fidzu : debian

I was reading a post on alexwlchan's blog¹ that referenced the concept of digital gardens, a concept/analogy for organising information which dates back to the 90s. This old concept is getting new traction today by contrasting the approach with "endless stream" as used and abused by social media, but also how blogs are typically presented.

This site, my homepage, has a blog, and that's the bit that most people who interact with the site will experience. Partly, because it's the bit that gets syndicated out: via feeds; on Planet Debian and downstream from it; once upon a time on Twitter; nowadays on the Fediverse.

However there's more to my homepage than that. The rest of it may be of little interest to anyone beside me, but it's useful to me, at least. So I may switch focus a little bit from mainly writing blog posts, and tend to the rest of the garden a bit more.

Some recent seeding and pruning: Recently my guest status at Newcastle University came up for renewal, so I wrote down my goals in the Historic Computing Committee for the next year or so, and put them here: nuhcc. I've also been pondering what I'm up to in Debian at the moment, so took some time to add my current projects to that page.

25 Mar 2026 11:20am GMT

At the end of last year I uninstalled the Twitter app on my phone.

In the past Twitter used to be very useful for providing feedback to large organisations. I had responses from supermarkets, chain restaurants, online stores, major computer companies, and even the IT department of a court. In recent times I have had less responses from corporations which significantly reduces the value of Twitter to me and to many other users. It seems that Elon's management style has discouraged not only advertising but also all forms of corporate interaction. Messing up the check mark on accounts to make it harder to work out which is a real corporate

Since Elon bought it Twitter has been increasingly pushing conservative Tweets and has done little to stop bot accounts. The incidence of useful discussions has steadily decreased. I know people who have quit Twitter entirely due to opposition to Elon, I am not doing that. I finally decided to stop using Twitter in any serious way when the notifications on my phone about popular Tweets started only being about Tweets from conservative influencers and Elon. This was obviously not any algorithm based on Tweets I was liking, it was based on political decisions. I didn't uninstall the app due to political disagreement, I uninstalled it because it was through deliberate design promoting material that any algorithm would know was something I wouldn't either like or "like".

I still announce new blog posts on Twitter for my 198 followers at the same time as announcing them on Mastodon and Facebook. I get the most reactions to such announcements on Mastodon, the second most on Facebook, and hardly any on Twitter. I'm wondering how long it will be worth announcing blog posts on Twitter or whether I should stop now.

I am sure that many other people are making similar decisions and this is going to affect Twitter overall.

The web site www.russellcoker.com has information on all the ways of following me.

25 Mar 2026 5:21am GMT

AI sure is a hot topic right now, and I see a lot of people arguing about it. To a lot of people around here, I'm the "computer person" they know and I get asked a lot about AI.

I'm going to suggest a lot of things can be true at once. For instance:

• LLMs are changing how we work and will continue to do so.
• LLMs are vastly over-hyped by vested interests, and may be in a bubble.

Or how about:

• Huge investment in GenAI is having many negative consequences, ranging from environmental to causing affordability problems in many industries that use hardware (ie, everywhere)
• Useful results can be had from models that run on local hardware, even battery-powered hardware, which may have negligible harm or even some benefit

And:

• GenAI is further concentrating wealth and power in megacorps, with the effect of squeezing out the smaller players even more.
• GenAI is lowering the cost of entry for people without a lot of resources already.

I have sympathy for the naysayers; those that say it's nothing but a stochastic parrot. But I don't have a lot of sympathy for the naysayers that deny ever using it; you can't form a credible argument against something without having an understanding of it informed by experience.

I also have sympathy for the cheerleaders. I have seen some impressive things from AI; for instance, a story from an engineer who has a child with a rare disease without a credible cure. The engineer did a lot of research on it, started feeding research papers into AI to analyze, and the AI started finding correlations between different areas of research that humans hadn't yet found - leading to a positive result for the child.

To be fair, I have rarely seen an AI deliver a 100% correct answer on anything with any real level of complexity. I have seen it both waste more time than it saves, and save a ton of time.

My point here is: It is neither always fantastic nor always terrible.

Let me talk you through an example.

I am a fan of inbox zero for email. That is, the inbox should be empty. Unfortunately, mine has 8000 messages in it. According to the oldest messages in my inbox, I last had inbox zero 8 years ago. But really, only a handful are older than 2020. I guess something must have happened that year…

I've been chipping away at this for quite some time now. The problem is, there are certain emails in there that really do still need some action - maybe it's photos to save off into our photo collection, for instance. But when looking at things sorted by date or thread, there are old shipping confirmations next to phishing attempts and family photos. One can't just scan down the list.

I've tried all the usual tricks, most of which involve selecting groups of message that are easy to bulk erase, or at least easy to scan visually for the occasional thing worth saving. Sort by sender or subject line, for instance. Then I can, for instance, delete all the old messages from the shopping sites I commonly use all at once. But then they start using different senders and different subject lines and that doesn't get all of them. I've tried keyword searches for this sort of thing too. Still, that got me down to about 8000 messages.

So I thought: why not see if an LLM could help me classify these? Maybe it could categorize them, and then I could look at emails grouped by category.

I have one machine with a discrete GPU, an Nvidia RTX 4070. It's a desktop machine I don't use all that often. But I set up Ollama on it, running in a Docker container. Ollama runs models locally.

I should also mention at this point that we are solar-powered, and this time of year is a time of peak production of excess solar, because it is sunny and not much heat or AC is required. So that machine is solar-powered and isn't causing environmental harm. In any case, charging the EV uses much more power than that GPU.

I figured I would do this in two passes. First, ask the LLM to classify each message (or a sampling of them would probably work too), letting it pick its own categories for each. Then, look at the patterns that emerge and give it a single, much smaller, set of broad categories to use and rerun it over that.

Then I can easily select messages from my Maildirs by category and process them in bulk.

I used open-interpreter pointing to that GPU on my network to help me write the scripts for this. It didn't get things right on its own; for instance, it didn't call the Ollama API correctly, and insisted on appending "/cur" to the path to the Maildir (which was not going to fly with Python's maildir module). It took roughly an hour to classify those 8000 messages (or, as I had it do, the first 2000 characters of them), and then the same to do it a second time. I had it output lines in the form of "filename\tcategory" and hand-wrote the shell script that processed those.

In the end, was it useful? Yes, quite. Its classifications weren't perfect (and it didn't even follow my prompt perfectly; sometimes it would give me a long discussion on why it picked a certain category rather than just that category, and occasionally it picked categories not on the list). But then, neither were my manual keyword searches. So far I've gotten rid of nearly 1000 more messages. Several categories were a "visual scan for sanity and then delete all" sort of thing.

My emails never left my network. I didn't rely on a cloud AI to process them. I didn't contribute to global warming (this may have even been a case of saving energy, since it no doubt will offset quite a bit of manual time that would keep screens and room lights energized and so forth). I used about as much energy as watching a movie on a TV.

Did it complete the task for me entirely autonomously? Also no. AI isn't a mind reader and it can't possibly evaluate exactly what my thought process would be for a given task. But it can do a decent enough job to save me some time.

Still, this didn't require hyperscaler datacenters. AI even runs on-phone (Google Translate being one of the most useful AI-driven apps I've ever seen, and it can run on-device).

25 Mar 2026 4:12am GMT