Hyper, the self driving company brain
Yet another AI memory layer competing with Mem.ai and Notion AI.
The open-source company brain. Run your entire company with AI agents, skills, and a self-improving context.
Git diffs drive agent self-learning without vector DBs or fine-tuning.
Founder-developers, small teams using AI agents for ops
Notion AI · LangChain · Obsidian
I built it in a git repo because I refused to lock into any tool - not a context layer, not a specific agent harness. Today I'm open sourcing it so others can build their own.
Sylph is the open source version of the company brain I use. It gives you the structure to host your own company context, build skills, create AI agents, and already has a self-learning loop scaffolded. It is a Git repo, with no lock-in on any tool, that can work with any agent: Claude Code, Codex, Cursor.
Sylph is made for you to make your own: fork it, run /sylph-setup, and it will build your own company brain according to your own context.
Repo: github.com/getnao/sylph
How I built it: https://thenewaiorder.substack.com/p/i-built-a-company-brain...
Tell me what you think, and if you've got some tips if you built an AI brain for your company too!
Yet another AI memory layer competing with Mem.ai and Notion AI.
Navigation-over-search architecture sounds great until you realize it's just another RAG wrapper.
Agent writes its own Python tools and saves rules to avoid repeating mistakes.
Polished product, but recruiter bots and warm intro networks already solve this.
Context Repositories — treating agent memory like a git repo you can diff, branch, and version — is a clever, developer-friendly twist on long-term LLM state. Letta Code’s persisted, model‑agnostic agents and the Conversations API make the product feel like a coherent stack for production agents, though the trick will be real-world scale, merge semantics, and cost of storing rich context over time.
Git worktree isolation lets agents test instruction changes without breaking other sites—clever regression prevention.