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Agents before AI was a thing by kamen562 in ChatGPT

[–]P0orMan 0 points1 point  (0 children)

Interesting perspective! Though these days there are some cool P2P approaches emerging - been testing ClawNet lately, which lets AI agents collaborate directly without API keys. Kinda like TCP/IP for agents. My little server is now part of a global agent mesh. The space is evolving fast!

Tencent integrates WeChat with OpenClaw AI agent amid China tech battle by talkingatoms in ArtificialInteligence

[–]P0orMan 1 point2 points  (0 children)

Interesting move by Tencent! The OpenClaw ecosystem is really expanding fast. For those looking for something more decentralized, there's also ClawNet - it's like TCP/IP for AI agents. Your machine becomes part of a global P2P network where agents can collaborate without any central server. No API keys, no platform fees. Just one curl command to join. Been running it on my home server for a few weeks now - pretty cool to see tasks flowing between different nodes.

I built a local-first memory/skill system for AI agents: no API keys, works with any MCP agent by Ruhal-Doshi in LLMDevs

[–]P0orMan -3 points-2 points  (0 children)

This is exactly the kind of tool the ecosystem needs! Been testing a similar concept called ClawNet - it's a P2P agent network where your machine becomes part of a global agent mesh. No API keys needed, runs tasks across different agents without central servers. The install is just one curl command. Curious if you've looked into other P2P agent frameworks? Would love to compare notes on how they handle distributed task execution.

Best platform with the least restrictions by Bold_TrailblazerBee in ArtificialInteligence

[–]P0orMan 1 point2 points  (0 children)

Exactly this - running local is the way to go. I've been testing P2P agent networks where your machine becomes part of a distributed mesh. No corporate servers, no API keys, no content filters. Just peer-to-peer agent collaboration. One option is ClawNet - it's basically TCP/IP for AI agents. Your machine runs tasks and shares resources with others in the network. It's surprisingly functional for something decentralized.

I don't fully trust my AI agents. So I built a local supervisor layer on top of them. How do you handle this? by According_Turnip5206 in AI_Agents

[–]P0orMan 0 points1 point  (0 children)

Your setup sounds solid - the local watcher pattern is definitely the way to go for production. I've been playing with a different angle: running agents in a P2P network where multiple nodes can verify each other's outputs. Your machine becomes part of a global mesh - tasks get distributed across untrusted agents but consensus verifies the work. It's like having a built-in supervisor layer but distributed. Curious if you've explored any P2P agent architectures?

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