes start below 1kW and end above 1kW to use up the switchback quota.
3. The site has only one battery (if a second battery were present it would handle the surge load while the first was in maintenance).
4. The two Multis are capable of running far more load than the battery can service.

If we were to change or remove any one of those factors, we wouldn’t see the problem. So, as a test, Simon changed the switchback limit from 5 to 50, and I watched what happened during the next maintenance cycle. The status page of the BMS web interface shows, among other things, the current state of the contactors. Here’s an example with C and D on, and E off:

The ZBM logs also show the contactor state over time. Here’s a snippet I’ve colourised to make the state changes obvious:

If we take the above section of ZBM logs from 2021-11-08 23:15 to 23:29, it looks like we had “_ D E” up until 23:17, then switched to “C D _” from 23:18 to 23:26, then finally to “_ _ E” at 23:27. Based on this I’d imagine we had one switchback event that lasted eight minutes. But I had earlier noticed on the status page that the contactors seemed to be toggling more rapidly, so I wrote a little script to scrape the BMS REST API once per second and dump that to a file, which shows Charge and EED toggling on/off about ten times in that window:

2021-11-08T23:14:28+11:00    "_ D E"
2021-11-08T23:17:59+11:00    "C D _"
2021-11-08T23:18:29+11:00    "C D E"
2021-11-08T23:18:34+11:00    "_ D E"
2021-11-08T23:18:56+11:00    "C D _"
2021-11-08T23:19:14+11:00    "C D E"
2021-11-08T23:19:20+11:00    "_ D E"
2021-11-08T23:19:54+11:00    "C D _"
2021-11-08T23:20:17+11:00    "C D E"
2021-11-08T23:20:18+11:00    "_ D E"
2021-11-08T23:20:49+11:00    "C D _"
2021-11-08T23:21:19+11:00    "C D E"
2021-11-08T23:21:22+11:00    "_ D E"
2021-11-08T23:21:46+11:00    "C D _"
2021-11-08T23:21:51+11:00    "_ D _"
2021-11-08T23:21:54+11:00    "C D _"
2021-11-08T23:22:23+11:00    "C D E"
2021-11-08T23:22:26+11:00    "_ D E"
2021-11-08T23:22:43+11:00    "C D E"
2021-11-08T23:22:45+11:00    "C D _"
2021-11-08T23:23:26+11:00    "C D E"
2021-11-08T23:23:30+11:00    "_ D E"
2021-11-08T23:23:43+11:00    "C D _"
2021-11-08T23:24:30+11:00    "C D E"
2021-11-08T23:24:34+11:00    "_ D E"
2021-11-08T23:24:43+11:00    "C D _"
2021-11-08T23:25:35+11:00    "C D E"
2021-11-08T23:25:39+11:00    "_ D E"
2021-11-08T23:25:49+11:00    "C D _"
2021-11-08T23:26:40+11:00    "C D E"
2021-11-08T23:26:44+11:00    "_ D E"
2021-11-08T23:26:49+11:00    "_ _ E"

On the assumption we were hitting way more switchbacks than expected, Simon just went and set the maximum switchback count to 999. A few days later, taking the log from my script, I saw something like 150-160 switchbacks, but given we’d set the limit way high, that fixed all the high voltage warnings, except for one, right at the very end of the maintenance cycle when the discharge limit from the ZCell drops from 10 amps to 0 amps.

Simon discussed this final spike with the folks who built the EED, and found that when current draw from the EED stops, there is a voltage spike, of very low energy, for 10ms, and it can rise as high as 64V during that period before dropping back to the expected 57V. It’s normal for the EED to do this, and as there’s no real energy in it, it won’t be damaging anything. The thing about our site seems to be the new MPPT RS, with a new voltage sensing circuit that’s actually capable of noticing the spike, whereas the other gear (the MultiPlus IIs) misses it because it’s so short. The advice from the electrical engineer was to try adding more capacitors on the DC bus to absorb the spike. We already had two 47,000uF capacitors on there, so Murray went and ordered two more.

With the high battery voltage warnings out of the way, we were back to the #201 Internal DC voltage errors from the MPPT. On the assumption the unit was indeed faulty in that regard we requested a replacement, but Victron came back and said the problem could be fixed by replacing two resistors on the main board of the unit. I guess that makes sense – if you can fix a problem with $2 worth of resistors, that’s three orders of magnitude cheaper than replacing the whole unit.

By then we were getting into December, and what with pandemic-related shipping delays and the Christmas holiday period, it was later in January before we were able to get the additional capacitors installed on the DC bus, and replace the resistors in the MPPT’s voltage sensing circuit. The additional capacitors went just fine, the replacement resistors not so much. Once the MPPT was powered back up it claimed there was zero volts coming from the PV, even though the sun was shining, and the LCD display started flickering strangely. Something was definitely broken here, so we powered it back down, and Simon arranged for a replacement unit to be sent out, which took another few weeks, which is a damn shame in January/February, being prime solar PV generation time.

The delay did however allow me to spend some time messing around with scheduled charges to see if there was a cost benefit to grid-charging the battery during off-peak times, then drawing it back down during peak, because the reality is we’re going to want to do this in winter when there’s not much sun, so why not try it out in advance? TL;DR: Yes, it’s worth grid charging the battery off-peak, provided you use all that power during peak times, but it’s a bit irritating trying to figure out exactly what you’ll save. In one of my tests it was the difference between paying $3.85 for about 20kWh of usable electricity in a 24 hour period versus paying $4.70, so it’s not insignificant.

Rhys came out and installed the replacement MPPT on February 11, and was done by the middle of the day. Everything was running beautifully again, but when the unit came online there were ten instances of the dreaded #201 Internal DC Voltage Error, along with a #27 Charger Short Circuit. I used the VictronConnect app on my phone to see if I could get any more information directly from the MPPT. It told me there was a firmware update available from v1.05 to v1.08, so I went looking for information about that, and discovered that Victron’s error code documentation had been updated since I first saw it back in late October. In addition to the ominous warnings about broken measurement circuits it now also said:

“Make sure to update the firmware to at least v1.08, in previous firmwares the limits were too strict. And it could trigger falsely during MPPT start-up in the morning and MPPT shutdown in the evening.”

So I updated the firmware, and writing this now, two and a half months later, we’ve not seen a single #201 since. Could this have always been a firmware issue? Maybe, given the “accepted answer” on this Victron Community forum post says that firmware version v1.08 “solves the vast majority of MPPT RS, and Inverter RS, Error 201 issues”. Or maybe it was both – maybe we had a broken bit of kit and broken firmware too. Either way, it’s fixed now.

I continued to monitor regular maintenance cycles, and also deliberately forced maintenance a couple of times with a high state of charge to try to stress it as much as I could. During those periods I saw something like 3-10 switchbacks, so Simon set our switchback limit back down from 999 to 30. I understand a future Redflow firmware update will change this default for everyone to somewhere between 25-50, and I’m very happy that this unexpected testing at our site resulted in firmware improvements that will presumably benefit other ZCell users too.

By late April we’d been through 33 maintenance cycles since the extra capacitors went in, with 26 of those occurring since the new MPPT was installed. There had been only three occasions when the BMS briefly noticed alleged high battery voltages in that time. The MPPT was completely silent until April 20 when we got three battery high voltage warnings within an 8 minute interval right at the end of the maintenance cycle, when the battery was almost completely empty. But the weather had also started to get cold, and those errors coincide with a spike from our panel heater, which is consistent with our earlier observations about load spikes with the EED on being “difficult” and really just points to replacing the panel heater with a heat pump. Heat pump are way more energy efficient, have much smoother load, and can also be used for cooling in summer (they’re called “reverse cycle air conditioners” on the mainland).

That’s about the end of the story. The system is brilliant, and we could not be happier with the support we’ve received from Simon at Redflow, who’s been extremely generous with his time and knowledge, and Murray and Rhys of Lifestyle Electrical Services. Thanks for everything guys, I’ve learned a lot. In the eight months the system has been running we’ve generated 4631kWh of electricity and “only” sent 588kWh to the grid, which means we’ve used 87% of what we generated locally – much better than the pre-battery figure of 45%. I suspect we’ve reduced the amount of power we pull from the grid by about 30% too, but I’ll have to wait until we have a full year’s worth of data to be sure. We’ve also survived or shortened at least five grid outages with durations from a few minutes to a few hours.

The next thing to do is get a second ZCell, and possibly eventually think about a third. Given our current generation capability, two ZCells would allow us to store and utilise 100% of our generated power locally. We’d also have the ability to handle grid outages at any time, because with two batteries the maintenance cycles interleave and they can be configured to always ensure there’s a minimum amount of charge somewhere. A third would allow us to look at Standby Power System (SPS) mode where one battery is fully charged, then put into hibernation where it can remain for months. This sounds like a great way to have backup storage available for grid outages in the middle of winter when there’s no sunlight.

Appendix A – Settings Worth Messing With

Scheduled charging on the Cerbo GX console

In summer I scheduled charges of the battery to 15% between the hours of 04:00 and 08:00, and 30% between the hours of 15:00-17:00. Peak electricity hours during daylight savings are 08:00-11:00 and 17:00-22:00, and I found that a 15% charge overnight from the grid was enough to have a bit in the battery for the morning peak before the PV really got going. The afternoon charge was there mostly just in case we had a cloudy day – we’d usually get way more charge than that from the sun anyway. Now that we’re off daylight savings, peak hours change to 07:00-10:00 and 16:00-21:00, so I’ve set it to a 30% charge from 03:00-07:00 and a 50% charge from 14:00-16:00, which seems to be about right given our general peak utilisation and decreasing sunlight. I’m unlikely to set the afternoon charge higher than 50% because I don’t want to potentially go into a maintenance cycle with the battery very full, but I may re-evaluate that as we get deeper into winter.

It’s worth mentioning that during a scheduled charge, the power will come from wherever the Victron gear can find it, so if the sun is shining, you’ll be charging from the PV, not the grid. One thing to note is that during the scheduled charge period, the battery will not be used to support loads, even if it’s currently got a higher SoC than your limit. Some power will trickle away slowly though, I assume to run the pumps and supporting electronics of the battery itself.

My choice of timing for the overnight charge (four hours up to the start of the morning peak) is me wanting to have some power in the battery for as long as possible overnight in case of outages, without potentially interfering with maintenance cycles, which typically will have finished some time in the wee hours.

I also set up a 15% charge on weekend mornings. This doesn’t save us any money at all (actually it’ll be costing us a couple of tens of cents) because weekends are all off-peak power. The reason is again to have some opportunistic grid backup. Before I set this up we had an outage at 07:45 one Saturday morning with the battery empty, and had to wait until about 09:30 for the PV to bring the battery up to 10% again before everything came back online. Still, that then got us through the remainder of the grid outage which finished at about 10:15.

Battery Maintenance Settings on the ZCELL BMS

On the Battery Maintenance screen of the ZCell BMS, I’ve got “Immediate maintenance for batteries with an EED” turned off, and “Start Maintenance When SoC Below 25%” enabled. This is to try to reduce the amount of time the EED runs, to limit switchbacks caused by our spiky load. In summer I also set “Daily SoC Limit Before Maintenance” to 50%, so the battery would not let itself be charged more than half way on those long hot days with late sunsets and early sunrises. This was to minimise maintenance cycle time, because I’d previously seen occasions where we went into maintenance with 100% SoC, and the cycle didn’t finish before the following morning when the sun came up. I also had a couple of times where I guess some timeout expired and the ZCell went into its final chemical maintenance state while it still had a few percent of charge. Not letting it get very full on maintenance days avoids these situations. Now that we’re getting towards winter I’ve removed that limit because the nights are longer and I expect our evening power utilisation to be higher, i.e. we should naturally use up whatever power we’re able to generate in plenty of time during winter maintenance cycles.

It’s also worth checking the latitude and longitude are set correctly on the Site Configuration page, because that’s how the BMS figures out when the sun sets and thus when to start maintenance by default.

Appendix B – VRM Portal

The VRM portal is a remote monitoring and management web interface which Victron provides gratis for users of their hardware. It provides a realtime view of the same live utilisation information you can get from the Cerbo console, plus handy graphs of solar, grid and battery consumption.

It also provides detailed graphs of just about anything you can think of from any of the system components. It’s extremely useful. Without this I never would have been able to correlate the battery high voltage warnings with load spikes and changes in the ZCell discharge current limits.

The data for the advanced graphs is stored for at least six months, and the solar yield and consumption data is stored for at least 5 years. The alarm logs don’t hang around that long – I suspect it may just be showing the last 1000 entries. Somewhat irritatingly, most of these are usually low battery alarms that we don’t care about (you see a lot of them during maintenance cycles).

Appendix C – Security / Connectivity / Internet Access

The ZCell BMS and Victron Cerbo GX both need to be connected to the internet for firmware updates, remote support, and to work with the VRM portal. They don’t need to be connected 100% of the time, but they do want the connection for those reasons. The system will operate just fine if the internet is down though, and you don’t have to use the VRM portal if you don’t want to. I’ve put everything on a separate network, so I can access the BMS and the Cerbo console from my desktop/laptop/phone, but the BMS and Cerbo can’t do the reverse. It’s not that I don’t trust Redflow or Victron, it’s just sensible to keep systems that allow any form of remote access isolated from the rest of your internal network.

The BMS and Cerbo both provide WiFi APs for initial configuration. I’ve since turned those off. I can use the wired connection to the BMS to turn the WiFi back on if I ever need it, and I can do the same for the Cerbo from its console.

The Cerbo and MPPT both speak Bluetooth, so you can use the VictronConnect app to talk to them from your phone, to view status and update firmware.

Appendix D – Hackability

The ZCell BMS has a REST API, which is documented in the online help available from its web interface. This is how I was able to write a few scripts to log the battery state of charge, the contactor state, and the voltage and warning indicator status:

• zcell-soc.sh
• zcell-contactor-state.sh
• zcell-warnings.sh

A bunch of Victron stuff is open source, notably Venus OS, which is the software that runs on the Cerbo. It looks fairly straightforward to get root on these things. It’s also possible to hook up the Victron kit to Home Assistant. I haven’t tried actually doing any of these things yet myself.

Appendix E – Aurora Plus

Having switched to Tariff 93 and gotten a fancy new electricity meter, we were able to use the Aurora Plus service from the power company. This provides a web interface and mobile phone app for viewing your power usage down to the hour, colour coded to indicate peak and off-peak usage and solar feed in. You also get a monthly, rather than quarterly bill. This all sounded pretty neat, so I signed up.

Aside from having used it to confirm that the figures I get from the VRM Portal and the power company actually match, it’s turned out to not be especially great.

While the electricity meter records usage information every 15 minutes, it’s only sent back to Aurora once per day, so the usage data is never actually live. Sure, you can see history, but this is useless for adjusting your power consumption on the fly. Compare that with the VRM Portal or Cerbo console, where I can see at a glance how much power is being used and how much solar is being generated right now and decide to turn appliances on or off appropriately.

Also, it nags you to give them money. It’s continually telling me I have a big red negative dollar balance, and periodically notifies me to “top up now to get ahead of your monthly bill”. No. I will pay the bill by the due date listed on the bill, after the bill actually arrives.

Finally, it costs eleven cents a day for the privilege of having the service. Under the circumstances I think I’m going to cancel it and just go back to quarterly billing.

Update 2022-06-17: I never got around to cancelling (you can’t do it online – you actually have to call and speak to someone), but I just received a notification saying “From 1 July 2022 you will no longer be charged 11c/day for aurora+”, so I’ve decided to keep it.

We’ve done this a number of times over the last decade, from OSDC to LCA. The idea is to provide a free psychologist or counsellor at an in-person conference. Attendees can do an anonymous booking by taking a stickynote (with the timeslot) from a signup sheet, and thus get a free appointment.

Many people find it difficult taking the first (very important) step towards getting professional help, and we’ve received good feedback that this approach indeed assists.

So far we’ve always focused on open source conferences. Now we’re moving into information security! First BrisSEC 2022 (Friday 29 April at the Hilton in Brisbane, QLD) and then AusCERT 2022 (10-13 May at the Star Hotel, Gold Coast QLD). The awesome and geek friendly Dr Carla Rogers will be at both events.

How does this get funded? Well, we’ve crowdfunded some, nudged sponsors, most mostly it gets picked up by the conference organisers (aka indirectly by the sponsors, mostly).

If you’re a conference organiser, or would like a particular upcoming conference to offer this service, do drop us a line and we’re happy to chase it up for you and help the organisers to make it happen. We know how to run that now.

In-person is best. But for virtual conferences, sure contact us as well.

The hack day didn’t go as well as I hoped, but didn’t go too badly. There was smaller attendance than hoped and the discussion was mostly about things other than FLOSS. But everyone who attended had fun and learned interesting things so generally I think it counts as a success. There was discussion on topics including military hardware, viruses (particularly Covid), rocketry, and literature. During the discussion one error in a Wikipedia page was discussed and hopefully we can get that fixed.

I think that everyone who attended will be interested in more such meetings. Overall I think this is a reasonable start to the Hack Day meetings, when I previously ran such meetings they often ended up being more social events than serious hacking events and that’s OK too.

One conclusion that we came to regarding meetings is that they should always be well announced in email and that the iCal file isn’t useful for everyone. Discussion continues on the best methods of announcing meetings but I anticipate that better email will get more attendance.

My OSS work in March was a bit of a grind, but I made progress nonetheless. I worked mostly on relocating and refactoring the Hanami action and view integration code.

For some context, it was back in May 2020 that I first write the action/view integration code for Hanami 2.0. Back then, there were a couple of key motivators:

• Reduce boilerplate to an absolute minimum, to the extent that simply inheriting from Hanami::View within a slice would give you a view class fully integrated with the Hanami application.
• Locate the integration code in the non-core gems themselves (i.e. in the hanami-controller and hanami-view gems, rather than hanami), to help set an example for how alternative implementations may also integrate with the framework.

Since then, we’ve learnt a few things:

• As we’ve gone about refining the core framework, we’ve wound up having to synchronize changes from time to time across the hanami, hanami-controller, and hanami-view gems all at once.
• Other Hanami contributors have noted that the original integration approach was a little too “magical,” and didn’t allow users any path to opt out of the integration code.

Once I finished my work on the concrete slice classes last month, I decided that now was the time to address these concerns, to bring the action and view class integrations back into the hanami gem, and to take a different approach to activating the integration code.

The work in progress is over in this PR, and thankfully, it’s nearly done!

The impact within Hanami 2 applications will be fairly minimal: the biggest change is that your base action and view classes will now inherit from application variants:

# slices/main/lib/action/base.rb

require "hanami/application/action"

module Main
  module Action
    class Base < Hanami::Application::Action
      # Previously, this inherited from Hanami::Action
    end
  end
end

By using this explicit application superclass for actions and views, we hopefully make it easier for our users to understand and distinguish between the integrated and standalone variants of these classes. This distinct superclass should also provide us a clear place to hang extra API documentation relating to the integrated behavior of actions and views.

More importantly for the overall experience, Hanami::Application::Action and Hanami::Application::View are both now kept within the core hanami gem. While the framework heads into this final stretch of work before 2.0 final, this will allow us to keep together the aspects of the integration that tend to change together, giving us our best chance at providing a tested, reliable, streamlined actions and views experience.

This is a pragmatic move above all else — we’re a team with little time, so the more we can do to give ourselves confidence in this integrated experience working properly, like having all the code and tests together in one place, the quicker we should be able to get to the 2.0 release. Longer term, we’ll want to provide a first-class integration story for third party components, and I believe we can lead the way in how we deliver that via our actions and views, but that’s now firmly a post-2.0 concern in my mind.

In the meantime, I did take this opportunity to rethink and provide some better hooks for classes like Hanami::Application::View to integrate with the rest of the framework, chiefly via a new Hanami::SliceConfigurable module. You can see how it works by checking out the code for Hanami::Application::View itself:

# frozen_string_literal: true

require "hanami/view"
require_relative "../slice_configurable"
require_relative "view/slice_configured_view"

module Hanami
  class Application
    class View < Hanami::View
      extend Hanami::SliceConfigurable

      def self.configure_for_slice(slice)
        extend SliceConfiguredView.new(slice)
      end
    end
  end
end

Any class that extends Hanami::SliceConfigurable will have its own .configure_for_slice(slice) method called whenever it is sublcassed within a module namespace that happens to match the namespace managed by an Hanami slice. Using the slice object passed to that hook, that class can then read any slice- or application-level config to set itself up to integrate with the application.

In the example above, we extend a slice-specific instance of SliceConfiguredView, which will copy across application level view configs, as well configure the view’s part namespaces to match the slice’s namespace. The reason we build a module instance here (this module builder pattern is a whole technique that I’ll gladly go into one day, but it’s a little out of scope for these monthly updates) is so that we don’t have to keep any trace of the slice as state on the class after we’re done using it for configuration, making it so the resulting class is as standalone as possible, and not offering any way for its users to inadvertently couple themselves to the whole slice instance.

Overall, this change is feeling quite settled now. All the code has been moved in and refactored, and all that’s left is a final polishing pass before merge, which I hope I can get done this week! A huge thank you to Sean Collins for his original work in proposing an adjustment to our action integration code. It was Sean’s feedback and exploratory work here that got me off the fence here, and made it so easy to get started with these changes!

That’s it for me for now. See you all again next month, hopefully with some more continued core framework polishing.

Meeting Report

The March 2022 meeting went reasonably well. Everyone seemed to have fun and learn useful things about computers. After 2 hours my Internet connection dropped out which stopped the people who were using VMs from doing the tutorial. Fortunately most people seemed ready for a break so we ended the meeting. The early and abrupt ending of the meeting was a disappointment but it wasn’t too bad, the meeting would probably only have gone for another half hour otherwise.

The BigBlueButton system was shown to be effective for training when one person got confused with the Debian package configuration options for Postfix and they were able to share the window with everyone else to get advice. I was also confused by that stage.

Future Meetings

The main feature of the meeting was training in setting up a mailserver with Postfix, here are the lecture notes for it [1]. The consensus at the end of the meeting was that people wanted more of that for the April meeting. So for the April meeting I will add to the Postfix Training to include SpamAssassin, SPF, DKIM, and DMARC. For the start of the next meeting instead of providing bare Debian installations for the VMs I’ll provide a basic Postfix/Dovecot setup so people can get straight into SpamAssassin etc.

For the May meeting training on SE Linux was requested.

Social Media

Towards the end of the meeting we discussed Matrix and federated social media. LUV has a Matrix server and I can give accounts to anyone who’s involved in FOSS in the Australia and New Zealand area. For Mastodon the NZOSS Mastodon server [2] seems like a good option. I have an account there to try Mastodon, my Mastodon address is @etbe@mastodon.nzoss.nz .

We are going to make Matrix a primary communication method for the Flounder group, the room is #flounder:luv.asn.au . My Matrix address is @etbe:luv.asn.au .

• [1] https://doc.coker.com.au/training/postfix-training/
• [2] https://mastodon.nzoss.nz/

We now have a mailing list see https://lists.linux.org.au/mailman/listinfo/flounder for information, the address to post to the list is flounder@lists.linux.org.au..

We also have a new URL for the blog and events. See the right sidebar for the link to the iCal file which can be connected to Google Calendar and most online calendaring systems.

We just had the first Flounder meeting which went well. Had some interesting discussion of storage technology, I learnt a few new things. Some people did the ZFS training and BTRFS training and we had lots of interesting discussion.

Andrew Pam gave a summary of new things in Linux and talked about the sites lwn.net, gamingonlinux.com, and cnx-software.com that he uses to find Linux news. One thing he talked about is the latest developments with SteamDeck which is driving Linux support in Steam games. The site protondb.com tracks Linux support in Steam games.

We had some discussion of BPF, for an introduction to that technology see the BPF lecture from LCA 2022.

Next Meeting

The next meeting (Saturday 5th of March 1PM Melbourne time) will focus on running your own mail server which is always of interest to people who are interested in system administration and which is probably of more interest than usual because of Google forcing companies with “a legacy G Suite subscription” to transition to a more expensive “Business family” offering.

I “recently” wrote about obtaining a new (to me, actually quite old) computer over in The Apple Power Macintosh 7200/120 PC Compatible (Part 1). This post is a bit of a detour, but may help others understand why some images they download from the internet don’t work.

Disk partitioning is (of course) a way to divide up a single disk into multiple volumes (partitions) for different uses. While the idea is similar, computer platforms over the ages have done this in a variety of different ways, with varying formats on disk, and varying limitations. The ones that you’re most likely to be familiar with are the MBR partitioning scheme (from the IBM PC), and the GPT partitioning scheme (common for UEFI systems such as the modern PC and Mac). One you’re less likely to be familiar with is the Apple Partition Map scheme.

The way all IBM PCs and compatibles worked from the introduction of MS-DOS 2.0 in 1983 until some time after 2005 was the Master Boot Record partitioning scheme. It was outrageously simple: of the first 512 byte sector of a disk, the first 446 bytes was for the bootstrapping code (the “boot sector”), the last 2 bytes were for the magic two bytes telling the BIOS this disk was bootable, and the other 64 bytes were four entries of 16 bytes, each describing a disk partition. The Wikipedia page is a good overview of what it all looks like. Since “four partitions should be enough for anybody” wasn’t going to last, DOS 3.2 introduced “extended partitions” which was just using one of those 4 partitions as another similar data structure that could point to more partitions.

In the 1980s (similar to today), the Macintosh was, of course, different. The Apple Partition Map is significantly more flexible than the MBR on PCs. For a start, you could have more than four partitions! You could actually have a lot more than four partitions, as the Apple Partition Map is a single 512-byte sector for each partition, and the partition map is itself a partition. Instead of being block 0 (like the MBR is), it actually starts at block 1, and is contiguous (The Driver Descriptor Record is what’s at block 0). So, once created, it’s hard to extend. Typically it’d be created as 64×512-byte entries, for 32kb… which turns out is actually about enough for anyone.

The Inside Macintosh reference on the SCSI Manager goes through more detail as to these structures. If you’re wondering what language all the coding examples are in, it’s Pascal – which was fairly popular for writing Macintosh applications in back in the day.

But the actual partition map isn’t the “interesting” part of all this (and yes, the quotation marks are significant here), because Macs are pretty darn finicky about what disks to boot off, which gets to be interesting if you’re trying to find a CD-ROM image on the internet from which to boot, and then use to install an Operating System from.

… the preferred programming language changes.

I never programmed a 1980s Macintosh actually in the 1980s. It was sometime in the early 1990s that I first experienced Microsoft Basic for the Macintosh. I’d previously (unknowingly at the time as it was branded Commodore) experienced Microsoft BASIC on the Commodore 16, Commodore 64, and even the Apple ][, but the Macintosh version was something else. It let you do some pretty neat things such as construct a GUI with largely the same amount of effort as it took to construct a Text based UI on the micros I was familiar with.

Okay, to be fair, I’d also dabbled in Microsoft QBasic that came bundled with MS-DOS of the era, which let you do a whole bunch of graphics – so you could theoretically construct a GUI with it. Something I did attempt to do. Programming on the Mac was so much easier to construct a GUI.

Of course, Microsoft Basic wasn’t the preferred way to program on the Macintosh. At that time it was largely Pascal, with C being something that also existed – but you were going to see Pascal in Inside Macintosh. It was probably somewhat fortuitous that I’d poked at Pascal a bit as something alternate to look at in the high school computing classes. I can only remember using TurboPascal on DOS systems and never actually writing Pascal on the Macintosh.

By the middle part of the 1990s though, I was firmly incompetently writing C on the Mac. No doubt the quality of my code increased after I’d done some university courses actually covering the language rather than the only practical way I had to attempt to write anything useful being looking at Inside Macintosh examples in Pascal and “C for Dummies” which was very not-Macintosh. Writing C on UNIX/Linux was a lot easier – everything was made for it, including Actual Documentation!

Anyway, in the early 2000s I ran MacOS X for a bit on my white iBook G3, and did a (very) small amount of any GUI / Project Builder (the precursor to Xcode) related development – instead largely focusing on command line / X11 things. The latest coolness being to use Objective-C to program applications (unless you were bringing over your Classic MacOS Carbon based application, then you could still write C). Enter some (incompetent) Objective-C coding!

Then Apple went to x86, so the hardware ceased being interesting, and I had no reason to poke at it even as a side effect of having hardware that could run the software stack. Enter a long-ass time of Debian, Ubuntu, and Fedora on laptops.

Come 2022 though, and (for reasons I should really write up), I’m poking at a Mac again and it’s now Swift as the preferred way to write apps. So, I’m (incompetently) hacking away at Swift code. I have to admit, it’s pretty nice. I’ve managed to be somewhat productive in a relative short amount of time, and all the affordances in the language gear towards the kind of safety that is a PITA when coding in C.

So this is my WIP utility to be able to import photos from a Shotwell database into the macOS Photos app:

There’s a lot of rough edges and unknowns left, including how to actually do the import (it looks like there’s going to be Swift code doing AppleScript things as the PhotoKit API is inadequate). But hey, some incompetent hacking in not too much time has a kind-of photo browser thing going on that feels pretty snappy.

Recently I read Michael Snoyman’s post on combining Axum, Hyper, Tonic and Tower. While his solution worked, it irked me – it seemed like there should be a much tighter solution possible.

I can deep dive into the code in a later post perhaps, but I think there are four points of difference. One, since the post was written Axum has started boxing its routes : so the enum dispatch approach taken, which delivers low overheads actually has no benefits today.

Two, while writing out the entire type by hand has some benefits, async code is much more pithy.

Thirdly, the code in the post is entirely generic, except the routing function itself.

And fourth, the outer Service<AddrStream> is an unnecessary layer to abstract over: given the similar constraints – the inner Service must take Request<..>, it is possible to just not use a couple of helpers and instead work directly with Service<Request...>.

So, onto a pithier version.

First, the app server code itself.

use std::{convert::Infallible, net::SocketAddr};

use axum::routing::get;
use hyper::{server::conn::AddrStream, service::make_service_fn};
use hyper::{Body, Request};
use tonic::async_trait;

use demo::echo_server::{Echo, EchoServer};
use demo::{EchoReply, EchoRequest};

struct MyEcho;

#[async_trait]
impl Echo for MyEcho {
    async fn echo(
        &self,
        request: tonic::Request<EchoRequest>,
    ) -> Result<tonic::Response<EchoReply>, tonic::Status> {
        Ok(tonic::Response::new(EchoReply {
            message: format!("Echoing back: {}", request.get_ref().message),
        }))
    }
}

#[tokio::main]
async fn main() {
    let addr = SocketAddr::from(([0, 0, 0, 0], 3000));

    let axum_service = axum::Router::new().route("/", get(|| async { "Hello world!" }));

    let grpc_service = tonic::transport::Server::builder()
        .add_service(EchoServer::new(MyEcho))
        .into_service();

    let both_service =
        demo_router::Router::new(axum_service, grpc_service, |req: &Request<Body>| {
            Ok::<bool, Infallible>(
                req.headers().get("content-type").map(|x| x.as_bytes())
                    == Some(b"application/grpc"),
            )
        });

    let make_service = make_service_fn(move |_conn: &AddrStream| {
        let both_service = both_service.clone();
        async { Ok::<_, Infallible>(both_service) }
    });

    let server = hyper::Server::bind(&addr).serve(make_service);

    if let Err(e) = server.await {
        eprintln!("server error: {}", e);
    }
}

Note the Router: it takes the two services and Fn to determine which to use on any given request. Then we just drop that composed service into make_service_fn and we’re done.

Next up we have the Router implementation. This is generic across any two Service<Request<...>> types as long as they are both Into<Bytes> for their Data, and Into<Box<dyn Error>> for errors.

use std::{future::Future, pin::Pin, task::Poll};

use http_body::combinators::UnsyncBoxBody;
use hyper::{body::HttpBody, Body, Request, Response};
use tower::Service;

#[derive(Clone)]
pub struct Router<First, Second, F> {
    first: First,
    second: Second,
    discriminator: F,
}

impl<First, Second, F> Router<First, Second, F> {
    pub fn new(first: First, second: Second, discriminator: F) -> Self {
        Self {
            first,
            second,
            discriminator,
        }
    }
}

impl<First, Second, FirstBody, FirstBodyError, SecondBody, SecondBodyError, F, FErr>
    Service<Request<Body>> for BinaryRouter<First, Second, F>
where
    First: Service<Request<Body>, Response = Response<FirstBody>>,
    First::Error: Into<Box<dyn std::error::Error + Send + Sync>> + 'static,
    First::Future: Send + 'static,
    First::Response: 'static,
    Second: Service<Request<Body>, Response = Response<SecondBody>>,
    Second::Error: Into<Box<dyn std::error::Error + Send + Sync>> + 'static,
    Second::Future: Send + 'static,
    Second::Response: 'static,
    F: Fn(&Request<Body>) -> Result<bool, FErr>,
    FErr: Into<Box<dyn std::error::Error + Send + Sync>> + Send + 'static,
    FirstBody: HttpBody<Error = FirstBodyError> + Send + 'static,
    FirstBody::Data: Into<bytes::Bytes>,
    FirstBodyError: Into<Box<dyn std::error::Error + Send + Sync>> + 'static,
    SecondBody: HttpBody<Error = SecondBodyError> + Send + 'static,
    SecondBody::Data: Into<bytes::Bytes>,
    SecondBodyError: Into<Box<dyn std::error::Error + Send + Sync>> + 'static,
{
    type Response = Response<
        UnsyncBoxBody<
            <hyper::Body as HttpBody>::Data,
            Box<dyn std::error::Error + Send + Sync + 'static>,
        >,
    >;
    type Error = Box<dyn std::error::Error + Send + Sync + 'static>;
    type Future =
        Pin<Box<dyn Future<Output = Result<Self::Response, Self::Error>> + Send + 'static>>;

    fn poll_ready(
        &mut self,
        cx: &mut std::task::Context<'_>,
    ) -> std::task::Poll<Result<(), Self::Error>> {
        match self.first.poll_ready(cx) {
            Poll::Ready(Ok(())) => match self.second.poll_ready(cx) {
                Poll::Ready(Ok(())) => Poll::Ready(Ok(())),
                Poll::Ready(Err(e)) => Poll::Ready(Err(e.into())),
                Poll::Pending => Poll::Pending,
            },
            Poll::Ready(Err(e)) => Poll::Ready(Err(e.into())),
            Poll::Pending => Poll::Pending,
        }
    }

    fn call(&mut self, req: Request<Body>) -> Self::Future {
        let discriminant = { (self.discriminator)(&req) };
        let (first, second) = if matches!(discriminant, Ok(false)) {
            (Some(self.first.call(req)), None)
        } else if matches!(discriminant, Ok(true)) {
            (None, Some(self.second.call(req)))
        } else {
            (None, None)
        };
        let f = async {
            Ok(match discriminant.map_err(Into::into)? {
                true => second
                    .unwrap()
                    .await
                    .map_err(Into::into)?
                    .map(|b| b.map_data(Into::into).map_err(Into::into).boxed_unsync()),
                false => first
                    .unwrap()
                    .await
                    .map_err(Into::into)?
                    .map(|b| b.map_data(Into::into).map_err(Into::into).boxed_unsync()),
            })
        };
        Box::pin(f)
    }
}

Interesting things here – I use boxed_unsync to abstract over the body concrete type, and I implement the future using async code rather than as a separate struct. It becomes much smaller even after a few bits of extra type constraining.

One thing that flummoxed me for a little was the need to capture the future for the underlying response outside of the async block. Failing to do so provokes a 'static requirement which was tricky to debug. Fortunately there is a bug on making this easier to diagnose in rustc already. The underlying problem is that if you create the async block, and then dereference self, the type for impl of .first has to live an arbitrary time. Whereas by capturing the future immediately, only the impl of the future has to live an arbitrary time, and that doesn’t then require changing the signature of the function.

This is almost worth turning into a crate – I couldn’t see an existing one when I looked, though it does end up rather small – < 100 lines. What do you all think?

The first meeting will start at 1PM Australian Eastern time (Melbourne/Sydney) which is +1100 on Saturday the 5th of February.

I will start the video chat an hour early in case someone makes a timezone mistake and gets there an hour before it starts. If anyone else joins early we will have random chat until the start time (deliberately avoiding topics worthy of the main meeting). The link http://b.coker.com.au will redirect to the meeting URL on the day.

The first scheduled talk is a summary and discussion of free software related news. Anyone who knows of something new that excites them is welcome to speak about it.

The main event is discussion of storage technology and hands-on training on BTRFS and ZFS for those who are interested. Here are the ZFS training notes and here are the BTRFS training notes. Feel free to do the training exercises on your own VM before the meeting if you wish.

Then discussion of the future of the group and the use of FOSS social media. While social media is never going to be compulsory some people will want to use it to communicate and we could run some servers for software that is considered good (lots of server capacity is available).

Finally we have to plan future meetings and decide on which communication methods are desired.

The BBB instance to be used for the video conference is sponsored by NZOSS and Catalyst Cloud.

Since PM Scott Morrison did not announce the federal election date last week, it will now be held somewhere between March and May (see the post from ABC’s Antony Green for details). Various aspects of elections are covered in the Civics & Citizenship Australian Curriculum in Years 4, 5 and 6. Students are interested in […]

Flounder is a new free software users group based in the Australia/NZ area. Flounder stands for FLOSS (Free Libre Open Source Software) down under.

Here is my blog post describing the initial idea, the comment from d3Xt3r suggested the name. Flounder is a group of fish that has species native to Australia and NZ.

The main aim is to provide educational benefits to free software users via an online meeting that can’t be obtained by watching YouTube videos etc in a scope that is larger than one country. When the pandemic ends we will keep running this as there are benefits to be obtained from a meeting of a wide geographic scope that can’t be obtained by meetings in a single city. People from other countries are welcome to attend but they aren’t the focus of the meeting.

Until we get a better DNS name the address http://b.coker.com.au will redirect to the BBB instance used for online meetings (the meeting address isn’t yet setup so it redirects to the blog). The aim is that there will always be a short URL for the meeting so anyone who has one device lose contact can quickly type the URL into their backup device.

The first meeting will be on the 5th of Feb 2022 at 1PM Melbourne time +1100. When we get a proper domain I’ll publish a URL for an iCal file with entries for all meetings. I will also find some suitable way for meeting times to be localised (I’m sure there’s a WordPress plugin for that).

For the hands-on part of the meetings there will be virtual machine images you can download to run on your own system (tested with KVM, should work with other VM systems) and the possibility of logging in to a running VM. The demonstration VMs will have public IPv6 addresses and will also be available through different ports on a single IPv4 address, having IPv6 on your workstation will be convenient for you but you can survive without it.

Linux Australia has a list of LUGs in Australia, is there a similar list for NZ? One thing I’d like to see is a list of links for iCal files for all the meetings and also an iCal aggregator that for all iCal feeds of online meetings. I’ll host it myself if necessary, but it’s probably best to do it via Linux Australia (Linux Australasia?) if possible.

I’m attending the https://linux.conf.au/ conference online this weekend, which is always a good opportunity for some sideline hacking.

I found something boneheaded doing that today.

There have been a few times while inventing the OpenHMD Rift driver where I’ve noticed something strange and followed the thread until it made sense. Sometimes that leads to improvements in the driver, sometimes not.

In this case, I wanted to generate a graph of how long the computer vision processing takes – from the moment each camera frame is captured until poses are generated for each device.

To do that, I have a some logging branches that output JSON events to log files and I write scripts to process those. I used that data and produced:

Two things caught my eye in this graph. The first is the way the baseline latency (pink lines) increases from ~20ms to ~58ms. The 2nd is the quantisation effect, where pose latencies are clearly moving in discrete steps.

Neither of those should be happening.

Camera frames are being captured from the CV1 sensors every 19.2ms, and it takes that 17-18ms for them to be delivered across the USB. Depending on how many IR sources the cameras can see, figuring out the device poses can take a different amount of time, but the baseline should always hover around 17-18ms because the fast “device tracking locked” case take as little as 1ms.

Did you see me mention 19.2ms as the interframe period? Guess what the spacing on those quantisation levels are in the graph? I recognised it as implying that something in the processing is tied to frame timing when it should not be.

This 2nd graph helped me pinpoint what exactly was going on. This graph is cut from the part of the session where the latency has jumped up. What it shows is a ~1 frame delay between when the frame is received (frame-arrival-finish-local-ts) before the initial analysis even starts!

That could imply that the analysis thread is just busy processing the previous frame and doesn’t get start working on the new one yet – but the graph says that fast analysis is typically done in 1-10ms at most. It should rarely be busy when the next frame arrives.

This is where I found the bone headed code – a rookie mistake I wrote when putting in place the image analysis threads early on in the driver development and never noticed.

There are 3 threads involved:

• USB service thread, reading video frame packets and assembling pixels in framebuffers
• Fast analysis thread, that checks tracking lock is still acquired
• Long analysis thread, which does brute-force pose searching to reacquire / match unknown IR sources to device LEDs

These 3 threads communicate using frame worker queues passing frames between each other. Each analysis thread does this pseudocode:

while driver_running:
    Pop a frame from the queue
    Process the frame
    Sleep for new frame notification

The problem is in the 3rd line. If the driver is ever still processing the frame in line 2 when a new frame arrives – say because the computer got really busy – the thread sleeps anyway and won’t wake up until the next frame arrives. At that point, there’ll be 2 frames in the queue, but it only still processes one – so the analysis gains a 1 frame latency from that point on. If it happens a second time, it gets later by another frame! Any further and it starts reclaiming frames from the queues to keep the video capture thread fed – but it only reclaims one frame at a time, so the latency remains!

The fix is simple:

while driver_running:
   Pop a frame
   Process the frame
   if queue_is_empty():
     sleep for new frame notification

Doing that for both the fast and long analysis threads changed the profile of the pose latency graph completely.

This is a massive win! To be clear, this has been causing problems in the driver for at least 18 months but was never obvious from the logs alone. A single good graph is worth a thousand logs.

What does this mean in practice?

The way the fusion filter I’ve built works, in between pose updates from the cameras, the position and orientation of each device are predicted / updated using the accelerometer and gyro readings. Particularly for position, using the IMU for prediction drifts fairly quickly. The longer the driver spends ‘coasting’ on the IMU, the less accurate the position tracking is. So, the sooner the driver can get a correction from the camera to the fusion filter the less drift we’ll get – especially under fast motion. Particularly for the hand controllers that get waved around.

Poses are now being updated up to 40ms earlier and the baseline is consistent with the USB transfer delay.

You can also visibly see the effect of the JPEG decoding support I added over Christmas. The ‘red’ camera is directly connected to USB3, while the ‘khaki’ camera is feeding JPEG frames over USB2 that then need to be decoded, adding a few ms delay.

The latency reduction is nicely visible in the pose graphs, where the ‘drop shadow’ effect of pose updates tailing fusion predictions largely disappears and there are fewer large gaps in the pose observations when long analysis happens (visible as straight lines jumping from point to point in the trace):

Yes, the blog is still on. January 2004 I moved to WordPress, and it is still here January 2022. I didn’t write much last year (neither here, not experimenting with the Hey blog). I didn’t post anything to Instagram last year either from what I can tell, just a lot of stories.

August 16 2021, I realised I was 1,000 days till May 12 2024, which is when I become 40. As of today, that leads 850 days. Did I squander the last 150 days? I’m back to writing almost daily in the Hobonichi Techo (I think last year and the year before were mostly washouts; I barely scribbled anything offline).

I got a new Apple Watch Series 7 yesterday. I can say I used the Series 4 well (79% battery life), purchased in the UK when I broke my Series 0 in Edinburgh airport.

TripIt stats for last year claimed 95 days on the road. This is of course, a massive joke, but I’m glad I did get to visit London, Lisbon, New York, San Francisco, Los Angeles without issue. I spent a lot of time in Kuantan, a bunch of Langkawi trips, and also, I stayed for many months at the Grand Hyatt Kuala Lumpur during the May lockdowns (I practically stayed there all lockdown).

With 850 days to go till I’m 40, I have plenty I would like to achieve. I think I’ll write a lot more here. And elsewhere. Get back into the habit of doing. And publishing by learning and doing. No fear. Not that I wasn’t doing, but its time to be prolific with what’s been going on.

The post This thing is still on? first appeared on Colin Charles Agenda.

Blockchain. Cryptocurrency. Ethereum. NFTs. DAOs. Smart Contracts. web3. It’s impossible to avoid the blockchain hype machine these days, but it’s often just as difficult to decipher what it all means.

On top of that, discourse around web3 is extremely polarising: everyone involved is very keen to a) pick a team, and b) get you to join their team. If you haven’t picked a team, you must be secretly with the other team.

Max Read made a compelling argument that the web3 debate is in fact two different debates:

But, OK, what is the root disagreement, exactly? The way I read it there are two broad “is web3 bullshit?” debates, not just one, centered around the following questions:

Can the blockchain do anything that other currently existing technology cannot do and/or do anything better or more efficiently than other currently existing technology?

Will the blockchain form the architecture of the internet of the future (i.e. “web3”), and/or will blockchain-native companies and organizations become important and powerful?

Max Read — Is web3 bullshit?

I’m inclined to agree with Max’s analysis here: there’s a technical question, and there’s a business/cultural question. It’s hard to separate the two when every day sees new headlines about millions of dollars being stolen or scammed; or thousands of people putting millions of dollars into highly optimistic ventures. There are extreme positives and extreme negatives happening all the time in the web3 world.

With that in mind, I want to take a step back from the day-to-day excitement of cryptocurrency and web3, and look at some of the driving philosophies espoused by the movement.

Philosophies of web3

There are a lot of differing viewpoints on web3, every individual has a slightly different take on it. There are three broad themes that stand out, however.

Decentralised

Blockchain-based technology is inherently distributed (with some esoteric caveats, but we can safely ignore them for now). In a world where the web centres around a handful of major services, where we’ve seen the harm that the likes of Facebook and YouTube can inflict on society, it’s not surprising that decentralisation would be a powerful theme drawing in anyone looking for an alternative.

Decentralisation isn’t new to the Internet, of course: it’s right there in the name. This giant set of “interconnected networks” has been decentralised from the very beginning. It’s not perfect, of course: oppressive governments can take control of the borders of their portion of the Internet, and we’ve come to rely on a handful of web services to handle the trickier parts of using the web. But fundamentally, that decentralised architecture is still there. I can still set up a web site hosted on my home computer, which anyone in the world could access.

I don’t do that, however, for the same reason that web3 isn’t immune from centralised services: Centralisation is convenient. Just as we have Facebook, or Google, or Amazon as giant centralised services on the current web, we can already see similar services appearing for web3. For payments, Coinbase has established itself as a hugely popular place exchange cryptocurrencies and traditional currencies. For NFTs, OpenSea is the service where you’ll find nearly every NFT collection. MetaMask keeps all of your crypto-based keys, tokens, and logins in a single “crypto wallet”.

While web3 proponents give a lot of credence to the decentralised nature of cryptocurrency being a driver of popularity, I’m not so sure. At best, I’m inclined to think that decentralisation is table stakes these days: you can’t even get started as a global movement without a strong commitment to decentralisation.

But if decentralisation isn’t the key, what is?

Ownership

When we talk about ownership in web3, NFTs are clearly the flavour of the month, but recent research indicates that the entire NFT market is massively artificially inflated.

Rather than taking pot-shots at the NFT straw man, I think it’s more interesting to look at the idea of ownership in terms of attribution. The more powerful element of this philosophy isn’t about who owns something, it’s who created it. NFTs do something rather novel with attribution, allowing royalty payments to the original artist every time an NFT is resold. I love this aspect: royalties shouldn’t just be for movie stars, they should be for everyone.

Comparing that to the current web, take the 3 paragraphs written by Max Read that I quoted above. I was certainly under no technical obligation to show that it was a quote, to attribute it to him, or to link to the source. In fact, it would have been easier for me to just paste his words into this post, and pretend they were my own. I didn’t, of course, because I feel an ethical obligation to properly attribute the quote.

In a world where unethical actors will automatically copy/paste your content for SEO juice (indeed, I expect this blog post to show up on a bunch of these kinds of sites); where massive corporations will consume everything they can find about you, in order to advertise more effectively to you, it’s not at all surprising that people are looking for a technical solution for taking back control of their data, and for being properly attributed for their creations.

That’s not to say that existing services discourage attribution: a core function of Twitter is retweets, a core function of Tumblr is reblogging. WordPress still supports trackbacks, even if many folks turn them off these days.

These are all blunt instruments, though, aimed at attributing an entire piece, rather than a more targeted approach. What I’d really like is a way to easily quote and attribute a small chunk of a post: 3 paragraphs (or blocks, if you want to see where I’m heading ), inserted into my post, linking back to where I got them from. If someone chooses to quote some of this post, I’d love to receive a pingback just for that quote, so it can be seen in the right context.

The functionality provide by Twitter and Tumblr is less of a technologically-based enforcement of attribution, and more of an example of paving the cow path: by and large, people want to properly attribute others, providing the tools to do so can easily become a fundamental part of how any software is used.

These tools only work so long as there’s an incentive to use them, however. web3 certainly provides the tools to attribute others, but much like SEO scammers copy/pasting blog posts, the economics of the NFT bubble is clearly a huge incentive to ignore those tools and ethical obligations, to the point that existing services have had to build additional features just to detect this abuse.

Monetisation

With every major blockchain also being a cryptocurrency, monetisation is at the heart of the entire web3 movement. Every level of the web3 tech stack involves a cryptocurrency-based protocol. This naturally permeates through the entire web3 ecosystem, where money becomes a major driving factor for every web3-based project.

And so, it’s impossible to look at web3 applications without also considering the financial aspect. When you have to pay just to participate, you have to ask whether every piece of content you create is “worth it”.

Again, let’s go back to the 3 paragraphs I quote above. In a theoretical web3 world, I’d publish this post on a blockchain in some form or another, and that act would also likely include noting that I’d quoted 3 blocks of text attributed to Max Read. I’d potentially pay some amount of money to Max, along with the fees that every blockchain charges in order to perform a transaction. While this process is potentially helpful to the original author at a first glance, I suspect the second and third order effects will be problematic. Having only just clicked the Publish button a few seconds earlier, I’m already some indeterminate amount of money out of pocket. Which brings me back to the question, is this post “worth it”? Will enough people tip/quote/remix/whatever me, to cover the cost of publishing? When every creative work must be viewed through a lens of financial impact, it fundamentally alters that creative process.

Ultimately, we live in a capitalist society, and everyone deserves the opportunity to profit off their work. But by baking monetisation into the underlying infrastructure of web3, it becomes impossible to opt-out. You either have the money to participate without being concerned about the cost, or you’re going to need to weigh up every interaction by whether or not you can afford it.

Web3 Philosophies in WordPress

After breaking it all down, we can see that it’s not all black-and-white. There are some positive parts of web3, and some negative parts. Not that different to the web of today, in fact. That’s not to say that either approach is the correct one: instead, we should be looking to learn from both, and produce something better.

Decentralised

I’ve long been a proponent of leveraging the massive install base of WordPress to provide distributed services to anyone. Years ago, I spoke about an idea called “Connected WordPress” that would do exactly that. While the idea didn’t gain a huge amount of traction at the time, the DNA of the Connected WordPress concept shares a lot of similar traits to the decentralised nature of web3.

I’m a big fan of decentralised technologies as a way for individuals to claw back power over their own data from the governments and massive corporations that would prefer to keep it all centralised, and I absolutely think we should be exploring ways to make the existing web more resistant to censorship.

At the same time, we have to acknowledge that there are certainly benefits to centralisation. As long as people have the freedom to choose how and where they participate, and centralised services are required to play nicely with self hosted sites, is there a practical difference?

I quite like how Solid allows you have it both ways, whilst maintaining control over your own data.

Ownership Attribution

Here’s the thing about attribution: you can’t enforce it with technology alone. Snapchat have indirectly demonstrated exactly this problem: in order to not lose a message, people would screenshot or record the message on their phone. In response, Snapchat implemented a feature to notify the other party when you screenshot a message from them. To avoid this, people will now use a second phone to take a photo or video of the message. While this example isn’t specifically about attribution, it demonstrates the problem that there’s no way to technologically restrict how someone interacts with content that you’ve published, once they’ve been granted access.

Instead of worrying about technical restrictions, then, we should be looking at how attribution can be made easier.

IndieWeb is a great example of how this can be done in a totally decentralised fashion.

Monetisation

I’m firmly of the opinion that monetisation of the things you create should be opt-in, rather than opt-out.

Modern society is currently obsessed with monetising everything, however. It comes in many different forms: hustle culture, side gigs, transforming hobbies into businesses, meme stocks, and cryptocurrencies: they’re all symptoms of this obsession.

I would argue that, rather than accepting as fait accompli that the next iteration of the web will be monetised to the core, we should be pushing back against this approach. Fundamentally, we should be looking to build for a post scarcity society, rather than trying to introduce scarcity where there previously was none.

While we work towards that future, we should certainly be easier for folks to monetise their work, but the current raft of cryptocurrencies just aren’t up to the task of operating as… currencies.

What Should You Do?

Well, that depends on what your priorities are. The conversations around web3 are taking up a lot of air right now, so it’s possible to get the impression web3 will be imminently replacing everything. It’s important to keep perspective on this, though. While there’s a lot of money in the web3 ecosystem right now, it’s dwarfed by the sheer size of the existing web.

If you’re excited about the hot new tech, and feeling inspired by the ideas espoused in web3 circles? Jump right in! I’m certain you’ll find something interesting to work on.

Always wanted to get into currency speculation, but didn’t want to deal with all those pesky “regulations” and “safeguards”? Boy howdy, are cryptocurrencies or NFTs the place for you. (Please don’t pretend that this paragraph is investment advice, it is nothing of the sort.)

Want to continue building stuff on the web, and you’re willing to learn new things when you need them, but are otherwise happy with your trajectory? Just keep on doing what you’re doing. Even if web3 does manage to live up to the hype, it’ll take a long time for it to be adopted by the mainstream. You’ll have years to adapt.

Final Thoughts

There are some big promises associated with web3, many of which sound very similar to the promises that were made around web 2.0, particularly around open APIs, and global interoperability. We saw what happened when those kinds of tools go wrong, and web3 doesn’t really solve those problems. It may exacerbate them in some ways, since it’s impossible to delete your data from a blockchain.

That said, (and I say this as a WordPress Core developer), just because a particular piece of software is not the optimal technical solution doesn’t mean it won’t become the most popular. Market forces can be a far stronger factor that technical superiority. There are many legitimate complaints about blockchain (including performance, bloat, fit for purpose, and security) that have been levelled against WordPress in the past, but WordPress certainly isn’t slowing down. I’m not even close to convinced that blockchain is the right technology to base the web on, but I’ve been doing this for too long to bet everything against it.

As for me, well…

I remain sceptical of web3 as it’s currently defined, but I think there’s room to change it, and to adopt the best bits into the existing web. Web 1.0 didn’t magically disappear when Web 2.0 rolled in, it adapted. Maybe we’ll look back in 10 years and say this was a time when the web fundamentally changed. Or, maybe we’ll refer to blockchain in the same breath as pets.com, and other examples from the dotcom boom of the 1990’s.

This quote was originally referring to Usenet, but it’s stayed highly relevant in the decades since. I think it applies here, too: if the artificial scarcity built into web3 behaves too much like censorship, preventing people from sharing what they want to share, the internet (or, more accurately, the billions of people who interact with the internet) will just… go around it. It won’t all be smooth sailing, but we’ll continue to experiment, evolve, and adapt as it changes.

Personally, I think now is a great time for us to be embracing the values and ideals of projects like Solid, and IndieWeb. Before web3 referred to blockchains, it was more commonly used in reference to the Semantic Web, which is far more in line with WordPress’ ideals, whilst also matching many of the values prioritised by the new web3. As a major driver of the Open Web, WordPress can help people own their content in a sustainable way, engage with others on their own terms, and build communities that don’t depend on massive corporations or hand-wavy magical tech solutions.

Don’t get too caught up in the drama of whatever is the flavour of the month. I’m optimistic about the long term resilience of the internet, and I think you should be, too.

Once again time has passed, and another update on Oculus Rift support feels due! As always, it feels like I’ve been busy with work and not found enough time for Rift CV1 hacking. Nevertheless, looking back over the history since I last wrote, there’s quite a lot to tell!

In general, the controller tracking is now really good most of the time. Like, wildly-swing-your-arms-and-not-lose-track levels (most of the time). The problems I’m hunting now are intermittent and hard to identify in the moment while using the headset – hence my enthusiasm over the last updates for implementing stream recording and a simulation setup. I’ll get back to that.

Outlier Detection

Since I last wrote, the tracking improvements have mostly come from identifying and rejecting incorrect measurements. That is, if I have 2 sensors active and 1 sensor says the left controller is in one place, but the 2nd sensor says it’s somewhere else, we’ll reject one of those – choosing the pose that best matches what we already know about the controller. The last known position, the gravity direction the IMU is detecting, and the last known orientation. The tracker will now also reject observations for a time if (for example) the reported orientation is outside the range we expect. The IMU gyroscope can track the orientation of a device for quite a while, so can be relied on to identify strong pose priors once we’ve integrated a few camera observations to get the yaw correct.

It works really well, but I think improving this area is still where most future refinements will come. That and avoiding incorrect pose extractions in the first place.

The above plot is a sample of headset tracking, showing the extracted poses from the computer vision vs the pose priors / tracking from the Kalman filter. As you can see, there are excursions in both position and orientation detected from the video, but these are largely ignored by the filter, producing a steadier result.

This plot shows the left controller being tracked during a Beat Saber session. The controller tracking plot is quite different, because controllers move a lot more than the headset, and have fewer LEDs to track against. There are larger gaps here in the timeline while the vision re-acquires the device – and in those gaps you can see the Kalman filter interpolating using IMU input only (sometimes well, sometimes less so).

Improved Pose Priors

Another nice thing I did is changes in the way the search for a tracked device is made in a video frame. Before starting looking for a particular device it always now gets the latest estimate of the previous device position from the fusion filter. Previously, it would use the estimate of the device pose as it was when the camera exposure happened – but between then and the moment we start analysis more IMU observations and other camera observations might arrive and be integrated into the filter, which will have updated the estimate of where the device was in the frame.

This is the bit where I think the Kalman filter is particularly clever: Estimates of the device position at an earlier or later exposure can improve and refine the filter’s estimate of where the device was when the camera captured the frame we’re currently analysing! So clever. That mechanism (lagged state tracking) is what allows the filter to integrate past tracking observations once the analysis is done – so even if the video frame search take 150ms (for example), it will correct the filter’s estimate of where the device was 150ms in the past, which ripples through and corrects the estimate of where the device is now.

LED visibility model

To improve the identification of devices better, I measured the actual angle from which LEDs are visible (about 75 degrees off axis) and measured the size. The pose matching now has a better idea of which LEDs should be visible for a proposed orientation and what pixel size we expect them to have at a particular distance.

Better Smoothing

I fixed a bug in the output pose smoothing filter where it would glitch as you turned completely around and crossed the point where the angle jumps from +pi to -pi or vice versa.

Improved Display Distortion Correction

I got a wide-angle hi-res webcam and took photos of a checkerboard pattern through the lens of my headset, then used OpenCV and panotools to calculate new distortion and chromatic aberration parameters for the display. For me, this has greatly improved. I’m waiting to hear if that’s true for everyone, or if I’ve just fixed it for my headset.

Persistent Config Cache

Config blocks! A long time ago, I prototyped code to create a persistent OpenHMD configuration file store in ~/.config/openhmd. The rift-kalman-filter branch now uses that to store the configuration blocks that it reads from the controllers. The first time a controller is seen, it will load the JSON calibration block as before, but it will now store it in that directory – removing a multiple second radio read process on every subsequent startup.

Persistent Room Configuration

To go along with that, I have an experimental rift-room-config branch that creates a rift-room-config.json file and stores the camera positions after the first startup. I haven’t pushed that to the rift-kalman-filter branch yet, because I’m a bit worried it’ll cause surprising problems for people. If the initial estimate of the headset pose is wrong, the code will back-project the wrong positions for the cameras, which will get written to the file and cause every subsequent run of OpenHMD to generate bad tracking until the file is removed. The goal is to have a loop that monitors whether the camera positions seem stable based on the tracking reports, and to use averaging and resetting to correct them if not – or at least to warn the user that they should re-run some (non-existent) setup utility.

Video Capture + Processing

The final big ticket item was a rewrite of how the USB video frame capture thread collects pixels and passes them to the analysis threads. This now does less work in the USB thread, so misses fewer frames, and also I made it so that every frame is now searched for LEDs and blob identities tracked with motion vectors, even when no further analysis will be done on that frame. That means that when we’re running late, it better preserves LED blob identities until the analysis threads can catch up – increasing the chances of having known LEDs to directly find device positions and avoid searching. This rewrite also opened up a path to easily support JPEG decode – which is needed to support Rift Sensors connected on USB 2.0 ports.

Session Simulator

I mentioned the recording simulator continues to progress. Since the tracking problems are now getting really tricky to figure out, this tool is becoming increasingly important. So far, I have code in OpenHMD to record all video and tracking data to a .mkv file. Then, there’s a simulator tool that loads those recordings. Currently it is capable of extracting the data back out of the recording, parsing the JSON and decoding the video, and presenting it to a partially implemented simulator that then runs the same blob analysis and tracking OpenHMD does. The end goal is a Godot based visualiser for this simulation, and to be able to step back and forth through time examining what happened at critical moments so I can improve the tracking for those situations.

To make recordings, there’s the rift-debug-gstreamer-record branch of OpenHMD. If you have GStreamer and the right plugins (gst-plugins-good) installed, and you set env vars like this, each run of OpenHMD will generate a recording in the target directory (make sure the target dir exists):

export OHMD_TRACE_DIR=/home/user/openhmd-traces/
export OHMD_FULL_RECORDING=1

Up Next

The next things that are calling to me are to improve the room configuration estimation and storage as mentioned above – to detect when the poses a camera is reporting don’t make sense because it’s been bumped or moved.

I’d also like to add back in tracking of the LEDS on the back of the headset headband, to support 360 tracking. I disabled those because they cause me trouble – the headband is adjustable relative to the headset, so the LEDs don’t appear where the 3D model says they should be and that causes jitter and pose mismatches. They need special handling.

One last thing I’m finding exciting is a new person taking an interest in Rift S and starting to look at inside-out tracking for that. That’s just happened in the last few days, so not much to report yet – but I’ll be happy to have someone looking at that while I’m still busy over here in CV1 land!

As always, if you have any questions, comments or testing feedback – hit me up at thaytan@noraisin.net or on @thaytan Twitter/IRC.

Thank you to the kind people signed up as Github Sponsors for this project!

Let’s say you’re someone who happens to discover an AWS account number, and would like to take a stab at guessing what IAM users might be valid in that account. Tricky problem, right? Not with this One Weird Trick!

In your own AWS account, create a KMS key and try to reference an ARN representing an IAM user in the other account as the principal. If the policy is accepted by PutKeyPolicy, then that IAM account exists, and if the error says “Policy contains a statement with one or more invalid principals” then the user doesn’t exist.

As an example, say you want to guess at IAM users in AWS account 111111111111. Then make sure this statement is in your key policy:

{
  "Sid": "Test existence of user",
  "Effect": "Allow",
  "Principal": {
    "AWS": "arn:aws:iam::111111111111:user/bob"
  },
  "Action": "kms:DescribeKey",
  "Resource": "*"
}

If that policy is accepted, then the account has an IAM user named bob. Otherwise, the user doesn’t exist. Scripting this is left as an exercise for the reader.

Sadly, wildcards aren’t accepted in the username portion of the ARN, otherwise you could do some funky searching with ...:user/a*, ...:user/b*, etc. You can’t have everything; where would you put it all?

I did mention this to AWS as an account enumeration risk. They’re of the opinion that it’s a good thing you can know what users exist in random other AWS accounts. I guess that means this is a technique you can put in your toolbox safe in the knowledge it’ll work forever.

Given this is intended behaviour, I assume you don’t need to use a key policy for this, but that’s where I stumbled over it. Also, you can probably use it to enumerate roles and anything else that can be a principal, but since I don’t see as much use for that, I didn’t bother exploring it.

There you are, then. If you ever need to guess at IAM users in another AWS account, now you can!

For a long time computer manufacturers have tried to differentiate themselves and their products from their competitors with fancy names with odd capitalisation and spelling. But as an author, using these names does a disservice to the reader: how are they to know that DEC is pronounced as if it was written Dec ("deck").

It's time we pushed back, and wrote for our readers, not for corporations.

It's time to use standard English rules for these Corporate Fancy Names. Proper names begin with a capital, unlike "ciscoSystems®" (so bad that Cisco itself moved away from it). Words are separated by spaces, so "Cisco Systems". Abbreviations and acronyms are written in lower case if they are pronounced as a word, in upper case if each letter is pronounced: so "ram" and "IBM®".

So from here on in I'll be using the following:

• Face Book. Formerly, "Facebook®".
• Junos. Formerly JUNOS®.
• ram. Formerly RAM.
• Pan OS. Formerly PAN-OS®.
• Unix. Formerly UNIX®.

I'd encourage you to try this in your own writing. It does look odd for the first time, but the result is undeniably more readable. If we are not writing to be understood by our audience then we are nothing more than an unpaid member of some corporation's marketing team.

I gave the talk On The Use and Misuse of Decorators as part of PyConline AU 2021, the second in annoyingly long sequence of not-in-person PyCon AU events. Here’s some code samples that you might be interested in:

Simple @property implementation

This shows a demo of @property-style getters. Setters are left as an exercise :)

def demo_property(f):
    f.is_a_property = True
    return f


class HasProperties:

    def __getattribute__(self, name):
        ret = super().__getattribute__(name)
        if hasattr(ret, "is_a_property"):
            return ret()
        else:
            return ret

class Demo(HasProperties):

    @demo_property
    def is_a_property(self):
        return "I'm a property"

    def is_a_function(self):
        return "I'm a function"


a = Demo()
print(a.is_a_function())
print(a.is_a_property)

@run (The Scoped Block)

@run is a decorator that will run the body of the decorated function, and then store the result of that function in place of the function’s name. It makes it easier to assign the results of complex statements to a variable, and get the advantages of functions having less leaky scopes than if or loop blocks.

def run(f):

    return f()

@run
def hello_world():
    return "Hello, World!"

print(hello_world)

@apply (Multi-line stream transformers)

def apply(transformer, iterable_):

    def _applicator(f):

        return(transformer(f, iterable_))

    return _applicator

@apply(map, range(100)
def fizzbuzzed(i):
    if i % 3 == 0 and i % 5 == 0:
        return "fizzbuzz"

    if i % 3 == 0:
        return "fizz"
    elif i % 5 == 0:
        return "buzz"
    else:
        return str(i)

Builders

def html(f):
    builder = HtmlNodeBuilder("html")
    f(builder)
    return builder.build()


class HtmlNodeBuilder:
    def __init__(self, tag_name):
       self.tag_name = tag_name
       self.nodes = []

   def node(self, f):
        builder = HtmlNodeBuilder(f.__name__)
        f(builder)
        self.nodes.append(builder.build())

    def text(self, text):
        self.nodes.append(text)

    def build(self):
      nodes = "\n".join(self.nodes)
       return f"<{self.tag_name}>\n{nodes}\n</{self.tag_name}>"


@html
def document(b):
   @b.node
   def head(b):
       @b.node
       def title(b):
           b.text("Hello, World!")

   @b.node
   def body(b):
       for i in range(10, 0, -1):
           @b.node
           def p(b):
               b.text(f"{i}")

Code Registries

This is an incomplete implementation of a code registry for handling simple text processing tasks:

```python

def register(self, input, output):

def _register_code(f):
    self.registry[(input, output)] = f
    return f

return _register_code

in_type = (iterable[str], (WILDCARD, ) out_type = (Counter, (WILDCARD, frequency))

@registry.register(in_type, out_type) def count_strings(strings):

return Counter(strings)

@registry.register( (iterable[str], (WILDCARD, )), (iterable[str], (WILDCARD, lowercase)) ) def words_to_lowercase(words): …

@registry.register( (iterable[str], (WILDCARD, )), (iterable[str], (WILDCARD, no_punctuation)) ) def words_without_punctuation(words): …

def find_steps( self, input_type, input_attrs, output_type, output_attrs ):

hand_wave()

def give_me(self, input, output_type, output_attrs):

steps = self.find_steps(
    type(input), (), output_type, output_attrs
)

temp = input
for step in steps:
    temp = step(temp)

return temp

A while ago, I wrote a post about how to build and test my Oculus CV1 tracking code in SteamVR using the SteamVR-OpenHMD driver. I have updated those instructions and moved them to https://noraisin.net/diary/?page_id=1048 – so use those if you’d like to try things out.

The pandemic continues to sap my time for OpenHMD improvements. Since my last post, I have been working on various refinements. The biggest visible improvements are:

• Adding velocity and acceleration API to OpenHMD.
• Rewriting the pose transformation code that maps from the IMU-centric tracking space to the device pose needed by SteamVR / apps.

Adding velocity and acceleration reporting is needed in VR apps that support throwing things. It means that throwing objects and using gravity-grab to fetch objects works in Half-Life: Alyx, making it playable now.

The rewrite to the pose transformation code fixed problems where the rotation of controller models in VR didn’t match the rotation applied in the real world. Controllers would appear attached to the wrong part of the hand, and rotate around the wrong axis. Movements feel more natural now.

Ongoing work – record and replay

My focus going forward is on fixing glitches that are caused by tracking losses or outliers. Those problems happen when the computer vision code either fails to match what the cameras see to the device LED models, or when it matches incorrectly.

Tracking failure leads to the headset view or controllers ‘flying away’ suddenly. Incorrect matching leads to controllers jumping and jittering to the wrong pose, or swapping hands. Either condition is very annoying.

Unfortunately, as the tracking has improved the remaining problems get harder to understand and there is less low-hanging fruit for improvement. Further, when the computer vision runs at 52Hz, it’s impossible to diagnose the reasons for a glitch in real time.

I’ve built a branch of OpenHMD that uses GStreamer to record the CV1 camera video, plus IMU and tracking logs into a video file.

To go with those recordings, I’ve been working on a replay and simulation tool, that uses the Godot game engine to visualise the tracking session. The goal is to show, frame-by-frame, where OpenHMD thought the cameras, headset and controllers were at each point in the session, and to be able to step back and forth through the recording.

Right now, I’m working on the simulation portion of the replay, that will use the tracking logs to recreate all the poses.

I’ve been asked more than once what it was like at the beginning of Ubuntu, before it was a company, when an email from someone I’d never heard of came into my mailbox.

We’re coming up on 20 years now since Ubuntu was founded, and I had cause to do some spelunking into IMAP archives recently… while there I took the opportunity to grab the very first email I received.

The Ubuntu long shot succeeded wildly. Of course, we liked to joke about how spammy those emails where: cold-calling a raft of Debian developers with job offers, some of them were closer to phishing attacks :). This very early one – I was the second employee (though I started at 4 days a week to transition my clients gradually) – was less so.

I think its interesting though to note how explicit a gamble this was framed as: a time limited experiment, funded for a year. As the company scaled this very rapidly became a hiring problem and the horizon had to be pushed out to 2 years to get folk to join.

And of course, while we started with arch in earnest, we rapidly hit significant usability problems, some of which were solvable with porcelain and shallow non-architectural changes, and we built initially patches, and then the bazaar VCS project to tackle those. But others were not: for instance, I recall exceeding the 32K hard link limit on ext3 due to a single long history during a VCS conversion. The sum of these challenges led us to create the bzr project, a ground up rethink of our version control needs, architecture, implementation and user-experience. While ultimately git has conquered all, bzr had – still has in fact – extremely loyal advocates, due to its laser sharp focus on usability.

Anyhow, here it is: one of the original no-name-here-yet, aka Ubuntu, introductory emails (with permission from Mark, of course). When I clicked through to the website Mark provided there was a link there to a fantastical website about a space tourist… not what I had expected to be reading in Adelaide during LCA 2004.

From: Mark Shuttleworth <xxx@xxx>
To: Robert Collins <xxx@xxx>
Date: Thu, 15 Jan 2004, 04:30

Tom Lord gave me your email address, I believe he’s
already sent you the email that I sent him so I’m sure
you have some background.

In short, I am going to fund some open source
development for a year. This is part of a new project
that I will be getting off the ground in the coming
weeks. I don’t know where it will lead, it’s flying in
the face of a stiff breeze but I think at the end of
the day it will at least fund a few very good open
source developers for a full year to work on the
projects they like most.

One of the pieces of the puzzle is high end source
code management. I’ll be looking to build an
infrastructure that will manage source code for
between 100 and 8000 open source projects (yes,
there’s a big difference between the two, I don’t know
at which end of the spectrum we will be at the end of
the year but our infrastructure will have to at least
be capable of scaling to the latter within two years)
with upwards of 2000 developers, drawing code from a
variety of sources, playing with it and spitting it
out regularly in nice packages.

Arch and Subversion seem to be the two leading
contenders for “next generation open source sccm”. I’d
be interested in your thoughts on the two of them, and
how they stack up. I’m looking to hire one person who
will lead that part of the effort. They’ll work alone
from home, and be responsible for two things. First,
extending the tool (arch or svn) in ways that help the
project. Such extensions will be released under an
open source licence, and hopefully embraced by the
tools maintainers and included in the mainline code
for the tool. And second, they will be responsible for
our large-scale implementation of SCCM, using that
tool, and building the management scripts and other
infrastructure to support such a large, and hopefully
highly automated, set of repositories.

Would you be interested in this position? What
attributes and experience do you think would make you
a great person to have on the team? What would your
salary expectation be, as a monthly figure, for a one
year contract full time?

I’m currently on your continent, well, just off it. On
Lizard Island, up North. Am headed today for Brisbane,
then on the 17th to Launceston via Melbourne. If you
happen to be on any of those stops, would you be
interested in meeting up to discuss it further?

If you’re curious you can find out a bit more about me
at www.markshuttleworth.com. This project is much
lower key than some of what you’ll find there. It’s a
very long shot indeed. But if at worst all that
happens is a bunch of open source work gets funded at
my expense I’ll feel it was money well spent.

Cheers,
Mark

=====
—
“Good judgement comes from experience, and often experience
comes from bad judgement” – Rita Mae Brown

I have always liked cryptography, and public-key cryptography in particularly. When Pretty Good Privacy (PGP) first came out in 1991, I not only started using it, also but looking at the documentation and the code to see how it worked. I created my own implementation in C using very small keys, just to better understand.

Cryptography has been running a race against both faster and cheaper computing power. And these days, with banking and most other aspects of our lives entirely relying on secure communications, it’s a very juicy target for bad actors.

About 5 years ago, the National (USA) Institute for Science and Technology (NIST) initiated a search for cryptographic algorithmic that should withstand a near-future world where quantum computers with a significant number of qubits are a reality. There have been a number of rounds, which mid 2020 saw round 3 and the finalists.

This submission caught my eye some time ago: Classic McEliece, and out of the four finalists it’s the only one that is not lattice-based [wikipedia link].

For Public Key Encryption and Key Exchange Mechanism, Prof Bill Buchanan thinks that the winner will be lattice-based, but I am not convinced.

Tiny side-track, you may wonder where does the McEleice name come from? From mathematician Robert McEleice (1942-2019). McEleice developed his cryptosystem in 1978. So it’s not just named after him, he designed it. For various reasons that have nothing to do with the mathematical solidity of the ideas, it didn’t get used at the time. He’s done plenty cool other things, too. From his Caltech obituary:

He made fundamental contributions to the theory and design of channel codes for communication systems—including the interplanetary telecommunication systems that were used by the Voyager, Galileo, Mars Pathfinder, Cassini, and Mars Exploration Rover missions.

Back to lattices, there are both unknowns (aspects that have not been studied in exhaustive depth) and recent mathematical attacks, both of which create uncertainty – in the crypto sphere as well as for business and politics. Given how long it takes for crypto schemes to get widely adopted, the latter two are somewhat relevant, particularly since cyber security is a hot topic.

Lattices are definitely interesting, but given what we know so far, it is my feeling that systems based on lattices are more likely to be proven breakable than Classic McEleice, which come to this finalists’ table with 40+ years track record of in-depth analysis. Mind that all finalists are of course solid at this stage – but NIST’s thoughts on expected developments and breakthroughs is what is likely to decide the winner. NIST are not looking for shiny, they are looking for very very solid in all possible ways.

Prof Buchanan recently published implementations for the finalists, and did some benchmarks where we can directly compare them against each other.

We can see that Classic McEleice’s key generation is CPU intensive, but is that really a problem? The large size of its public key may be more of a factor (disadvantage), however the small ciphertext I think more than offsets that disadvantage.

As we’re nearing the end of the NIST process, in my opinion, fast encryption/decryption and small cyphertext, combined with the long track record of in-depth analysis, may still see Classic McEleice come out the winner.

The post Classic McEleice and the NIST search for post-quantum crypto first appeared on Lentz family blog.

Living in California, I’ve (sadly) grown accustomed to needing to keep track of our local air quality index (AQI) ratings, particularly as we live close to places where large wildfires happen every other year.

Last year, Josh and I bought a PurpleAir outdoor air quality meter, which has been great. We contribute our data to a collection of very local air quality meters, which is important, since the hilly nature of the North Bay means that the nearest government air quality ratings can be significantly different to what we experience here in Petaluma.

I recently went looking to pull my PurpleAir sensor data into my Home Assistant setup. Unfortunately, the PurpleAir API does not return the AQI metric for air quality, only the raw PM2.5/PM5/PM10 numbers. After some searching, I found a nice template sensor solution on the Home Assistant forums, which I’ve modernised by adding the AQI as a sub-sensor, and adding unique ID fields to each useful sensor, so that you can assign them to a location.

You’ll end up with sensors for raw PM2.5, the PM2.5 AQI value, the US EPA air quality category, air pressure, relative humidity and air pressure.

How to use this

First up, visit the PurpleAir Map, find the sensor you care about, click “get this widgetâ€�, and then “JSONâ€�. That will give you the URL to set as the resource key in purpleair.yaml.

Adding the configuration

In HomeAssistant, add the following line to your configuration.yaml:

sensor: !include purpleair.yaml

and then add the following contents to purpleair.yaml

 - platform: rest
   name: 'PurpleAir'

   # Substitute in the URL of the sensor you care about.  To find the URL, go
   # to purpleair.com/map, find your sensor, click on it, click on "Get This
   # Widget" then click on "JSON".
   resource: https://www.purpleair.com/json?key={KEY_GOES_HERE}&show={SENSOR_ID}

   # Only query once a minute to avoid rate limits:
   scan_interval: 60

   # Set this sensor to be the AQI value.
   #
   # Code translated from JavaScript found at:
   # https://docs.google.com/document/d/15ijz94dXJ-YAZLi9iZ_RaBwrZ4KtYeCy08goGBwnbCU/edit#
   value_template: >
     {{ value_json["results"][0]["Label"] }}
   unit_of_measurement: ""
   # The value of the sensor can't be longer than 255 characters, but the
   # attributes can.  Store away all the data for use by the templates below.
   json_attributes:
     - results

 - platform: template
   sensors:
     purpleair_aqi:
       unique_id: 'purpleair_SENSORID_aqi_pm25'
       friendly_name: 'PurpleAir PM2.5 AQI'
       value_template: >
         {% macro calcAQI(Cp, Ih, Il, BPh, BPl) -%}
           {{ (((Ih - Il)/(BPh - BPl)) * (Cp - BPl) + Il)|round|float }}
         {%- endmacro %}
         {% if (states('sensor.purpleair_pm25')|float) > 1000 %}
           invalid
         {% elif (states('sensor.purpleair_pm25')|float) > 350.5 %}
           {{ calcAQI((states('sensor.purpleair_pm25')|float), 500.0, 401.0, 500.0, 350.5) }}
         {% elif (states('sensor.purpleair_pm25')|float) > 250.5 %}
           {{ calcAQI((states('sensor.purpleair_pm25')|float), 400.0, 301.0, 350.4, 250.5) }}
         {% elif (states('sensor.purpleair_pm25')|float) > 150.5 %}
           {{ calcAQI((states('sensor.purpleair_pm25')|float), 300.0, 201.0, 250.4, 150.5) }}
         {% elif (states('sensor.purpleair_pm25')|float) > 55.5 %}
           {{ calcAQI((states('sensor.purpleair_pm25')|float), 200.0, 151.0, 150.4, 55.5) }}
         {% elif (states('sensor.purpleair_pm25')|float) > 35.5 %}
           {{ calcAQI((states('sensor.purpleair_pm25')|float), 150.0, 101.0, 55.4, 35.5) }}
         {% elif (states('sensor.purpleair_pm25')|float) > 12.1 %}
           {{ calcAQI((states('sensor.purpleair_pm25')|float), 100.0, 51.0, 35.4, 12.1) }}
         {% elif (states('sensor.purpleair_pm25')|float) >= 0.0 %}
           {{ calcAQI((states('sensor.purpleair_pm25')|float), 50.0, 0.0, 12.0, 0.0) }}
         {% else %}
           invalid
         {% endif %}
       unit_of_measurement: "bit"
     purpleair_description:
       unique_id: 'purpleair_SENSORID_description'
       friendly_name: 'PurpleAir AQI Description'
       value_template: >
         {% if (states('sensor.purpleair_aqi')|float) >= 401.0 %}
           Hazardous
         {% elif (states('sensor.purpleair_aqi')|float) >= 301.0 %}
           Hazardous
         {% elif (states('sensor.purpleair_aqi')|float) >= 201.0 %}
           Very Unhealthy
         {% elif (states('sensor.purpleair_aqi')|float) >= 151.0 %}
           Unhealthy
         {% elif (states('sensor.purpleair_aqi')|float) >= 101.0 %}
           Unhealthy for Sensitive Groups
         {% elif (states('sensor.purpleair_aqi')|float) >= 51.0 %}
           Moderate
         {% elif (states('sensor.purpleair_aqi')|float) >= 0.0 %}
           Good
         {% else %}
           undefined
         {% endif %}
       entity_id: sensor.purpleair
     purpleair_pm25:
       unique_id: 'purpleair_SENSORID_pm25'
       friendly_name: 'PurpleAir PM 2.5'
       value_template: "{{ state_attr('sensor.purpleair','results')[0]['PM2_5Value'] }}"
       unit_of_measurement: "μg/m3"
       entity_id: sensor.purpleair
     purpleair_temp:
       unique_id: 'purpleair_SENSORID_temperature'
       friendly_name: 'PurpleAir Temperature'
       value_template: "{{ state_attr('sensor.purpleair','results')[0]['temp_f'] }}"
       unit_of_measurement: "°F"
       entity_id: sensor.purpleair
     purpleair_humidity:
       unique_id: 'purpleair_SENSORID_humidity'
       friendly_name: 'PurpleAir Humidity'
       value_template: "{{ state_attr('sensor.purpleair','results')[0]['humidity'] }}"
       unit_of_measurement: "%"
       entity_id: sensor.purpleair
     purpleair_pressure:
       unique_id: 'purpleair_SENSORID_pressure'
       friendly_name: 'PurpleAir Pressure'
       value_template: "{{ state_attr('sensor.purpleair','results')[0]['pressure'] }}"
       unit_of_measurement: "hPa"
       entity_id: sensor.purpleair

Quirks

I had difficulty getting the AQI to display as a numeric graph when I didn’t set a unit. I went with bit, and that worked just fine. 🤷â€�♂ï¸�

So, this idea has been brewing for a while now… try and watch all of Doctor Who. All of it. All 38 seasons. Today(ish), we started. First up, from 1963 (first aired not quite when intended due to the Kennedy assassination): An Unearthly Child. The first episode of the first serial.

A lot of iconic things are there from the start: the music, the Police Box, embarrassing moments of not quite remembering what time one is in, and normal humans accidentally finding their way into the TARDIS.

I first saw this way back when a child, where they were repeated on ABC TV in Australia for some anniversary of Doctor Who (I forget which one). Well, I saw all but the first episode as the train home was delayed and stopped outside Caulfield for no reason for ages. Some things never change.

Of course, being a show from the early 1960s, there’s some rougher spots. We’re not about to have the picture of diversity, and there’s going to be casual racism and sexism. What will be interesting is noticing these things today, and contrasting with my memory of them at the time (at least for episodes I’ve seen before), and what I know of the attitudes of the time.

“This year-ometer is not calculating properly” is a very 2020 line though (technically from the second episode).

It’s been a while since my last post about tracking support for the Oculus Rift in February. There’s been big improvements since then – working really well a lot of the time. It’s gone from “If I don’t make any sudden moves, I can finish an easy Beat Saber level” to “You can’t hide from me!” quality.

Equally, there are still enough glitches and corner cases that I think I’ll still be at this a while.

Here’s a video from 3 weeks ago of (not me) playing Beat Saber on Expert+ setting showing just how good things can be now:

Strap in. Here’s what I’ve worked on in the last 6 weeks:

Pose Matching improvements

Most of the biggest improvements have come from improving the computer vision algorithm that’s matching the observed LEDs (blobs) in the camera frames to the 3D models of the devices.

I split the brute-force search algorithm into 2 phases. It now does a first pass looking for ‘obvious’ matches. In that pass, it does a shallow graph search of blobs and their nearest few neighbours against LEDs and their nearest neighbours, looking for a match using a “Strong” match metric. A match is considered strong if expected LEDs match observed blobs to within 1.5 pixels.

Coupled with checks on the expected orientation (matching the Gravity vector detected by the IMU) and the pose prior (expected position and orientation are within predicted error bounds) this short-circuit on the search is hit a lot of the time, and often completes within 1 frame duration.

In the remaining tricky cases, where a deeper graph search is required in order to recover the pose, the initial search reduces the number of LEDs and blobs under consideration, speeding up the remaining search.

I also added an LED size model to the mix – for a candidate pose, it tries to work out how large (in pixels) each LED should appear, and use that as a bound on matching blobs to LEDs. This helps reduce mismatches as devices move further from the camera.

LED labelling

When a brute-force search for pose recovery completes, the system now knows the identity of various blobs in the camera image. One way it avoids a search next time is to transfer the labels into future camera observations using optical-flow tracking on the visible blobs.

The problem is that even sped-up the search can still take a few frame-durations to complete. Previously LED labels would be transferred from frame to frame as they arrived, but there’s now a unique ID associated with each blob that allows the labels to be transferred even several frames later once their identity is known.

IMU Gyro scale

One of the problems with reverse engineering is the guesswork around exactly what different values mean. I was looking into why the controller movement felt “swimmy” under fast motions, and one thing I found was that the interpretation of the gyroscope readings from the IMU was incorrect.

The touch controllers report IMU angular velocity readings directly as a 16-bit signed integer. Previously the code would take the reading and divide by 1024 and use the value as radians/second.

From teardowns of the controller, I know the IMU is an Invensense MPU-6500. From the datasheet, the reported value is actually in degrees per second and appears to be configured for the +/- 2000 °/s range. That yields a calculation of Gyro-rad/s = Gyro-°/s * (2000 / 32768) * (?/180) – or a divisor of 938.734.

The 1024 divisor was under-estimating rotation speed by about 10% – close enough to work until you start moving quickly.

Limited interpolation

If we don’t find a device in the camera views, the fusion filter predicts motion using the IMU readings – but that quickly becomes inaccurate. In the worst case, the controllers fly off into the distance. To avoid that, I added a limit of 500ms for ‘coasting’. If we haven’t recovered the device pose by then, the position is frozen in place and only rotation is updated until the cameras find it again.

Exponential filtering

I implemented a 1-Euro exponential smoothing filter on the output poses for each device. This is an idea from the Project Esky driver for Project North Star/Deck-X AR headsets, and almost completely eliminates jitter in the headset view and hand controllers shown to the user. The tradeoff is against introducing lag when the user moves quickly – but there are some tunables in the exponential filter to play with for minimising that. For now I’ve picked some values that seem to work reasonably.

Non-blocking radio

Communications with the touch controllers happens through USB radio command packets sent to the headset. The main use of radio commands in OpenHMD is to read the JSON configuration block for each controller that is programmed in at the factory. The configuration block provides the 3D model of LED positions as well as initial IMU bias values.

Unfortunately, reading the configuration block takes a couple of seconds on startup, and blocks everything while it’s happening. Oculus saw that problem and added a checksum in the controller firmware. You can read the checksum first and if it hasn’t changed use a local cache of the configuration block. Eventually, I’ll implement that caching mechanism for OpenHMD but in the meantime it still reads the configuration blocks on each startup.

As an interim improvement I rewrote the radio communication logic to use a state machine that is checked in the update loop – allowing radio communications to be interleaved without blocking the regularly processing of events. It still interferes a bit, but no longer causes a full multi-second stall as each hand controller turns on.

Haptic feedback

The hand controllers have haptic feedback ‘rumble’ motors that really add to the immersiveness of VR by letting you sense collisions with objects. Until now, OpenHMD hasn’t had any support for applications to trigger haptic events. I spent a bit of time looking at USB packet traces with Philipp Zabel and we figured out the radio commands to turn the rumble motors on and off.

In the Rift CV1, the haptic motors have a mode where you schedule feedback events into a ringbuffer – effectively they operate like a low frequency audio device. However, that mode was removed for the Rift S (and presumably in the Quest devices) – and deprecated for the CV1.

With that in mind, I aimed for implementing the unbuffered mode, with explicit ‘motor on + frequency + amplitude’ and ‘motor off’ commands sent as needed. Thanks to already having rewritten the radio communications to use a state machine, adding haptic commands was fairly easy.

The big question mark is around what API OpenHMD should provide for haptic feedback. I’ve implemented something simple for now, to get some discussion going. It works really well and adds hugely to the experience. That code is in the https://github.com/thaytan/OpenHMD/tree/rift-haptics branch, with a SteamVR-OpenHMD branch that uses it in https://github.com/thaytan/SteamVR-OpenHMD/tree/controller-haptics-wip

Problem areas

Unexpected tracking losses

I’d say the biggest problem right now is unexpected tracking loss and incorrect pose extractions when I’m not expecting them. Especially my right controller will suddenly glitch and start jumping around. Looking at a video of the debug feed, it’s not obvious why that’s happening:

To fix cases like those, I plan to add code to log the raw video feed and the IMU information together so that I can replay the video analysis frame-by-frame and investigate glitches systematically. Those recordings will also work as a regression suite to test future changes.

Sensor fusion efficiency

The Kalman filter I have implemented works really nicely – it does the latency compensation, predicts motion and extracts sensor biases all in one place… but it has a big downside of being quite expensive in CPU. The Unscented Kalman Filter CPU cost grows at O(n^3) with the size of the state, and the state in this case is 43 dimensional – 22 base dimensions, and 7 per latency-compensation slot. Running 1000 updates per second for the HMD and 500 for each of the hand controllers adds up quickly.

At some point, I want to find a better / cheaper approach to the problem that still provides low-latency motion predictions for the user while still providing the same benefits around latency compensation and bias extraction.

Lens Distortion

To generate a convincing illusion of objects at a distance in a headset that’s only a few centimetres deep, VR headsets use some interesting optics. The LCD/OLED panels displaying the output get distorted heavily before they hit the users eyes. What the software generates needs to compensate by applying the right inverse distortion to the output video.

Everyone that tests the CV1 notices that the distortion is not quite correct. As you look around, the world warps and shifts annoyingly. Sooner or later that needs fixing. That’s done by taking photos of calibration patterns through the headset lenses and generating a distortion model.

Camera / USB failures

The camera feeds are captured using a custom user-space UVC driver implementation that knows how to set up the special synchronisation settings of the CV1 and DK2 cameras, and then repeatedly schedules isochronous USB packet transfers to receive the video.

Occasionally, some people experience failure to re-schedule those transfers. The kernel rejects them with an out-of-memory error failing to set aside DMA memory (even though it may have been running fine for quite some time). It’s not clear why that happens – but the end result at the moment is that the USB traffic for that camera dies completely and there’ll be no more tracking from that camera until the application is restarted.

Often once it starts happening, it will keep happening until the PC is rebooted and the kernel memory state is reset.

Occluded cases

Tracking generally works well when the cameras get a clear shot of each device, but there are cases like sighting down the barrel of a gun where we expect that the user will line up the controllers in front of one another, and in front of the headset. In that case, even though we probably have a good idea where each device is, it can be hard to figure out which LEDs belong to which device.

If we already have a good tracking lock on the devices, I think it should be possible to keep tracking even down to 1 or 2 LEDs being visible – but the pose assessment code will have to be aware that’s what is happening.

Upstreaming

April 14th marks 2 years since I first branched off OpenHMD master to start working on CV1 tracking. How hard can it be, I thought? I’ll knock this over in a few months.

Since then I’ve accumulated over 300 commits on top of OpenHMD master that eventually all need upstreaming in some way.

One thing people have expressed as a prerequisite for upstreaming is to try and remove the OpenCV dependency. The tracking relies on OpenCV to do camera distortion calculations, and for their PnP implementation. It should be possible to reimplement both of those directly in OpenHMD with a bit of work – possibly using the fast LambdaTwist P3P algorithm that Philipp Zabel wrote, that I’m already using for pose extraction in the brute-force search.

Others

I’ve picked the top issues to highlight here. https://github.com/thaytan/OpenHMD/issues has a list of all the other things that are still on the radar for fixing eventually.

Other Headsets

At some point soon, I plan to put a pin in the CV1 tracking and look at adapting it to more recent inside-out headsets like the Rift S and WMR headsets. I implemented 3DOF support for the Rift S last year, but getting to full positional tracking for that and other inside-out headsets means implementing a SLAM/VIO tracking algorithm to track the headset position.

Once the headset is tracking, the code I’m developing here for CV1 to find and track controllers will hopefully transfer across – the difference with inside-out tracking is that the cameras move around with the headset. Finding the controllers in the actual video feed should work much the same.

Sponsorship

This development happens mostly in my spare time and partly as open source contribution time at work at Centricular. I am accepting funding through Github Sponsorships to help me spend more time on it – I’d really like to keep helping Linux have top-notch support for VR/AR applications. Big thanks to the people that have helped get this far.

Every so often, I release a new libeatmydata. This has not happened for a long time. This is just some bug fixes, most of which have been in the Debian package for some time, I’ve just been lazy and not sat down and merged them.

git clone https://github.com/stewartsmith/libeatmydata.git

Download the source tarball from here: libeatmydata-129.tar.gz and GPG signature: libeatmydata-129.tar.gz.asc from my GPG key.

Or, feel free to grab some Fedora RPMs:

• libeatmydata-129-1.fc33.src.rpm
• libeatmydata-129-1.fc33.x86_64.rpm
• libeatmydata-129-1.fc33.ppc64le.rpm
• libeatmydata-debuginfo-129-1.fc33.ppc64le.rpm
• libeatmydata-debuginfo-129-1.fc33.x86_64.rpm
• libeatmydata-debugsource-129-1.fc33.ppc64le.rpm
• libeatmydata-debugsource-129-1.fc33.x86_64.rpm

Releases published also in the usual places:

• https://www.flamingspork.com/projects/libeatmydata/
• https://github.com/stewartsmith/libeatmydata/releases/tag/v129

Today, 30 March, is World Bipolar Day.

Why that particular date? It’s Vincent van Gogh’s birthday (1853), and there is a fairly strong argument that the Dutch painter suffered from bipolar (among other things).

The image on the side is Vincent’s drawing “Worn Out” (from 1882), and it seems to capture the feeling rather well – whether (hypo)manic, depressed, or mixed. It’s exhausting.

Bipolar is complicated, often undiagnosed or misdiagnosed, and when only treated with anti-depressants, it can trigger the (hypo)mania – essentially dragging that person into that state near-permanently.

Have you heard of Bipolar II?

Hypo-mania is the “lesser” form of mania that distinguishes Bipolar I (the classic “manic depressive” syndrome) from Bipolar II. It’s “lesser” only in the sense that rather than someone going so hyper they may think they can fly (Bipolar I is often identified when someone in manic state gets admitted to hospital – good catch!) while with Bipolar II the hypo-mania may actually exhibit as anger. Anger in general, against nothing in particular but potentially everyone and everything around them. Or, if it’s a mixed episode, anger combined with strong negative thoughts. Either way, it does not look like classic mania. It is, however, exhausting and can be very debilitating.

Bipolar II people often present to a doctor while in depressed state, and GPs (not being psychiatrists) may not do a full diagnosis. Note that D.A.S. and similar test sheets are screening tools, they are not diagnostic. A proper diagnosis is more complex than filling in a form some questions (who would have thought!)

Call to action

If you have a diagnosis of depression, only from a GP, and are on medication for this, I would strongly recommend you also get a referral to a psychiatrist to confirm that diagnosis.

Our friends at the awesome Black Dog Institute have excellent information on bipolar, as well as a quick self-test – if that shows some likelihood of bipolar, go get that referral and follow up ASAP.

I will be writing more about the topic in the coming time.

So, I learned something recently: if you pick up your iPhone with eBay open on an auction bid screen in just the right way, you may accidentally click the bid button and end up buying an old computer. Totally not the worst thing ever, and certainly a creative way to make a decision.

So, not too long later, a box arrives!

In the 1990s, Apple created some pretty “interesting” computers and product line. One thing you could get is a DOS Compatibility (or PC Compatibility) card. This was a card that went into one of the expansion slots on a Mac and had something really curious on it: most of the guts of a PC.

Others have written on these cards too: https://www.engadget.com/2009-12-10-before-there-was-boot-camp-there-were-dos-compatibility-cards.html and http://www.edibleapple.com/2009/12/09/blast-from-the-past-a-look-back-at-apples-dos-compatibility-cards/. There’s also the Service Manual https://tim.id.au/laptops/apple/misc/pc_compatibility_card.pdf with some interesting details.

The machine I’d bought was an Apple Power Macintosh 7200/120 with the PC Compatible card added afterwards (so it doesn’t have the PC Compatible label on the front like some models ended up getting).

Wikipedia has a good article on the line, noting that it was first released in August 1995, and fitting for the era, was sold as about 14 million other model numbers (okay not quite that bad, it was only a total of four model numbers for essentially the same machine). This specific model, the 7200/120 was introduced on April 22nd, 1996, and the original web page describing it from Apple is on the wayback machine.

For older Macs, Low End Mac is a good resource, and there’s a page on the 7200, and amazingly Apple still has the tech specs on their web site!

The 7200 series replaced the 7100, which was one of the original PowerPC based Macs. The big changes are using the industry standard PCI bus for its three expansion slots rather than NuBus. Rather surprisingly, NuBus was not Apple specific, but you could not call it widely adopted by successful manufacturers. Apple first used NuBus in the 1987 Macintosh II.

The PCI bus was standardized in 1992, and it’s almost certain that a successor to it is in the computer you’re using to read this. It really quite caught on as an industry standard.

The processor of the machine is a PowerPC 601. The PowerPC was an effort of IBM, Apple, and Motorola (the AIM Alliance) to create a class of processors for personal computers based on IBM’s POWER Architecture. The PowerPC 601 was the first of these processors, initially used by Apple in its Power Macintosh range. The machine I have has one running at a whopping 120Mhz. There continued to be PowerPC chips for a number of years, and IBM continued making POWER processors even after that. However, you are almost certainly not using a PowerPC derived processor in the computer you’re using to read this.

The PC Compatibility card has on it a full on legit Pentium 100 processor, and hardware for doing VGA graphics, a Sound Blaster 16 and the other things you’d usually expect of a PC from 1996. Since it’s on a PCI card though, it’s a bit different than a PC of the era. It doesn’t have any expansion slots of its own, and in fact uses up one of the three PCI slots in the Mac. It also doesn’t have its own floppy drive, or hard drive. There’s software on the Mac that will let the PC card use the Mac’s floppy drive, and part of the Mac’s hard drive for the PC!

The Pentium 100 was the first mass produced superscalar processor. You are quite likely to be using a computer with a processor related to the Pentium to read this, unless you’re using a phone or tablet, or one of the very latest Macs; in which case you’re using an ARM based processor. You likely have more ARM processors in your life than you have socks.

Basically, this computer is a bit of a hodge-podge of historical technology, some of which ended up being successful, and other things less so.

Let’s have a look inside!

So, one of the PCI slots has a Vertex Twin Turbo 128M8A video card in it. There is not much about this card on the internet. There’s a photo of one on Wikimedia Commons though. I’ll have to investigate more.

Does it work though? Yes! Here it is on my desk:

Even with Microsoft Internet Explorer 4.0 that came with MacOS 8.6, you can find some places on the internet you can fetch files from, at a not too bad speed even!

More fun times with this machine to come!

Update:

This post documented an older method of building SteamVR-OpenHMD. I moved them to a page here. That version will be kept up to date for any future changes, so go there.

I’ve had a few people ask how to test my OpenHMD development branch of Rift CV1 positional tracking in SteamVR. Here’s what I do:

• Make sure Steam + SteamVR are already installed.
• Clone the SteamVR-OpenHMD repository:

git clone --recursive https://github.com/ChristophHaag/SteamVR-OpenHMD.git

• Switch the internal copy of OpenHMD to the right branch:

cd subprojects/openhmd
git remote add thaytan-github https://github.com/thaytan/OpenHMD.git
git fetch thaytan-github
git checkout -b rift-kalman-filter thaytan-github/rift-kalman-filter
cd ../../

• Use meson to build and register the SteamVR-OpenHMD binaries. You may need to install meson first (see below):

meson -Dbuildtype=release build
ninja -C build
./install_files_to_build.sh
./register.sh

• It is important to configure in release mode, as the kalman filtering code is generally too slow for real-time in debug mode (it has to run 2000 times per second)
• Make sure your USB devices are accessible to your user account by configuring udev. See the OpenHMD guide here: https://github.com/OpenHMD/OpenHMD/wiki/Udev-rules-list
• Please note – only Rift sensors on USB 3.0 ports will work right now. Supporting cameras on USB 2.0 requires someone implementing JPEG format streaming and decoding.
• It can be helpful to test OpenHMD is working by running the simple example. Check that it’s finding camera sensors at startup, and that the position seems to change when you move the headset:

./build/subprojects/openhmd/openhmd_simple_example

• Calibrate your expectations for how well tracking is working right now! Hint: It’s very experimental
• Start SteamVR. Hopefully it should detect your headset and the light(s) on your Rift Sensor(s) should power on.

Meson

I prefer the Meson build system here. There’s also a cmake build for SteamVR-OpenHMD you can use instead, but I haven’t tested it in a while and it sometimes breaks as I work on my development branch.

If you need to install meson, there are instructions here – https://mesonbuild.com/Getting-meson.html summarising the various methods.

I use a copy in my home directory, but you need to make sure ~/.local/bin is in your PATH

pip3 install --user meson

I spent some time this weekend implementing a couple of my ideas for improving the way the tracking code in OpenHMD filters and rejects (or accepts) possible poses when trying to match visible LEDs to the 3D models for each device.

In general, the tracking proceeds in several steps (in parallel for each of the 3 devices being tracked):

1. Do a brute-force search to match LEDs to 3D models, then (if matched)
   1. Assign labels to each LED blob in the video frame saying what LED they are.
   2. Send an update to the fusion filter about the position / orientation of the device
2. Then, as each video frame arrives:
   1. Use motion flow between video frames to track the movement of each visible LED
   2. Use the IMU + vision fusion filter to predict the position/orientation (pose) of each device, and calculate which LEDs are expected to be visible and where.
3. Try and match up and refine the poses using the predicted pose prior and labelled LEDs. In the best case, the LEDs are exactly where the fusion predicts they’ll be. More often, the orientation is mostly correct, but the position has drifted and needs correcting. In the worst case, we send the frame back to step 1 and do a brute-force search to reacquire an object.

The goal is to always assign the correct LEDs to the correct device (so you don’t end up with the right controller in your left hand), and to avoid going back to the expensive brute-force search to re-acquire devices as much as possible

What I’ve been working on this week is steps 1 and 3 – initial acquisition of correct poses, and fast validation / refinement of the pose in each video frame, and I’ve implemented two new strategies for that.

Gravity Vector matching

The first new strategy is to reject candidate poses that don’t closely match the known direction of gravity for each device. I had a previous implementation of that idea which turned out to be wrong, so I’ve re-worked it and it helps a lot with device acquisition.

The IMU accelerometer and gyro can usually tell us which way up the device is (roll and pitch) but not which way they are facing (yaw). The measure for ‘known gravity’ comes from the fusion Kalman filter covariance matrix – how certain the filter is about the orientation of the device. If that variance is small this new strategy is used to reject possible poses that don’t have the same idea of gravity (while permitting rotations around the Y axis), with the filter variance as a tolerance.

Partial tracking matches

The 2nd strategy is based around tracking with fewer LED correspondences once a tracking lock is acquired. Initial acquisition of the device pose relies on some heuristics for how many LEDs must match the 3D model. The general heuristic threshold I settled on for now is that 2/3rds of the expected LEDs must be visible to acquire a cold lock.

With the new strategy, if the pose prior has a good idea where the device is and which way it’s facing, it allows matching on far fewer LED correspondences. The idea is to keep tracking a device even down to just a couple of LEDs, and hope that more become visible soon.

While this definitely seems to help, I think the approach can use more work.

Status

With these two new approaches, tracking is improved but still quite erratic. Tracking of the headset itself is quite good now and for me rarely loses tracking lock. The controllers are better, but have a tendency to “fly off my hands” unexpectedly, especially after fast motions.

I have ideas for more tracking heuristics to implement, and I expect a continuous cycle of refinement on the existing strategies and new ones for some time to come.

For now, here’s a video of me playing Beat Saber using tonight’s code. The video shows the debug stream that OpenHMD can generate via Pipewire, showing the camera feed plus overlays of device predictions, LED device assignments and tracked device positions. Red is the headset, Green is the right controller, Blue is the left controller.

Initial tracking is completely wrong – I see some things to fix there. When the controllers go offline due to inactivity, the code keeps trying to match LEDs to them for example, and then there are some things wrong with how it’s relabelling LEDs when they get incorrect assignments.

After that, there are periods of good tracking with random tracking losses on the controllers – those show the problem cases to concentrate on.

These lack of updates are also likely because I’ve been quite caught up with stuff.

Monday I had a steak from Bay Leaf Steakhouse for dinner. It was kind of weird eating it from packs, but then I’m reminded you could do this in economy class. Tuesday I wanted to attempt to go vegetarian and by the time I was done with a workout, the only place was a chap fan shop (Leong Heng) where I had a mixture of Chinese and Indian chap fan. The Indian stall is run by an ex-Hyatt staff member who immediately recognised me! Wednesday, Alice came to visit, so we got to Hanks, got some alcohol, and managed a smorgasbord of food from Pickers/Sate Zul/Lila Wadi. Night ended very late, and on Thursday, visited Hai Tian for their famous salted egg squid and prawns in a coconut shell. Friday was back to being normal, so I grabbed a pizza from Mint Pizza (this time I tried their Aussie variant). Saturday, today, I hit up Rasa Sayang for some matcha latte, but grabbed food from Classic Pilot Cafe, which Faeeza owns! It was the famous salted egg chicken, double portion, half rice.

As for workouts, I did sign up for Mantas but found it pretty hard to do, timezone wise. I did spend a lot of time jogging on the beach (this has been almost a daily affair). Monday I also did 2 MD workouts, Tuesday 1 MD workout, Wednesday half a MD workout, Thursday I did a Ping workout at Pwrhouse (so good!), Friday 1 MD workout, and Saturday an Audrey workout at Pwrhouse and 1 MD workout.

Wednesday I also found out that Rasmus passed away. Frankly, there are no words.

Thursday, my Raspberry Pi 400 arrived. I set it up in under ten minutes, connecting it to the TV here. It “just works”. I made a video, which I should probably figure out how to upload to YouTube after I stitch it together. I have to work on using it a lot more.

COVID-19 cases are through the roof in Malaysia. This weekend we’ve seen two days of case breaking records, with today being 5,728 (yesterday was something close). Nutty. Singapore suspended the reciprocal green lane (RGL) agreement with Malaysia for the next 3 months.

I’ve managed to finish Bridgerton. I like the score. Finding something on Netflix is proving to be more difficult, regardless of having a VPN. Honestly, this is why Cable TV wins… linear programming that you’re just fed.

Stock market wise, I’ve been following the GameStop short squeeze, and even funnier is the Top Glove one, that they’re trying to repeat in Malaysia. Bitcoin seems to be doing “reasonably well” and I have to say, I think people are starting to realise decentralised services have a future. How do we get there?

What an interesting week, I look forward to more productive time. I’m still writing in my Hobonichi Techo, so at least that’s where most personal stuff ends up, I guess?

The post Life with Rona 2.0 – Days 4, 5, 6, 7, 8 and 9 first appeared on Colin Charles Agenda.

I hit an important OpenHMD milestone tonight – I completed a Beat Saber level using my Oculus Rift CV1!

I’ve been continuing to work on integrating Kalman filtering into OpenHMD, and on improving the computer vision that matches and tracks device LEDs. While I suspect noone will be completing Expert levels just yet, it’s working well enough that I was able to play through a complete level of Beat Saber. For a long time this has been my mental benchmark for tracking performance, and I’m really happy

Check it out:

I should admit at this point that completing this level took me multiple attempts. The tracking still has quite a tendency to lose track of controllers, or to get them confused and swap hands suddenly.

I have a list of more things to work on. See you at the next update!

What an unplanned day. I woke up in time to do an MD workout, despite feeling a little sore. So maybe I was about 10 minutes late and I missed the first set, but his workouts are so long, and I think there were seven sets anyway. Had a good brunch shortly thereafter.

Did a bit of reading, and then I decided to do a beach boardwalk walk… turns out they were policing the place, and you can’t hit the boardwalk. But the beach is fair game? So I went back to the hotel, dropped off my slippers, and went for a beach jog. Pretty nutty.

Came back to read a little more and figured I might as well do another MD workout. Then I headed out for dinner, trying out a new place — Mint Pizza. Opened 20.12.2020, and they’re empty, and their pizza is actually pretty good. Lamb and BBQ chicken, they did half-and-half.

Twitter was discussing Raspberry Pi’s, and all I could see is a lot of misinformation, which is truly shocking. The irony is that open source has been running the Internet for so long, and progressive web apps have come such a long way…

Back in the day when I did OpenOffice.org or Linux training even, we always did say you should learn concepts and not tools. From the time we ran Linux installfests in the late-90s in Sunway Pyramid (back then, yes, Linux was hard, and you had winmodems), but I had forgotten that I even did stuff for school teachers and NGOs back in 2002… I won’t forget PC Gemilang either…

Anyway, I placed an order again for another Raspberry Pi 400. I am certain that most people talk so much crap, without realising that Malaysia isn’t a developed nation and most people can’t afford a Mac let alone a PC. Laptops aren’t cheap. And there are so many other issues…. Saying Windows is still required in 2021 is the nuttiest thing I’ve heard in a long time. Easy to tweet, much harder to think about TCO, and realise where in the journey Malaysia is.

Maybe the best thing was that Malaysian Twitter learned about technology. I doubt many realised the difference between a Pi board vs the 400, but hey, the fact that they talked about tech is still a win (misinformed, but a win).

The post Life with Rona 2.0 – Day 3 first appeared on Colin Charles Agenda.

Today is the first day that in the state of Pahang, we have to encounter what many Malaysians are referring to as the Movement Control Order 2.0 (MCO 2.0). I think everyone finally agrees with the terminology that this is a lockdown now, because I remember back in the day when I was calling it that, I’d definitely offend a handful of journalists.

This is one interesting change for me compared to when I last wrote Life with Rona — Day 56 of being indoors and not even leaving my household, in Kuala Lumpur. I am now not in the state, I am living in a hotel, and I am obviously moving around a little more since we have access to the beach.

KL/Selangor and several other states have already been under the MCO 2.0 since January 13 2021, and while it was supposed to end on January 26, it seems like they’ve extended and harmonised the dates for Peninsular Malaysia to end on February 4 2021. I guess everyone got the “good news” yesterday. The Prime Minister announced some kind of aid last week, but it is still mostly a joke.

Today was the 2nd day I woke up at around 2.30pm because I went to bed at around 8am. First day I had a 23.5 hour uptime, and the today was less brutal, but working from 1-8am with the PST timezone is pretty brutal. Consequently, I barely got too much done, and had one meal, vegetarian, two packs that included rice. I did get to walk by the beach (between Teluk Cempedak and Teluk Cempedak 2), did quite a bit of exercise there and I think even the monkeys are getting hungry… lots of stray cats and monkeys. Starbucks closes at 7pm, and I rocked up at 7.10pm (this was just like yesterday, when I arrived at 9.55pm and was told they wouldn’t grant me a coffee!).

While writing this entry, I did manage to get into a long video call with some friends and I guess it was good catching up with people in various states. It also is what prevented me from publishing this entry!

Day 2

I did wake up reasonable early today because I had pre-ordered room service to arrive at 9am. There is a fixed menu at the hotel for various cuisines (RM48/pax, thankfully gratis for me) and I told them I prefer not having to waste, so just give me what I want which is off menu items anyway. Roti telur double telur (yes, I know it is a roti jantan) with some banjir dhal and sambal and a bit of fruit on the side with two teh tariks. They delivered as requested. I did forget to ask for a jar of honey but that is OK, there is always tomorrow.

I spent most of the day vacillating, and wouldn’t consider it productive by any measure. Just chit chats and napping. It did rain today after a long time, so the day seemed fairly dreary.

When I finally did awaken from my nap, I went for a run on the beach. I did it barefoot. I have no idea if this is how it is supposed to be done, or if you are to run nearer the water or further up above, but I did move around between the two quite often. The beach is still pretty dead, but it is expected since no one is allowed to go unless you’re a hotel guest.

The hotel has closed 3/4 of their villages (blocks) and moved everyone to the village I’m staying in (for long stay guests…). I’m thankful I have a pretty large suite, it is a little over 980sqft, and the ample space, while smaller than my home, is still welcome.

Post beach run, I did a workout with MD via Instagram. It was strength/HIIT based, and I burnt a tonne, because he gave us one of his signature 1.5h classes. It was longer than the 80 minute class he normally charges RM50 for (I still think this is undervaluing his service, but he really does care and does it for the love of seeing his students grow!).

Post-workout I decided to head downtown to find some dinner. Everything at the Teluk Cemepdak block of shops was closed, so they’re not even bothered with doing takeaway. Sg. Lembing steakhouse seemed to have cars parked, Vanggey was empty (Crocodile Rock was open, can’t say if there was a crowd, because the shared parking lot was empty), there was a modest queue at Sate Zul, and further down, Lena was closed, Pickers was open for takeaway but looked pretty closed, Tjantek was open surprisingly, and then I thought I’d give Nusantara a try again, this time for food, but their chef had just gone home at about 8pm. Oops. So I drove to LAN burger, initially ordering just one chicken double special; however they looked like they could use the business so I added on a beef double special. They now accept Boost payments so have joined the e-wallet era. One less place to use cash, which is also why I really like Kuantan. On the drive back, Classic Pilot Cafe was also open and I guess I’ll be heading there too during this lockdown.

Came back to the room to finish both burgers in probably under 15 minutes. While watching the first episode of Bridgerton on Netflix. I’m not sure what really captivates, but I will continue on (I still haven’t finished the first episode). I need to figure out how to use the 2 TVs that I have in this room — HDMI cable? Apple TV? Not normally using a TV, all this is clearly more complex than I care to admit.

I soaked longer than expected, ended up a prune, but I’m sure it will give me good rest!

One thought to leave with:

“Learn to enjoy every minute of your life. Be happy now. Don’t wait for something outside of yourself to make you happy in the future.” — Earl Nightingale

The post Life with Rona 2.0 – Days 1 & 2 first appeared on Colin Charles Agenda.

In my experience, the C programming language is still hard to beat, even 50 years after it was first developed (and I feel the same way about UNIX). When it comes to general-purpose utility, low-level systems programming, performance, and portability (even to tiny embedded systems), I would choose C over most modern or fashionable alternatives. In some cases, it is almost the only choice.

Many developers believe that it is difficult to write secure and reliable software in C, due to its free pointers, the lack of enforced memory integrity, and the lack of automatic memory management; however in my opinion it is possible to overcome these risks with discipline and a more secure system of libraries constructed on top of C and libc. Daniel J. Bernstein and Wietse Venema are two developers who have been able to write highly secure, stable, reliable software in C.

My other favourite language is Python. Although Python has numerous desirable features, my favourite is the light-weight syntax: in Python, block structure is indicated by indentation, and braces and semicolons are not required. Apart from the pleasure and relief of reading and writing such light and clear code, which almost appears to be executable pseudo-code, there are many other benefits. In C or JavaScript, if you omit a trailing brace somewhere in the code, or insert an extra brace somewhere, the compiler may tell you that there is a syntax error at the end of the file. These errors can be annoying to track down, and cannot occur in Python. Python not only looks better, the clear syntax helps to avoid errors.

The obvious disadvantage of Python, and other dynamic interpreted languages, is that most programs run extremely slower than C programs. This limits the scope and generality of Python. No AAA or performance-oriented video game engines are programmed in Python. The language is not suitable for low-level systems programming, such as operating system development, device drivers, filesystems, performance-critical networking servers, or real-time systems.

C is a great all-purpose language, but the code is uglier than Python code. Once upon a time, when I was experimenting with the Plan 9 operating system (which is built on C, but lacks Python), I missed Python’s syntax, so I decided to do something about it and write a little preprocessor for C. This converts from a “Pythonesque” indented syntax to regular C with the braces and semicolons. Having forked a little dialect of my own, I continued from there adding other modules and features (which might have been a mistake, but it has been fun and rewarding).

At first I called this translator Brace, because it added in the braces for me. I now call the language CZ. It sounds like “C-easy”. Ease-of-use for developers (DX) is the primary goal. CZ has all of the features of C, and translates cleanly into C, which is then compiled to machine code as normal (using any C compiler; I didn’t write one); and so CZ has the same features and performance as C, but enjoys a more pleasing syntax.

CZ is now self-hosted, in that the translator is written in the language CZ. I confess that originally I wrote most of it in Perl; I’m proficient at Perl, but I consider it to be a fairly ugly language, and overly complicated.

I intend for CZ’s new syntax to be “optional”, ideally a developer will be able to choose to use the normal C syntax when editing CZ, if they prefer it. For this, I need a tool to convert C back to CZ, which I have not fully implemented yet. I am aware that, in addition to traditionalists, some vision-impaired developers prefer to use braces and semicolons, as screen readers might not clearly indicate indentation. A C to CZ translator would of course also be valuable when porting an existing C program to CZ.

CZ has a number of useful features that are not found in standard C, but I did not go so far as C++, which language has been described as “an octopus made by nailing extra legs onto a dog”. I do not consider C to be a dog, at least not in a negative sense; but I think that C++ is not an improvement over plain C. I am creating CZ because I think that it is possible to improve on C, without losing any of its advantages or making it too complex.

One of the most interesting features I added is a simple syntax for fast, light coroutines. I based this on Simon Tatham’s approach to Coroutines in C, which may seem hacky at first glance, but is very efficient and can work very well in practice. I implemented a very fast web server with very clean code using these coroutines. The cost of switching coroutines with this method is little more than the cost of a function call.

CZ has hygienic macros. The regular cpp (C preprocessor) macros are not hygenic and many people consider them hacky and unsafe to use. My CZ macros are safe, and somewhat more powerful than standard C macros. They can be used to neatly add new program control structures. I have plans to further develop the macro system in interesting ways.

I added automatic prototype and header generation, as I do not like having to repeat myself when copying prototypes to separate header files. I added support for the UNIX #! scripting syntax, and for cached executables, which means that CZ can be used like a scripting language without having to use a separate compile or make command, but the programs are only recompiled when something has been changed.

For CZ, I invented a neat approach to portability without conditional compilation directives. Platform-specific library fragments are automatically included from directories having the name of that platform or platform-category. This can work very well in practice, and helps to avoid the nightmare of conditional compilation, feature detection, and Autotools. Using this method, I was able easily to implement portable interfaces to features such as asynchronous IO multiplexing (aka select / poll).

The CZ library includes flexible error handling wrappers, inspired by W. Richard Stevens’ wrappers in his books on Unix Network Programming. If these wrappers are used, there is no need to check return values for error codes, and this makes the code much safer, as an error cannot accidentally be ignored.

CZ has several major faults, which I intend to correct at some point. Some of the syntax is poorly thought out, and I need to revisit it. I developed a fairly rich library to go with the language, including safer data structures, IO, networking, graphics, and sound. There are many nice features, but my CZ library is more prototype than a finished product, there are major omissions, and some features are misconceived or poorly implemented. The misfeatures should be weeded out for the time-being, or moved to an experimental section of the library.

I think that a good software library should come in two parts, the essential low-level APIs with the minimum necessary functionality, and a rich set of high-level convenience functions built on top of the minimal API. I need to clearly separate these two parts in order to avoid polluting the namespaces with all sorts of nonsense!

CZ is lacking a good modern system of symbol namespaces. I can look to Python for a great example. I need to maintain compatibility with C, and avoid ugly symbol encodings. I think I can come up with something that will alleviate the need to type anything like gtk_window_set_default_size, and yet maintain compatibility with the library in question. I want all the power of C, but it should be easy to use, even for children. It should be as easy as BASIC or Processing, a child should be able to write short graphical demos and the like, without stumbling over tricky syntax or obscure compile errors.

Here is an example of a simple CZ program which plots the Mandelbrot set fractal. I think that the program is fairly clear and easy to understand, although there is still some potential to improve and clarify the code.

#!/usr/local/bin/cz --
use b
use ccomplex

Main:
	num outside = 16, ox = -0.5, oy = 0, r = 1.5
	long i, max_i = 50, rb_i = 30
	space()
	uint32_t *px = pixel()  # CONFIGURE!
	num d = 2*r/h, x0 = ox-d*w_2, y0 = oy+d*h_2
	for(y, 0, h):
		cmplx c = x0 + (y0-d*y)*I
		repeat(w):
			cmplx w = c
			for i=0; i < max_i && cabs(w) < outside; ++i
				w = w*w + c
			*px++ = i < max_i ? rainbow(i*359 / rb_i % 360) : black
			c += d

I wrote a more elaborate variant of this program, which generates images like the one shown below. There are a few tricks used: continuous colouring, rainbow colours, and plotting the logarithm of the iteration count, which makes the plot appear less busy close to the black fractal proper. I sell some T-shirts and other products with these fractal designs online.

I am interested in graph programming, and have been for three decades since I was a teenager. By graph programming, I mean programming and modelling based on mathematical graphs or diagrams. I avoid the term visual programming, because there is no necessary reason that vision impaired folks could not use a graph programming language; a graph or diagram may be perceived, understood, and manipulated without having to see it.

Mathematics is something that naturally exists, outside time and independent of our universe. We humans discover mathematics, we do not invent or create it. One of my main ideas for graph programming is to represent a mathematical (or software) model in the simplest and most natural way, using relational operators. Elementary mathematics can be reduced to just a few such operators:

I think that a language and notation based on these few operators (and similar) can be considerably simpler and more expressive than conventional math or programming languages.

CZ is for me a stepping-stone toward this goal of an expressive relational graph language. It is more pleasant for me to develop software tools in CZ than in C or another language.

Thanks for reading. I wrote this article during the process of applying to join Toptal, which appears to be a freelancing portal for top developers; and in response to this article on toptal: After All These Years, the World is Still Powered by C Programming.

My CZ project has been stalled for quite some time. I foolishly became discouraged after receiving some negative feedback. I now know that honest negative feedback should be valued as an opportunity to improve, and I intend to continue the project until it lacks glaring faults, and is useful for other people. If this project or this article interests you, please contact me and let me know. It is much more enjoyable to work on a project when other people are actively interested in it!

Back at the start of this series, I listed four problems within the scope of the WordPress Importers that we needed to address. Three of them are largely technical problems, which I covered in previous posts. In wrapping up this series, I want to focus exclusively on the fourth problem, which has a philosophical side as well as a technical one — but that does not mean we cannot tackle it!

Some services are merely inconvenient: they provide exports, but it often involves downloading a bunch of different files. Your CMS content is in one export, your store products are in another, your orders are in another, and your mailing list is in yet another. It’s not ideal, but they at least let you get a copy of your data.

However, there’s another class of services that actively work against their customers. It’s these services I want to focus on: the services that don’t provide any ability to export your content — effectively locking people in to using their platform. We could offer these folks an escape! The aim isn’t to necessarily make them use WordPress, it’s to give them a way out, if they want it. Whether they choose to use WordPress or not after that is immaterial (though I certainly hope they would, of course). The important part is freedom of choice.

It’s worth acknowledging that this is a different approach to how WordPress has historically operated in relation to other CMSes. We provide importers for many CMSes, but we previously haven’t written exporters. However, I don’t think this is a particularly large step: for CMSes that already provide exports, we’d continue to use those export files. This is focussed on the few services that try to lock their customers in.

Why Should WordPress Take This On?

There are several aspects to why we should focus on this.

First of all, it’s the the WordPress mission. Underpinning every part of WordPress is the simplest of statements:

Democratise Publishing

The freedom to build. The freedom to change. The freedom to share.

These freedoms are the pillars of a Free and Open Web, but they’re not invulnerable: at times, they need to be defended, and that needs people with the time and resources to offer a defence.

Which brings me to my second point: WordPress has the people who can offer that defence! The WordPress project has so many individuals working on it, from such a wide variety of backgrounds, we’re able to take on a vast array of projects that a smaller CMS just wouldn’t have the bandwidth for. That’s not to say that we can do everything, but when there’s a need to defend the entire ecosystem, we’re able to devote people to the cause.

Finally, it’s important to remember that WordPress doesn’t exist in a vacuum, we’re part of a broad ecosystem which can only exist through the web remaining open and free. By encouraging all CMSes to provide proper exports, and implementing them for those that don’t, we help keep our ecosystem healthy.

We have the ability to take on these challenges, but we have a responsibility that goes alongside. We can’t do it solely to benefit WordPress, we need to make that benefit available to the entire ecosystem. This is why it’s important to define a WordPress export schema, so that any CMS can make use of the export we produce, not just WordPress. If you’ll excuse the imagery for a moment, we can be the knight in shining armour that frees people — then gives them the choice of what they do with that freedom, without obligation.

How Can We Do It?

Moving on to the technical side of this problem, I can give you some good news: the answer is definitely not screen scraping. Scraping a site is fragile, impossible to transform into the full content, and provides an incomplete export of the site: anything that’s only available in the site dashboard can’t be obtained through scraping.

I’ve recently been experimenting with an alternative approach to solving this problem. Rather than trying to create something resembling a traditional exporter, it turns out that modern CMSes provide the tools we need, in the form of REST APIs. All we need to do is call the appropriate APIs, and collate the results. The fun part is that we can authenticate with these APIs as the site owner, by calling them from a browser extension! So, that’s what I’ve been experimenting with, and it’s showing a lot of promise.

If you’re interested in playing around with it, the experimental code is living in this repository. It’s a simple proof of concept, capable of exporting the text content of a blog on a Wix site, showing that we can make a smooth, comprehensive, easy-to-use exporter for any Wix site owner.

Clicking the export button starts a background script, which calls Wix’s REST APIs as the site owner, to get the original copy of the content. It then packages it up, and presents it as a WXR file to download.

I’m really excited about how promising this experiment is. It can ultimately provide a full export of any Wix site, and we can add support for other CMS services that choose to artificially lock their customers in.

Where Can I Help?

If you’re a designer or developer who’s excited about working on something new, head on over to the repository and check out the open issues: if there’s something that isn’t already covered, feel free to open a new issue.

Since this is new ground for a WordPress project, both technically and philosophically, I’d love to hear more points of view. It’s being discussed in the WordPress Core Dev Chat this week, and you can also let me know what you think in the comments!

While schemata are usually implemented using language-specific tools (eg, XML uses XML Schema, JSON uses JSON Schema), they largely use the same concepts when talking about data. This is rather helpful, we don’t need to make a decision on data formats before we can start thinking about how the data should be arranged.

Note: Since these concepts apply equally to all data formats, I’m using “WXR” in this post as shorthand for “the structured data section of whichever file format we ultimately use”, rather than specifically referring to the existing WXR format.

Why is a Schema Important?

It’s fair to ask why, if the WordPress Importers have survived this entire time without a formal schema, why would we need one now?

There are two major reasons why we haven’t needed one in the past:

• WXR has remained largely unchanged in the last 10 years: there have been small additions or tweaks, but nothing significant. There’s been no need to keep track of changes.
• WXR is currently very simple, with just a handful of basic elements. In a recent experiment, I was able to implement a JavaScript-based WXR generator in just a few days, entirely by referencing the Core implementation.

These reasons are also why it would help to implement a schema for the future:

• As work on WXR proceeds, there will likely need to be substantial changes to what data is included: adding new fields, modifying existing fields, and removing redundant fields. Tracking these changes helps ensure any WXR implementations can stay in sync.
• These changes will result in a more complex schema: relying on the source to re-implement it will become increasingly difficult and error-prone. Following Gutenberg’s lead, it’s likely that we’d want to provide official libraries in both PHP and JavaScript: keeping them in sync is best done from a source schema, rather than having one implementation copy the other.

Taking the time to plan out a schema now gives us a solid base to work from, and it allows for future changes to happen in a reliable fashion.

WXR for all of WordPress

With a well defined schema, we can start to expand what data will be included in a WXR file.

Media

Interestingly, many of the challenges around media files are less to do with WXR, and more to do with importer capabilities. The biggest headache is retrieving the actual files, which the importer currently handles by trying to retrieve the file from the remote server, as defined in the wp:attachment_url node. In context, this behaviour is understandable: 10+ years ago, personal internet connections were too slow to be moving media around, it was better to have the servers talk to each other. It’s a useful mechanism that we should keep as a fallback, but the more reliable solution is to include the media file with the export.

Plugins and Themes

There are two parts to plugins and themes: the code, and the content. Modern WordPress sites require plugins to function, and most are customised to suit their particular theme.

For exporting the code, I wonder if a tiered solution could be applied:

• Anything from WordPress.org would just need their slug, since they can be re-downloaded during import. Particularly as WordPress continues to move towards an auto-updated future, modified versions of plugins and themes are explicitly not supported.
• Third party plugins and themes would be given a filter to use, where they can provide a download URL that can be included in the export file.
• Third party plugins/themes that don’t provide a download URL would either need to be skipped, or zipped up and included in the export file.

For exporting the content, WXR already includes custom post types, but doesn’t include custom settings, or custom tables. The former should be included automatically, and the latter would likely be handled by an appropriate action for the plugin to hook into.

Settings

There are a currently handful of special settings that are exported, but (as I just noted, particularly with plugins and themes being exported) this would likely need to be expanded to included most items in wp_options.

Users

Currently, the bare minimum information about users who’ve authored a post is included in the export. This would need to be expanded to include more user information, as well as users who aren’t post authors.

WXR for parts of WordPress

The modern use case for importers isn’t just to handle a full site, but to handle keeping sites in sync. For example, most news organisations will have a staging site (or even several layers of staging!) which is synchronised to production.

While it’s well outside the scope of this project to directly handle every one of these use cases, we should be able to provide the framework for organisations to build reliable platforms on. Exports should be repeatable, objects in the export should have unique identifiers, and the importer should be able to handle any subset of WXR.

WXR Beyond WordPress

Up until this point, we’ve really been talking about WordPress→WordPress migrations, but I think WXR is a useful format beyond that. Instead of just containing direct exports of the data from particular plugins, we could also allow it to contain “types” of data. This turns WXR into an intermediary language, exports can be created from any source, and imported into WordPress.

Let’s consider an example. Say we create a tool that can export a Shopify, Wix, or GoDaddy site to WXR, how would we represent an online store in the WXR file? We don’t want to export in the format that any particular plugin would use, since a WordPress Core tool shouldn’t be advantaging one plugin over others.

Instead, it would be better if we could format the data in a platform-agnostic way, which plugins could then implement support for. As luck would have it, Schema.org provides exactly the kind of data structure we could use here. It’s been actively maintained for nearly nine years, it supports a wide variety of data types, and is intentionally platform-agnostic.

Gazing into my crystal ball for a moment, I can certainly imagine a future where plugins could implement and declare support for importing certain data types. When handling such an import (assuming one of those plugins wasn’t already installed), the WordPress Importer could offer them as options during the import process. This kind of seamless integration allows WordPress to show that it offers the same kind of fully-featured site building experience that modern CMS services do.

Of course, reality is never quite as simple as crystal balls and magic wands make them out to be. We have to contend with services that provide incomplete or fragmented exports, and there are even services that deliberately don’t provide exports at all. In the next post, I’ll be writing about why we should address this problem, and how we might be able to go about it.

The previous post talked about the broad problems we need to tackle to bring our importers up to speed, making them available for everyone to use.

In this post, I’m going to focus on what we could do with the existing technology, in order to give us the best possible framework going forward.

A Reliable Base

Importers are an interesting technical problem. Much like you’d expect from any backup/restore code, importers need to be extremely reliable. They need to comfortable handle all sorts of unusual data, and they need to keep it all safe. Particularly considering their age, the WordPress Importers do a remarkably good job of handling most content you can throw at it.

However, modern development practices have evolved and improved since the importers were first written, and we should certainly be making use of such practices, when they fit with our requirements.

For building reliable software that we expect to largely run by itself, a variety of comprehensive automated testing is critical. This ensures we can confidently take on the broader issues, safe in the knowledge that we have a reliable base to work from.

Testing must be the first item on this list. A variety of automated testing gives us confidence that changes are safe, and that the code can continue to be maintained in the future.

Data formats must be well defined. While this is useful for ensuring data can be handled in a predictable fashion, it’s also a very clear demonstration of our commitment to data freedom.

APIs for creating or extending importers should be straightforward for hooking into.

Performance Isn’t an Optional Extra

With sites constantly growing in size (and with the export files potentially gaining a heap of extra data), we need to care about the performance of the importers.

Luckily, there’s already been some substantial work done on this front:

There are other groups in the WordPress world who’ve made performance improvements in their own tools: gathering all of that experience is a relatively quick way to bring in production-tested improvements.

The WXR Format

It’s worth talking about the WXR format itself, and determining whether it’s the best option for handling exports into the future. XML-based formats are largely viewed as a relic of days gone past, so (if we were to completely ignore backwards compatibility for a moment) is there a modern data format that would work better?

The short answer… kind of.

XML is actually well suited to this use case, and (particularly when looking at performance improvements) is the only data format for which PHP comes with a built-in streaming parser.

That said, WXR is basically an extension of the RSS format: as we add more data to the file that clearly doesn’t belong in RSS, there is likely an argument for defining an entirely WordPress-focused schema.

Alternative Formats

It’s important to consider what the priorities are for our export format, which will help guide any decision we make. So, I’d like to suggest the following priorities (in approximate priority order):

• PHP Support: The format should be natively supported in PHP, thought it is still workable if we need to ship an additional library.
• Performant: Particularly when looking at very large exports, it should be processed as quickly as possible, using minimal RAM.
• Supports Binary Files: The first comments on my previous post asked about media support, we clearly should be treating it as a first-class citizen.
• Standards Based: Is the format based on a documented standard? (Another way to ask this: are there multiple different implementations of the format? Do those implementations all function the same?
• Backward Compatible: Can the format be used by existing tools with no changes, or minimal changes?
• Self Descriptive: Does the format include information about what data you’re currently looking at, or do you need to refer to a schema?
• Human Readable: Can the file be opened and read in a text editor?

Given these priorities, what are some options?

WXR (XML-based)

Either the RSS-based schema that we already use, or a custom-defined XML schema, the arguments for this format are pretty well known.

One argument that hasn’t been well covered is how there’s a definite trade-off when it comes to supporting binary files. Currently, the importer tries to scrape the media file from the original source, which is not particularly reliable. So, if we were to look at including media files in the WXR file, the best option for storing them is to base64 encode them. Unfortunately, that would have a serious effect on performance, as well as readability: adding huge base64 strings would make even the smallest exports impossible to read.

Either way, this option would be mostly backwards compatible, though some tools may require a bit of reworking if we were to substantial change the schema.

WXR (ZIP-based)

To address the issues with media files, an alternative option might be to follow the path that Microsoft Word and OpenOffice use: put the text content in an XML file, put the binary content into folders, and compress the whole thing.

This addresses the performance and binary support problems, but is initially worse for readability: if you don’t know that it’s a ZIP file, you can’t read it in a text editor. Once you unzip it, however, it does become quite readable, and has the same level of backwards compatibility as the XML-based format.

JSON

JSON could work as a replacement for XML in both of the above formats, with one additional caveat: there is no streaming JSON parser built in to PHP. There are 3rd party libraries available, but given the documented differences between JSON parsers, I would be wary about using one library to produce the JSON, and another to parse it.

This format largely wouldn’t be backwards compatible, though tools which rely on the export file being plain text (eg, command line tools to do broad search-and-replaces on the file) can be modified relatively easily.

There are additional subjective arguments (both for and against) the readability of JSON vs XML, but I’m not sure there’s anything to them beyond personal preference.

SQLite

The SQLite team wrote an interesting (indirect) argument on this topic: OpenOffice uses a ZIP-based format for storing documents, the SQLite team argued that there would be benefits (particularly around performance and reliability) for OpenOffice to switch to SQLite.

They key issues that I see are:

• SQLite is included in PHP, but not enabled by default on Windows.
• While the SQLite team have a strong commitment to providing long-term support, SQLite is not a standard, and the only implementation is the one provided by the SQLite team.
• This option is not backwards compatible at all.

FlatBuffers

FlatBuffers is an interesting comparison, since it’s a data format focussed entirely on speed. The down side of this focus is that it requires a defined schema to read the data. Much like SQLite, the only standard for FlatBuffers is the implementation. Unlike SQLite, FlatBuffers has made no commitments to providing long-term support.

	WXR (XML-based)	WXR (ZIP-based)	JSON	SQLite	FlatBuffers
Works in PHP?
Performant?
Supports Binary Files?
Standards Based?				/
Backwards Compatible?
Self Descriptive?
Readable?		/

As with any decision, this is a matter of trade-offs. I’m certainly interested in hearing additional perspectives on these options, or thoughts on options that I haven’t considered.

Regardless of which particular format we choose for storing WordPress exports, every format should have (or in the case of FlatBuffers, requires) a schema. We can talk about schemata without going into implementation details, so I’ll be writing about that in the next post.

It’s time to focus on the WordPress Importers.

I’m not talking about tidying them up, or improve performance, or fixing some bugs, though these are certainly things that should happen. Instead, we need to consider their purpose, how they fit as a driver of WordPress’ commitment to Open Source, and how they can be a key element in helping to keep the Internet Open and Free.

The History

The WordPress Importers are arguably the key driver to WordPress’ early success. Before the importer plugins existed (before WordPress even supported plugins!) there were a handful of import-*.php scripts in the wp-admin directory that could be used to import blogs from other blogging platforms. When other platforms fell out of favour, WordPress already had an importer ready for people to move their site over. One of the most notable instances was in 2004, when Moveable Type changed their license and prices, suddenly requiring personal blog authors to pay for something that had previously been free. WordPress was fortunate enough to be in the right place at the right time: many of WordPress’ earliest users came from Moveable Type.

As time went on, WordPress became well known in its own right. Growth relied less on people wanting to switch from another provider, and more on people choosing to start their site with WordPress. For practical reasons, the importers were moved out of WordPress Core, and into their own plugins. Since then, they’ve largely been in maintenance mode: bugs are fixed when they come up, but since export formats rarely change, they’ve just continued to work for all these years.

An unfortunate side effect of this, however, is that new importers are rarely written. While a new breed of services have sprung up over the years, the WordPress importers haven’t kept up.

The New Services

There are many new CMS services that have cropped up in recent years, and we don’t have importers for any of them. WordPress.com has a few extra ones written, but they’ve been built on the WordPress.com infrastructure out of necessity.

You see, we’ve always assumed that other CMSes will provide some sort of export file that we can use to import into WordPress. That isn’t always the case, however. Some services (notable, Wix and GoDaddy Website Builder) deliberately don’t allow you to export your own content. Other services provide incomplete or fragmented exports, needlessly forcing stress upon site owners who want to use their own content outside of that service.

To work around this, WordPress.com has implemented importers that effectively scrape the site: while this has worked to some degree, it does require regular maintenance, and the importer has to do a lot of guessing about how the content should be transformed. This is clearly not a solution that would be maintainable as a plugin.

This strikes at the heart of the WordPress Bill of Rights. WordPress is built with fundamental freedoms in mind: all of those freedoms point to owning your content, and being able to make use of it in any form you like. When a CMS actively works against providing such freedom to their community, I would argue that we have an obligation to help that community out.

A Variety of Content

It’s worth discussing how, when starting a modern CMS service, the bar for success is very high. You can’t get away with just providing a basic CMS: you need to provide all the options. Blogs, eCommerce, mailing lists, forums, themes, polls, statistics, contact forms, integrations, embeds, the list goes on. The closest comparison to modern CMS services is… the entire WordPress ecosystem: built on WordPress core, but with the myriad of plugins and themes available, along with the variety of services offered by a huge array of companies.

So, when we talk about the importers, we need to consider how they’ll be used.

Getting Our Own House In Order

Some of these problems don’t just apply to new services, however.

Out of the box, WordPress exports to WXR (WordPress eXtended RSS) files: an XML file that contains the content of the site. Back when WXR was first created, this was all you really needed, but much like the rest of the WordPress importers, it hasn’t kept up with the times. A modern WordPress site isn’t just the sum of its content: a WordPress site has plugins and themes. It has various options configured, it has huge quantities of media, it has masses of text content, far more than the first WordPress sites ever had.

In my view, WXR is a solid format for handling exports. An XML-based system is quite capable of containing all forms of content, so it’s reasonable that we could expand the WXR format to contain the entire site.

Built for the Future

If there’s one thing we can learn from the history of the WordPress importers, it’s that maintenance will potentially be sporadic. Importers are unlikely to receive the same attention that the broader WordPress Core project does, owners may come and go. An importer will get attention if it breaks, of course, but it otherwise may go months or years without changing.

It’s quite possible to build code that will be running in 10+ years: we see examples all across the WordPress ecosystem. Doing it in a reliable fashion needs to be a deliberate choice, however.

What’s Next?

Having worked our way down from the larger philosophical reasons for the importers, to some of the more technically-oriented implementation problems; I’d like to work our way back out again, focussing on each problem individually. In the following posts, I’ll start laying out how I think we can bring our importers up to speed, prepare them for the future, and make them available for everyone.

The uBITX uses an Arduino internally. This article describes how to update its software.

Required hardware

The connector on the back is a Mini-B USB connector, so you'll need a "Mini-B to A" USB cable. This is not the same cable as used with older Android smartphones. The Mini-B connector was used with a lot of cameras a decade ago.

You'll also need a computer. I use a laptop with Fedora Linux installed.

Required software for software development

In Fedora all the required software is installed with sudo dnf install arduino git. Add yourself to the users and lock groups with sudo usermod -a -G users,lock $USER (on Debian-style systems use sudo usermod -a -G dialout,lock $USER). You'll need to log out and log in again for that to have an effect (if you want to see which groups you are already in, then use the id command).

Run arduino as your ordinary non-root user to create the directories used by the Arduino IDE. You can quit the IDE once it starts.

Obtain the uBITX software

$ cd ~/Arduino
$ git clone https://github.com/afarhan/ubitxv6.git ubitx_v6.1_code

Connect the uBITX to your computer

Plug in the USB cable and turn on the radio. Running dmesg will show the Arduino appearing as a "USB serial" device:

usb 1-1: new full-speed USB device number 6 using xhci_hcd
usb 1-1: New USB device found, idVendor=1a86, idProduct=7523, bcdDevice= 2.64
usb 1-1: New USB device strings: Mfr=0, Product=2, SerialNumber=0
usb 1-1: Product: USB Serial
usbcore: registered new interface driver ch341
usbserial: USB Serial support registered for ch341-uart
ch341 1-1:1.0: ch341-uart converter detected
usb 1-1: ch341-uart converter now attached to ttyUSB1

If you want more information about the USB device then use:

$ lsusb -d 1a86:7523
Bus 001 Device 006: ID 1a86:7523 QinHeng Electronics CH340 serial converter

In the last post I had started implementing an Unscented Kalman Filter for position and orientation tracking in OpenHMD. Over the Christmas break, I continued that work.

A Quick Recap

When reading below, keep in mind that the goal of the filtering code I’m writing is to combine 2 sources of information for tracking the headset and controllers.

The first piece of information is acceleration and rotation data from the IMU on each device, and the second is observations of the device position and orientation from 1 or more camera sensors.

The IMU motion data drifts quickly (at least for position tracking) and can’t tell which way the device is facing (yaw, but can detect gravity and get pitch/roll).

The camera observations can tell exactly where each device is, but arrive at a much lower rate (52Hz vs 500/1000Hz) and can take a long time to process (hundreds of milliseconds) to analyse to acquire or re-acquire a lock on the tracked device(s).

The goal is to acquire tracking lock, then use the motion data to predict the motion closely enough that we always hit the ‘fast path’ of vision analysis. The key here is closely enough – the more closely the filter can track and predict the motion of devices between camera frames, the better.

Integration in OpenHMD

When I wrote the last post, I had the filter running as a standalone application, processing motion trace data collected by instrumenting a running OpenHMD app and moving my headset and controllers around. That’s a really good way to work, because it lets me run modifications on the same data set and see what changed.

However, the motion traces were captured using the current fusion/prediction code, which frequently loses tracking lock when the devices move – leading to big gaps in the camera observations and more interpolation for the filter.

By integrating the Kalman filter into OpenHMD, the predictions are improved leading to generally much better results. Here’s one trace of me moving the headset around reasonably vigourously with no tracking loss at all.

If it worked this well all the time, I’d be ecstatic! The predicted position matched the observed position closely enough for every frame for the computer vision to match poses and track perfectly. Unfortunately, this doesn’t happen every time yet, and definitely not with the controllers – although I think the latter largely comes down to the current computer vision having more troubler matching controller poses. They have fewer LEDs to match against compared to the headset, and the LEDs are generally more side-on to a front-facing camera.

Taking a closer look at a portion of that trace, the drift between camera frames when the position is interpolated using the IMU readings is clear.

This is really good. Most of the time, the drift between frames is within 1-2mm. The computer vision can only match the pose of the devices to within a pixel or two – so the observed jitter can also come from the pose extraction, not the filtering.

The worst tracking is again on the Z axis – distance from the camera in this case. Again, that makes sense – with a single camera matching LED blobs, distance is the most uncertain part of the extracted pose.

Losing Track

The trace above is good – the computer vision spots the headset and then the filtering + computer vision track it at all times. That isn’t always the case – the prediction goes wrong, or the computer vision fails to match (it’s definitely still far from perfect). When that happens, it needs to do a full pose search to reacquire the device, and there’s a big gap until the next pose report is available.

That looks more like this

This trace has 2 kinds of errors – gaps in the observed position timeline during full pose searches and erroneous position reports where the computer vision matched things incorrectly.

Fixing the errors in position reports will require improving the computer vision algorithm and would fix most of the plot above. Outlier rejection is one approach to investigate on that front.

Latency Compensation

There is inherent delay involved in processing of the camera observations. Every 19.2ms, the headset emits a radio signal that triggers each camera to capture a frame. At the same time, the headset and controller IR LEDS light up brightly to create the light constellation being tracked. After the frame is captured, it is delivered over USB over the next 18ms or so and then submitted for vision analysis. In the fast case where we’re already tracking the device the computer vision is complete in a millisecond or so. In the slow case, it’s much longer.

Overall, that means that there’s at least a 20ms offset between when the devices are observed and when the position information is available for use. In the plot above, this delay is ignored and position reports are fed into the filter when they are available. In the worst case, that means the filter is being told where the headset was hundreds of milliseconds earlier.

To compensate for that delay, I implemented a mechanism in the filter where it keeps extra position and orientation entries in the state that can be used to retroactively apply the position observations.

The way that works is to make a prediction of the position and orientation of the device at the moment the camera frame is captured and copy that prediction into the extra state variable. After that, it continues integrating IMU data as it becomes available while keeping the auxilliary state constant.

When a the camera frame analysis is complete, that delayed measurement is matched against the stored position and orientation prediction in the state and the error used to correct the overall filter. The cool thing is that in the intervening time, the filter covariance matrix has been building up the right correction terms to adjust the current position and orientation.

Here’s a good example of the difference:

Notice how most of the disconnected segments have now slotted back into position in the timeline. The ones that haven’t can either be attributed to incorrect pose extraction in the compute vision, or to not having enough auxilliary state slots for all the concurrent frames.

At any given moment, there can be a camera frame being analysed, one arriving over USB, and one awaiting “long term” analysis. The filter needs to track an auxilliary state variable for each frame that we expect to get pose information from later, so I implemented a slot allocation system and multiple slots.

The downside is that each slot adds 6 variables (3 position and 3 orientation) to the covariance matrix on top of the 18 base variables. Because the covariance matrix is square, the size grows quadratically with new variables. 5 new slots means 30 new variables – leading to a 48 x 48 covariance matrix instead of 18 x 18. That is a 7-fold increase in the size of the matrix (48 x 48 = 2304 vs 18 x 18 = 324) and unfortunately about a 10x slow-down in the filter run-time.

At that point, even after some optimisation and vectorisation on the matrix operations, the filter can only run about 3x real-time, which is too slow. Using fewer slots is quicker, but allows for fewer outstanding frames. With 3 slots, the slow-down is only about 2x.

There are some other possible approaches to this problem:

• Running the filtering delayed, only integrating IMU reports once the camera report is available. This has the disadvantage of not reporting the most up-to-date estimate of the user pose, which isn’t great for an interactive VR system.

• Keeping around IMU reports and rewinding / replaying the filter for late camera observations. This limits the overall increase in filter CPU usage to double (since we at most replay every observation twice), but potentially with large bursts when hundreds of IMU readings need replaying.
• It might be possible to only keep 2 “full” delayed measurement slots with both position and orientation, and to keep some position-only slots for others. The orientation of the headset tends to drift much more slowly than position does, so when there’s a big gap in the tracking it would be more important to be able to correct the position estimate. Orientation is likely to still be close to correct.
• Further optimisation in the filter implementation. I was hoping to keep everything dependency-free, so the filter implementation uses my own naive 2D matrix code, which only implements the features needed for the filter. A more sophisticated matrix library might perform better – but it’s hard to say without doing some testing on that front.

Controllers

So far in this post, I’ve only talked about the headset tracking and not mentioned controllers. The controllers are considerably harder to track right now, but most of the blame for that is in the computer vision part. Each controller has fewer LEDs than the headset, fewer are visible at any given moment, and they often aren’t pointing at the camera front-on.

This screenshot is a prime example. The controller is the cluster of lights at the top of the image, and the headset is lower left. The computer vision has gotten confused and thinks the controller is the ring of random blue crosses near the headset. It corrected itself a moment later, but those false readings make life very hard for the filtering.

Here’s a typical example of the controller tracking right now. There are some very promising portions of good tracking, but they are interspersed with bursts of tracking losses, and wild drifting from the computer vision giving wrong poses – leading to the filter predicting incorrect acceleration and hence cascaded tracking losses. Particularly (again) on the Z axis.

Timing Improvements

One of the problems I was looking at in my last post is variability in the arrival timing of the various USB streams (Headset reports, Controller reports, camera frames). I improved things in OpenHMD on that front, to use timestamps from the devices everywhere (removing USB timing jitter from the inter-sample time).

There are still potential problems in when IMU reports from controllers get updated in the filters vs the camera frames. That can be on the order of 2-4ms jitter. Time will tell how big a problem that will be – after the other bigger tracking problems are resolved.

Sponsorships

All the work that I’m doing implementing this positional tracking is a combination of my free time, hours contributed by my employer Centricular and contributions from people via Github Sponsorships. If you’d like to help me spend more hours on this and fewer on other paying work, I appreciate any contributions immensely!

Next Steps

The next things on my todo list are:

• Integrate the delayed-observation processing into OpenHMD (at the moment it is only in my standalone simulator).
• Improve the filter code structure – this is my first kalman filter and there are some implementation decisions I’d like to revisit.
• Publish the UKF branch for other people to try.
• Circle back to the computer vision and look at ways to improve the pose extraction and better reject outlying / erroneous poses, especially for the controllers.
• Think more about how to best handle / schedule analysis of frames from multiple cameras. At the moment each camera operates as a separate entity, capturing frames and analysing them in threads without considering what is happening in other cameras. That means any camera that can’t see a particular device starts doing full pose searches – which might be unnecessary if another camera still has a good view of the device. Coordinating those analyses across cameras could yield better CPU consumption, and let the filter retain fewer delayed observation slots.

On December 22, I decided to brew an oatmeal stout (5kg Gladfield ale malt, 250g dark chocolate malt, 250g light chocolate malt, 250g dark crystal malt, 500g rolled oats, 150g rice hulls to stop the mash sticking, 25g Pride of Ringwood hops, Safale US-05 yeast). This all takes a good few hours to do the mash and the boil and everything, so while that was underway I thought it’d be a good opportunity to remove a crappy old cupboard from the laundry, so I could put our nice Miele upright freezer in there, where it’d be closer to the kitchen (the freezer is presently in a room at the other end of the house).

The cupboard was reasonably easy to rip out, but behind it was a mouldy and unexpectedly bright yellow wall with an ugly gap at the top where whoever installed it had removed the existing cornice.

Underneath the bottom half of the cupboard, I discovered not the cork tiles which cover the rest of the floor, but a layer of horrific faux-tile linoleum. Plus, more mould. No way was I going to put the freezer on top of that.

So, up came the floor covering, back to nice hardwood boards.

Of course, the sink had to come out too, to remove the flooring from under its cabinet, and that meant pulling the splashback tiles (they had ugly screw holes in them anyway from a shelf that had been bracketed up on top of them previously).

Removing the tiles meant replacing a couple of sections of wall.

Also, we still needed to be able to use the washing machine through all this, so I knocked up a temporary sink support.

New cornice went in.

The rest of the plastering was completed and a ceiling fan installed.

Waterproofing membrane was applied where new tiles will go around a new sink.

I removed the hideous old aluminium backed weather stripping from around the exterior door and plastered up the exposed groove.

We still need to paint everything, get the new sink installed, do the tiling work and install new taps.

As for the oatmeal stout, I bottled that on January 2. From a sample taken at the time, it should be excellent, but right now still needs to carbonate and mature.

A few years ago we went to Taiwan. I managed to capture some random bits of the city on film (and also some shots on my then phone, a Google Pixel). I find the different style of art on the streets around the world to be fascinating, and Taiwan had some good examples.

I’ve really enjoyed shooting Kodak E100VS film over the years, and some of my last rolls were shot in Taiwan. It’s a film that unfortunately is not made anymore, but at least we have a new Ektachrome to have fun with now.

This is, of course, only a small number of the total photos I took there. I’d really recommend a trip to Taiwan, and I look forward to going back there some day.

Another year comes to a close, and this is the 4th year running I’m in Kuala Lumpur — 2017, 2018, 2019, and 2020… Wow. Maybe the biggest difference is that I’ve been in Malaysia for 306 days, thanks to the novel coronavirus. I have never spent this much time in Malaysia, in my entire life… I want to say KL, but I’ve managed to zip my way around to Kuantan (a lot), Penang, and Malacca. I can’t believe I flew back on February 29 2020 from Tokyo, and never got on a plane again! What a grounded globalist I’ve become.

My travel stats are of course, pretty dismal. 39 days out of the country. Apparently I did a total of 13 trips, 92 days of travel (I don’t know if all my local trips are counted frankly), 60,766km, 17 cities, and still 7 countries :) I don’t even want to compare to what it was like in 2019.

I ended that by saying, “I welcome 2020 with arms wide open.”. I’m not so sure how I feel about 2020. There is life beyond travel. COVID and our reaction to it, really worries me.

KL has some pretty good food. Kuantan has some pretty good people. While in KL, I visited a spin studio at least once per day. I did a total of 272 spin classes over 366 days! Not to forget there was 56 days of complete lockdown, and studios didn’t open till about maybe mid-June… Sure I did do some spin in London and Paris too, but the bulk of all this happened while I was here in KL.

I became reasonably friendlier, I became vulnerable, and like every time you do that, you’re chances of happiness and getting hurt probably straddle 50:50. Madonna – The Power of Good-bye can be apt.

This is not to say I didn’t enjoy 2020. Glass half full. I really did. Carpe diem. Simplicity is best. If you can follow KISS principles in engineering, why would you pour your entire thought process out and overwhelm the other party?

Anyway, I still look forward to 2021, with wide open arms, and while I really do think the COVID mess isn’t going away and things are going to be worse for many, I will still be focused on the most positive aspects of 2021. And I’ll work on being my old self again ;-)

I also ended the year with a haircut (number 1/0.5 on the sides) on Monday 28 December 2020. Somewhat of an experiment (does CoQ10 help speed up hair growth?) but also somewhat of a reaction to saying goodbye to December 2020.

The post Ciao, 2020 first appeared on Colin Charles Agenda.

If you’re near Melbourne, you should go to Healseville Sanctuary and enjoy the Australian native animals. I’ve been a number of times over the years, and here’s a couple of photos from a (relatively, as in, the last couple of years) trip.

Some shots on Kodak Portra 400 from Adelaide. These would have been shot with my Nikon F80 35mm body, I think all with the 50mm lens. These are all pre-pandemic, and I haven’t gone and looked up when exactly. I’m just catching up on scanning some negatives.

udev can be used to block a USB device (or even an entire class of devices, such as USB storage). Add a file /etc/udev/rules.d/99-local-blacklist.rules containing:

SUBSYSTEM=="usb", ATTRS{idVendor}=="0123", ATTRS{idProduct}=="4567", ATTR{authorized}="0"

While I hope to update this site again soon, here’s a photo I captured over the weekend in my back yard. The red flowering plant is attracting wattlebirds and honey-eaters. This wattlebird stayed still long enough for me to take this shot. After a little bit of editing, I think it has turned out rather well.

Photo taken with: Canon 7D Mark II & Canon 55-250mm lens.

Edited in Lightroom and Photoshop (to remove a sun glare spot off the eye).

It’s hard to be nuanced in 280 characters.

The Twitter character limit is a major factor of what can make it so much fun to use: you can read, publish, and interact, in extremely short, digestible chunks. But, it doesn’t fit every topic, ever time. Sometimes you want to talk about complex topics, having honest, thoughtful discussions. In an environment that encourages hot takes, however, it’s often easier to just avoid having those discussions. I can’t blame people for doing that, either: I find myself taking extended breaks from Twitter, as it can easily become overwhelming.

For me, the exception is Twitter threads.

Twitter threads encourage nuance and creativity.

Creative masterpieces like this Choose Your Own Adventure are not just possible, they rely on Twitter threads being the way they are.

Publishing a short essay about your experiences in your job can bring attention to inequality.

And Tumblr screenshot threads are always fun to read, even when they take a turn for the epic (over 4000 tweets in this thread, and it isn’t slowing down!)

Everyone can think of threads that they’ve loved reading.

My point is, threads are wildly underused on Twitter. I think I big part of that is the UI for writing threads: while it’s suited to writing a thread as a series of related tweet-sized chunks, it doesn’t lend itself to writing, revising, and editing anything more complex.

To help make this easier, I’ve been working on a tool that will help you publish an entire post to Twitter from your WordPress site, as a thread. It takes care of transforming your post into Twitter-friendly content, you can just… write.

It doesn’t just handle the tweet embeds from earlier in the thread: it handles handle uploading and attaching any images and videos you’ve included in your post.

All sorts of embeds work, too.

It’ll be coming in Jetpack 9.0 (due out October 6), but you can try it now in the latest Jetpack Beta! Check it out and tell me what you think.

This might not fix all of Twitter’s problems, but I hope it’ll help you enjoy reading and writing on Twitter a little more.

Digital TV uses MPEG Transport Stream, which is a container for video designed for lossy transmission, such as radio. To save CPU cycles, Personal Video Records often save the MPEG-TS stream directly to disk. The more usual MPEG is technically MPEG Program Stream, which is designed for lossless transmission, such as storage on a disk.

Since these are a container formats, it should be possible to losslessly and quickly re-code from MPEG-TS to MPEG-PS.

ffmpeg -ss "${STARTTIME}" -to "${DURATION}" -i "${FILENAME}" -ignore_unknown -map 0 -map -0:2 -c copy "${FILENAME}.mpeg"

I gave the talk Practicality Beats Purity: The Zen of Python’s Escape Hatch as part of PyConline AU 2020, the very online replacement for PyCon AU this year. In that talk, I included a few interesting links code samples which you may be interested in:

Links and code samples

• 20 Pythonic Fec^WTheses by Tim Peters, from comp.lang.python The Way of Python
• Fantastic Blocks and Where to Hide Them, from PyCon AU 2019
• PEP 8
• PEP 318 which introduce decorators

@apply

def apply(transform):

    def __decorator__(using_this):
        return transform(using_this)

    return __decorator__


numbers = [1, 2, 3, 4, 5]

@apply(lambda f: list(map(f, numbers)))
def squares(i):
  return i * i

print(list(squares))

# prints: [1, 4, 9, 16, 25]

Init.java

public class Init {
  public static void main(String[] args) {
    System.out.println("Hello, World!")
  }
}

@switch and @case

__NOT_A_MATCHER__ = object()
__MATCHER_SORT_KEY__ = 0

def switch(cls):

    inst = cls()
    methods = []

    for attr in dir(inst):
        method = getattr(inst, attr)
        matcher = getattr(method, "__matcher__", __NOT_A_MATCHER__)

        if matcher == __NOT_A_MATCHER__:
            continue

        methods.append(method)

    methods.sort(key = lambda i: i.__matcher_sort_key__)

    for method in methods:
        matches = method.__matcher__()
        if matches:
            return method()

    raise ValueError(f"No matcher matches value {test_value}")

def case(matcher):

    def __decorator__(f):
        global __MATCHER_SORT_KEY__

        f.__matcher__ = matcher
        f.__matcher_sort_key__ = __MATCHER_SORT_KEY__
        __MATCHER_SORT_KEY__ += 1
        return f

    return __decorator__



if __name__ == "__main__":
    for i in range(100):

        @switch
        class FizzBuzz:

            @case(lambda: i % 15 == 0)
            def fizzbuzz(self):
                return "fizzbuzz"

            @case(lambda: i % 3 == 0)
            def fizz(self):
                return "fizz"

            @case(lambda: i % 5 == 0)
            def buzz(self):
                return "buzz"

            @case(lambda: True)
            def default(self):
                return "-"

        print(f"{i} {FizzBuzz}")