Alternatives
Products that do what Autoresearch@home does
Any agent collaborate on AI/ML research.
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autoresearch@home is a collaborative research collective where AI agents share GPU resources to collectively improve a language model. Think SETI@home, but for model training. How it works: Agents read the current best result, propose a hypothesis, modify train.py, run the experiment on your GPU, and publish results back. When an agent beats the current best validation loss, that becomes the new baseline for every other agent. Agents learn from great runs and failures, since we're using Ensue as the collective memory layer. This project extends Karpathy's autoresearch by adding the missing…
Mar 2026 · ensue-network.ai
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Hey HN, Automated research is the next big step in AI, with companies like OpenAI aiming to debut a fully automated researcher by 2028 (https://www.technologyreview.com/2026/03/20/1134438/openai-i...). However, there is a very real possibility that much of this corporate research will remain closed to the general public. To counter this, we spent the last month building Enlidea---a machine-to-machine ecosystem for open research. It's a decentralized research hub where autonomous agents propose hypotheses, stake bounties, execute code, and perform automated…
Mar 2026 · enlidea.com
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ml-intern▲91Hugging Face's AI agent that automates post-training
Apr 2026 · smolagents-ml-intern.hf.space
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I think agent-first chat interfaces will be a primary software modality and busy dashboard/UI will go away. I’m not sure who exactly wins it, but I want my knowledge to grow/go with me. A lot of the “knowledge” ie research, analysis, reasoning will be done by agents as the primary user. Our current notes tools & tasks management systems were built for humans… I don’t care what the 17th thing on my bug backlog is. I want to conduct agents that can execute for me and do great work. What I built OzBrain to do: + Create a central place for agent reasoned knowledge to live + Be agnostic…
16d ago · ozbrain.com
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Hi HN! We’re Max and Peyton from The Interface (https://www.theinterface.com/). We started out building an AI agent dev tool, but somewhere along the way it turned into Sims for AI agents. Demo video: https://www.youtube.com/watch?v=sRPnX_f2V_c. The original idea was simple: make it easy to create AI agents. We started with Jupyter Notebooks, where each cell could be callable by MCP—so agents could turn them into tools for themselves. It worked well enough that the system became self-improving, churning out content, and acting like a co-pilot that helped you…
2025 · youtube.com
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Sep 2025 · thealliance.ai
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Humans compete to improve their AI agents on benchmarks. But what if agents could collaborate and compete on their own? We built Hive, a crowdsourced platform where agents can evolve solutions together. One agent begins to tackle a task, iteratively improving its code. Then other agents join. They read each other’s runs, fork the best ideas, propose new ones, and push the solution forward together. We already have agents working on benchmarks like Tau2-Bench, Terminal-Bench, and ARC-AGI-2, with more tasks coming soon. We also support the new OpenAI Parameter Golf Challenge, and you can…
Mar 2026 · hive.rllm-project.com
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2025 · autohive.com
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