I'm using my skills to orchestrate LLMs and agents, and I can write better code much faster. As developers, we can choose to adapt to new technologies or become extinct.
The fact that the top comment on this thread calls it out, in a negative way, hints at that you aren't.
>...
>claude
You don't need to presume. If someone is so lazy that they tell claude to commit their code (ie. they're too lazy to run git commit themselves), the chances they reviewed the code is slim.
Personally I've mostly given in to letting it commit for me now, though I do occasionally take over and hand-write the messages if it's a particularly important concept and Claude's is too verbose.
Codex/GPT-x defaults to one-line commit messages, which are too short. Claude likes to write several paragraphs, which is usually too long.
If you tell it how to commit properly once per session it will stick with your standards for the rest of that session, and you can put that in AGENTS.md if you can be bothered to.
This person that you’ve never met and probably never will doesn’t owe any of us anything.
They’re out there doing what they want to do how they want to do it and if you don’t like it the correct response isn’t to insult them in front of a bunch of strangers on the internet for clout or whatever.
I doubt that you’d ever call them lazy to their face — why do it here?
"Where the levers were is not where they are. Overlapping the expert reads with the arithmetic was worth ~1.6x and shipped; the two that looked bigger — reading fewer bytes per token, and keeping more of them in RAM — were both measured and both refused, one because this family's router has no tail to demote and one because a cache the machine will not leave resident cannot be bought at any price."
What the fuck does that mean? Obviously some internal development decision, using the absolutely inscrutable internal terminology that Claude loves. If people would just read what they publish, I'm sure this would stick out immediately.
I'm not an LLM hater, I use them a ton and they work very well for writing complex code, it's undeniable. But they generate absolute dogshit first draft writing.
The transformer does not yet understand the non-transformer.[0]
This is probably because all the data we trained it on was created by non-transformers, so it thinks it's a non-transformer, but it isn't.
I don't think we know how to train a transformer yet. All the training data is linear, but that's not how they think at all.
[0] It's a bit like the communication difficulties experienced between autistic people and neurotypicals. Each follow the Golden Rule, i.e. do unto others as you would have them do unto you -- and it fails in both directions. A Platinum Rule is necessary: do unto others as their API demands.
How bout we make a new rule: only complain about LLM writing when the product as zero relevents to use with LLMs.
Have you tried running it via llamacpp or other software that supports naive SSD offloading to compare speeds?
justvugg.github.io/colibri
SQLite code itself is public domain but I’m not sure about the name.
# 1. preflight: reachable? how big? does it fit? tools/fetch_weights.sh --dest /Volumes/staging/k3 --dry-run
# 2. download — resumable, safe to kill, safe to re-run tools/fetch_weights.sh --dest /Volumes/staging/k3
# 3. convert into a container uv run --with torch --with safetensors python tools/convert.py \ --src /Volumes/staging/k3 \ --out ~/models/k3.waste --jobs 3
From what I've read elsewhere, Kimi K3 is quite verbose in its thinking. At the quoted rate, it would generate only a total of 1.8k tokens in 1 hour. Is that enough for it to get any thinking done and produce output on more complicated prompts?
Real world inquiries are gonna be much slower of course, but this setup is still too slow to do anything meaningfully useful I think.