This is a tool I created that gives you 4-10 expert Claude Codes for different sections of your codebase (auth, DB, API) with their own memory, tasks, and skills. They can:
> spawn a swarm of other Claude Codes,
> talk to each other
> manage parallel work
Open-source for anyone interested.
it's called OctoGent, a prototype I made out of curiosity about what an Agent Engineering dashboard might look like.
OctoGent is simply a thin agent orchestration dashboard running locally, and it's built AROUND Claude Code, not to hide it under abstraction.
with OctoGent you can have:
> specialized context and skills for multiple sections of your codebase. (I call them tentacles)
> a list of tasks for each tentacle, defined by you or Claude.
> a CLI for Claude Code to create other Claude Codes, prompt them, and check their work.
> you can control these children and see their work.
> all visible in a graph canvas, so you have a God-view of what's going on.
> Claude usage limits always visible so you don't get surprised.
> a token heatmap of your Claude usage, like GitHub's.
> a list of your past conversations with Claude Code that you can search through, or export to use elsewhere.
It is pretty straightforward:
spawn a new Claude Code under the database tentacle,
it reads all the memories and context it wrote down,
reads the to-do list,
and starts working on tasks,
Basically, the way you use Claude Code, just with more utilities.
This is still in early stages and experimental, so if you hit any issues, submit it on the repo. You can also fork it and change it however you want.
There are docs on how you can use Claude Code as a coordinator agent, how to make it use other agents, message them, how to work with its hooks, inject prompts into Claude, and use all the things it exposes.
Check it out: https://t.co/95pqACH57l
People have too inflated sense of what it means to "ask an AI" about something. The AI are language models trained basically by imitation on data from human labelers. Instead of the mysticism of "asking an AI", think of it more as "asking the average data labeler" on the internet.
Few caveats apply because e.g. in many domains (e.g. code, math, creative writing) the companies hire skilled data labelers (so think of it as asking them instead), and this is not 100% true when reinforcement learning is involved, though I have an earlier rant on how RLHF is just barely RL, and "actual RL" is still too early and/or constrained to domains that offer easy reward functions (math etc.).
But roughly speaking (and today), you're not asking some magical AI. You're asking a human data labeler. Whose average essence was lossily distilled into statistical token tumblers that are LLMs. This can still be super useful ofc ourse. Post triggered by someone suggesting we ask an AI how to run the government etc. TLDR you're not asking an AI, you're asking some mashup spirit of its average data labeler.
타계한 스티븐 호킹이 블랙 홀과 우울증을 비교해 강의를 했었다 함. 블랙홀은 검지도 않고 우리가 생각하는 것처럼 영원한 감옥도 아니라고 함. 이를 타고 다른 우주로 나갈 수도 있다고. 우울증도 이와 비슷하다고 우울증 환자들을 격려했다고. 별이 잠든 그곳에 호킹이 있기를..RIP. https://t.co/R52n5mKByt