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The way I star GitHub repositories is pretty much the same as how I like and collect videos on bilibili: I just click them on a whim. Whenever I see a project that looks even remotely interesting, my brain always goes “might be useful someday” or “I’ll take a look later” — and my finger clicks the button before my brain finishes the thought.

That’s how, over the years, my star count quietly climbed past 800. To be honest, I have no idea what those 800+ projects actually are — most of them were starred because they seemed useful back then, and then never opened again.

Recently I submitted a PR to BananaSlice (#11), adding configurable Gemini-compatible API endpoint support. The whole process made me realize: AI Agents not only help people write code faster, but also change how we leverage open source software.

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AI

Recently, I spent several days deeply experiencing multiple AI Agent products, from WorkBoddy, OpenClaw, LobeHub, DeerFlow2, LirbeChat, DingTalk Wukong to OpenCode, trying almost every tool I could find. Everyone has different needs, but my main focus is on open source and self‑deployable, feature completeness, and practical usability. Below is my personal comparison from a practical usage perspective.

I’ve been trying to use AI Agents / workflows to build efficient agent teams or AI workflows to improve productivity. After two weeks of experimentation, I’ve encountered several pitfalls…