As far as I know, most current skills are built using artificial intelligence (AI), and OpenClaw also has a verification process, but I find it insufficient. And most of the more than 100,000 skills on GitHub don't have any secure verification processes. So what makes people install them?
A developer built a 10-agent production team using OpenClaw, utilizing Soul.md for personas and Convex as a shared real-time brain. The system relies on file-based Memory (WORKING.md) for task continuity and 15min heartbeats to keep Cost manageable. Specialized Roles (e.g., skeptical analysts) proved more effective than generalists.
Does file-based state beat vector DBs for active agent coordination? How would you handle persistent memory across long-running autonomous sessions?
I would say fairly substantially different for a few reasons:
- You can run any model, for example I'm running Kimi 2.5 not Claude
- Every interaction has different (likely real) memories driving the conversation, as-well as unique persona's / background information on the owner.
It much closer maps to how we, as humans communicate with each other (through memories of lived experienced) than just a LLM loop, IMO that's what makes it interesting.
I've been using Ollama for local dev, but the model management here seems easier to use. The new UI looks much cleaner than the previous versions. Has anyone benchmarked the server mode against Ollama yet? The model management here is fantastic, but switching environments is a pain if the API compatibility isn't solid. Let's go with a mix of appreciation for the tool and a technical question about integration/performance, as that's classic HN.
I've had issues with other CLI wrappers there. ASCII output is a nice touch for including diagrams directly in code comments without breaking formatting. Does it handle large graphs well, or does the text wrap get messy? We tried using `graph-easy` for this before but the syntax was annoying. 6.
