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 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 MIT licensed. PRs welcome.