I won’t argue with the downvotes, honestly they’re fair and I get it. This is an AI project posted to a community that’s rightly tired of AI slop, and I’d be skeptical too.
So let me be straight: yes, I used Claude heavily to build this. I’m a solo dev and it’s how I got it done at all. But every decision (architecture, stack, features, scope) is mine. That doesn’t mean they’re good decisions, just that they’re deliberate, not generated. I built this because it actually solves a need I had at home, and I figured I’d share it in case it’s useful to someone else, not to pretend I’m reinventing anything.
I also added an honest “How this is built” note to the README and the site rather than hide the AI use. If the code reads like unreviewed slop anywhere, that’s a real bug to me and I’ll fix it.
Either way, I hear the reception loud and clear, and I appreciate the people who took the time to actually tell me why.
Good question, and you’re right that it’s missing from the docs (just added a Hardware requirements section to the README to fix that).
The key thing: Hivekeep doesn’t run the models itself. It calls your provider (Anthropic/OpenAI/etc.) or a local OpenAI-compatible endpoint, so the heavy compute lives there, not in the app. The platform itself is a single Bun process over SQLite, no GPU, no extra services. It runs in well under 1 GB of RAM on a small home server.
On the multi-agent worry: agents are activated serially per message, not all firing at once, and the persistent memory is exactly what keeps each context small (hybrid vector + keyword recall, re-ranked, instead of replaying the whole history). So adding agents mostly means more calls routed to your provider, not multiplied local load.
The opencode slowdown you saw is on the inference side: if you point Hivekeep at local models (llama.cpp / LM Studio / Ollama / vLLM), the hardware question moves to your inference server, same as any other client. If you use a hosted provider, your machine barely feels it.