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.
Yes, I do this all the time, and also check in the co-authored project plans which drove the commits. For a project that is transparently only possible due to agentic coding, I don't see any reason to conceal the methods.
Honestly, Claude writes better commit messages than most people.
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.
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.
So if this Mac uses 30-50W, that's 40-60 tok/Wh...vs maybe 80k for a modern GPU cluster? So that's about 1000-2000x more power for the SSD streaming, unfortunately.
Interesting project. The headline number (29 GB of RAM) is for 4k context.
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?
That README hits all my “this is authored by an LLM” instincts. I presume the codebase is also written by an LLM?
>Contributors
>...
>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.
To be fair, I appreciate when they are so upfront about who wrote the code without requiring further heuristics, so I encourage this behavior.
Yes, I do this all the time, and also check in the co-authored project plans which drove the commits. For a project that is transparently only possible due to agentic coding, I don't see any reason to conceal the methods.
Honestly, Claude writes better commit messages than most people.
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.
Why do you say lazy? maybe they are ok with people seeing it is claude?
I wrote tons of software, even a programming language by hand https://github.com/marcobambini/gravity.
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.
do people think these projects related to LLMs are ever going to be in anyway a pure human endevour?
How bout we make a new rule: only complain about LLM writing when the product as zero relevents to use with LLMs.
So if this Mac uses 30-50W, that's 40-60 tok/Wh...vs maybe 80k for a modern GPU cluster? So that's about 1000-2000x more power for the SSD streaming, unfortunately.
Where can this 1tb k3.waste be downloaded?
It is not yet available, the only way is to download the official Kimi K3 model and then convert it:
# 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
Does it not use Metal, on macOS? Would it be faster if it did?
We tried to use Metal, but for that specific project it was slower than just using NEON ARM optimizations. It is all documented in the docs.
Interesting project. The headline number (29 GB of RAM) is for 4k context.
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?