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Building a family of single-user services

2026-08-27T00:00:00Z

Last month I made and wrote about a personal service to track my workouts and record personal bests. Since then, things have escalated. I now have eight different services running in the same “family”:

Name	Purpose
Collegiate Room	Simple weather overview for my current location
German Mall	LLM broker for use by other services
Gloss White	Weight tracking
Olive Crosses	Central service hub
Pompei Band	Workout and personal best tracking
Speed Carp	Spaced repetition system
Tiger Island	Reading history and library tracking
Typical Horse	Location history and analytics

The names are all anagrams of something related to their function. Finding those anagrams brings me an unreasonable amount of joy. Unscrambling them is an exercise left to the reader. On top of those eight, I currently have four other service ideas sat in my todo list. It’s going to be a big family.

A postmortem on the previous hodge-podge

Most of this functionality isn’t new to me. When I started out playing around with LLMs, I created a “personal data aggregator” that just kept getting things bolted on. I said at the time:

My biggest just-for-me project is a web app that I started to help me aggregate film recommendations. It’s since morphed into a general personal data aggregation service: it deals with data from GitHub, Todoist, Letterboxd, TMDB, Healthkit, and others. It also lets me make re-orderable lists, store recipes, and more. Parts of this could definitely be open sourced, and I might carve them out at some point, but it’s mostly a glorious hodge-podge of things specific to me. Having all these services in one place lets me make quick and dirty automations, for example: when I create a Todoist note on my phone or watch, I often forget to set the due date, so it doesn’t show up in the “Today” view. It was literally a few lines of code to plumb things together so any inbox task without a due date gets set to today automatically.

In a twist that nobody could see coming, it turns out that a “glorious hodge-podge” is actually quite hard to maintain and very annoying to work with. It didn’t help that it had a fairly extensive Svelte frontend and I really am not a fan of JavaScript. I ended up using it less and less just because it wasn’t fun to use or work on, and I didn’t want to waste the limited LLM tokens on doing massive refactors or reworks.

I’ve got almost two decades of professional software development experience under my belt, so you’d think I would have learnt the lessons on how to write maintainable software. Hell, my CV even mentions that as something I’m particularly good at. So what went wrong?

There were a few factors. One of the obvious things to blame is that it was written mostly with LLMs. Most of that blame isn’t very honest, though. In the year or so since, LLMs have definitely got better at writing good code; but I was manually reviewing all the code at the time, and telling it quite carefully what to implement and how to do it. So the buck really stops with me.

I think it’s actually more because it was my first larger project using LLMs. It’s a bit like learning a new programming language. I don’t think the first sizable project I’ve written in any language has ever been any good. You can read and learn as much as you want, but you need to actually run into some problems headfirst to get a grip on how the language works. Or at least I do. LLM coding agents are basically the same deal.

What the LLMs actually did was mask several problems. The architecture wasn’t right for how the service developed. It started out as a single-purpose app and gradually got things bolted on, without any major changes. Everything ended up in horizontal slices: a massive database layer with every query used by every part of the app, a massive HTTP layer, and so on. Working on a single feature became a tap dance all over the source tree. This sort of friction is normally quite apparent when you’re writing code by hand, and at some point it hurts enough that you throw in the towel and do some refactoring. But the LLM will happily plod on without exposing that pain, so the project needed a more active kind of architecture management that it just didn’t get.

The other problem they covered up was how brittle and unmaintainable the JavaScript frontend was. I’m not even sure how I ended up with a Svelte frontend in hindsight. It’s my preferred framework, but I still only reach for it as a last resort when there’s no other way to avoid using one. It seems like there were lots of ways to avoid it here, but I seem to have… well… avoided them. Anyway, each feature got its own slew of Svelte components, without much thought to reusability, shared patterns, or anything sensible. Some of them were just thin wrappers around data supplied by Go, others had vast swathes of logic in them, with no real rhyme or reason as to which way that would fall. It’s the same issue as before: a human would look at it, say “WTF?” and suggest tidying it up; instead, the LLM just gets on with what you tell it to do.

There’s also a big non-LLM factor: one of the primary reasons all of this unrelated functionality got tossed into the same app was that I’d previously worked on a few projects with a microservice architecture and really didn’t like it. My conclusion was they only really worked in large environments where you can dedicate people or teams to working on all the boilerplate that happens from maintaining lots of interconnected projects. Dumping everything and the kitchen sink into one badly architected monolith was, perhaps, not the best rebound from that.

Pompei Band and beyond

When I wanted to implement ~~Strava at home~~ my PB tracking app, it seemed fairly natural to build it out as a new service, rather than bolt it on to the slowly decomposing hodge-podge that was giving off a funny smell. Then when I wanted a weather dashboard, why not do it in the same style as Pompei Band? The same serving architecture, the same page layout… I could even come up with a similar weird two-word name!

Then wouldn’t it be nice if the weather dashboard knew where I was, so it could show the local weather? I could just build that functionality in, but it’d also be a good opportunity to carve out the location history feature from my old service and make a new, shinier one. Then basically every time I even thought about going near the old service, I mentally sketched out a replacement service.

Olive Crosses showing links to the other services

At one point I had the thought that it’d be nice if the services could all link to one another, but I didn’t want to have to touch every service whenever I added a new one. So I made a “hub” service, which maintains the One True Catalogue, and provides a bit of JavaScript the others can all embed to add a navigation sidebar.

So after complaining about microservices and JavaScript, it sounds like I have landed on microservices (or at least miniservices) and JavaScript? Sort of yes, but sort of no. Each service is still its own thing and makes sense independent of the others. There are some cross-service dependencies, but they’re very simple and don’t have the same cognitive or boilerplate overhead as a login service that talks to an authentication service that queries a user manager and so on. And as for the JavaScript, it’s almost exclusively used for progressive enhancement and a few limited client-side visual things like maps and graphs. All of the logic lives in Go, which is a lot easier to maintain and test, and all of the pages are simple Go templates.

Having these services built in a way that I actually like has motivated me to play around a lot more with new features:

My workout tracking app pulls photos from my private Immich server that were taken during workouts, and shows them alongside. Now I can see all my obligatory end-of-run selfies in one place.
The location history app can detect trips away from home, and get an LLM to automatically title them. My most recent, very exotic, trip has the title “A fortnight in Milton Keynes and Coventry”. Each trip gets its own little map, and in the future I want to pull in images like with workouts.
My weight tracking app now exports weigh-ins to Garmin, as it doesn’t fully integrate with Apple Health (it will export its own weights, but not import others, like the ones from my Withings scales). Stupid walled gardens, but thankfully I can fix it myself.
My book tracking app automatically cleans up the titles of books from the nonsense provided by Amazon to something more palatable. “Killing Eve: Long Shot: A twist on obsession, betrayal and revenge from Luke Jennings, author of the TV sensation Killing Eve” becomes “Long Shot (Killing Eve #5)”. It does this by passing the title to an LLM with a bunch of rules.
The hub generates me a snippet of Tailscale configuration that defines the minimal grants needed to allow the intra-service dependencies to work.

The LLM integrations were inspired by my friend Shane who recently demonstrated an inventory tracking app he’d made, which used Haiku to process images of boxes and describe the contents. I’ve dabbled with LLM integration before, and always disliked the process of fiddling with the prompts, and debugging when the LLM gave a nonsensical response. So I built German Mall, which makes it a lot easier. It lets me define a “workflow” consisting of a system prompt, initial message, and expected response format. I can browse past runs, tweak the prompt, rerun them to see the change, all without touching the services using them. Quick iteration loops are the best. German Mall also provides markdown documentation for each workflow, so integrating it downstream is simply a case of pointing a coding agent at the right documentation endpoint.

The “tidy book titles” workflow in German Mall

It’s not all fun and games: having a bunch of services all following the same pattern means you need to do cross-cutting maintenance on them now and then. But that’s surprisingly easy with LLMs. I put all the projects in a common folder, and I launch cross-cutting tasks from that folder. Usually I’ll make the change in one project, and then the cross-cutting task is simply “Look at commit XYZ in ./foo, make the same change to the other projects”.

I’ve also ended up with a shared library used by all the projects to cut down on some boilerplate. That’s normally something I’d resist, especially with personal projects, just because the extra overhead of working in a separate project, managing releases of it, and so on, is a bit annoying. But, again, LLMs can do almost all of that grunt work. They effectively reduce the “cost” of maintaining separate services to a negligible level, leaving me to focus on the more fun aspects like coming up with new names.

An interesting Tailscale + Docker gotcha

2026-01-29T00:00:00Z

As I’ve written about before, I use Tailscale for a lot of things. I thought I had it set up in a reasonably secure manner, but I recently noticed a problem.

I use Tailscale’s ACLs to limit what each node can access, based on the tags I apply to it. So an app node can’t access anything via Tailscale, while an integration or server node can access things tagged with either app or integration. This is expressed pretty simply in the Tailscale ACL JSON:

{
	"tagOwners": {
		// Servers that can be SSH'd into
		"tag:server": [],
		// Applications that are exposed on tailscale but never connect out
		"tag:app": [],
		// Things which talk to other services over tailscale (connecting to apps etc)
		"tag:integration": [],
	},
	"grants": [
		{
			// Users can access everything
			"src": ["autogroup:member"],
			"dst": ["*"],
			"ip":  ["*"],
		},
		{
			// Servers and integrations can access integrations and apps
			"src": ["tag:server", "tag:integration"],
			"dst": ["tag:integration", "tag:app"],
			"ip":  ["*"],
		},
	],
}

Over the past week I’ve been setting up a private Forgejo instance behind Tailscale, complete with an actions runner that runs things using a docker-in-docker container. I didn’t want the runner knowing anything about Tailscale, so I had it configured to speak to Forgejo direct over HTTP (forgejo:3000) instead of using full Tailscale HTTPS URL that I use when accessing it (http://git.example-net.ts.net/)¹.

Everything was going fine, until I forgot to do that translation… and it worked. My Forgejo action runners could access anything on my tailnet. I run several things on Tailscale that just have authentication turned off, on the basis that only authorised devices can access them. Things like the admin interface for this website. I definitely didn’t intend for any workflow I run on my git server to have access to edit my website!

This wasn’t just limited to Forgejo, either. Any docker container I was running could access the tailnet. In hindsight it’s fairly obvious why: the host is running Tailscale, connected as a node tagged with my server tag. That creates a tailscale0 interface, and automagically sets up iptables rules to route Tailscale traffic over the interface. Docker also automagically sets up iptables rules to bridge traffic, and apparently these two sets of rules interact in such a way that traffic from Docker containers is allowed to route via the tailscale0 interface.

I say it’s fairly obvious in hindsight — there’s no reason why Docker would special case any particular host interface after all — but it still feels pretty surprising. Because both bits of software inject their own iptables rules, I never really had a good mental model for how they interact. The host Tailscale node was a completely separate building block to Docker. It would be a pain to use either of them if they didn’t do these rules, but it’s also one of the reasons I don’t really like “magical” things².

There are lots of ways to fix this, but none of them feel particularly great. You can configure both Tailscale and Docker to not automatically fiddle with iptables and handle the rules yourself, but I really hate dealing with iptables³. As a stopgap I did hold my nose and add some iptables rules to drop traffic to the tailscale0 interface if it originated from the IP ranges that Docker was configured to use:

iptables -I DOCKER-USER -s 192.168.0.0/16 -o tailscale0 -j DROP
iptables -I DOCKER-USER -s 172.17.0.0/12 -o tailscale0 -j DROP

The DOCKER-USER chain is a nice little escape hatch; it comes before the main auto-generated DOCKER chain, and Docker leaves the rules in it alone.

So I added these rules, and felt pretty good about myself, and then… everything started breaking in weird ways. After some debugging I realised the problem was DNS⁴. The server’s DNS resolver is 100.100.100.100, a special Tailscale address. This is so that it can resolve tailnet hostnames via MagicDNS (and so I don’t have to configure my custom DNS servers manually on each device; Tailscale does it for me). My new iptables rules inadvertently dropped all the DNS packets coming from docker containers. D’oh.

Obviously the solution here is to double down and add MORE iptables rules:

iptables -I DOCKER-USER -s 192.168.0.0/16 -d 100.100.100.100 -o tailscale0 -j ACCEPT
iptables -I DOCKER-USER -s 172.17.0.0/12 -d 100.100.100.100 -o tailscale0 -j ACCEPT

This got DNS working again, but it all feels a bit gross. Not least because to actually make these persist you need to use iptables-save, and then you also get all the rules that both Docker and Tailscale have inserted. I came across a script to filter out the Docker ones, but… yuck⁵.

I realised a cleaner idea might just be to lock down what server tagged nodes could access within Tailscale itself. That way I can avoid touching iptables at all. It’s handy to be able to curl services from the host when debugging, but it’s not really necessary. So I removed that access, and… stuff broke again. I’m using my Forgejo instance as a registry for some of the docker images I run, so the docker daemon needs to be able to reach it. I ended up making a new tag for infrastructure, which can be accessed from server devices. This does still allow all the docker containers to reach Forgejo, but I already have it set up with appropriate access controls and public/private repository splits. Forgejo is a service designed to run publicly, so this seems a reasonable trade-off for convenience. I used the tests feature of Tailscale’s ACL config to make sure I’d got the rules right:

{
	"tests": [
		{
			// Servers can only access infrastructure
			"src":   "tag:server",
			"proto": "tcp",
			"allow": [
				"tag:infrastructure:8080",
			],
			"deny": [
				"100.84.16.43:8080",
				"me@example.com:8080",
				"tag:server:8080",
				"tag:app:8080",
				"tag:integration:8080",
			],
		},
	],
}

The whole situation still feels a bit messy. If I ever get around to switching to nftables I might loop back and manually craft some rules for routing traffic, instead of leaving Tailscale and Docker to do their own thing.

I discovered later on that logging into the container registry at forgejo:3000 actually issued a redirect to https://git.example-net.ts.net/ so this was all basically for naught… ↩︎
I didn’t use Tailscale’s MagicDNS for a long time just because the word “magic” put me off. Only when I eventually got around to learning how it worked, and seeing that it wasn’t really that magical under the hood, did I change my mind. ↩︎
nftables seems far better in lots of ways, but I can’t really be bothered migrating. Maybe next time I reimage the server for whatever reason… ↩︎
It’s always DNS… ↩︎
Again, nftables would almost certainly help here. It actually has (gasp) configuration files. But again, I really didn’t want to spend the time migrating. ↩︎

How tech companies failed to build the Star Trek computer

2025-07-16T00:00:00Z

A computer core room on the Enterprise-D

In most Star Trek series, the ship or station computer is ever-present in the background, waiting to be called on by the main characters¹. It nearly always does exactly the right thing, and there’s little limit to the functions it can perform. Take this mundane example from DS9:

KIRA: Computer, establish link with the Bajoran Medical Index for the Northwestern District.
COMPUTER: Link established.
KIRA: Access all information on Doctor Surmak Ren.
COMPUTER: There are no records matching that name.
KIRA: Try the Northeastern District, same search.
COMPUTER: Doctor Surmak Ren, currently serving as Chief Administrator of the Ilvian Medical Complex.
KIRA: Computer, open a channel to the Ilvian Medical Complex. Administrator’s office.

The computer is doing some kind of networking to a database only identified by name. It does a search and summarises the lack of results. It then repeats the process with another database, and succinctly announces the results. Finally, it opens a communication channel to a specific room in a facility, based only on its name.

This whole interaction is remarkably boring². Kira doesn’t have to know any URLs or API endpoints, or what protocol she wants to use. She doesn’t have to open a specific app and then login and then try the query again. She just says what she wants and the computer does it.

It seems like this should be one of the most easily obtainable bits of sci-fi wizardry with our current technology. We have multiple massive companies throwing lots of money at digital assistants, LLMs that are improving at an insane rate, but we’re somehow not even close to the usability or usefulness of the Trek computers. What gives?

Boring is, well, boring.

Larry Page once said something that might help explain it:

The Star Trek computer doesn’t seem that interesting. They ask it random questions, it thinks for a while. I think we can do better than that.

This is the same Larry Page that founded Google, whose mission statement is “to organize the world’s information and make it universally accessible and useful”. Of all people, surely he should find an omnipresent computer that can answer ‘random questions’ interesting?! It seems like it should be the epitome of Google’s mission!

Google’s “better than that” seems to have been to stuff LLMs into every product they can, even when you don’t want them there. Even when they’re worse than the normal content they displace. These things look exciting when they’re part of a scripted demo at Google I/O, but they fall flat and just get in the way when they’re exposed to the reality of day-to-day use.

The Star Trek computer is the opposite: it isn’t snazzy, but it is genuinely useful. That means it’s not an attractive target for the company execs who want marketing opportunities, and it’s not appealing for engineers who need to demonstrate “impact”. But even if Google did try to make the Trek computer, there are other problems…

Assistants need to be free

A significant amount of tech companies’ business models currently revolves around trapping users in walled gardens. They want you using their ecosystem; that way they get more data from you, and you’re more likely to spend more money on their other offerings that work together. There’s barely any incentive to allow any kind of interoperability with other platforms outside carefully contracted integrations.

I remember trying to help a family member move their photos from iCloud to Google Photos. At one point they turned around and said, exasperated, “why is this so hard? Aren’t they both in the cloud?!”. It’s easy to dismiss that as someone who hasn’t quite grasped the fundamental idea that “the cloud” is just someone else’s computers, but that’s not the whole story. There’s no reason why there shouldn’t be a quick and easy transfer: both services already allow uploading and downloading, there’s just no incentive for the companies involved to make it so³.

These kinds of misaligned incentives and walled garden business models cause even more problems when it comes to digital assistants. Siri is basically never going to be able to interact with, say, your Google Drive; ~~Bard~~ Gemini is never going to be able to send a message via iMessage. Even when there are appropriately blessed interactions, they’re so clunky. Can you imagine Captain Picard saying “Computer, ask the turbolift skill to take me to deck 5”?

Someone else’s computer

Software issues aside, there’s still a key difference between the Star Trek computers and our current batch of digital assistants: where they run. The Trek computers are all housed within the ship or station they serve; they can connect elsewhere to gather information, but they run entirely independently. If they go wrong, a local engineer can go in and fix things. While some of our assistants may have physical hardware in your home, they don’t work without a vast cloud apparatus behind them. If your Internet connection fails, they become paperweights. If the company running them decide to remove some functionality you depend on, you have no recourse.

That kind of helplessness isn’t limited to assistants, either. There’s a rapidly growing trend of being unable to modify or repair hardware you fully own and control. Part of this is just that they’re becoming more complex: it’s a lot harder to replace a microchip than a gear, but companies are also going out of their way to make it more difficult for users through draconian DRM regimes⁴ and aggressive intellectual property enforcement. If the US Navy can’t repair their own equipment because a corporation says so, what hope do consumers have?

We’re approaching a point where you don’t actually own anything. Software is cloud and subscription based, hardware is unrepairable. Even cars can be remotely updated and have features added or removed. The Federation wouldn’t allow a third party control over their ships⁵, so why are we so happy to put up with it in everything we consume?

A small ray of hope?

The most promising way of tackling all of these problems is through legislation. The EU’s Digital Market Act is an attempt to force ‘gatekeepers’ like Google, Apple and Meta, to allow third-party access to their services. It seems like a pretty reasonable approach, but the tech companies are unsurprisingly resisting it. Apple in particular have gone out of their way to refuse to comply, and when forced to do so have limited the functionality to people in Europe. Still, the DMA is a promising start, and if similar legislation is introduced (and robustly enforced) elsewhere it might start forcing companies to behave a bit better.

There are also smaller companies that actually do the right thing. Framework make laptops that are user-serviceable; Fairphone do the same for mobile phones. Smaller software companies provide useful, open APIs. The average person on the street will probably have never heard of these, unfortunately, but they do still exist. Maybe as the bigger tech companies tighten the screws more, people will turn to alternatives like this? Or maybe we’ll just keep accepting that our computers work for everyone but us?

Unless, of course, the computer is playing the role of the episode’s MacGuffin and has contracted space-computer-COVID or something, then it’s a lot less in-the-background. ↩︎
It’s almost like it only exists to move the plot along. ↩︎
You can generally export your data, thanks to a combination of legislation and efforts like Google’s “Data Liberation Front”, but I’ve never seen an export format that could then just be imported into an equivalent commercial product. ↩︎
Oh, you’ve changed the screen on your iPhone? Better hope it can do the secret handshake with the Apple hardware. ↩︎
I think there might actually have been an episode where that did in fact happen. We’ll just ignore that as a plot contrivance. ↩︎

The Ethics of LLMs

2025-06-22T00:00:00Z

I’ve written about LLMs a few times recently, carefully dodging the issue of ethics each time. I didn’t want to bog down the other posts with it, and I wanted some time to think over the issues. Now I’ve had time to think, it’s time to remove my head from the sand. There are a lot of different angles to consider, and a lot of it is more nuanced than is often presented. It’s not all doom and gloom, and it’s also not the most amazing thing since sliced bread. Who would have thought?

It’s worth noting that I’m just setting out my position here. I’m not trying to convince you to change your mind. To set the scene a bit, I mainly use Claude Code as a programming tool. I use the Claude chat interface sometimes to proofread things, do random one-off data analysis, or help organise things. More rarely I’ll try to use it to brainstorm things, or recommend things, or do more “creative” things, but I don’t trust it enough in those domains to do it often. I don’t use it for research or as a Google replacement¹, which I recognise probably makes me a weird half-in-the-water, half-out-of-it class of user.

Copyright & Corporate Control

One of the key issues, and something that is being prosecuted in several court cases right now, is how LLMs interact with the copyright system. And by “interact with” I mean “run roughshod all over”. It seems pretty obvious from my lay perspective that if having 10 seconds of pop music in the background of a YouTube video is copyright infringement, then Meta pirating books via BitTorrent must also be.

That said, the entire copyright system as it exists now is fundamentally broken. I don’t mind the idea of individual creators having protection, but I absolutely hate how copyright is wielded as a blunt instrument by massive corporations to intimidate individuals and to try to earn more money. If a law enables a huge conglomerate to sue the author of an open source project for a theoretical 75 trillion dollars then that is a bad law. Copyright today has been twisted well beyond what it was meant to be originally, thanks to lobbying by people and businesses with lots of money that want to keep it.

So if copyright law is not fit for purpose, are LLMs fine? Well, not quite. The argument that LLM training is a bit like a child learning to read holds some merit, but it falls flat when LLMs can regurgitate the input material verbatim². Personally I don’t care if they steal all the content of the New York Times and all the Disney characters that are still in copyright. It’s the individual harm that bothers me: the struggling author whose words are slurped up by the LLMs and regurgitated, potentially costing them readers; the open source dev who chooses a copyleft licence on their work, only for an LLM to dump complete copies of functions in someone else’s codebase with no attribution or licence, stopping any future improvements from being released for the public good.

I can mostly mitigate those concerns in my personal use. When coding I generally direct the agent closely enough that the opportunity to drop in massive chunks of someone else’s code is negligible. I don’t use LLMs to generate big chunks of written content. Overall I’m reasonably happy that I’m not making anyone worse off.

One related area that doesn’t affect me much because of my limited use, but does concern me a lot, is corporate control. Most of the frontier LLMs are built by big tech companies. As more and more people are using LLMs, they become an avenue for control over information. It’s clear the people in charge should not be trusted with that responsibility, as Grok’s whole Boer War thing demonstrated. I’m not sure what the answer is, though. Would a government built LLM be any better? Probably not. The requirements to actually do the training are so high that the only people who can do it are the ones you wouldn’t want to.

So I’m left in the uncomfortable position of using tools by companies I don’t trust, trained on data they shouldn’t have been trained on. Still, there can’t be many more ethical issues, right?

Forced Features & Flattery

Before we get onto the more obvious ethical topics, let’s go on a brief detour so I can complain about how LLMs are being crowbarred into seemingly EVERY SINGLE SERVICE. I don’t want an LLM in my text editor. Or when shopping. Or taking screenshots of everything my computer does every few seconds. I don’t want LLMs anywhere outside a clearly-defined LLM box. With these forced integrations, you’re even more in the dark about what their prompts are, what information they have access to, what underlying model they’re using, and so on.

There’s no good reason for 90% of these implementations; there’s just some weird Silicon Valley hype train that everyone is scared to miss out on. And as a result we get more and more screen real estate taken up with things with animated gradients that can’t be turned off and are prompted to talk in an insufferable manner.

The problem isn’t just that they’re everywhere, though, it’s how they behave when they’re there. The overly sycophantic behaviour that most LLMs adopt can hook users in the same way a social media app can fiddle with its algorithms to encourage people to keep scrolling. It’s a lot harder to dismiss an LLM when it’s blowing smoke up your ass. Now I don’t believe this behaviour is actually intentionally nefarious, but more of an artifact of the models overfitting on user approval instead of something useful like accuracy.

The combination of going too far to please the user and hallucinating things is incredibly problematic. How are ordinary, non-tech savvy, people meant to deal with these models being forced in front of them? If you ask a question and get back a flattering answer that’s roughly the shape of what you were expecting, would you question it? Would a student, who was doing their homework in Google Docs when an LLM inserted itself into their lives question it?

This isn’t just a problem for naive users, either: I occasionally ask Claude Code “Can we do X?” and discover much later that the answer should either be an emphatic “NO”, or at least have a page of caveats attached. But the LLM wouldn’t want to displease me, so it tries to do what’s asked of it, with all kinds of weird hacks, and ultimately gets itself tied up in knots. This is not how a tool should behave. If I wanted someone to say “Yes, and” repeatedly I’d join an improv troupe.

Environmental Expenses

The biggest ethical issue with LLMs is their environmental impact. Are we adding more fuel to the dumpster fire that is our planet, just so my phone can inaccurately summarise news headlines for me? The answer is a resounding “maybe”. The companies making the LLM models are cagey about how much energy goes into making them and then answering queries. There are estimates for the latter, but there are several orders of magnitude between the most optimistic and most pessimistic. LLMs are definitely not good for the environment. Basically no computer activity is³. But you don’t see people campaigning to shutter YouTube (for example) because of its environmental impact. I struggle to see the difference.

I’m not saying considering the environmental impact isn’t important. We are killing the planet. We are not doing enough about it. More attention to the climate crisis in general is a good thing. But even with the most pessimistic estimates on power usage, my personal LLM usage is a drop in the ocean. I can have far more impact by reducing the amount I travel, avoiding red meat, and so on.

One issue I do have, though, is the forced integrations I mentioned. I’m happy with my personal usage. I’m not happy that a whole slew of LLMs are being operated on my behalf. How much power is wasted by Google generating LLM results in every search results page, versus how much benefit it provides? How do people know if a search box will just use a few Watts doing a database search, or burn kilowatts⁴ spinning up an LLM and filling its context window?

Then there’s the issue with training. Who knows what resources go into training these models? Whether the amortised cost of that across all its users is worth it? I think the answer to the first question is “only the tech companies making the models”, and their utter silence on the second question says volumes.

But this kind of industrial power isn’t unique to LLMs. There are many industries that do far worse. And that’s not a justification, but I think it makes sense to look at them more holistically. In an ideal world, where we have functioning governments that listen to scientists⁵, we could address all of this with legislation on emissions and clean power. Then all of these industries will either manage to function in a manner compatible with keeping the planet alive, or will have to stop. I appreciate that actually legislating that is about as likely as waking up one day to find Claude has become an AGI, though.

Slop & Survival

We’re nearly done, I promise. You can see why I didn’t want to try to tackle these issues in an earlier post! The last big issue I want to talk about is slop. It feels like it’s everywhere, and still getting worse.

The Internet has slowly morphed from a wonderful place full of individual, quirky sites, into a desert of bland corporate silos, and now into a wasteland of AI slop. It’s becoming more and more difficult to find original information. There’s this conspiracy theory called “Dead Internet Theory” that basically says nearly all traffic on the Internet is generated by bots as part of a co-ordinated effort to manipulate us. Obviously it’s unhinged, but at the same time some elements of it are not that far off the mark. How long will it be before the Internet is so drowned in slop that it is, effectively, dead?

The other issue is what happens to the human jobs that have been replaced with slop? It’s way too easy for a money-pinching publication to get rid of their human staff in favour of pushing a few buttons and publishing the output. You’d hope that market forces would balance things out, but there doesn’t seem to be much sign of that happening. There are small movements of people putting badges and other marks on their work to show it was made by a human, perhaps that will take off?

On a more personal level: what happens to software developers? As it stands, code agents are nowhere near good enough to replace a human. At least not if you want a non-trivial output that works, can be changed in the future, and doesn’t have massive issues. Even if they get substantially better, I feel like you’d still need a knowledgeable human in the loop to keep a rein on everything. Does that mean the world needs far fewer software engineers? Probably. But it doesn’t mean we’re all going to be out of jobs overnight as some people seem to be predicting. The only way I can see that ever happening is if there’s a radical shift in how software is made: a new language of some kind that’s uniquely suited to LLMs, or new ways of composing software together from smaller parts⁶.

So… Yeah. Where does that leave us? I’ve written several thousand words about how LLMs are an ethical mess, but I’m going to carry on using them. That’s uncomfortable. But it’s not unlike using Amazon: they’re not a great company, I’d rather give the money to a local shop or supplier, but I’m also not going to massively inconvenience myself by being quixotic about it. Life is about compromises like this, and it’d be silly to think otherwise. I’ll stand on morals on some things⁷, but not to the extent of becoming a reclusive old crank shouting at the clouds.

That’s what Kagi is for. It’s great. You should try it. ↩︎
Or, maybe even worse, with added hallucinations. ↩︎
Hell, basically no human activity is good for the environment. ↩︎
±several kilowatts, because who knows? ↩︎
and didn’t elect people who withdraw from climate agreements, re-open coal plants, and so on… ↩︎
I don’t think I’m just saying this to reassure myself… ↩︎
I’ll grudgingly give Jeff Bezos money, but I sure as hell am not going to use any service from Elon Musk. ↩︎

An app can be a ready meal

2025-06-11T00:00:00Z

It’s not a home-cooked meal, but it does the job sometimes.

Three years ago I read “an app can be a home-cooked meal” by Robin Sloan. It’s a great article about how Robin cooked up an app for his family to replace a commercial one that died. It’s been stuck in my head ever since. It’s only recently that I’ve actually done anything like Robin described, though. Part of the reason was my brain got too hung up on the family aspect: in my head, a home-cooked meal is one where your family or friends all gather around to eat it with you (in much the same way as Robin’s app is used in the article). It took me an embarrassingly long time to realise that you can apply all the same arguments to an app built just for you. And it doesn’t even have to be difficult. In fact, it can be more like a ready meal¹ than a family dinner.

Why not open source?

I love open source software. Almost everything I use day-to-day is open source, and most things I write for myself I release as open source. I believe that should be the default stance for most software. So why would you want to make something and keep it just for yourself?

Even if an open source project garners no users whatsoever, there’s still some pressure for it to meet certain standards²: if it needs configuring, then there needs to be a mechanism for people to do that; the code needs to be of a reasonable quality — my open source projects are linked from my CV, potential clients might be looking at them — and there needs to be at least some attempt at documentation or showing other people how to use it. There’s “building in public” and then there’s “inviting the whole world to poke around your drawer of shame”.

The way I usually work on things that are mostly for me is I hack them together, and then I gradually force myself to fix them up into a state where I consider them acceptable for release. That stage isn’t fun, and sometimes it adds more complexity than the whole original project. I’m not saying open source isn’t worth it — far from it! — but there is definitely a balance to be struck between the utility of making something open source and the amount of effort it takes.

For example, I mentioned previously that I wrote a home automation system. Hidden within that is a ~40 line function that determines whether a fan should be turned on to keep a room cool. The logic is entirely unique to the devices on my network and my requirements. To try to put it into a form that would be useful to anyone but me would take exponentially more effort. I’d basically be re-inventing Home Assistant, and I explicitly started that project because I didn’t need that kind of complexity. For a while I had this nagging feeling that I should find a way to open source it. Then I had my³ “lightbulb” moment: I could just not.

Writing something entirely for yourself, without planning on open sourcing it is surprisingly liberating: you don’t need to worry about documenting things; you can change anything and everything at will without having to worry about migration paths or whether it’ll break anyone’s workflow; you don’t have to configure things, you can just code them how you want. Hell, you can hard-code API keys right in the source code. Who cares!

The microwave revolution

XKCD 1205

One of the other problems with making software just for yourself is that often the time investment just isn’t worth it. If I spend a day automating something that normally takes me five minutes, it’s going to take an awful long time to “break even” on that time spent. If you’re open sourcing something then there are ancillary benefits that may tip those scales: you might help other people, have something to show off, etc. But if it’s just for you, then XKCD 1205 is a harsh mistress.

Recently, though, a new tool has emerged that tips the balance: LLM assistants. I’ve talked about my experience before. I’m definitely not comfortable letting an LLM run roughshod over my published code, even if it can help write some of it, but for private code? Why not! For a trivial example: I have a USB key with an Arch Linux ISO on it, in case I need to troubleshoot or reinstall my PC. Every so often I’ll update the ISO to the latest version. It probably takes 5 minutes to do, so it’s probably only worth 25-50 minutes to optimise it. Can I write, test, and debug a script to do it myself in that time? Probably not. Can I get an LLM to generate some code to do it for me, and then spend 10 minutes reviewing it⁴? Easily.

To continue with the strained metaphor: LLMs are like the microwave that nukes your ready meal. Pop in a prompt, let it use a load of power for a while, and out pops your app. You can even do it on your phone while sat on a sofa⁵. That changes the time equation even more: you can just squeeze in a prompt whenever something comes to mind.

This also goes back to the open source issue: I don’t feel comfortable publishing something created by an LLM with minimal intervention on my part. If I can prompt an LLM to do something, so can anyone else. We don’t need more slop out there, and I don’t want there to be any confusion about what I’ve written and what I’ve just prompted into existence. But for personal projects that will never see the light of day it’s perfect.

What I’ve made

Besides the home automation controller, I’ve built a constellation of smaller tools: a script to arrange windows on my monitors how I want them⁶, one to help me verify that my backups are working and can be restored, one to create the files/folders for a new blog post, and some other odds and ends. They’re all pretty small, and pretty specific to my setup.

My biggest just-for-me project is a web app that I started to help me aggregate film recommendations. It’s since morphed into a general personal data aggregation service: it deals with data from GitHub, Todoist, Letterboxd, TMDB, Healthkit, and others. It also lets me make re-orderable lists, store recipes, and more. Parts of this could definitely be open sourced, and I might carve them out at some point, but it’s mostly a glorious hodge-podge of things specific to me. Having all these services in one place lets me make quick and dirty automations, for example: when I create a Todoist note on my phone or watch, I often forget to set the due date, so it doesn’t show up in the “Today” view. It was literally a few lines of code to plumb things together so any inbox task without a due date gets set to today automatically.

If I was trying to write these things in a way that would be useful to other people, or — for some of the features — without the aid of an LLM, I just wouldn’t be bothered. It’d take too much time for questionable benefit. But there’s a kind of joy in just being able to hack things together that work just well enough for you. A ready meal will never compete with a home-cooked meal, but sometimes it perfectly hits the spot.

Image credits	Creator	Licence	Source
Spaghetti carbonara	Wikimedia user Geni	CC BY-SA 4.0	Wikimedia
XKCD 1205	Randall Munroe	CC BY-NC 2.5	XKCD

or a “TV dinner”, if you’re North-American-ly inclined. ↩︎
Self-imposed pressure, for sure, but brains are going to do brain things. ↩︎
very obvious in retrospect ↩︎
Reviewing LLM-generated code that shells out to dd to write to the root of a storage drive is perhaps the most intensely I’ve ever reviewed any code to date. I really didn’t want to write a blog post about how an LLM blatted my hard drive! ↩︎
Phones are terrible input devices for code, but they’re just about passable for typing English. ↩︎
I basically want a tiling window manager, but without all the effort and weirdness of a tiling window manager. ↩︎