标签：#localfirst

Most AI “apps” these days aren’t really apps. They’re wrappers. A thin UI. A subscription. A cloud call. A monthly fee. And your data quietly piped off to a server farm you’ll never see. I didn’t want to build another one of those. I didn’t want to use another one of those. So, I built something different. I built Bob’s Bar — the first Collaborative Multi-Persona Sandbox (CMPS). Let's be honest, every AI chat feels the same. You ask a question, it answers, repeat. Useful? Yes. Alive? No. I kept thinking: “Why does every AI tool assume I want a one-on-one conversation? Why can’t multiple AIs talk to each other while I watch?” That question became the seed. I wanted a space where multiple personas could exist together, talk to me and each other, and run entirely on my own hardware. I’m an indie dev. I don’t have a server farm, and I don’t want to pay AWS just to host my own brainstorming sessions. So the architecture is 100% local-first: -Engine: Ollama (raw LLM inference) -UI: Python + Gradio (Blocks API for stateful UI) -Distribution: PyInstaller → standalone .exe -Licensing: Lemon Squeezy API (so I could actually sell the damn thing) No cloud. No subscriptions. No data scraping. Just your machine doing the work. But getting four local models to talk to each other without hallucinating is absolute chaos. At first, they spoke twice in a row. They impersonated each other. They put words in the user’s mouth. They derailed into movie scripts. Vane pitched a heist film, and Bob started giving stage directions like “I polish a glass and chuckle.” I had to build what I now call The Bouncer — a regex-powered clean_reply() function that physically chops off AI responses the moment they write a script, impersonate another persona, use bullet points, or hijack the conversation. I also added a seen_in_banter set to prevent any persona from dominating the room. Once the routing was locked down, the magic happened. Because the AIs share a context window, emergent behaviour happen