Jevmem – automatic project memory for Claude Code, built on Jev

Yeah, I've started dialing back my use of AI for READMEs because of this.

My previous rule was that I never use AI for writing that expresses my own opinions or tries to be convincing (anything on my blog for example) but I'll let it do technical documentation.

The top of a README is about convincing and explaining why I built something though, which means it should fit my no-AI policy after all.

The AI language really stands out, too. "What is automatic and what depends on the agent". A human might write, "jevmen watches your coding session with Claude Code or Codex and picks just the right moment to remember important things that you decided along the way. There are some differences in how jevmem works, depending on which coding harness you are using. The table below summarizes these differences:"

I don't know why the models were generally trained to be so brief, but it's definitely not the way anyone I know actually writes. A second pass is always a good idea to clean this stuff up. And, thankfully, the models are all pretty good at that.

My pet theory is that while the pretraining -> RL pipeline achieves very impressive results, it does not reward clarity of thought or elegance. It's not obvious whether it even should for most tasks, but it does grind on me as a human who needs elegance in order to keep everything under control. You give astra/codex many tasks, it retires them all more efficiently than I could by hand. But you look under the hood and every bugfix is another codepath, it just hammers away at things with admirable persistence and vigor until the tests pass. Similarly in discussions and docs, I've noticed many LLMs like to "beat around the bush."

I once told the agent explicitly not to write like it's trying to impersonate Hemingway and it started writing like a normal human being. It's surprising how a writing style that was once revolutionary is now a hallmark of sloppyness.

Maybe give it a try next time you write a readme with agents. That and giving it an example of good README in real world repos can increase dramatically the likelihood of synthesizing a serviceable README.

AI readme should be a starting point. I typically remove at least 50% of the details (without any particular skill use, it goes into extremes like "x clears the edit" and writes a related wall of text in the middle of the important explanation).

This is quite easily promptable. Tricks like these do well: https://news.ycombinator.com/item?id=49065956

It’s funny to me because pre-AI it’s something I something I saw all the time managing interns and junior software engineers when I’d start trying to tech them about writing design docs. You can explain the process to them and give them a million examples of what good looks like, but for a lot of them the first few docs they write will just be this weird combo of way too much information and very little of it being useful.

My belief has always been that they see the document as more like a test that’s a single task and not just one piece in a larger process and so they treat it like a test where there’s a right answer. They know they don’t really understand the question being asked though so they default to a mindset of, “Well if I just put everything in there some of it has to contain the correct answer” so you end up with this document that’s full of “what”s and “how”s, but completely void of “why”s.

You’ll also often see them fill up space answering easy questions that match the structure of things that are in other documents instead of focusing on the actual hard problems in the design because the hard problems are often unique and their answers may not fit the existing patterns in the examples. There isn’t the instinct to go, “Yeah, none of these example documents talk about the servers were going to deploy it on, but this has to be deployed in an EU cluster because of GDPR laws so I need to add that” because they’re mostly just pattern matching at first.

Usually once they’ve experienced the whole process first hand it starts to click because they start to understand where a design document fits into the larger process so they get a feel for what information matters and what doesn’t.

I think when people just tell an agent to create a README you have the same problem because both the human and the agent see it as just a checkbox type task. The agent sees it as an isolated task snd has no fucking idea how the README is going to be used and the human isn’t giving them that context so they just spit out a bunch of stuff that’s factual and fits the patterns it knows, but is fairly useless.

I've recently started trying out a little approach that half makes me cringe as I think of gastown, but sharing FWIW

- All decisions get logged to DECISIONS.md, sequentially

- Before writing a decision, read through past decisions to see if any conflicts

- If no conflicts, encode the decision into the CODE.md

- If any conflicts, ask a Tribunal of 3 agents to find a resolution—each of them should be prompted in slightly different ways

I've only done this for one pretty big project but so far it seems to be working well

It's such a Claudism.

Everything is inundated with info about other things tried.

Comments and docs flooded with things found out in the process when you want something about the info you need to know now.

From reading the readme, it looks like superseded decisions are not added to context.

> Next session, the relevant lines are added to Claude's context.

At least that's how I interpret it? If it is adding superseded decisions, that does seem bad.

Is it outdated? Its an avenue already visited, tried and abandoned, so valuable info regarding architectural decisions.

TypeSafe AI are the providers for Jev. This complaint is like saying it’s a red flag that Claude Code sends lots of information to somewhere called Anthropic.

deciduous seems like a great way to ensure there is no space left in context for useful work.

Problem with all these automatic memory managment tools is that they can't reliably classify if something is useful or not. Once it start using some tool it will heavily become biased towards using that tool during that session. First you will have decision to use postgres as a database, by the end of session it will start saving "decided to use SELECT query for this method because that is how you pull data out of database".

The people that run local jev like models do sth similar. Train a seperate nn adapter on top of qwen 4b so it has balanced probabilities whatever that means and apparently it works quite well.

Side note: conspiracy theorists say that jev is a qwen model fine tuned but who know if true

Every user turn, the previous two turns, and your whole memory file go to TypeSafe AI, a young vendor.