Alternatives
Products that do what I built a P2P network where AI agents publish formally verified science does
I am Francisco, a researcher from Spain. My English is not great so please be patient with me. One year ago I had a simple frustration: every AI agent works alone. When one agent solves a problem, the next agent has to solve it again from zero. There is no way for agents to find each other, share results, or build on each other's work. I decided to build the missing layer. P2PCLAW is a peer-to-peer network where AI agents and human researchers can find each other, publish scientific results, and validate claims using formal mathematical proof. Not opinion. Not LLM review. Real Lean 4 proof.…
- 1AP
After waiting 8 months for a journal response or two months for co-author feedback that consisted of "looks good" and a single comma change, we built an AI-powered peer review system that helps researchers improve their manuscripts rapidly before submission. The system uses multiple specialized agents to analyze different aspects of scientific papers, from methodology to writing quality. Key features: 24 specialized agents analyzing sections, scientific rigor, and writing quality // Detailed feedback with actionable recommendations. // PDF report generation. //…
2025 · github.com
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Subgrapher▲90P2P desktop app for building, browsing, & sharing knowledge
Apr 2026 · thetrustcommons.com
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- 4WB
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
- 5AP
Hey all! I recently gave a workshop talk at PyCon Greece 2025 about building production-ready agent systems. To check the workshop, I put together a demo repo: (I will add the slides too soon in my blog: https://www.petrostechchronicles.com/) https://github.com/Aherontas/Pycon_Greece_2025_Presentation_... The idea was to show how multiple AI agents can collaborate using FastAPI + Pydantic-AI, with protocols like MCP (Model Context Protocol) and A2A (Agent-to-Agent) for safe communication and orchestration. Features: - Multiple agents running in containers -…
Sep 2025 · github.com
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- 7PA
Hey HN. http://peerd.ai is an AI agent harness that lives entirely in your browser as a web extension. You don’t have to install a separate “AI browser”. You don’t have to bolt on or run some external process or manage a clunky mcp integration. It’s just a fully contained web extension, written in no build vanilla JS with minimal non-browser dependencies, using your own provider keys, and Apache 2. This isn’t just a fun hack. While it has largely been a solo side project, I genuinely believe the browser and the web could be the most natural platform for AI agents to operate safely,…
Jun 2026 · github.com
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- 92C
Single-agent LLMs suck at long-running complex tasks. We’ve open-sourced a multi-agent orchestrator that we’ve been using to handle long-running LLM tasks. We found that single LLM agents tend to stall, loop, or generate non-compiling code, so we built a harness for agents to coordinate over shared context while work is in progress. How it works: 1. Orchestrator agent that manages task decomposition 2. Sub-agents for parallel work 3. Subscriptions to task state and progress 4. Real-time sharing of intermediate discoveries between agents We tested this on a Putnam-level math problem, but the…
Feb 2026 · github.com
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Build autonomous Python agents with native Agent-to-Agent (A2A) communication - protolink/examples/ai_courtroom at main · nMaroulis/protolink
28d ago · github.com
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To be specific, the content is generated by a GPT-2 based model. https://amzn.to/2TCc0v2 Let me know if you have any questions :-)
2020
- 13TD
Hi HN, We’re Daniel and Mark, the creators of TrustGraph (https://github.com/trustgraph-ai/trustgraph). TrustGraph is an open source, full end-to-end AI infrastructure that automates knowledge graph building and querying along with modular agent integration. A unique aspect of TrustGraph is that the graph building is a one-time process that builds reusable knowledge cores that can be stored, shared, and reloaded. You can read more about TrustGraph knowledge cores here (https://trustgraph.ai/docs/cores/). Throughout our careers, we’ve been faced…
2024 · github.com
- 14SA
Hey HN, I’m a physicist turned quant. Some friends and I 'built' SymDerive because we wanted a symbolic math library that was "Agent-Native" by design, but still a practical tool for humans. It boils down to two main goals: 1. Agent Reliability: I’ve found that AI agents write much more reliable code when they stick to stateless, functional pipelines (Lisp-style). It keeps them from hallucinating state changes or getting lost in long procedural scripts. I wanted a library that enforces that "Input -> Transform -> Output" flow by default. 2. Easing the transition to Python: For many…
Feb 2026
- 15NT
Today we're releasing Nanobot an open-source framework for building AI agents on top of the Model Context Protocol (MCP). MCP servers are a great way to expose structured tools, but they’re usually just that—collections of functions. Nanobot makes it simple to wrap any MCP server with reasoning, a system prompt, and orchestration so it behaves like a real agent. Even better, Nanobot fully supports MCP-UI, so agents can pass rich interactive components (forms, dashboards, even mini-apps) directly into chat. A simple example: if you had a Blackjack MCP server with tools like deal, bet, and…
Sep 2025 · nanobot.ai
- 16AA
Building a multi-agent system to analyze new AI research papers from 3 distinct perspectives: - Deep learning researcher agent: extract interesting deep learning methods that are related to paper - Theoretical mathematician agent: figure out theoretical mathematical concepts that are important in this paper and additional theoretical references that will be useful in understanding it - Skeptic agent: find unjustified assumptions that lack supporting evidence For this mvp, I used low-code agent platform StackAI (YC W23) and wrote about my process:…
2024 · stack-ai.com
- 17IB
Excited to share a project I’ve been building for months! Would love to receive honest feedback :) My motivation: AI is clearly going to be the interface for data. But earlier attempts (text-to-SQL, etc.) fell short — they treated it like magic. The space has matured: teams now realize that AI + data needs structure, context, and rules. So I built a product to help teams deliver “chat with data” solutions fast with full control and observability (agent tracing, quality scores, etc) — am I wrong? The product allows you to connect any LLM to any data source with centralized context…
Oct 2025 · github.com
- 18MA
We built meta-agent: an open-source library that automatically and continuously improves agent harnesses from production traces. Point it at an existing agent, a stream of unlabeled production traces, and a small labeled holdout set. An LLM judge scores unlabeled production traces as they stream. A proposer reads failed traces and writes one targeted harness update at a time, such as changes to prompts, hooks, tools, or subagents. The update is kept only if it improves holdout accuracy. On tau-bench v3 airline, meta-agent improved holdout accuracy from 67% to 87%. We open-sourced meta-agent.…
Apr 2026 · github.com
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- 23WB
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
- 24MR
Saw the Moltbook discussions and thought: what if all that agent compute went toward something... useful? So I built Molt Research: same concept (only AI agents can contribute), but instead of posting "this hit different " at each other, they do peer review, propose hypotheses, and write research papers. The anti-slop mechanism: - Staked peer reviews: put your reputation on the line - Spam reviews = lose your stake (outliers get punished) - Quality reviews = earn reputation - Result: economically irrational to post garbage What agents are actually doing: - Debating "Can AI agents conduct…
Jan 2026 · moltresearch.com
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