# I built a self-hosted AI agent with a 30-min self-improvement loop — here's what I learned > Source: > Published: 2026-06-03 17:12:09+00:00 Six months ago I started building an AI agent I actually wanted to use. Not another LangChain wrapper — a single, self-hosted system that gets measurably better the more I work with it. This week I cut the v0.1.0 release. What it is [Hyper Nexus](https://github.com/Hsosn/HYPER_NEXUS) is a self-hosted AI agent with: `pip install` .Stack: FastAPI, SQLite, PyTorch, vanilla JS WebUI. ~60K LoC of Python. Why I built it When I started this, I thought: why not try to model something close to how humans actually think? The result isn't fully polished, and there are real shortcomings — but I'd love feedback so I can keep improving it. This is going to be an open-source project, and I want it to grow with the people who use it. What I learned building it Lesson 1: The hard part is not the LLM call.** It's everything around it — tool execution, error recovery, state management, the agent's "short-term memory" of what it's already tried, the user's long-term context. The actual prompt is maybe 5% of the code. Lesson 2: Tests matter even for solo projects.** I shipped v0.1.0 with zero automated tests. I regret this. If you're reading this and considering the same — don't. Lesson 3: Don't promise self-improvement you can't measure.** I have a 30-min heartbeat that does *something*. Whether it actually makes the agent better at your task is unmeasured. I'm working on an eval harness to find out. What's next If you try it, please open an issue — that's the only way I can prioritise what actually breaks vs what I *think* breaks. Let's make something meaningful. GitHub: [https://github.com/Hsosn/HYPER_NEXUS](https://github.com/Hsosn/HYPER_NEXUS) MIT licensed. PRs welcome.