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 full-tour.webm If OpenClaw is an employee, Paperclip is the company.
- What happened: The open-source app everyone uses to manage agents at work Quickstart · Docs · GitHub · Discord · Twitter full-tour.webm If OpenClaw is an employee, Paperclip is the.
- Why it matters: The open-source app everyone uses to manage agents at work Quickstart · Docs · GitHub · Discord · Twitter full-tour.webm If OpenClaw is an employee, Paperclip is the.
- 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 full-tour.webm 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...
What's new
The open-source app everyone uses to manage agents at work Quickstart · Docs · GitHub · Discord · Twitter full-tour.webm 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...
Key details
- Bring your own agents, assign goals, and track your agents' work and costs from one dashboard.
- It looks like a task manager — but under the hood it has org charts, budgets, governance, goal alignment, and agent coordination.
- Manage business goals, not pull requests.
- | 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.
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 agent...
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: 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.
Source: arxiv | Overall 6.2/10 | Corroboration: 1
Signal 9.4
Novelty 4.0
Impact 2.0
Confidence 8.7
Actionability 6.5
Summary: arXiv:2605.26533v1 Announce Type: cross Abstract: Automated industrial inspection requires both precise defect localization and structured maintenance report generation; in.
- What happened: arXiv:2605.26533v1 Announce Type: cross Abstract: Automated industrial inspection requires both precise defect localization and structured maintenance report generation.
- Why it matters: arXiv:2605.26533v1 Announce Type: cross Abstract: Automated industrial inspection requires both precise defect localization and structured maintenance report generation.
- What to do: Validate with one small internal benchmark and compare against your current baseline this week.
Deep
Context
Submission history From: Malikussaid Malikussaid [view email][v1] Tue, 26 May 2026 04:27:38 UTC (1,874 KB) Current browse context: cs.CV References & Citations Loading...
What's new
arXiv:2605.26533v1 Announce Type: cross Abstract: Automated industrial inspection requires both precise defect localization and structured maintenance report generation; in current practice these tasks are handled separately, with linguistic interpretation...
Key details
- This paper describes a decoupled, edge-deployable pipeline for wind turbine blade inspection built from three components that each handle a distinct sub-task.
- The Eyes a YOLO26-x-obb oriented bounding-box detector localizes defects at dataset-native resolution.
- The Bridge a deterministic, parameter-free encoding module maps each detected bounding box to grid-referenced spatial tokens embedded in a structured prompt.
- The Brain a 4-bit quantized Qwen-2.5-1.5B model adapted with Quantized Low-Rank Adaptation (QLoRA) on 947 synthetically generated maintenance reports generates a structured JSON report from that prompt.
Results & evidence
- arXiv:2605.26533v1 Announce Type: cross Abstract: Automated industrial inspection requires both precise defect localization and structured maintenance report generation; in current practice these tasks are handled separately, with linguistic interpretation...
- The Brain a 4-bit quantized Qwen-2.5-1.5B model adapted with Quantized Low-Rank Adaptation (QLoRA) on 947 synthetically generated maintenance reports generates a structured JSON report from that prompt.
- Five ablation experiments, scored by BLEU-4, ROUGE-L, Hallucination Rate (HR), and an LLM-as-a-Judge rubric, compare the pipeline against a monolithic vision-language model (VLM) baseline and against partial configurations in which one component is removed.
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.
Source: hackernews | Overall 6.2/10 | Corroboration: 1
Signal 8.4
Novelty 5.1
Impact 2.4
Confidence 7.5
Actionability 6.5
Summary: Govern AI coding agents before they generate the code.
- What happened: Govern AI coding agents before they generate the code.
- Why it matters: Coding assistants generate code faster than teams can review it.
- What to do: Validate with one small internal benchmark and compare against your current baseline this week.
Deep
Context
Adjacent tools solve adjacent problems.
What's new
Govern AI coding agents before they generate the code.
Key details
- Stop architectural drift before it reaches review.
- Mneme catches violations at the moment AI generates code so your standards are enforced, not just documented.
- - Block banned frameworks, cross-boundary calls, and superseded decisions before generation - No re-prompting constraints apply on every call, every session, across every agent - Surface violations before the PR, not during it cut review overhead at the s...
- Coding assistants generate code faster than teams can review it.
Results & evidence
- No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.
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.
Source: hackernews | Overall 6.0/10 | Corroboration: 1
Signal 8.4
Novelty 5.1
Impact 2.8
Confidence 6.2
Actionability 5.2
Summary: North Korea tests AI-guided missiles for the first time
- What happened: North Korea tests AI-guided missiles for the first time
- Why it matters: Could materially affect near-term AI workflows.
- What to do: Track for corroboration and benchmark data before adopting.
Deep
Context
North Korea tests AI-guided missiles for the first time
What's new
North Korea tests AI-guided missiles for the first time
Key details
- North Korea tests AI-guided missiles for the first time
Results & evidence
- No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.
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.
Source: rss | Overall 4.7/10 | Corroboration: 1
Signal 7.3
Novelty 5.1
Impact 2.0
Confidence 3.0
Actionability 3.5
Summary: See how OpenAI, Thrive, and Crete built a self-improving tax agent with Codex, automating filings, improving accuracy, and accelerating workflows.
- What happened: See how OpenAI, Thrive, and Crete built a self-improving tax agent with Codex, automating filings, improving accuracy, and accelerating workflows.
- Why it matters: See how OpenAI, Thrive, and Crete built a self-improving tax agent with Codex, automating filings, improving accuracy, and accelerating workflows.
- What to do: Track for corroboration and benchmark data before adopting.
Deep
Context
See how OpenAI, Thrive, and Crete built a self-improving tax agent with Codex, automating filings, improving accuracy, and accelerating workflows.
What's new
See how OpenAI, Thrive, and Crete built a self-improving tax agent with Codex, automating filings, improving accuracy, and accelerating workflows.
Key details
- See how OpenAI, Thrive, and Crete built a self-improving tax agent with Codex, automating filings, improving accuracy, and accelerating workflows.
Results & evidence
- No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.
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.