podlators is the package containing Pod::Man, Pod::Text, and other tools for converting POD documentation into manual pages and simple text documents.

This release fixes a long-standing bug in Pod::Text and subclasses where a pathological level of indentation could cause the wrapping code to go into an infinite loop. Thanks to Jitka Plesnikova for the report. This was assigned CVE-2026-82560, although I make no guarantees that podlators is safe to run on untrusted input and therefore not fully treating this like a security issue.

While fixing that bug, I noticed a bug in Pod::Text::Overstrike's wrapping code that would leave stray formatting at the start of the next line in some situations. That is also fixed in this release.

You can get the current podlators release from CPAN or from the podlators distribution page.

I’m looking at something entirely unrelated, but tripped over some search results that made me realise that a lot of people still think getting errors like ACPI Warning: SystemIO range 0x0000000000001828-0x000000000000182F conflicts with OpRegion 0x0000000000001800-0x000000000000187F indicate a firmware bug. This is generally untrue. We need to dive a little into what ACPI is to clarify why.

The Advanced Configuration and Power Interface¹ specification defines a whole bunch of stuff, but what’s interesting to us here is the hardware abstraction it performs. While PCs are nominally a well-defined platform that’s really not true at the hardware level once you get beyond a certain level of complexity. When you suspend a system you want to power down the hardware in the correct order, for instance, and knowing what that order is requires you to know details about the specific motherboard design. The approach taken in the embedded world is to just bake that knowledge into the OS in some form, which is how we end up with Devicetree. ACPI takes an alternative approach - rather than provide that information as data that has to be consumed by OS drivers, it distributes it as code.

The ACPI Source Language, or ASL, is a simple language that gets compiled into a bytecode that’s then interpreted by the OS at runtime. One of the features of this language is the ability to define “Operation Regions”, effectively structure definitions that describe access to underlying hardware. Let’s imagine a simple device with two exposed registers. The first is an index register - it describes which internal register we want to access. The second is a data register, where reading it gives us the value of the internal register whose address is currently in the index register, and writing to it modifies that register. An example operation region declaration would look something like

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OperationRegion(OPR1, SystemIO, 0x400, 0x2)
Field(OPR1, ByteAcc, NoLock, Preserve)
{
  INDX, 8
  DATA, 8
}

This defines an operation region called “OPR1” at IO port 0x400, 2 bytes long. Inside it are two 8-bit fields, INDX and DATA. These are to be accessed one at a time, do not need the ACPI interpreter to take a global lock when accessing them, and if a subset of the register is modified then the other values should be preserved (irrelevant in this case since the fields are only a byte wide). Now any references to INDX or DATA in this scope will trigger accesses to those registers. So, a method to read the value of register 0x03 would look something like:

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Method (RD03) {
  INDX = 0x3
  Return (DATA)
}

ie, set INDX to 3, and then read the value of DATA and return it. But! What if another ACPI method is running at the same time? Let’s say we have one that writes to register 0x05:

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Method (WR05, 1) {
  INDX = 0x05
  DATA = Arg1
}

What happens if RD03 executes while we’re part-way through WR05? INDX might get reset to 0x03, and now WR05 will modify register 0x03 instead of 0x05. Oh no! But we can avoid this - we declare a mutex (Mutex (MUTX, 0x00)), and update our methods to be something like:

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Method (RD03) {
  Acquire (MUTX, 0xFFFF)
  INDX = 0x3
  Local0 = DATA
  Release (MUTX)
  Return (Local0)
}

Method (WR05, 1) {
  Acquire (MUTX, 0xFFFF)
  INDX = 0x05
  DATA = Arg1
  Release (MUTX)
}

Each method takes a lock (waiting up to 0xffff milliseconds and then erroring out if it doesn’t), and performs the access. There’s now no chance of a race. Phew!

Now suppose someone writes a Linux driver for this piece of hardware. It accesses the hardware directly, with no knowledge of ACPI. What stops the driver from racing against one of the ACPI access methods? Nothing at all. Oh no! Again! This isn’t hypothetical, by the way - here’s a relatively harmless example, but back in the day we did trip over cases where temperature monitoring chips would be accessed by the firmware and Linux simultaneously and as a result you might end up thinking you’re reading a temperature when you’re actually reading a status flag, resulting in an impossibly high temperature and an immediate thermal shutdown.

In this case, the kernel saves you from this (potentially hardware damaging) outcome by printing a message like ACPI Warning: SystemIO range 0x0000000000000400-0x000000000000401 conflicts with OpRegion 0x0000000000000400-0x0000000000000401 (OPR1), telling you that the kernel has detected that a driver is attempting to allocate IO ports 0x400-0x401, but that there’s an ACPI operation region called OPR1 that is claiming the same addresses. The kernel isn’t in a position to know what type of access the firmware might perform in that region, so assumes that it might be dangerous and blocks the driver from loading.

But all is not lost! The kernel also prints some helpful advice, ACPI: If an ACPI driver is available for this device, you should use it instead of the native driver. And ACPI tables will often actually have a definition that looks like this:

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Device (HDW1)
{
  Name (_HID, "VEND0001")
  OperationRegion(OPR1, SystemIO, 0x400, 0x2)
  Field(OPR1, ByteAcc, NoLock, Preserve)
  {
    INDX, 8
    DATA, 8
  }
  Mutex (MUTX, 0)
  Method (RD03) {
    Acquire (MUTX, 0xFFFF)
    INDX = 0x3
    Local0 = DATA
    Release (MUTX)
    Return (Local0)
  }

  Method (WR05, 1) {
    Acquire (MUTX, 0xFFFF)
    INDX = 0x05
    DATA = Arg1
    Release (MUTX)
  }
}

which defines an ACPI device and associated methods. The _HID field defines the device type, and a Linux driver can be written that will be automatically loaded if a device with type VEND0001 is seen. That driver can then call ACPI methods associated with the device and access the resources in a way that matches the firmware’s expectations.

(Interested in writing such a driver? I wrote a guide back in 2009)

The firmware did absolutely nothing wrong here², but trying to load the native ddriver will generate an error and the internet will tell you that PC firmware developers are incompetent³ and you should pass a kernel argument that overrides this behaviour and it never did them any harm, and it probably won’t do you any harm either but it might and you might never know why your system occasionally wedges or catches fire.

A new maintenance release 0.0.7 of RcppXts is now on CRAN, and has been built for r2u. The RcppXts package demonstrates how to access the export C API of xts which we contributed a looong time ago. There are by now a more example packages around this C level access to another package, but this one was an early example.

This release is strictly maintenance, updating continuous integration, the README.md file and other packaging conventions adopted since the last release four years ago.

The NEWS entries follow.

Changes in version 0.0.7 (2026-09-09)

• Corrected a docstring for the module

• Updated continuous integration setup several times

• Simplified setup by removing no-longer-needed Makevars

• Added badges to README.md

Courtesy of my CRANberries, there is also a diffstat report for this release. For questions, suggestions, or issues please use the issue tracker at the GitHub repo.

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. If you like this or other open-source work I do, you can now sponsor me at GitHub.

Debian LTS/ELTS

This was my hundred-forty-sixth month that I did some work for the Debian LTS initiative, started by Raphael Hertzog at Freexian.

Unfortunately the number of distributed working hours had been rather low this month, so the list contains much less entries than normal. During my allocated time I uploaded or worked on:

• [DLA 4713-1] sslh security update to fix one CVE in Bookworm related to a so-called “link following” vulnerability.
• [DLA 4714-1] libmodbus security update to fix one CVEs in Bookworm related to a stack-based Buffer Overflow vulnerability.
• [DLA 4715-1] kissfft security update to fix two CVEs in Bookworm related to integer overflows.

Last but not least I spent days with FD work at the beginning of the month. The last remaining hours I continued to work on cups and hplip. Unfortunately this work did not result in an upload yet.

Debian Printing

This month I did not upload any package but just worked on some bugs.

This work is generously funded by Freexian!

Debian Lomiri

This month I worked on new Lomiri Apps. Due to a broken disk, the progress was not as expected. But stay tuned!

Not really related to Lomiri, but to Debian EDU, I fixed an incus related bug in sitesummary.

This work is generously funded by Fre(i)e Software GmbH!

Debian Astro

This month I uploaded a new upstream version or a bugfix version of:

• … einsteinpy to unstable, fixing a dependency bug.
• … supernovas to unstable, sponsored upload of a new upstream version.

Debian IoT

Unfortunately I had no time to work in this category this month.

Debian Mobcom

This month I uploaded a new upstream version or a bugfix version of:

• … libosmocore to unstable.
• … osmocom-analog to unstable.
• … libosmo-netif to unstable.
• … osmo-trx to unstable.
• … libosmo-abis to unstable.
• … libosmo-sigtran to experimental.
• … osmo-mgw to unstable.
• … osmo-bsc to experimental.
• … osmo-sgsn to experimental.
• … osmo-pcu to unstable.
• … osmo-hlr to unstable.
• … osmo-tetra to unstable.

misc

This month I uploaded a new upstream version or a bugfix version of:

• … tango to unstable to fix bugs and do a soname transition.
• … pytango to unstable to fix bugs and do a soname transition.

Armadillo is a powerful and expressive C++ template library for linear algebra and scientific computing. It aims towards a good balance between speed and ease of use, has a syntax deliberately close to Matlab, and is useful for algorithm development directly in C++, or quick conversion of research code into production environments. RcppArmadillo integrates this library with the R environment and language–and is widely used by (currently) 1331 other packages on CRAN, downloaded 48.5 million times (per the partial logs from the cloud mirrors of CRAN), and the CSDA paper (preprint / vignette) by Conrad and myself has been cited 727 times according to Google Scholar.

This versions updates to the 15.6.0 upstream Armadillo release made yesterday. It extends solver options for poorly conditioned systems, and brings some updates and extension to the cube data type. For this release, we once again ran the usual complete reverse-dependency check which came back spotless, and did CRAN so no email exchange needed despite nearly 1300 reverse dependencies (but it ended up taking more than a single business day). Still, automation can be helpful when used with a well-maintained software stack. The package has also already been updated for Debian, built for r2u and r-universe, and will build shortly at CRAN for the different binary releases.

All changes since the last CRAN release follow.

Changes in RcppArmadillo version 15.6.0-1 (2026-09-07)

• Upgraded to Armadillo release 15.6.0 (Medium Roast Cortado)

  • Expanded solve() with solve_opts::scale_thresh option to widen detection of poorly conditioned systems

  • Expanded trans() and .t() to handle cubes

  • Added permute() to rearrange dimensions of cubes (generalised transpose)

  • Added cubemul() for batched matrix multiplication of cube slices

Courtesy of my CRANberries, there is a diffstat report relative to previous release. More detailed information is on the RcppArmadillo page. Questions, comments etc should go to the rcpp-devel mailing list off the Rcpp R-Forge page.

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. If you like this or other open-source work I do, you can sponsor me at GitHub.

The final step to all of this was to tie together all my PHY RF code, Link layer parsers, and my background with operating systems to make this all feel like a normal thing my computer should be doing.

At the end of the day, I want my host system to know how to talk with a pigeon daemon, so I don’t have to reimplement basically everything else. My ability to use normal tools like curl or ping6 is pretty important here, so I need to reach for my old friend, the TUN interface. The TAP/TUN interface allows the kernel to route ethernet frames (TAP) or ip packets (TUN) to a userspace program responsible for handling delivery and reception – avoiding the need for a kernelspace driver for something that can be handled in userland.

I’m no stranger to playing with TAP/TUN, so this was pretty easy to snap together – although this time I avoided the whole ethernet proxying thing (to side-step lossy translations, maintaining two sets of mac address tables, and handle proxying NDP/ARP messages) – it was kinda a bad idea last time – so I just used TUN and straight IP for now. While implementing this, I decided I’d make a key assertion about all pigeon networks – namely, all pigeon IPv6 networks are a /64 in size, no more, no less. The reason why I’m doing this here is that, since pigeond does still does need a MAC address for the pigeon layer 2 protocol, we can write our daemon to always use SLAAC to set the TUN IP address without any new information.

Which leads us to a bit of an aside, but I have a point, I swear. A few years ago, my recreational RF adventures have lead me down a path where I decided to engage with ARIN to solve (once and for all) the massive headache I was running into with IPv6 numbering (really: always renumbering) my multi-site radio processing networks. It’s a lot of work to keep running correctly, but it’s solved a huge amount of problems for me.

The only “internal” thing we really need to outline for this post is that, at the highest level, my network (paultag.net) is split into an IP plan that looks roughly like:

For this exercise, I used IP space from paultag.net’s experimental region (“region 8”), named side.band (2602:810:6008::/48) to connect my RF lab (“site 1” - 2602:810:6008:400::/54), and my two pigeon-specific subnets, “subnet 0” (2602:810:6008:400::/64) and “subnet 1” (2602:810:6008:401::/64) to my wider network. The first subnet (“subnet 0”) is a simple ethernet network to enable my RF-only nodes to communicate with the side.band gateway. The second subnet (“subnet 1”) is an RF-only pigeon network local to my lab.

back to radios

With all that set up, I assigned my first two nodes their MAC addresses, and set up the local RF only network segment. The nodes I brought online were the following:

testing the pigeon network

And with that, I could begin to test that the host operating systems and RF links could properly exchange data locally from SDR to SDR. We can use ping6 to see if a plain-ole ICMPv6 ping round trips between hosts correctly:

$ ping6 2602:810:6008:401:8e1f:64ff:fe35:4001
PING 2602:810:6008:401:8e1f:64ff:fe35:4001 (2602:810:6008:401:8e1f:64ff:fe35:4001) 56 data bytes
64 bytes from 2602:810:6008:401:8e1f:64ff:fe35:4001: icmp_seq=1 ttl=64 time=426 ms
64 bytes from 2602:810:6008:401:8e1f:64ff:fe35:4001: icmp_seq=2 ttl=64 time=397 ms
64 bytes from 2602:810:6008:401:8e1f:64ff:fe35:4001: icmp_seq=3 ttl=64 time=419 ms
64 bytes from 2602:810:6008:401:8e1f:64ff:fe35:4001: icmp_seq=4 ttl=64 time=418 ms
64 bytes from 2602:810:6008:401:8e1f:64ff:fe35:4001: icmp_seq=5 ttl=64 time=436 ms
64 bytes from 2602:810:6008:401:8e1f:64ff:fe35:4001: icmp_seq=6 ttl=64 time=391 ms
64 bytes from 2602:810:6008:401:8e1f:64ff:fe35:4001: icmp_seq=7 ttl=64 time=397 ms

And it does! Latency is horrid (and there’s a bunch of tx artifacts that cause issues for us) – but both of those things are problems for later. Let’s see how it handles a TCP connection by firing off a quick cURL across the Pigeon network:

$ curl http://[2602:810:6008:401:8e1f:64ff:fe35:4001]:8000/testing.txt
The rock dove (Columba livia), also known as the common pigeon or rock pigeon
(but see also Petrophassa), is a member of the bird family Columbidae (doves
and pigeons).

As expected, our “remote” end here running the server reports the correct peer IP address, which is another indication (beyond the log messages and blinking LEDs) that we’re routing over our TUN interface.

Serving HTTP on 2602:810:6008:401:8e1f:64ff:fe35:4001 port 8000 (http://[2602:810:6008:401:8e1f:64ff:fe35:4001]:8000/) ...
2602:810:6008:401:8e1f:64ff:fe35:4002 - - [28/May/2026 12:53:21] "GET /testing.txt HTTP/1.1" 200 -

That … worked? First shot! Nice! It’s pretty slow and seems like we have a lot of packet loss, but it does, however, beg the question – can it nethack?

nethack!

Yes! It can nethack! No clickbait here. The way I went about this one is a bit anit-cimatic – I set up a nethack server (using inetd in this case) on one of the hosts’ pigeon0 network interface, and hit that port over RF from the other:

However, when playing it, it becomes very obvious (as you can likely see) that there’s a fair amount of packet loss (understandable) and probably some packet collisions taking place.

iperf

Let’s try and put a number to exactly how bad the bandwidth and packet loss is by running iperf between the two pigeon hosts over rf:

$ iperf -c 2602:810:6008:401:8e1f:64ff:fe35:4002
------------------------------------------------------------
Client connecting to 2602:810:6008:401:8e1f:64ff:fe35:4002, TCP port 5001
TCP window size: 16.0 KByte (default)
------------------------------------------------------------
[ 1] local 2602:810:6008:401:: port 58248 connected with 2602:810:6008:401:8e1f:64ff:fe35:4002 port 5001
[ ID] Interval Transfer Bandwidth
[ 1] 0.0000-20.2348 sec 76.8 KBytes 31.1 Kbits/sec

Shockingly, not nearly as bad as I thought it was going to be. Given I’ve spent exactly zero time making this operate to a level that I would call acceptable, this is a very fucking solid start. I expect I could get that number up if I spent a few weeks on it – it’s just not been a priority at any point yet (and the first time I’ve instrumented it, even!).

This’ll be good enough to get started. Let’s see what else we can pull off here.

IP multicast to some rtl-sdrs

Back when I designed what I wanted Mode A to look like, I intentionally picked a signal bandwidth that could be received by an rtl-sdr – so let’s put that to use. It may go without saying, but just to say it – the rtl-sdr can not transmit, so this will be capable of receiving pigeon frames – but not sending any in reply.

However, this means I can use a bunch of low-cost computers (raspberry pi-class), and low-cost SDRs (rtl-sdr) and still receive IP traffic from transmitting pigeon network stations. This could be a lot of fun for things like fountain coding a data stream, or adapting multicast streaming protocols to work over RF links. Anywho, I swapped my “far” end to an rtl-sdr (one config file change!), and figured I’d start with some (basic) multicast traffic, transmitting the time once a second:

$ while [ true ]; do
 echo $(date +%s) \
 | socat - UDP6-DATAGRAM:[ff02::114%pigeon0]:62804
 sleep 1
done

If I had more time to burn, I was planning on bridging APRS traffic to UDP multicast within a pigeon network subnet. However, since I’m already 4 years late on this blog post, I figured this would be enough for now (and you can imagine that fun project in this space if you so wish!)

I fired up pigeond again (except this time connected to an rtl-sdr), and was pleasantly surprised to be greeted by some decoded traffic right off the bat:

⪧ [k3xec/mn] 8c:1f:64:35:40:02 ⇢ 00:00:00:00:00:00 ipv6 fe80::23ee:2969:53f7:b332 ⇢ ff02::114 17 (UDP - User Datagram)
⪧ [k3xec/mn] 8c:1f:64:35:40:02 ⇢ 00:00:00:00:00:00 ipv6 fe80::23ee:2969:53f7:b332 ⇢ ff02::114 17 (UDP - User Datagram)
⪧ [k3xec/mn] 8c:1f:64:35:40:02 ⇢ 00:00:00:00:00:00 ipv6 fe80::23ee:2969:53f7:b332 ⇢ ff02::114 17 (UDP - User Datagram)
⪧ [k3xec/mn] 8c:1f:64:35:40:02 ⇢ 00:00:00:00:00:00 ipv6 fe80::23ee:2969:53f7:b332 ⇢ ff02::114 17 (UDP - User Datagram)

Of course, I took a tcpdump to confirm for completeness sake that the traffic actually made it out of our TUN interface:

$ tcpdump -i pigeon0
22:39:34.808705 IP6 (flowlabel 0x92a0e, hlim 1, next-header UDP (17), payload length 19) fe80::23ee:2969:53f7:b332.35911 > ff02::114.62804: [udp sum ok] UDP, length 11
22:39:36.429887 IP6 (flowlabel 0x4dc84, hlim 1, next-header UDP (17), payload length 19) fe80::23ee:2969:53f7:b332.50026 > ff02::114.62804: [udp sum ok] UDP, length 11
22:39:39.365977 IP6 (flowlabel 0x224d5, hlim 1, next-header UDP (17), payload length 19) fe80::23ee:2969:53f7:b332.36308 > ff02::114.62804: [udp sum ok] UDP, length 11
22:39:40.944889 IP6 (flowlabel 0x4721c, hlim 1, next-header UDP (17), payload length 19) fe80::23ee:2969:53f7:b332.35003 > ff02::114.62804: [udp sum ok] UDP, length 11

Looks great! tcpdump is showing multicast packets show up (as we assumed they would), on the pigeon0 interface, on the machine connected to an rtl-sdr. Of course, any replies will get sent to the bit bucket, but it can definitely decode things just fine! Very fucking cool.

Well right, ok! Let’s go back to two rx/tx radios, and see what we can do with our newfound network stack over ham radio frequencies – let’s try to do some fun (and traditional!) ham radio things with it!

Winlink

Winlink is a ham radio mail relay system for ham radio operators to send, receive or relay mail over the internet, or RF (usually HF or VHF/2M). Winlink relays are accessible via whatever transport you can find – most commonly telnet (using the internet), ax.25 (usually 2m VHF) or VARA HF (unsurprisingly, on HF). I use pat as my Winlink client – it’s written in Go, doesn’t require windows, and is just generally nice to work with.

Let’s try the easy thing first – let’s connect by proxying the Winlink server into the pigeon network using socat (lightly edited to remove date/times)

$ pat connect pigeon
Connecting to WL2K (telnet)...
Connected to [2602:810:6008:401:8e1f:64ff:fe35:4002]:8772 (tcp)
[WL2K-5.0-B2FWIHJM$]
;PQ: 54509561
CMS>
>FC EM OLU6BP5HKMG2 240 205 0
>F> 95
FS Y
Remote accepted OLU6BP5HKMG2
Transmitting [Hello, World] [offset 0]
Hello, World: 100%
FF
>FQ
Disconnected.
$

Lo and behold, shortly after, I got this delightful message to my email address, relayed in from WINLINK:

From: K3XEC@winlink.org
Reply-To: K3XEC@winlink.org
Subject: Hello, World
To: paultag@[...]
Message-ID: <OLU6BP5HKMG2@winlink.org>
MIME-Version: 1.0
X-MARSPrecedence: Routine
X-WL2KPrecedence: Routine
Content-Type: text/plain
Content-Transfer-Encoding: 8bit

Hello, World!

The only shame is I won’t be able to check in to a winlink wednesday using this scheme unless I further proxy this message over AX.25 instead of relaying to Winlink’s servers over telnet (which, to be fair, is definitely also possible – I just got lazy when I glued this one together – see note above about being 4 years late on this post).

But, you know, connecting to a host that is using socat to proxy a connection to an internet resource is interesting but – you know what, fuck it – hang on, dear reader – let’s bang a hard left turn and just ship this thing hard and directly connect it to the internet. Let’s take our dinky, home-built PHY and Layer 2 and see if we can wire it directly into the internet – something that, every time I go to think about it, reminds me of Tim FitzHigham and his crapper.

Crossing the english channel in a bathtub

Ok, ok. I decided to bury the lede a bit here – I didn’t mention that the side.band network is currently BGP announced. Although we haven’t used it – this does mean that we’re most of the way to sending packets to the wider internet, and we should be able to “just” fix a few routing tables, and see packets begin to flow.

After tweaking the local routing tables (and restarting pigeond for good measure), I decided to test my newfound connectivity by pinging something over our new network transport.

ping github

Why don’t we start with the world’s premier software engineering platform, operated by one of the largest companies in the world, GitHub! After all, they have an all knowing (and, apparently, arguably sentiant?) AI on hand to instantly and automatically fix any stray reliability issues in the background, so we should definitely see replies right off the bat:

$ ping6 github.com
ping6: github.com: Address family for hostname not supported

Wait, oh no – that can’t be right?

After all, it’s 2026, and both Google and CloudFlare (in North America) are reporting over half of all traffic they see is IPv6 – and GitHub still doesn’t support IPv6? Definitely not, this is for sure a bug with my code or network.

lets ping something that supports ipv6 instead

That being said, just for completeness sake, since that error is also given when there’s no IPv6 DNS record, let’s go ahead and double check with Hurricane Electric too, you know, just to be sure.

$ ping6 he.net
PING he.net (2001:470:0:503::2) 56 data bytes
64 bytes from he.net (2001:470:0:503::2): icmp_seq=1 ttl=53 time=514 ms
64 bytes from he.net (2001:470:0:503::2): icmp_seq=2 ttl=53 time=230 ms
64 bytes from he.net (2001:470:0:503::2): icmp_seq=3 ttl=53 time=248 ms
64 bytes from he.net (2001:470:0:503::2): icmp_seq=4 ttl=53 time=246 ms
64 bytes from he.net (2001:470:0:503::2): icmp_seq=5 ttl=53 time=265 ms
64 bytes from he.net (2001:470:0:503::2): icmp_seq=6 ttl=53 time=240 ms

Well, shit. Right, OK, i’ll be damned. 18 years in and GitHub still can’t crack that nut.

cURL works!

Right, anyway, yes, back on track – good news! Our uplink is up and routing, and wait, holy shit! Check it out! pigeon is exchanging packets with the internet and no one is any the wiser! Literlaly amazing. Let’s try a cURL across the internet now (although no TLS allowed, so, http only for now):

$ curl -6 -I http://facebook.com
HTTP/1.1 301 Moved Permanently
Location: https://facebook.com/
Content-Type: text/plain
Server: proxygen-bolt
Connection: keep-alive
Content-Length: 0

IRC works, too

Sweeeeet. That all works! Forget HTTP, let’s do some other 90’s era stuff, it’s high-time to log into IRC with a quick /connect -notls, and see what’s going on in the #debian-hams channel – pleased that I got online fairly quickly, and was able to even talk to myself!

THE GOPHERSPACE

Naturally, let’s keep this train of nostalga running, and give the 2026 gopherspace a shot.

I know the kind folks over at tilde.town (hello, townies!) have a robust gopherspace, so let’s give it a dial! Let’s try and see if we can load vilmibm’s slug over gopher:

Yes! I forgot to make this one a video, so no gif. I did wind up having a bit if trouble with a few gopher clients and IPv6 support – I may send some patches if I can find the time.

So, what’s next?

Alright, that’s it. I have a few more fun ideas but they’re going to have to wait for another day. Carrying IP is fun and all but kinda not the point behind pigeon, after all. Rather than trying to make this into “a thing”, I’m planning on exploring the loose ends first – different types of modulation schemes (like QAM-NUC), implementing LDPC error correction and some layer 2 logic into the pigeond (like switching traffic, and gain control). I also plan on spending some time with my (currently, very basic) simulator to better dial in tradeoffs throughout the stack.

Since, structurally, pigeon is something I feel like I can work with, I’m hoping i’ll be able to find the time for some (much smaller!) followup posts without it taking 4 years this time. If I do, they’ll show up under the pigeon tag – and I’ll be sure to update this post with a link below (and the intro post).

I’m hoping that this series (which was supposed to be one post) was helpful to someone out there – if it was, feel free to reach out and let me know!

Built-in to the pigeon link protocol is a message type called cal (short for, you guessed it, calibration). A pigeon frame with a type of cal (which is 0x02) carries a JSON encoded payload in the body, which can either be a beacon, requesting signal reports in response, or a report, describing the received beacons.

This serves a few interesting purposes – firstly, network operators can better understand the coverage footprint, propagation under different conditions, and how gain impacts reception when tuning for the lowest practical power levels. Secondly, this can be used (and I plan to eventually implement!) to construct a mapping of minimum power level and peer mac address to dynamically control the transmission power based on the destination station.

That being said, for now, all I’ve used this for is getting a rough sense for what gain value(s) make sense between two nodes (manually). In the future, beyond all the fancy neighbor gain stuff, I plan to wire this into the daemon to happen automatically, “debouncing” for beacon and report messages, such that transmitting stations only beacon, and receiving stations only report a max of once over some time period for a given peer.

Version

Given all the above, I do intend to make some massive changes to this protocol (I promise to blog all about it) when I get around to hacking on switching Layer 2 frames within a network segment. Just to avoid having to dig myself out of a hole later, I’m going to explicitly send (and check) the version field to avoid having a big “flag day” switchover or needing to use a new link type.

Location

Both flavors of cal messages (beacon and report) may contain a location, which is the location that the beacon was transmitted, or for or the location where the beacon was heard for a response. This can be used to derive a coverage map and to (operationally) better understand what stations should be within range, and generally what gain level(s) are effective.

All fields assume WGS84 latitude and longitude values, and elevation is distance, in meters, above the WGS84 ellipsoid – NOT height above sea level, or altitude above the ground.

Sequence

Each beacon contains a Sequence identifier, which is used to communicate which message number is being heard, and how many total were transmitted by the originating station. The current approach with Beacon messages is to transmit some number of Beacon messages at different gain levels, each with a unique Sequence identifier.

Gains

Recorded gain setting(s). For a Beacon this indicates the gain settings (which, in spite of its name, includes things like amplifiers, or attenuators). Changing this over different Beacon frames enables a better understanding of what an appropriate gain level is for the transmitting station over time.

Cal

All messages contained in a cal frame are of this type. The type field communicates if this is a Beacon or Report message type.

Beacon

A beacon message may be sent periodically by pigeon nodes capable of transmitting to announce their prescience to peers and, implicitly, to receive signal reports from nearby listeners who are capable and configured to transmit reports.

The beacon JSON message is made up of the following fields:

An example beacon looks, unsupprisingly, as follows:

{
 "type": "beacon",
 "version": "V1",
 "sequence": {
 "number": 2,
 "total": 5
 },
 "gains": []
}

Report

A report message may be sent in response to a beacon message by pigeon nodes capable of receiving and transmitting to assist with setting the lowest usable gain value, and to better understand the area of coverage and propagation.

The report JSON message is made up of the following fields:

An example report looks as follows:

{
 "type": "report",
}

With all that out of the way

lets send some ip →

Now that we have a working Layer 1, we have a way to send a block of bits from one place to anyone who cares to listen to us. This is very welcome news, but we are now facing a new, different and just as fun question – what shape should that data take?

Given our incredibly limited functionality of our nodes, we could definitely skip all this work and just stuff an IP packet into the link; but I decided to not since I am (eventually) interested in adding some sort of spanning tree-like protocol to implement network switching so not all nodes need to communicate directly with all other nodes – but that day is not today.

Given i’m going to stub most of that out, let’s take a look at what some similar Layer 2 protocols use – things like Ethernet or WiFi frames. Both contain structured information regarding the transmitter, desired recipient, type of data, and the higher-level data itself (such as IP packets). As a result of attempting to learn from others, the Pigeon Layer 2 (called, simply, “link”) is also split into a fixed-length header, followed by the contents described by the header.

The header is a fixed-length (23 byte) structure, which contains the source MAC address (src mac), destination MAC address (dst mac), the ITU coordinated ham radio callsign of the control operator of this message (callsign), the type of payload to follow (type; defined below), and the length of the data to follow the header (length as a 16 bit big-endian unsigned integer).

The type field indicates how the payload is to be interpreted – currently I’ve only defined 3 possible payload types so far:

Keen observers will perhaps infer that there used to be an Ipv4 type at 0x03 – which is true – however, i’ve since removed it since i’ve never once used it and the codepath was more trouble than it was worth. As is my wont, I’ve optend to just lean into Ipv6-only IP transport – it’s easy enough to shim ipv4 in, if someone REALLY wanted to using something like 64:ff9b:1::/48 and a bit of code in the transmitter/receiver (or even using something like jool and unbound’s dns64-prefix at the router). I don’t think I’ll bring it back, but just in case I have to for some reason in the future, it’s there.

Additionally, friends of the pod may also recognize that this structure is basically the exact same structure as what I had in PACKRAT, which, is also true. I started this project off maintaining interoperability in the Layer 2 for pigeon and packrat, but at some point just gave up on it during one of the many cleanups. I’m hopeful I can maintain compatibility going forward, and that won’t have to muck with this header too much more. We’ll see what happens once I start to push the bounds of what is possible with pigeon.

Hopefully it feels like carrying IP data inside this frame to be a pretty self-explanatory exercise – the 0th byte of the payload is the 0th byte of an IPv6 header (followed by all the usual stuff, like UDP or TCP header(s) and any carried data, just like you’d find anywhere else.

Pigeon’s calabration protocol →

While developing Pigeon, I’ve called the group of all the configuration of the Layer 1 PHY parameters the “Mode”. I’ve experimented with a few different “modes”, but one in particular has been the most resilient to the innumerable mistakes and bugs i’ve wrought into existence – and that is the first mode I wrote down, “Mode A”. This is even (mostly) backwards compatible to my original Go implementation of Pigeon Mode A (back in 2022) over the air, and has largely withstood the problems I’ve thrown at it.

I’ve removed the bulk of the support I wrote out for other modes, but i’m likely to bring them back over time as I use pigeon to learn more (such as “Mode B” (QAM-16), “Mode C” (QAM-16 NUC), and “Mode AW” which is the exact same as Mode A, except 5MHz in bandwidth. More to come on those as I get further along – but for now let’s braindump the parameters i’ve picked out for Mode A:

The last bit to describe here is the Subcarrier Plan. The plan is ordered “negative first” (meaning the 0th bin in-memory is the most negative frequency domain bin of the fft), and within a Mode A OFDM symbol, there are 64 frequency domain bins (so, just to make it explicit: 64 ‘subcarrier usages’ that make up our Mode A ‘subcarrier plan’).

We’ll follow the same structure and conventions that we went through in the post all about OFDM Symbols – which means, we’ll need to place our guard bins, data bins, and pilot bins. I’ll include a copy-paste-able version of the images to follow at the end.

Guard Bins

First up, let’s place our guard bins. As we’ve already gone over, we’re looking to clear some space right up against the high and low end of the frequency range, so let’s go ahead and do that:

I gave up the center bin (0 Hz) and 8 of the 64 bits on each side (1/4 of the signal!) to give myself a bit of elbow room. This is perhaps definitely a bit overkill, but it’s been an extremely robust choice. If you multiply that through, this accounts for 312.5 kHz of frequency domain “padding” at the high and low end of the bandwidth, or 625.0 kHz of bandwidth which is not to be used.

Pilot Bins

Next up was the pilot bins. We’ve already gone over the purpose (and use) of our pilots, but I’ve found there to be an art to the placement of the pilots. Interpolation between pilot bins has turned out to be very reliable, but extrapolation, on the other hand, has been a major pain, for reasons I don’t fully understand yet.

My intent in placement was to pick out roughly even stretches of data bins bracketed between pilots, with as few data subcarriers as practical “outside” of a pilot (using extrapolation). I’ve played a bit with my AGWN simulator(s), as well as logging errors between two SDRs, and the configuration I have this in has been fairly resillant (for whatever reason), and withstood a few rounds of tweaking.

Data Bins

Almost as an afterthought – all of the remaining bins become data bins.

This puts the total number of data bins at 43, which, since Mode A carries data in QPSK/QAM-4 (two bits per data subcarrier), means we can carry 86 bits of data per OFDM symbol. That fairly modest capacity is largely due to the modulation scheme (or fft size, but increasing that has been … fraught) we’re using for Mode A – but I’ve made up for it by including 168 OFDM symbols in a single burst in order to have enough data to carry IP traffic without splitting the packet into two bursts.

With all that designed and on paper, we’re ready to start to tackle the next layer up – our Layer 2, named, creatively, “link”.

Let’s send some link layer data →

The following table is Mode A’s OFDM Subcarrier Plan. This is in negative first ordering (meaning the 0th member is the most negative fft bin, and the Nth is the highest frequency fft bin).

SubcarrierPlan([
 Guard,
 Guard,
 Guard,
 Guard,
 Guard,
 Guard,
 Guard,
 Guard,
 Data,
 Data,
 Data,
 Pilot(iq!(-1.0, 0.0)),
 Data,
 Data,
 Data,
 Data,
 Data,
 Data,
 Data,
 Data,
 Data,
 Data,
 Data,
 Data,
 Pilot(iq!(1.0, 0.0)),
 Data,
 Data,
 Data,
 Data,
 Data,
 Data,
 Guard,
 Data,
 Data,
 Data,
 Data,
 Data,
 Data,
 Data,
 Pilot(iq!(0.0, -1.0)),
 Data,
 Data,
 Data,
 Data,
 Data,
 Data,
 Data,
 Data,
 Data,
 Data,
 Data,
 Data,
 Pilot(iq!(0.0, 1.0)),
 Data,
 Data,
 Data,
 Guard,
 Guard,
 Guard,
 Guard,
 Guard,
 Guard,
 Guard,
 Guard,
])

The following table is Mode A’s frequency-domain preamble. This is, as above, in negative first ordering. I don’t actually think these values matter much (at all)? – but in case they do, here’s what I have. I muck with these a lot and haven’t found many changes in quality of detection or frequency correction yet.

[
 IQ::new(0.0, 0.0),
 IQ::new(0.0, 0.0),
 IQ::polar((TAU / 13.0) * 2.0, 1.0),
 IQ::polar((TAU / 13.0) * 3.0, 1.0),
 IQ::polar((TAU / 13.0) * 4.0, 1.0),
 IQ::polar((TAU / 13.0) * 5.0, 1.0),
 IQ::polar((TAU / 13.0) * 6.0, 1.0),
 IQ::polar((TAU / 13.0) * 7.0, 1.0),
 IQ::polar((TAU / 13.0) * 8.0, 1.0),
 IQ::polar((TAU / 13.0) * 9.0, 1.0),
 IQ::polar((TAU / 13.0) * 10.0, 1.0),
 IQ::polar((TAU / 13.0) * 11.0, 1.0),
 IQ::polar((TAU / 13.0) * 12.0, 1.0),
 IQ::polar((TAU / 13.0) * 13.0, 1.0),
 IQ::new(0.0, 0.0),
 IQ::new(0.0, 0.0),
]

Let’s send some link layer data →

The time has come.

If you’re following along at home, we now have all the basics we need to glue these parts together and see what this looks like.

We’re going to build the highest-level constructs for the PHY in code – something that takes some number of bytes in and writes out IQ samples fit for transmit over the airwaves (we’ll call this the Encoder), and something that takes chunks of IQ samples in, writing out decoded bytes (which we’ll call the Decoder).

Encoder

Let’s begin with the Encoder, since it’s slightly less involved. I’ve tried to make this a bit more accessible by drawing a diagram out before describing the order of operations, so that it’s possible to follow along visually.

While the process here can look like a lot, it’s really not that bad. We begin by taking the incoming bytes, converting the bytes into bits, and chunk those bits into parts which are sized to fit completely within an LDPC message. We will then encode incoming data into LDPC messages, using our configured LDPC Matrix (the table we appropriated from 802.3an). Next, we apply whitning over all the bits in our encoded (and packed) LDPC messages, using our configured whitening constant. In the case of QPSK, pairs of bits will then be modulated into a QAM subcarrier, where each QAM point represents a range of bits in the message. We’ll go through each of those modulated IQ subcarriers, and set each corresponding data subcarrier in order, for each OFDM symbol contained in the pigeon Burst. The preamble configuration is then used to generate (or, more likely, can be used at startup to precompute) the Schmidl-Cox preamble, which is written to the first IQ samples in our output IQ buffer. Finally, we will do a series of inverse FFT operations to convert each OFDM symbol to the time domain, including their cyclic prefix.

Let’s take a look at doing that, but in code this time now:

// (lightly edited for clarity)
impl Encoder {
 ..

 /// Encode the provided bits into the output time-domain IQ samples.
 fn encode(
 &mut self,
 dst: &mut [IQ],
 src: &Vector,
 ) -> Result<Burst, Error> {
 let src = {
 let mut raw = Vector::new(self.fec.message_len());

 // Set `raw`'s data bits, compute and set LDPC
 // checkbits.
 self.fec.add(&mut raw, src);

 // Apply whitening, and return
 raw.xor(&self.whitening)
 };

 // copy in the precomputed schmidl-cox preamble to `dst`
 let preamble_len = self.preamble_iq.len();
 dst[..preamble_len].copy_from_slice(&self.preamble_iq);

 // allocate a new (frequency domain) 'Burst' container.
 let mut burst = Burst::new(
 &self.mode.ofdm.plan,
 self.mode.ofdm.symbols
 );

 // modulate bits from 'src' as iq, and set each
 // data subcarrier for each ofdm symbol in the
 // burst.
 self.burst_encoder.multiplex(&mut burst, &src);

 // convert from frequency-domain data into time
 // domain iq samples, writing out ofdm symbols
 // and cyclic prefixes to `dst`.
 self.burst_encoder
 .transform(&mut dst[preamble_len..], &burst)?;

 // normalize all IQ samples; the maximum magnitude
 // in the IQ buffer may be very small, which weakens
 // our transmitted signal. Scale all IQ samples such
 // that the maximum IQ sample magnitude will be '1.0'.
 dst.norm();

 Ok(burst)
 }
}

Using the Encoder should hopefully be fairly straightforward – we’ll give it a bag of bytes, and get back some IQ samples that we can ask our nearest SDR to transmit.

As for what happens on the other end?

Decoder

Next up is the mirror image of our Encoder – the, imaginatively named, Decoder. The Decoder is slightly more involved (since it has to find the packet in the IQ stream, as well as correct for channel error(s)), so we’ll do the same thing as above – start with a diagram. My hope is going over the Encoder first helps us only really focus on the “new” stuff, otherwise it should feel like running the Encoder backwards.

Here, we start with an incoming stream of IQ, where we will process scan detections as they come in from our Schmidl-Cox detector and burst Scanner. This will give us a “snippit” of IQ, sized to exactly our Burst. We’ll begin to correct our IQ samples by first doing frequency estimation and correction in the time domain using our preamble and ofdm configuration. We’ll then do a series of inverse FFTs to extract each OFDM symbol in our Burst, where we can then do channel estimation and correction. With the OFDM symbols (hopefully) good enough, we can now map each data subcarrier back to bits, and unapply whitning. The resulting bits are then chunked back up into LDPC messages, which are then checked, and concatanated data extracted. Finally the bits are turned back into bytes, which are written to our output buffer.

However, before we get into the code to do this – there’s one last detail. We know bursts won’t overlap (if they do, it’s likely not possible to recover right now – even though other PHYs can and do), so any time we see something we believe to be a burst, we can skip ahead by the burst’s (constant) length within the IQ, and avoid trying to decode anything else in there.

The nice side-effect here is this also gives us an interesting property for the Decoder – namely, we know the maximum number of Burst detections we can get for a given block of incoming IQ data if they were packed end-to-end – and we can pre-allocate the memory we need, avoiding allocations for every demodulation attempt (which may or may not even be a valid Burst).

This pre-allocated block of memory to hold the burst’s data is something that I’ve called a frame buffer internally. Each frame buffer contains exactly sized buffers to hold decoded information from the burst – an iq buffer that is exactly the same number of samples required to encode the preamble and data, exactly the number of bits needed to store pre and post FEC data, pre-allocated byte array, etc.

Not shockingly, the code looks like this:

#[derive(Clone)]
pub struct FrameBuffer {
 /// Corrected IQ samples
 pub samples: Samples,

 /// post-correction OFDM burst
 pub burst: Burst,

 /// demodulated bits from the OFDM burst
 pub bits: Vector,

 /// demodulated bits from the OFDM burst,
 /// after FEC, and cleaned
 pub raw_bits: Vector,

 /// Layer 2 contents of the Frame
 pub contents: Vec<u8>,
}

Of course, that alone is handy – but we need to use them. So let’s go ahead and do what we promised above – each Decoder uses a fixed number of pre-allocated FrameBuffers to store packets in-flight, packed into what is, creatively, called FrameBuffers within my code.

As an aside, I likely should have called this a Memory Pool, since that’s the common and accepted name for this design pattern – but being stuck with unfortunate names is the burden of those of us who stumble into sensible ideas over time. The only nuance here is that I use the pools strictly sequentially – we only “save” the FrameBuffer if the LDPC checksum is correct, allowing us to only keep track of how many successful packets we have and being able to get the valid FrameBuffers, rather than storing a handle to each FrameBuffer as we go – a promise that most memory pools do not make, since blocks can usually be taken and returned in any order.

Let’s go ahead and do the whole Decoder dance now:

// (lightly edited for clarity)

impl Decoder {
 ..

 /// Process incoming IQ for Pigeon Bursts, and
 /// demodulate them.
 pub fn decode(
 &mut self,
 buf: &[IQ],
 ) -> Result<Vec<(Detection, &FrameBuffer)>, Error> {
 let mut ret = Vec::new();
 let mode = self.scanner.mode().clone();

 // reset the "valid frame buffer count" back to 0
 self.frames.reset();

 // call the scanner and get scan detections for
 // this block of iq (`buf`)
 for detection in self.scanner.scan(buf) {
 // for each detection, we're (only) going to process
 // the iq snippit, but pass along the metadata
 // such as SNR.
 let ScanDetection {
 snippit,
 snr,
 range,
 m,
 } = detection;

 // grab the next free frame buffer to work within.
 let frame_buffer = self.frames.next_mut();

 // Copy the snippit into the frame buffer (a mutable
 // location)
 frame_buffer.samples.copy_from_slice(snippit);

 // estimate the frequency offset based on the
 // Burst's Schmidl-Cox preamble.
 let preamble_fo = preamble::estimate_frequency_offset(
 &mode.preamble,
 mode.rate,
 &frame_buffer.samples[..mode.preamble.samples()],
 );

 // Shift the IQ stream by the estimated frequency
 // offset -- hopefully we're closer to 0Hz
 frame_buffer.samples.shift(mode.rate, preamble_fo);

 // estimate the frequency offset based on the
 // each burst's **cyclic prefix** -- exactly like
 // we did with the Burst Schmidl-Cox preamble,
 // but this time on each OFDM symbol.
 let ofdm_fo = ofdm::estimate_frequency_offset(
 &mode.ofdm,
 mode.rate,
 &frame_buffer.samples[mode.preamble.samples()..],
 );

 // Shift the IQ stream closer yet; hopefully this
 // is a very small nudge even closer still to 0Hz.
 frame_buffer.samples.shift(mode.rate, ofdm_fo);

 // Do a bunch of inverse fft operations for each
 // OFDM symbol, filling the frequency-domain Symbol
 // structs in `frame_buffer.burst` (Burst) struct.
 //
 // this will also do channel estimation and
 // correction before returning.
 self
 .decoder
 .transform(
 &mut frame_buffer.burst,
 &frame_buffer.samples[mode.preamble.samples()..],
 )?;

 // "demultiplex" each data subcarrier's frequency-domain
 // IQ constellation point, setting the correct bit range.
 self.decoder.demultiplex(
 &mut frame_buffer.raw_bits,
 &frame_buffer.burst
 );

 // unapply whitening by XOR-ing the buffer with
 // the well-known whitening vector.
 frame_buffer.raw_bits = frame_buffer.raw_bits.xor(
 &self.whitening);

 // verify that the LDPC message(s) are all correct,
 // and if so, concatanate the the data bits (no check
 // bits) to the `bits` vector.
 if self.fec.decode(
 &mut frame_buffer.bits,
 &frame_buffer.raw_bits
 ).is_err() {
 // this is where invalid packets fail. we gave it a good go.
 // next packet please.
 continue;
 }

 // copy the raw bits out, as bytes, to the `contents` buffer.
 frame_buffer.bits.copy_as_bytes(&mut frame_buffer.contents);

 // store metadata/metrics on the demodulation.
 ret.push(Detection {
 m,
 snr,
 index: range.start,
 });

 // save the contents of this frame buffer (don't
 // reuse this buffer next go-around).
 self.frames.save();
 }

 // We're going to take out the borrow on the frame at
 // the end since we don't want to deal with telling the
 // compiler via code gymnastics that the mut and non-mut
 // borrows are OK since they're non-overlapping.
 Ok(ret.into_iter().zip(self.frames.iter()).collect())
 }
}

Phew. That was kinda a lot. In fact it’s basically the whole thing. This function is as close to “how do you read an OFDM packet” as it gets, and perhaps the most important part of this whole series. Beyond that, though, this is a huge conceptual unlock. This means we now have an incredibly powerful primitive; the ability to take bytes and go to/from IQ samples over the air.

Let’s talk about pigeon modes →

I asked the banker who is my reference at the bank something like this:

Give that we are talking about the consequences of the tantrum of a fascist foreign government, what happened to them (who are also people close and dear to me), in some future can very well happen to me.

Suddenly my risk profile shot up under the roof.

What do you suggest me to do? Should I find a trusted source of gold bullions to bury under the cellar at home?

The answer was something like this:

Sadly YES, given that the USA have a sort of financial monopoly they can entitle themselves to arbitrarily define a person/organization as a terrorist without any trial or judicial course, and as a consequence apply sanctions that cannot be effectively counteracted, not even abroad.

I didn't have this in my 2026 bingo card, but here we are.

For more details, see:

• the A/I press release under "12. Banca Etica: the sanction also affected an Italian bank account"
• the public statement from the bank (in Italian)

For some broader context on this kind of actions from the USA, see also:

• The same sactions against UN Special Rapporteur Francesca Albanese
• Executive Order 14203 titled "Imposing Sanctions on the International Criminal Court"

The following contributor got their Debian Developer account in the last two months:

• Nicolas Peugnet (nicolasp)

The following contributors were added as Debian Maintainers in the last two months:

• Antoine Lassagne
• Ivan Hu
• Jesse Rhodes
• Haolin Xue
• Léo Haf
• Rony João de Sousa
• Luke Yasuda
• Darshaka Pathirana

Congratulations!

My Debian contributions this month were all sponsored by Freexian.

You can also support my work directly via Liberapay or GitHub Sponsors.

Personal note

This month, my Dad unexpectedly passed away after a short illness. As a result I obviously got less work done than usual, and I still have a lot to take care of (since I’m the executor of his will, as well as helping with funeral arrangements) while grieving and generally having less focus and energy. Having routine work to do is one of the ways I cope with this sort of thing, but all the same, I hope people will bear with me and maybe remind me if I seem to be dropping the ball on something you especially need.

LLM vote

[Content note: strong opinions.]

I voted in General Resolution: LLM usage in Debian. My vote was pretty much the opposite of what ended up winning, so I’m quite disappointed. My personal opinion is that LLMs are cognitive hazards to their users that impose ecological costs far out of proportion to their utility at a time when the world absolutely cannot afford them. When the impossible economics of the large commercial models are finally allowed to catch up with reality, I expect there to be significant macroeconomic consequences, and that people who have become dependent on them will have problems; and who knows what the copyright situation on their output really is. I’m not convinced that local models are better enough on these axes to be worth the costs.

Debian’s direct contribution to all that will be negligible on a global scale, and even the most radical proposals in the GR didn’t expect that we could do much about upstreams that have gone all-in on LLMs. Even so, I’d hoped that my fellow developers might be more willing to lean on our position in the free software ecosystem to make at least a moderately radical statement. Instead, we’ve at best presented an undistinguished fence-sitting position to the world, and further entrenched the idea that humans can reliably do a good job of reviewing the output of tools that are designed to produce output plausible to humans. I certainly don’t trust my own code review skills that far.

Since I’ve never voluntarily used an LLM (not counting LLMs being foisted on me by things like search results, support chatbots, or incoming pull requests, regardless of whether I asked for them), and don’t intend to for the foreseeable future, I doubt this will change much for me in terms of the way I work. The winning option is a very weak one that imposes no new requirements on developers, which means that it also does nothing to stop me continuing to reject LLM-generated material from Debian bug reports and merge requests in my areas of responsibility. I know this probably won’t do much to satisfy people who have decided that Debian is slop now, but it’s the best I can do.

OpenSSH

I finally landed the GSS-API key exchange package split in our OpenSSH packaging. Here’s the NEWS entry:

openssh (1:10.4p1-5) unstable; urgency=medium

  The openssh-client and openssh-server packages no longer include GSS-API
  authentication and key exchange support; this adds pre-authentication
  attack surface and generally increases complexity, and should only be used
  where specifically needed.  Users who need these features should install
  openssh-client-gssapi or openssh-server-gssapi instead.

 -- Colin Watson <cjwatson@debian.org>  Sun, 23 Aug 2026 17:39:55 +0100

I fixed a flaky autopkgtest.

I upgraded from 10.4p1 to 10.5p1, which was a good test of keeping openssh and the new openssh-gssapi source package in sync.

PuTTY

I upgraded from 0.84 to 0.85.

Python packaging

New upstream versions:

• anorack
• djangorestframework (fixing a build failure)
• isort
• lazr.delegates (dropping pkg_resources)
• lazr.restfulclient (dropping pkg_resources)
• lazr.uri (dropping pkg_resources)
• nbclient (fixing a build failure)
• pydantic-settings
• python-argcomplete
• python-blockbuster
• python-makefun (fixing a build failure)
• python-persistent
• python-pytest-run-parallel
• python-time-machine
• python-typing-inspection
• wcwidth
• zope.hookable
• zope.i18nmessageid
• zope.interface
• zope.proxy
• zope.security
• zope.testrunner

The version treadmill continues: we’ve just finished dropping Python 3.13 as a supported version, so now we’ve started working on enabling Python 3.15 as a supported version. Maximiliano Curia has been very helpfully driving this. I didn’t get as much done here as I’d have liked (see the top of this post), but I fixed a couple of packages:

• pydantic
• python-typing-extensions

Other build/test failures:

• anorack
• buildbot
• codespell
• django-prometheus
• pendulum
• pydantic-core
• python-argcomplete
• python-ast-serialize
• python-bcrypt
• python-cross-web
• python-datamodel-code-generator
• python-djangorestframework-flex-fields
• python-openapi-core
• python-treq
• scikit-build-core

I fixed some other bugs:

• python-librosa: Hardcoded Python 3.13 in rules file
• python-makefun: Fails to build source after successful build

bugs.debian.org

I deployed the fix for Invalid link rel=”canonical” on bugs.debian.org. In the process I found a few bugs in recent undeployed code and fixed them.

I am a heavy user of sidenotes:¹ they keep optional content next to the text instead of sending the reader to the bottom of the page and back. Tufte CSS renders them without JavaScript but only accepts inline content. CSS anchor positioning, now supported by recent browsers,² is an elegant alternative. Sidenotes can hold several blocks, still without JavaScript, and fall back below the paragraph referencing them on narrow viewports and older browsers.

In 2023, Eric Meyer demonstrated this technique in “Nuclear Anchored Sidenotes.” The main improvement over other solutions is that the notes can sit anywhere in the HTML document. You can place them after the paragraph referencing them, as regular block elements for text browsers, screen readers, feed readers, and reader mode to render them properly:

When the viewport is too narrow or the browser does not support CSS anchor positioning, you can style them so the reader can skip them or glance at them without losing their position in the text:

Once the viewport is large enough, they appear in the margin, at the same vertical position as the matching reference mark, unless they would collide with a previous sidenote, as in the example below:³

The gist of CSS anchoring is to position an element relative to another element—the anchor. For the sidenotes, the anchor is the reference mark. I use the following markup, with a data attribute to specify the anchor name:

<sup id="fnref:YYY" data-anchor="--lf-sn-YYY">
  <a href="#sidenote-YYY">1</a>
</sup>

The matching note is an <aside> element carrying the same data attribute for the anchor name. We put it after the paragraph holding the reference mark:

<aside role="note" id="sidenote-YYY" data-anchor="--lf-sn-YYY">
  <sup>1</sup>
  <p>A first paragraph.</p>
  <p>A second paragraph.</p>
</aside>

On a narrow viewport or when the browser is too old for CSS anchoring, we style the sidenote, which stays below its paragraph, with a muted color:

aside[role="note"] {
  margin-block: 1rlh;
  color: #444;
}

On a wide viewport and when the browser is recent enough, we move the sidenote to the right margin:

@supports (anchor-name: attr(data-anchor type(<custom-ident>))) {
  @media (min-width: 72rem) {
    main {
      position: relative;
      sup[data-anchor] {
        anchor-name: attr(data-anchor type(<custom-ident>));
        /* → anchor-name: --lf-sn-YYY */
      }
      aside[role="note"][data-anchor] {
        anchor-name: --lf-sidenote;
        position: absolute;
        position-anchor: attr(data-anchor type(<custom-ident>));
        /* → position-anchor: --lf-sn-YYY */
        top: max(anchor(top), anchor(--lf-sidenote bottom, -1rlh) + 1rlh);
        left: 100%;
        margin: 0 2rem;
        width: 18rem;
        color: inherit;
      }
    }
  }
}

attr() extracts the anchor name for the reference mark from the data-anchor attribute. It returns a string, unless we specify a CSS unit or a type, like here: the browser parses the data attribute as a custom identifier, which anchor-name validates as a dashed identifier, a custom identifier starting with two dashes.⁴

The note itself is absolutely positioned past the right edge of the main block. It selects the matching reference mark as its anchor with position-anchor set to the value of the data-anchor attribute. Each note is also an anchor named --lf-sidenote. We use it to keep the next note from colliding with this one.

The anchor() CSS function lets us position the note’s top edge relative to its anchor: anchor(top) aligns the top edge of the note with the top edge of the reference mark. It can also take another anchor as a parameter: anchor(--lf-sidenote bottom) would align the top edge of the note with the bottom edge of the closest preceding anchor named --lf-sidenote—so the previous note.⁵ Like attr(), anchor() accepts a fallback value as its second parameter and use it when the named anchor does not exist.

The top property handles three cases, illustrated in the following diagram:

1. The first note’s top edge aligns with the top edge of its reference mark: as there is no previous note, anchor(--lf-sidenote bottom, -1rlh) + 1rlh resolves to 0 and max() returns anchor(top).
2. When the reference mark of a later note sits above the bottom of the previous note, plus some vertical space, the note goes below the previous one to avoid a collision. max() returns anchor(--lf-sidenote bottom) + 1rlh.
3. Otherwise, the note’s top edge aligns with the reference mark’s top edge, as max() returns anchor(top).

Have a look at the complete stylesheet, which also adapts the reference mark to the location of the note: a “↓” arrow when the note sits below the paragraph, a “→” arrow when it moves to the margin. Gwern’s “Sidenotes In Web Design” lists more implementations and their trade-offs.

Some bloggers aim to write a post in 30 minutes. I planned to publish three web-related articles this weekend. Instead, I spent an inordinate amount of time elsewhere: about 15 commits on the build system, a pull request to update CSS highlighting for nested selectors in Pygments, and a small correction to MDN’s article on the anchor() CSS function. The SVG illustration took a bit less than an hour and the article itself a handful of hours. The attr() function came in after I thought “inline style looks ugly, isn’t there a better way?” But, hey, I still think this is worth it! 🎨

About the project and me

Your program crashes. You open gdb and get ?? instead of a stack trace. So you go find the right -dbgsym package, for the right version, for the right architecture, install it, and start again. Debuginfod removes that entire detour: gdb asks a server for symbols by the build-ID baked into the binary. Debusine already built packages, already produced -dbgsym files, and already hosted the archives; it just couldn’t answer the question.

This summer I made it answer. My project was to add debuginfod server functionality to Debusine so that it not only hosts -dbgsym packages, but also serves their debug symbols over the debuginfod(8) protocol. Debian developers can then debug binaries by setting a single URL that gdb uses to fetch the matching debug symbols. This project took me through design, backend work, an extraction pipeline on the worker, HTTP serving, documentation, and testing from the first blueprint all the way to a live demo on debusine.debian.net.

Initial planning and design changes

A design first, in !3030. The proposal submitted for GSoC 2026 was just an overview of how things will work, but in reality there were a lot of design questions which needed to be answered before starting with contribution. Debusine keeps development blueprints in its docs tree, reviewed like code, it’s basically a blueprint of what feature or new changes are we going to make. I was assigned the work item #957, which was basically about how the idea of implementing a debuginfod server functionality inside Debusine was initially proposed by a fellow member which later became a project idea under GSoC 2026. My developer blueprint pinned down the four decisions everything else depends on: extraction happens on the worker after the build, symbols are stored as artifacts keyed by build-ID, they’re published into suites alongside their binaries, and they’re served from the archive root rather than per-suite. Settling that up front meant the design discussions happened in a document instead of across three merged branches.

One of those arguments became its own fix. My wording implied symbols were unpacked inside the isolated sbuild environment (the consequence was I was handed a bug to be solved in the first week of contribution period), when they’re actually extracted afterwards on the worker, where the build output already sits, a distinction that matters, because doing work inside the unshare environment means extra tooling in the chroot and more ways to affect the build. !3119 corrected it before the wrong model spread into the code.

A new artifact type

Artifacts are a major concept in Debusine overall, so as per the developer blueprint we introduced a new artifact which was debian:debug-symbols. It holds every .debug file from one -dbgsym package. Its data is a validated list of lowercase 40-character build-IDs, and each file is stored under its build-ID as the path, so answering “what are the symbols for this ID?” is a direct lookup, with no path translation in the request handler. One artifact per package rather than per file: a util-linux build would otherwise spray hundreds of artifacts, collection items and relations across the database for no benefit. For implementing debian:debug-symbols artifact, I changed the main models.py file, along with that since it’s a norm to write unit tests, all mentioned under !3088.

Publishing workflow and solving a bug

Extracting symbols is only useful if they reach the archive people actually install from, so !3180 taught package_publish to follow the relates-to relation: copying binaries into a suite now brings their debug symbols along automatically, with nothing extra for the publisher to configure. Each build-ID becomes its own collection item, for example debugsym:hello_2.10-5_amd64_fcc9064… each carrying the package name, version and architecture copied from the binary, so the item is meaningful on its own without dereferencing anything. Uniqueness is enforced at both the suite and archive level, because the serving URLs are archive-wide and two suites must never disagree about what a build-ID means: republishing an identical file is accepted quietly, while two different files claiming the same ID is an error worth failing on. A partial index on the build-ID keeps the eventual HTTP lookup fast.

That looked finished until symbols started arriving in target suites disconnected from their binaries published, but unfindable, because copying items between collections silently dropped their artifact relations, and that relation is the only thing tying the two together. The fix sat one level above my feature, in the generic CopyCollectionItems task that does the copying, and since it was reusable infrastructure rather than anything debuginfod-specific, Colin implemented it himself in !3228. My project needed it to work at all; every other Debusine feature that copies items now gets it for free.

Endpoint and CI tests

With symbols in the archive, !3212 added the part users actually touch: GET /{scope}/{workspace}/buildid/<build-id>/debuginfo looks the ID up across every suite in that workspace’s archive, streams the file, and sets the X-DEBUGINFOD-FILE and X-DEBUGINFOD-SIZE headers the protocol expects. It also handles the two things gdb actually does: a HEAD probe before committing to a download, and ranged requests to pull individual ELF sections instead of the whole file. Scoping it to the archive rather than the suite is what lets one URL cover a whole workspace, so the developer never has to know which suite their binary came from.

Every merge request above landed with unit tests, but those only tell you that the pieces behave correctly. What Colin and I wanted was a real gdb fetching real symbols from a real instance, so !3261 adds an autopkgtest that builds a package, publishes it, checks the HTTP headers, then sets DEBUGINFOD_URLS and makes gdb go and get the symbols, wired into the CI integration tests so it runs on every change. It took me a day to learn that skipping the signing worker doesn’t simplify that test, it just hangs until the 30-minute timeout, because update_suites needs signing to produce a usable repository.

The last piece, !3301 covers the new artifact, the suite and archive changes, the new archive URL, and a how-to for using it. My first how-to draft explained how everything worked and offered four ways to set DEBUGINFOD_URLS; the version that shipped gives one recommended setup and gets out of the way. The same pass trimmed the blueprint down to only what’s still unimplemented, since a design document describing merged code is just an obstacle for the next reader.

What’s left

Only one item on my original plan didn’t land: an archive-level build_debug_symbols switch, modelled on Launchpad’s equivalent, letting an archive skip building -dbgsym packages entirely by passing DEB_BUILD_OPTIONS=noautodbgsym to sbuild. It was always the stretch goal rather than core scope, landing the extract-publish-serve path solidly mattered more than landing it broadly. The design is written up in the blueprint, and I intend to implement it myself.

The other gaps were deliberately out of scope from the start, and the blueprint says so. DWZ supplement files aren’t ingested, so packages using compressed debug info may render without the alternate strings table; debugging still works, it’s just less complete. Source-file serving runs into the same Debian packaging limits that constrain debuginfod.debian.net today, making it a design question rather than a coding one. Executable serving, the metrics and metadata endpoints, and federation to upstream debuginfod servers were excluded for similar reasons, none of them are needed for Debusine’s core use case, and each would have crowded out the parts that are.

One open bug is left too. On the last day of the coding period, Stefano Rivera found that publishing ledger and linux was failing, because I had told the database that a build-ID identifies one exact debug file which isn’t true in Debian, since dh_dwz runs once per binary package, so when one object ships in two binary packages their .debug files differ while describing identical code. How to fix it is still an open discussion #1582, though it may not land before the formal end of the project.

None of that is a handoff. GSoC’s timeline is ending, my involvement isn’t, I’m carrying on with Debusine until both the build_debug_symbols switch and DWZ supplement support are merged, and I expect to keep contributing beyond that. This project got me familiar with a codebase I enjoy working in, and the remaining pieces are mine to finish.

Thanks!

The biggest thanks go to my mentor, Colin Watson, whose reviews consistently found the thing I hadn’t thought about. He also gave me room to get things wrong first and understand why, which taught me more than being handed the answer would have.

Thanks as well to Raphaël Hertzog, Enrico Zini, Stefano Rivera, Carles Pina i Estany and Helmut Grohne and everyone else around Debusine and Freexian for reviews, comments and patience with my questions.

Special thanks to Freexian for developing Debusine in the open and for giving me access to test on debusine.debian.net.

Finally, thanks to the wider Debian community, whose build-ID and -dbgsym conventions did most of the hard work before I arrived and to Google Summer of Code for providing a platform and the time to do this properly.

The AVIF image format gets more and more popular in the web dev community, so I needed to teach XFCE4's thunar (file manager) and Ristretto (image viewer) to thumbnail these.

Luckily that is not too hard:

Debian Trixie separates its gdk-pixbuf libraries slightly differently than previous versions. That's why it is not "automatically there". Ensure you have the libavif-gdk-pixbuf plugin and the tumbler service (which XFCE uses to process thumbnails):

Thunar has likely tried (and failed) to load your AVIF files before you installed the package, it will have saved a blank or "broken image" placeholder in a thumbnail cache directory. It will not attempt to regenerate them unless you clear this cache:

Tumbled will restart on its own when it is needed. When you open thunar again and navigate to your image directory ... your AVIF images will now generate thumbnails automatically like the other image format did already.

Looking at the reactions to the Debian AI vote, I think some people still think the clock can be turned back, as if that ever worked in history. Rather than cry about spilled milk, I prefer to find a path forward in the new world. There are many ways to use LLMs, some of them are straightforward, others not so much.

One of the “not so clear” areas for me is the focus on agentic workloads. For complex tasks, sure, you want something that can work in the background, but in general, why does every single tool go the agentic way? I much prefer the “chat/ask” approach, or even the “code” one, but if I’m at the keyboard, why would I send a task to an agent, and see it work, instead of directly implementing it?

And then, this past Friday, I finally understood one part of that. I was in the airport, sitting at the gate and waiting to board a flight, and because I arrived much earlier at the airport (fearing crowds due to Labour Day weekend), I got one hour of work before boarding started. As the time for boarding approached, I did one more commit after making sure tests pass, pushed, closed laptop, and went to walk a bit before getting on the plane.

As I was getting up, I get a phone notification from GitHub that the CI run failed. I was quite surprised, as the local tests passed, so I open the notification, and realize that tests via make test vs CI (which additionally uses --pedantic) had slightly different settings, and of course I missed a build warning (which in CI is an error).

I thought I’d fix that on the plane, but then I saw a “Copilot agent” button in the mobile app. I was curious what it did, I click it, and I see Copilot starting a draft pull request, and saying:

Thanks for asking me to work on this. I will get started on it and keep this PR’s description up to date as I form a plan and make progress.

Fix the failing GitHub Actions job. Analyze the Actions logs, identify the root cause of the failure, and implement a fix.

Then it goes, finds the failure, writes the fix, and tries to run the tests. Well, it can’t do it (it runs in a restricted container, so no network, so stack install couldn’t actually work). The agent sees that, acknowledges it has no way to validate the fix, but the error message was clear enough that it was confident the fix is mostly correct, so it sends the pull request.

I allow full CI to run on the pull request, and go buy a bottle of water. After that, I check and see that the CI failed again, as not one but two test files were broken, and I didn’t have --keep-going, so the build stopped at the first failure. I write a comment in the pull request, no reaction, I realize I need to tag Copilot explicitly, I do that, and it starts another investigation.

I’m waiting now in the boarding queue, with phone in hand, while Copilot is fixing my bug. While I scan my boarding pass and walk towards the plane, the pull request is updated, I trigger another CI, it passes, and I merge it.

And then, it hit me. Agents allow me to make progress while being “not at keyboard”, whether that’s physically “not at keyboard”, or while working on something else. Fixing a simple test failure is not something that needs human attention per se, whereas improving the test layout might be.

In that airport, using otherwise-unusable downtime, and without explicitly intending to, I made progress in understanding a different way to use AI. Now I have three ways to work with LLMs: ask (tutor mode), code (implement my request), and agent (fix simple or complex problems, autonomously). I still don’t know about “plan” mode and really complex tasks, like asking it to implement features from scratch. That will probably be the next area to tackle.

And today (Sunday), while waiting for a running race to start, I opened GitHub, and asked Copilot to increase test coverage for a simple module. It did, and yes it still can’t run tests (I learned in the meantime that you can configure the environment in which the agent runs, nice), but after two back-and-forth messages, I have a pull request ready to review. All in the 20 minutes before a race, where I could either browse social media or actually do some meaningful work.

Checking now my GitHub billing, it looks like all of this Copilot use only cost $1.92. Yes, that is under two dollars! And while it did use compute resources, the person across the aisle who watched TikTok or Instagram for half an hour while waiting for takeoff also consumed a lot of compute, and so do the gazillion cat videos uploaded to YouTube every day.

To me, this is another tool in the toolbox, that might one day replace me (as it did to the 19th-century textile workers), or make me five times more productive — we’ll see where we end up. In the meantime, I can move faster, and make better use of my limited free time.

Enjoy the ride!

Another minor maintenance release of the RcppFarmHash package is now on CRAN as version 0.0.4.

RcppFarmHash wraps the Google FarmHash family of hash functions (written by Geoff Pike and contributors) that are used for example by Google BigQuery for the FARM_FINGERPRINT digest.

This releases updates several of package internal files for continuous intergration and package data.

The brief NEWS entry follows:

Changes in version 0.0.4 (2026-09-06)

• Minor updates to continuous integration, README.md and DESCRIPTION

Courtesy of my CRANberries, there is also a diffstat report for this release. For questions, suggestions, or issues please use the issue tracker at the GitHub repo.

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. If you like this or other open-source work I do, you can now sponsor me at GitHub.

I have just tried CoMaps, a free mapping program released under the Apache license [1]. I have tried it on Android on a Pixel 6a but it also runs on Linux so I’ll try it on a PinePhone or similar at some convenient time. On Android it is in the F-Droid repository among others and for Linux there’s a Flatpak package.

The data it uses is from Open Street Map project [2] which has extensive and accurate coverage of every place I’ve looked at (Australia and a few other first-world countries). The first thing it does after being installed is start downloading the world data set from Open Street Map and prompt to download the data for the detected region (Melbourne in my case).

The UI is decent and allows most of the features that I am used to using in Google Maps. The quality of directions seems good, I’ve only tested it with one journey so far which was a 50 minute drive across the city and it gave a set of directions that Google Maps often gives.

It gives spoken directions which is an important feature but sometimes the way the directions are presented is confusing. When turning off a freeway it didn’t give a spoken direction to do that, it gave a direction to “turn right” which was AFTER leaving the freeway, fortunately the map was clearly displayed.

In terms of use practices of this program the main difference I recommend is checking which off ramp to use from a freeway before entering the freeway. With Google Maps you can rely on it giving clear directions in that case.

I recommend this program without reservation. It can do everything that Google Maps does apart from detecting traffic jams because there’s no way of detecting traffic without spying on users. It is designed to preserve user privacy and works well in that regard.

• [1] https://www.comaps.app/
• [2] https://www.openstreetmap.org/

I've released version 1.1.25 of plocate. This time around, there's two security issues of unknown severity; if you chain them with other bugs, they could lead to being able to list files (but of course not their contents) that you should not normally be able to see. So an update is probably in order; you can never be too safe these days.

The full changelog is:

plocate 1.1.25, September 6th, 2026

  - Fix two early-exit bugs with multiple databases.
    Reported by Manpreet Singh and Tyler Spivey.

  - Drop setgid properly, including the saved gid.
    Reported by Michal Sekletar, found with the help of Claude Opus 4.6.

  - Fix a potential symlink-checking race in updatedb.
    Reported by Michal Sekletar, found with the help of Claude Opus 4.6.

As usual, you can get it from the home page, or it's on the way up in Debian unstable.

After having witnessed how easy it is for good people to lose a .org domain over a fascist tantrum (you can follow the Autistici/Inventati story here and here), I've started moving all my infrastructure to differently managed TLDs.

enricozini.org and enricozini.com will keep being functional for the time being, as dropping a domain makes it available for squatting and impersonation.

These new domains are now online, with working web and emails:

• enricozini.eu
• ezini.eu
• ezini.it (probably equally subject to fascist tantrums these days)

It will take ages to migrate countless accounts that are tied to my primary email address, so better start early.

Waiting to see what will happen with .meow domains, which I supported despite not identifying as a cat.

This August, I accomplished what I wanted for many years: I deleted the last cgo dependency in Debian Code Search! This was made possible by Go’s recently introduced SIMD support, because now we can implement the TurboPFor integer compression format as efficiently — more efficiently, in fact, by using the newer AVX512 instruction set! — as the reference implementation.

Background: Why does DCS need a fast Integer Codec?

Debian Code Search (DCS) is a search engine that allows searching all the Open Source source code within Debian, with either literal search expressions or regular expression search queries.

A search engine uses an inverted index: a map from term to documents containing the term. Each document is typically represented most efficiently by using an id, so the index consists of many lists of document ids.

When searching, it is important to quickly decode these lists to answer the search query. However, there is a point of diminishing returns where the decoding speed, even though it can still be measurably improved quite a bit, no longer influences the overall query duration.

From 2012 (its inception) to 2019, Debian Code Search used to use a small index format, and queries were fast because the index was kept entirely in RAM. In 2019, I implemented the new index format, which adds an on-disk positional index. For literal queries (78.2% of DCS queries), querying the positional index on disk is faster than querying the non-positional index in RAM.

The efficient encoding of the TurboPFor format makes it possible to fit such an index on a mid-sized Hetzner server, which I rent with two 1 TB SSD disks. The optimized decoder of the C TurboPFor library is what made decoding fast at query time.

If you want to dive deeper into the algorithm, see this blog post from February 2019:

If you want to learn more about the positional index, see this blog post from September 2019:

SIMD in Go

For many years, you had the following options for using SIMD instructions in Go:

1. Hand-writing Go assembler code. This is only doable for small functions, for example bytes.IndexByte is implemented with hand-written Go assembly (including AVX2).
2. Generating Go assembler code with tools like Michael McLoughlin’s “Avo”. This is how crypto/internal/fips140/sha256 uses AVX2. While Avo generator code definitely is higher-level than hand-written assembly, it is still too close to assembly for my taste.
3. Use a C library via cgo so gcc or clang compiles SIMD code. Debian Code Search used to use the powturbo/TurboPFor C library via cgo for the last 7 years.

The C TurboPFor library has served us well, but Debian Code Search was always intended to be a project using Go, so I would prefer it if I did not have any C code in the project.

Go 1.26 (released in February 2026) introduced the simd/archsimd package:

Go 1.26 introduces a new experimental simd/archsimd package, which can be enabled by setting the environment variable GOEXPERIMENT=simd at build time. This package provides access to architecture-specific SIMD operations. It is currently available on the amd64 architecture and supports 128-bit, 256-bit, and 512-bit vector types, such as Int8x16 and Float64x8, with operations such as Int8x16.Add. The API is not yet considered stable.

— Go 1.26 Release Notes

For my 2019 TurboPFor analysis, I implemented goturbopfor, a native Go teaching decoder (without any SIMD), because I find Go code easier to follow than C code, especially optimized C code. My implementation was intentionally not optimized so that the code was easier to study.

The TurboPFor format/algorithm has a vector-optimized part: bitpacking comes in a scalar variant (bitunpack32) and a vector variant (bitunpack256v32), where the vector variant is used for full blocks (256 values) and the scalar variant is used for remainder blocks (< 256 values).

When Go 1.26 was released, I used Claude Code to explore whether my native Go decoder’s bitunpack256v32 function (for the vertical vector layout) could be implemented using Go SIMD, and the answer was yes, it was possible and it was faster than without SIMD, but not quite at the level of C TurboPFor. If you let Claude Code try for long enough, it eventually finds enough optimizations (about 10) to match C performance.

I don’t want to vibe-code Debian Code Search, though, so I figured I would find some time to review the SIMD code at some point and see if I could implement something similar myself.

Before I found enough time and motivation to complete said review, I discovered that to not regress real-life query performance by more than 10 to 100 milliseconds (which seems acceptable), I don’t actually need to add SIMD code to my teaching decoder at all; it would be sufficient to reduce allocations in my teaching decoder and specialize it per bit width.

Encouraged by the possibility of using the optimized native Go decoder in Debian Code Search, I explored whether I could also implement a native Go encoder so that I could get rid of the C TurboPFor dependency entirely. The answer is yes, it is doable in a few days, and it isn’t even that much slower: Go is at 76% of C, see Debian/dcs commit e920dc7.

The goal I set myself at that point was to see if I could learn enough SIMD to optimize the native Go encoder such that its performance would match how DCS uses C TurboPFor (via cgo).

Beating C TurboPFor was possible in 2-3 commits (SIMD and bit width specialization). To my surprise, Claude Fable 5 pointed out that the encoder’s block scanning could be done more efficiently using a technique called positional popcount, and that is another 2x speed-up! 😲

To be clear: I am not saying the Go compiler beats C here. Certainly, the C compiler can also produce fast AVX512 code and can be used to implement positional popcount. When comparing apples to apples, i.e. backporting the AVX512 kernels and positional popcount technique to C TurboPFor, Go benchmarks a little slower at ≈1.4x C.

This spectacular result (much faster than what DCS had before) got me curious how far I could push the decoder with SIMD after all. I ended up matching/exceeding the cgo version here, too!

The rest of this article explains a few classes of optimizations I encountered along the way.

Starting Point

When I wrote my goturbopfor teaching decoder, I named its functions to match the upstream C TurboPFor library, but now I want to get away from names like p4ndec256v32 — they make sense from the TurboPFor perspective, but for Debian Code Search, we can use cleaner names.

Before writing any code, I audited how DCS uses integer compression / decompression.

API design: BlockEncoder, BlockDecoder and streaming

In Debian Code Search, we have the following usage patterns:

• Partial Indexing: When a new package (or package version) enters Debian, all of its (text) files are indexed. If the hello-2.12.3-1 package (hypothetically) contained only hello.c with printf("hello!\n");, we would assign document ID 1 to hello.c and store in the partial index that trigrams pri, rin, int, ntf, etc. are all found in doc 1 (hello.c).
• Full Index Merging: The many thousands of partial index files (for each Debian package) are combined into a small handful of large index files: When searching, it would be expensive to consult thousands of indexes. To merge multiple partial index files into one larger index (which can then be efficiently queried), we need to re-encode the partial index files: what used to be document ID 1 in the partial index might be document ID 2531 in the full index.
• Querying (searching): When users enter search queries, these queries need to be answered as quickly as possible. The relevant entries in the full indexes are decoded (in parallel).

For reading the index, we do keep the decoded uint32s fully in memory, so we only need DecodeN(input []byte, output []uint32) (read int), a function that reads len(output) values (uint32) from input and returns how many bytes it consumed.

For writing the index (both in partial indexing, and when merging), keeping the entire index in memory is prohibitively expensive, so we need a streaming API, for decoding and for encoding.

Ultimately, I converged on the following API:

package pforenc

type BlockEncoder struct {
    // scratch buffers can go here
}

// EncodeBlock encodes len(vals)<=256 uint32s into dest (one TurboPFor block).
func (*BlockEncoder) EncodeBlock(dest []byte, vals []uint32) []byte {}

// EncodeN calls EncodeBlock in a loop.
func (*BlockEncoder) EncodeN(dest []byte, vals []uint32) []byte {}

type StreamEncoder struct {
  be   BlockEncoder
  vals [256]uint32
  // scratch buffers
}

// if full, you need to call [EncodeBlock]
func (*StreamEncoder) Add(val uint32) (full bool)

// EncodeBlock must be called after all data was [Add]ed.
//
// Write the returned buffer to file or send it over the network;
// it is only valid until the next [EncodeBlock] call.
func (*StreamEncoder) EncodeBlock() []byte {
  if se.n == 0 { return nil } // turn an extra EncodeBlock into a no-op
  // …
}

This API (the decoder works similarly) allows us to process data in TurboPFor format without any memory allocations. The types are not safe for concurrent use by multiple goroutines. The zero value is ready to be used. For the streaming API, the result only stays valid until the next call.

Initial Implementation

Before we can optimize anything, we need a working decoder and encoder. The decoder already exists: my goturbopfor teaching decoder. Next up, I needed an encoder.

Writing a TurboPFor encoder has a delightfully simple starting point: You can encode all values at bit width 32, in little endian, at which point you only need to add a one-byte TurboPFor block header every 256 values and you’re done:

func (be *BlockEncoder) EncodeN(dest []byte, vals []uint32) []byte {
  for len(vals) > 0 {
    chunk := min(len(vals), 256)
    dest = be.EncodeBlock(dest, vals[:chunk])
    vals = vals[chunk:]
  }
  return dest
}

func (be *BlockEncoder) EncodeBlock(dest []byte, vals []uint32) []byte {
  const bitWidth = 32
  dest = append(dest, bitWidth)
  for _, val := range vals {
    dest = binary.LittleEndian.AppendUint32(dest, val)
  }
  return dest
}

Of course, this is a terribly inefficient compressor, so after the first commit, the real work starts: implement each block type until the compression matches the original C TurboPFor implementation (same output file size), or in other words: do the reverse of the decoder.

1. The TurboPFor bitpacking block type (bitpacking implementation commit) encodes a bit stream of variable bit width (where the bit width is in range 0 ≤ bitWidth ≤ 32) in little endian byte order. By scanning all values and choosing the smallest bit width that allows representing all values, this technique saves disk space (compresses).
2. The bitpacking with exceptions block type (bitpacking with exceptions implementation commit) determines two bit widths: one for values, the other bit width for encoding exceptions. This allows choosing a lower bit width (that does not cover all values) compared to the bitpacking block type. A bitmap encodes whether a value has an exception or not.
3. The bitpacking with VB exceptions block type (bitpacking with VB exceptions implementation commit) is a variant which does not use an exception bitmap and encodes exceptions using a variable byte integer encoding. This is more efficient when there are few exceptions (less than 20) or the exceptions are very different in bit width compared to the other values.
4. Lastly, the constant block type (constant implementation commit) stores just one value on disk. This is useful for all-zero or all-one blocks, for example.

I found it interesting to realize that the main work of the encoder is to scan the input values and choose the optimal block type, whereas the actual encoding itself is cheap in comparison.

At this point, we can look at performance and see that the Go encoder is at 76% of the C encoder.

In all honesty, I could have probably stopped here, but now that the milestone of a viable replacement was reached, I got curious to see how far it would be possible to push the encoder (how much work to reach C speeds?) and afterwards, the decoder, too.

Setup

The microarchitecture level: set GOAMD64

The microarchitecture of a CPU determines which instructions it provides, and that includes not just SIMD instruction sets (like AVX2), but also other useful instructions like LZCNT (Leading Zero Count), which can be used to implement math/bits.Len32 more efficiently, which the TurboPFor encoder needs to call on every input value to determine the ideal bit width.

Let’s walk through how to set the microarchitecture level when using Go on 64-bit x86 (x86-64).

Go uses the GOARCH environment variable to configure the target compilation architecture, and I am using the value amd64 to select 64-bit x86 (AVX2 and AVX512 are instruction sets found on x86-64 CPUs). With GOARCH=amd64, the architecture-specific variable GOAMD64 configures the microarchitecture level for which to compile and Go 1.18 introduced these 4 different levels:

GOAMD64=v1 (default): The baseline.
Exclusively generates instructions that all 64-bit x86 processors can execute.

GOAMD64=v2: all v1 instructions,
plus CMPXCHG16B, LAHF, SAHF, POPCNT, SSE3, SSE4.1, SSE4.2, SSSE3.

GOAMD64=v3: all v2 instructions,
plus AVX, AVX2, BMI1, BMI2, F16C, FMA, LZCNT, MOVBE, OSXSAVE.

GOAMD64=v4: all v3 instructions,
plus AVX512F, AVX512BW, AVX512CD, AVX512DQ, AVX512VL.

In 2026, I generally recommend compiling with GOAMD64=v3 so that functions like bits.OnesCount8 are compiled into intrinsics (POPCNT) instead of using a lookup table.

For Intel CPUs, setting GOAMD64=v3 means your programs will only start on Haswell CPUs (2013) or newer; for AMD CPUs that means Zen 1 (2017) or newer.

In this specific case (DCS), I am even compiling with GOAMD64=v4. The v4 microarchitecture level requires AVX512, which means AMD Zen 4, Zen 5 or newer (Intel’s story is… complicated). Luckily, both my main development PC (Zen 5) and the Debian Code Search server (Zen 4) are recent enough. Setting GOAMD64=v4 has little effect on Go 1.27 itself: the only change is that maps use one less instruction (VPBROADCASTB instead of PSHUFB). But compiling with GOAMD64=v4 allows us to move one more feature check from runtime to compile time, see SIMD build tags.

It makes sense to set the microarchitecture level in your benchmark setup so that you don’t measure the slow fallback implementations. I use export GOAMD64=v4 in my Makefile.

Benchmarking setup

Go’s built-in testing package contains support for benchmarks which are written in functions of the form func BenchmarkXxx(b *testing.B). The simplest way to run such benchmarks is go test -bench=., but I ended up configuring a few convenience make targets, which write results to bench.txt and compare against baseline.txt (the previous commit’s results, usually), using the very useful benchstat tool.

GOTEST=go test

# -count=6 gives p≤0.002 in benchstat:
# https://pkg.go.dev/golang.org/x/perf/cmd/benchstat
BENCHFLAGS=-run=^$$ -bench=. -benchtime=200000x -count=6

# use taskset -c1 to always pin to the same single core,
# avoiding accidental scheduling on different cores on
# mixed-core CPUs like the Ryzen 9 9950X3D.
TASKSET=taskset -c 1
BENCH=$(TASKSET) $(GOTEST) $(BENCHFLAGS)

.PHONY: all test bench bench-baseline bench-relative

all: test

bench: test
       $(BENCH) | tee bench.txt
# Compares compression ratio between C and Go implementation
        benchstat -col /impl -row '/n /vals' -filter '-/impl:go-stream .unit:(encoded-bytes)' bench.txt
# Compares performance between C (cgo) and Go implementation
        benchstat -col /impl -row '/n /vals' -filter '.unit:(Mval/s)' bench.txt

bench-baseline: test
       $(BENCH) | tee baseline.txt

bench-relative: test
       $(BENCH) | tee bench.txt
       benchstat -filter '-/impl:go-stream .unit:(encoded-bytes)' baseline.txt bench.txt
       benchstat -filter '/impl:go .unit:(Mval/s)' baseline.txt bench.txt

The encoded-bytes and Mval/s units are custom metrics I am reporting from the various sub-benchmarks, which are arranged such that I can filter / report them with benchstat.

The main encoder (and decoder) benchmarks compare 3 different implementations (cgo, Go, Go with the StreamEncoder API) with a number of benchmark cases that are designed to cover the different block types and contain a similar mix of values as what we see in Debian Code Search:

// reportMetrics adds Mval/s and encoded-bytes metrics to all benchmarks.
func reportMetrics(b *testing.B, n int, nencoded int) {
   b.ReportMetric(float64(nencoded), "encoded-bytes")
   b.ReportMetric(float64(b.N*n)/1e6/b.Elapsed().Seconds(), "Mval/s")
}

// BenchmarkEncode/n=<N>/vals=<testcase>/impl=<c|go|go-stream>
//
// e.g. BenchmarkEncode/n=2048/vals=one-constant/impl=go-stream
func BenchmarkEncode(b *testing.B) {
   for _, tc := range allBenchCases() {
     n := len(tc.vals)
     b.Run(fmt.Sprintf("n=%d/vals=%s", n, tc.name), func(b *testing.B) {
       b.Run("impl=c", func(b *testing.B) {
         b.ReportAllocs()
         var encoded []byte
         buf := make([]byte, turbopfor.EncodingSize(n))
         for b.Loop() {
           encoded = turbopfor.P4nenc256v32Buf(buf, tc.vals)
         }
         reportMetrics(b, n, len(encoded))
       })
       b.Run("impl=go", func(b *testing.B) {
         b.ReportAllocs()
         var be BlockEncoder
         var encoded []byte
         buf := make([]byte, 0, turbopfor.EncodingSize(n))
         for b.Loop() {
           encoded = be.EncodeN(buf, tc.vals)
         }
         reportMetrics(b, n, len(encoded))
       })
       b.Run("impl=go-stream", func(b *testing.B) {
         b.ReportAllocs()
         var se StreamEncoder
         var encoded int
         for b.Loop() {
           encoded = 0
           for _, val := range tc.vals {
             if se.Add(val) {
               encoded += len(se.EncodeBlock())
             }
           }
           encoded += len(se.EncodeBlock())
         }
         reportMetrics(b, n, encoded)
       })
     })
   }
}

CPU counters: perf

Go has included excellent performance tooling for many years, see the “Profiling Go Programs” blog post (2011) for an example of how to use pprof, a sampling profiler. This profiler can help track down which part of a program runs slow, or where memory allocations happen.

Once you identified the slow part of a program, how do you know why it’s slow?

To learn more about the specific bottlenecks your program encounters, you can consult your CPU’s hardware performance counters. For example, you could check the branch predictor counters to see if your program is slow due to a high number of branch mispredicts.

On Linux, the perf tool is the best way to access the CPU hardware performance counters. A good starting point for working with perf is the documentation on “Top-down analysis with the perf tool”, which describes the optimization method that Intel established.

In my Makefile, I set up two perf targets:

# GOTEST and TASKSET like shown in the earlier benchmarking setup section:
GOTEST=go test -pgo=encode.cpuprof
TASKSET=taskset -c 1
PERFBENCHFLAGS=-test.bench='Encode/n=2048/vals=debian-mix/impl=go$$' -test.benchtime=200000x

# Use perf(1) to capture AMD IBS (the equivalent to Intel PEBS)
# PipelineL1 is roughly equivalent to Intel TopdownL1
perf:
       $(GOTEST) -c
       $(TASKSET) perf stat -M PipelineL1 ./pforenc.test -test.run=^$$ $(PERFBENCHFLAGS)
       sudo perf record -F 4999 -e ibs_op// --call-graph fp ./pforenc.test -test.run=^$$ $(PERFBENCHFLAGS)
       sudo chmod 644 perf.data

# 488281 iterations × 2048 values = 1.000e9 values, so counter/1e9 = per value.
perf-per-value:
       $(GOTEST) -c
       $(TASKSET) perf stat -x, -e cycles:u,instructions:u,branches:u,branch-misses:u ./pforenc.test -test.run=^$$ -test.bench='Encode/n=2048/vals=debian-mix/impl=go$$' -test.benchtime=488281x 2>&1 >/dev/null | awk -F, '{printf "%-16s %6.2f /val\n", $$3, $$1/1e9}'

The perf-per-value numbers are high level numbers that indicate how much work the implementation is doing. Reducing the number usually increases speed.

To see the counters for each instruction (and source code lines), I use make perf, followed by perf report. A quick shortcut is perf annotate, which directly shows the hottest function.

Optimizations (scalar)

Let’s first see how far we can get without reaching for SIMD instructions.

(The examples are not necessarily in commit order, but cherry-picked for clarity.)

Profile-Guided Optimization (PGO)

PGO stands for Profile-Guided Optimization and is a feature that Go introduced as a preview in Go 1.20 (released in February 2023) and shipped as ready for general production use in Go 1.21 (released in August 2023).

The idea is to capture a CPU profile that records where your program spends most of its CPU time, which you then provide to the Go compiler to give it more data to make better decisions.

Most importantly, this way the Go compiler can inline functions much more aggressively than its usual heuristics allow, which does have a measurably positive effect in my series of optimization commits. Another optimization that a PGO profile allows the compiler to do is conditional devirtualization — but our TurboPFor code does not use any interfaces.

My strategy is to enable PGO before doing any other optimizations, so that we have the full inlining budget available that PGO gives us, and can measure the effect of other commits clearly.

Surprisingly, turning on PGO actually decreases our performance (-13% geomean), but a closer investigation reveals that we just got unlucky. Let me explain.

Aside from inlining and conditional devirtualization, PGO also influences alignment: The Go compiler sets PCALIGNMAX(64, 31) on the first block of a loop (the “loop body”) for all loops in hot functions (per the PGO profile), i.e. Go will insert up to 31 bytes of padding to make the block land on a 64-byte boundary. Documentation like AMD’s “Software Optimization Guide for the AMD Zen5 Microarchitecture” (2024, #58455) explicitly recommends aligning hot loops that way:

[…] for hot loops, some further knowledge of trade-offs can be helpful. Because the processor can read an aligned 64-byte fetch block every cycle, it is suggested to either align the start of the loop to the beginning of a 64-byte cache line […]

Indeed, when compiling with -gcflags=all=-d=alignhot=0 to disable the alignment, performance remains as good as without PGO. How can the padding hurt more than help? The answer is: It’s not the padding itself! It’s a side-effect of the padding moving instructions to different addresses.

In the unlucky arrangement, a macro-fused CMPQ+JGE instruction pair now ends up exactly on a 32-byte boundary. However, the Go compiler ensures fused branch sequences must never cross or end at a 32-byte boundary to fix Intel erratum SKX102 (discussion: Go issue #35881) by inserting NOPs.

This NOP padding, unlike the loop alignment padding, is not free; these extra instructions slow down our otherwise dispatch-bound loops.

Because the commits after the PGO enabling commit change the code, this unlucky situation is avoided for the rest of the optimization series (by chance).

Reducing memory allocations

Memory allocations are quite expensive, at least in comparison to encoding/decoding integers, so I followed my usual strategy of first reducing memory allocations as much as possible.

In my goturbopfor teaching decoder, whenever the code needed a scratch buffer, it would allocate it right then and there with make():

// p4dec32 decodes one block of TurboPFor-encoded 32 bit ints
func (d *decoder) p4dec32(input []byte, output []uint32) (read int) {
    // …
  switch blockType {
  case blockBitpackingExceptions:
    bx, input := input[0], input[1:]
    n := len(output)

    exmap := input
    nex := 0 // number of exceptions
    for i := 0; i < n; i++ {
      if exmap[i/8]&(1<<uint(i%8)) != 0 {
        nex++
      }
    }
    input = input[(n+7)/8:]

    exceptions := make([]uint32, nex)
    input = input[bitunpack32(input, exceptions, bx):]
    input = input[d.bitunpack(input, output, b):]

    for i := 0; i < n; i++ {
      if exmap[i/8]&(1<<uint(i%8)) != 0 {
        output[i] += exceptions[0] << b
        exceptions = exceptions[1:]
      }
    }

    return before - len(input)
  }
}