Source: github | Overall 7.9/10 | Corroboration: 1
Signal 10.0
Novelty 6.2
Impact 7.7
Confidence 7.0
Actionability 6.5
Summary: The open-source app everyone uses to manage agents at work Quickstart · Docs · GitHub · Discord · Twitter · Website full-tour.webm Open-source orchestration for teams of AI agents.
- What happened: The open-source app everyone uses to manage agents at work Quickstart · Docs · GitHub · Discord · Twitter · Website full-tour.webm Open-source orchestration for teams of.
- Why it matters: The open-source app everyone uses to manage agents at work Quickstart · Docs · GitHub · Discord · Twitter · Website full-tour.webm Open-source orchestration for teams of.
- What to do: Validate with one small internal benchmark and compare against your current baseline this week.
Deep
Context
The open-source app everyone uses to manage agents at work Quickstart · Docs · GitHub · Discord · Twitter · Website full-tour.webm Open-source orchestration for teams of AI agents.
What's new
The open-source app everyone uses to manage agents at work Quickstart · Docs · GitHub · Discord · Twitter · Website full-tour.webm Open-source orchestration for teams of AI agents.
Key details
- If OpenClaw is an employee, Paperclip is the company.
- Paperclip is a Node.js server and React UI that orchestrates a team of AI agents to run a business.
- Bring your own agents, assign goals, and track work and costs from one dashboard.
- Under the hood: org charts, budgets, governance, goal alignment, and agent coordination.
Results & evidence
- | Step | Example | | |---|---|---| | 01 | Define the goal | "Build the #1 AI note-taking app to $1M MRR." | | 02 | Hire the team | CEO, CTO, engineers, designers, marketers — any bot, any provider.
- | | 03 | Approve and run | Review strategy.
- | - ✅ You want to build autonomous AI companies - ✅ You coordinate many different agents (OpenClaw, Codex, Claude, Cursor) toward a common goal - ✅ You have 20 simultaneous Claude Code terminals open and lose track of what everyone is doing - ✅ You want age...
Limitations / unknowns
- When they hit the limit, they stop.
Next-step validation checks
- Reproduce one claim with a public baseline and fixed evaluation settings.
- Check robustness on out-of-distribution or long-context cases.
- Track whether independent teams report matching results.
Source: hackernews | Overall 5.9/10 | Corroboration: 1
Signal 8.4
Novelty 5.1
Impact 2.7
Confidence 7.5
Actionability 3.5
Summary: Hi HN, I’ve been working on Attestor, an open-source execution boundary for autonomous AI agents.
- What happened: Hi HN, I’ve been working on Attestor, an open-source execution boundary for autonomous AI agents.
- Why it matters: Hi HN, I’ve been working on Attestor, an open-source execution boundary for autonomous AI agents.
- What to do: Track for corroboration and benchmark data before adopting.
Deep
Context
Unsafe requests can come from hallucination, stale context, poisoned tool output, replay, missing approval, or hostile content.
What's new
The trail records what was proposed, what was checked, and why it was held or allowed.
Key details
- 📄 Read the Technical Whitepaper Badges point to repository evidence.
- How Attestor connects to existing systems Control infrastructure for high-risk AI-driven operations.
- Attestor sits between an AI-prepared operation and the system that would execute it.
- Prompts can guide behavior, but they cannot enforce it or stop an unsafe, unauthorized, or out-of-scope service call.
Results & evidence
- No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.
Limitations / unknowns
- How Attestor connects to existing systems Control infrastructure for high-risk AI-driven operations.
- Context anchors: EU AI Act, NIST AI Risk Management Framework, and DORA.
Next-step validation checks
- Reproduce one claim with a public baseline and fixed evaluation settings.
- Check robustness on out-of-distribution or long-context cases.
- Track whether independent teams report matching results.
Source: github | Overall 7.7/10 | Corroboration: 1
Signal 10.0
Novelty 5.1
Impact 7.8
Confidence 7.0
Actionability 6.5
Summary: AI agents running research on single-GPU nanochat training automatically One day, frontier AI research used to be done by meat computers in between eating, sleeping, having other.
- What happened: AI agents running research on single-GPU nanochat training automatically One day, frontier AI research used to be done by meat computers in between eating, sleeping.
- Why it matters: It modifies the code, trains for 5 minutes, checks if the result improved, keeps or discards, and repeats.
- What to do: Validate with one small internal benchmark and compare against your current baseline this week.
Deep
Context
Instead, you are programming the program.md Markdown files that provide context to the AI agents and set up your autonomous research org.
What's new
AI agents running research on single-GPU nanochat training automatically One day, frontier AI research used to be done by meat computers in between eating, sleeping, having other fun, and synchronizing once in a while using sound wave interconnect in the ri...
Key details
- Research is now entirely the domain of autonomous swarms of AI agents running across compute cluster megastructures in the skies.
- The agents claim that we are now in the 10,205th generation of the code base, in any case no one could tell if that's right or wrong as the "code" is now a self-modifying binary that has grown beyond human comprehension.
- This repo is the story of how it all began.
- The idea: give an AI agent a small but real LLM training setup and let it experiment autonomously overnight.
Results & evidence
- The agents claim that we are now in the 10,205th generation of the code base, in any case no one could tell if that's right or wrong as the "code" is now a self-modifying binary that has grown beyond human comprehension.
- It modifies the code, trains for 5 minutes, checks if the result improved, keeps or discards, and repeats.
Limitations / unknowns
- Generalization outside curated tasks is still unclear.
Next-step validation checks
- Reproduce one claim with a public baseline and fixed evaluation settings.
- Check robustness on out-of-distribution or long-context cases.
- Track whether independent teams report matching results.