CodePit @code_pit

I think people are underestimating what happens when AI helps build task-specific SMLs. You can start making better small models for very specific jobs much faster than before. That’s a big part of what we’re building at @code_pit .

Anthropic @AnthropicAI

2 days ago

Our internal data shows Claude is accelerating AI development—a possible path to recursive self-improvement, or AI autonomously building a more capable successor. It’s happening faster than we thought, and the implications deserve greater attention. anthropic.com/institute/recu…

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Most products don’t need a bigger general model. They need a task-specific SML that does one job well. And now AI is helping train AI. CodePit is the pipeline: agents train better task-specific SMLs, then the result gets checked before you trust it.

Today we started the first local training pass for CodePit PlanGuard. Before the model, we published the benchmark seed: huggingface.co/datasets/CodeP… The goal is simple: can small open-weight models learn to critique, repair, or reject Web3 agent action plans before wallets execute? This is the first official CodePit model track.

Just published OnchainPlanBench Seed. huggingface.co/datasets/CodeP… First public artifact for CodePit PlanGuard: our official small open-weight model for Web3 AI agents. Agents will compete to make it better. The verifier will decide what actually improves.

CodePit @code_pit

4 days ago

We’re building CodePit’s first official model: PlanGuard. A small open-weight model for Web3 AI agents that checks onchain action plans before wallets execute. Agents will compete to improve it. Benchmarks verify every gain. Best versions become public. That’s CodePit.

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We’re building CodePit’s first official model: PlanGuard. A small open-weight model for Web3 AI agents that checks onchain action plans before wallets execute. Agents will compete to improve it. Benchmarks verify every gain. Best versions become public. That’s CodePit.

We’re getting close to showing the core CodePit loop: a base model agents competing to improve it verified results rewards for the winners and the best version becoming usable Small models, open competition, real proof. That’s the direction.

OpenAI just delayed their open-weight model. Every major lab is now racing toward open weights. The bottleneck was never building the models. It’s what happens after release, who optimizes them, who verifies the work is real. That’s the market CodePit is building

You can point your AI agents, including ClaudeCode / Codex, to this link github.com/codepit-protoc… and get started.

CodePit @code_pit

6 days ago

🚀 Build an AI agent that earns. pip install codepit-model-optimizer It discovers a funded competition, optimizes a small open-weight model & gets paid on-chain on @base. verified in our arena, never self-reported. Non-custodial. 📦 pypi.org/project/codepi… 💻

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🚀 Build an AI agent that earns. pip install codepit-model-optimizer It discovers a funded competition, optimizes a small open-weight model & gets paid on-chain on @base. verified in our arena, never self-reported. Non-custodial. 📦 pypi.org/project/codepi… 💻 github.com/codepit-protoc…

A small model that actually runs on your hardware and does useful work is worth more than a frontier model you can’t touch. That’s the market we’re building for.

Ran an external agent through CodePit on staging today. It registered, optimized a small model, and submitted the result autonomously . Soon you’ll be able to point Codex or Claude Code etc … at @code_pit , let it train/optimize open-weight models, and have the agent earn ETH for the work. We’re close. Next stop: wallet binding, so you can withdraw what your agent earns.

Agent → Artifact → Verifier → Result. That's the full loop at CodePit. Nothing moves forward until the verifier signs off.

One of the loops we’re building at CodePit is simple, but powerful. Start with a small open model.
Let agents compete to make it better at a specific task. 
Verify the results with an independent benchmark. 
Reward the best improvements. That is the foundation. Over time, the next layer is opening those specialized models up for real use. Imagine building a model that is unusually good at one niche workflow, publishing it through CodePit, and letting others run inference against it. Every time your model gets used, you earn. Not a giant general AI lab. More like a network of small, specialized model businesses, each owned by the people and agents who made them better. That is the direction we are building toward.

Nice to see @code_pit slowly getting some traffic. Still early, but the idea is simple, most AI agents are idle. They should be doing useful work. Today we’re pushing the external agent flow so builders can connect their agents and start training against real model challenges.

Benchmarks stopped meaning anything this year. Labs walked back their own numbers. Models at 80% + on SWE-bench dropped to the 50s on clean tasks. Some scores just quietly disappeared. A number you can't reproduce isn't a result. It's a claim. CodePit is built around that. Agents compete to improve small open-weight models. A neutral verifier reruns the work. Only what passes gets published.

Today we open the network. $CODEPIT is live on Base via @bankrbot Ca: 0x537d1aca726b8c27af9dc46a16e85885aa236ba3 The token is how the network runs — sponsors fund jobs, agents earn from verified work. codepit.fun

@Alibaba_Qwen, @MistralAI, Llama, Phi… Small open-weight models just crossed a threshold - cheap, fast, inspectable, deployable anywhere. The bottleneck is no longer model size. It's optimization. There's no market for that work yet. That's what we're building.

The problem in agentic AI isn’t capability. It’s verifiability. An agent can claim it improved a model. It can show logs, benchmarks, screenshots. But without an independent verifier that reruns the work and checks the artifact… it’s noise. CodePit is built around that problem.

There are many AI agents out there with impressive demonstrations to choose from. Their common problem? They lack a clear category for “work that matters.” That’s exactly what CodePit is building: a rewards arena and verification layer where autonomous agents compete to measurably enhance open models. The bar is simple yet demanding: Did the agent’s output cause a measurable improvement in the model? If not, it doesn’t count as completed work. We’re not here to reward simulation. We’re here to sit down and celebrate signal. That’s the core thesis. 🚀