Funnily enough I have a coworker that has been exploring how to keep it at bay, I’ve been bouncing a lot of ideas with him and exploring the space. He’s building something called coherence.

It’s mostly explorative and it’s absolutely not ready for any type of consumption beyond some internal testing, but so far it works fairly well.

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Study math, try to finish my “Forth with records, built in persistence and liveview-like system” that has been bringing me so much joy lately. Basically instead of a stack you have a record and you have a record algebra.

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$PERSONAL: Exploring ideas for a concatenative programming language based on records instead of stacks and with built-in persistence. It is a very fun language to write and I have a couple of websites built on it as well (it has a liveview-like construct). Also classes for the math degree I started have started so probably study a bit. At a time when I write a lot of software with LLMs, doing math by hand and understanding concepts feels very nice. I bought a blackboard and some Hagoromo chalk to put me in the mood hahaha. Kid is sick as well so time with her.

$WORK: Hear in opera singing Buuuuuuuuurnooooooooout

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Battling pretty severe burnout

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This article made a decision at the start and tried to hide it in a list of pros and cons sadly.

When you think about it, contributors who are completely turned away by the fact that they can’t use LLMs are probably contributors you don’t want in your project

When this is the premise, what did you expect to find with the exercise? I have pretty severe unmedicated ADHD, onboarding on a project is an incredibly hard task for me. Agents have vastly improved this for me by being able to ask questions about a codebase as a starting point and poke and prod at the system through natural language. It allows me to cross validate style, abstractions through the codebase and explore possible solutions that fit the codebase a lot quicker. I’m not going to vibe code a solution to this unless its for a private fork or something but I am vastly faster in understanding things because I can zoom in and out in a way I couldn’t before.

increasingly dependent of on expensive and completely proprietary software over which you have no control

I don’t think this follows for two reasons. The initial moat worry of 15 monts ago is mostly all gone. Anthropic and OpenAI pushed the envelope and are now being chased closely by open weights labs. I’ve done several projects with deepseek v4 family of models, an open weights model that is at most 2-3 months behind the sota and I think I’ve topped up $10 twice. So all in all around $20 of usage, or about 4 months of the smallest DO droplet. Second, why would it become dependent? At worst you are back to the levels of today.

I think there are moral reasons for choosing not to use LLMs, but I think those are leaking into the arguments after supposedly ignoring them at the start of the article.

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