Source: github | Overall 8.0/10 | Corroboration: 1
Signal 10.0
Novelty 6.2
Impact 8.2
Confidence 7.0
Actionability 6.5
Summary: The agent harness performance optimization system.
- What happened: The agent harness performance optimization system.
- Why it matters: The agent harness performance optimization system.
- What to do: Validate with one small internal benchmark and compare against your current baseline this week.
Deep
Context
The agent harness performance optimization system.
What's new
Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Key details
- Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
- Language: English | Português (Brasil) | 简体中文 | 繁體中文 | 日本語 | 한국어 | Türkçe | Русский | Tiếng Việt | ไทย | Deutsch | Español 211.9K+ stars | 32.5K+ forks | 230+ contributors | 12+ language ecosystems | Cross-harness agent workflows Language / 语言 / 語言 / Dil /...
- Built from real-world multi-harness engineering workflows.
- A complete system: skills, instincts, memory optimization, continuous learning, security scanning, and research-first development.
Results & evidence
- Language: English | Português (Brasil) | 简体中文 | 繁體中文 | 日本語 | 한국어 | Türkçe | Русский | Tiếng Việt | ไทย | Deutsch | Español 211.9K+ stars | 32.5K+ forks | 230+ contributors | 12+ language ecosystems | Cross-harness agent workflows Language / 语言 / 語言 / Dil /...
- Production-ready agents, skills, hooks, rules, MCP configurations, and legacy command shims evolved over 10+ months of intensive daily use building real products.
- ECC v2.0.0 adds the public Hermes operator story on top of that reusable layer: start with the Hermes setup guide, then review the 2.0.0 release notes and cross-harness architecture.
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: 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: arxiv | Overall 6.3/10 | Corroboration: 1
Signal 9.4
Novelty 4.0
Impact 2.0
Confidence 9.5
Actionability 6.5
Summary: arXiv:2606.14516v1 Announce Type: new Abstract: AI evaluations are widely used for testing and understanding progress.
- What happened: We introduce Every Eval Ever, the first shared schema and community-crowdsourced repository for AI evaluation results.
- Why it matters: arXiv:2606.14516v1 Announce Type: new Abstract: AI evaluations are widely used for testing and understanding progress.
- What to do: Validate with one small internal benchmark and compare against your current baseline this week.
Deep
Context
However, the diverse evaluators bring with them inconsistencies that challenge analysis and comparison.
What's new
arXiv:2606.14516v1 Announce Type: new Abstract: AI evaluations are widely used for testing and understanding progress.
Key details
- However, the diverse evaluators bring with them inconsistencies that challenge analysis and comparison.
- First, results are saved in incompatible formats, scattered across leaderboards, papers, blog posts, evaluation harness logs, and custom repositories.
- Second, results are created by different evaluation frameworks, which produce divergent scores for nominally identical evaluations and record metadata inconsistently, hindering comparison, cross-community evaluation science, cost reduction, and reuse.
- We introduce Every Eval Ever, the first shared schema and community-crowdsourced repository for AI evaluation results.
Results & evidence
- arXiv:2606.14516v1 Announce Type: new Abstract: AI evaluations are widely used for testing and understanding progress.
- Computer Science > Artificial Intelligence [Submitted on 12 Jun 2026] Title:Every Eval Ever: A Unifying Schema and Community Repository for AI Evaluation Results View PDF HTML (experimental)Abstract:AI evaluations are widely used for testing and understandi...
Limitations / unknowns
- However, the diverse evaluators bring with them inconsistencies that challenge analysis and comparison.
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 6.4/10 | Corroboration: 1
Signal 9.4
Novelty 5.1
Impact 2.0
Confidence 8.7
Actionability 6.5
Summary: arXiv:2606.02320v2 Announce Type: replace Abstract: Deep Research Agents have shown strong capability in multi-step information retrieval, reasoning, and long-form report.
- What happened: To address this gap, we introduce TVIR (Text-Visual Interleaved Report Generation), which includes TVIR-Bench, a benchmark of 100 expert-curated multimodal deep research.
- Why it matters: arXiv:2606.02320v2 Announce Type: replace Abstract: Deep Research Agents have shown strong capability in multi-step information retrieval, reasoning, and long-form.
- What to do: Validate with one small internal benchmark and compare against your current baseline this week.
Deep
Context
arXiv:2606.02320v2 Announce Type: replace Abstract: Deep Research Agents have shown strong capability in multi-step information retrieval, reasoning, and long-form report generation, but existing benchmarks and systems remain predominantly text-centric, wit...
What's new
arXiv:2606.02320v2 Announce Type: replace Abstract: Deep Research Agents have shown strong capability in multi-step information retrieval, reasoning, and long-form report generation, but existing benchmarks and systems remain predominantly text-centric, wit...
Key details
- To address this gap, we introduce TVIR (Text-Visual Interleaved Report Generation), which includes TVIR-Bench, a benchmark of 100 expert-curated multimodal deep research tasks that require visual elements to serve specific analytical sub-goals, and TVIR-Age...
- We further develop a dual-path evaluation framework that combines Textual Assessment and Visual Assessment.
- Experiments across nine deep research systems show that TVIR-Agent achieves strong overall performance, underscoring the importance of explicit multimodal design and evaluation for evidence-driven report generation.
- Computer Science > Computation and Language [Submitted on 1 Jun 2026 (v1), last revised 11 Jun 2026 (this version, v2)] Title:TVIR: Building Deep Research Agents Towards Text-Visual Interleaved Report Generation View PDF HTML (experimental)Abstract:Deep Res...
Results & evidence
- arXiv:2606.02320v2 Announce Type: replace Abstract: Deep Research Agents have shown strong capability in multi-step information retrieval, reasoning, and long-form report generation, but existing benchmarks and systems remain predominantly text-centric, wit...
- To address this gap, we introduce TVIR (Text-Visual Interleaved Report Generation), which includes TVIR-Bench, a benchmark of 100 expert-curated multimodal deep research tasks that require visual elements to serve specific analytical sub-goals, and TVIR-Age...
- Computer Science > Computation and Language [Submitted on 1 Jun 2026 (v1), last revised 11 Jun 2026 (this version, v2)] Title:TVIR: Building Deep Research Agents Towards Text-Visual Interleaved Report Generation View PDF HTML (experimental)Abstract:Deep Res...
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 8.6
Novelty 4.0
Impact 5.1
Confidence 7.5
Actionability 3.5
Summary: A sourced model and short report on a single question: Can Europe stand up a sovereign frontier-class AI model now, by federating the public compute it already owns, while the.
- What happened: A sourced model and short report on a single question: Can Europe stand up a sovereign frontier-class AI model now, by federating the public compute it already owns.
- Why it matters: A sourced model and short report on a single question: Can Europe stand up a sovereign frontier-class AI model now, by federating the public compute it already owns.
- What to do: Track for corroboration and benchmark data before adopting.
Deep
Context
A sourced model and short report on a single question: Can Europe stand up a sovereign frontier-class AI model now, by federating the public compute it already owns, while the gigawatt datacenters it is planning take years to connect to the grid?
What's new
Federated with low-communication (DiLoCo-style) training, the compute Europe already has can deliver a frontier-class model around 2028, against around 2033 for a new gigawatt campus.
Key details
- The answer the model gives is yes, as a stopgap.
- Europe already operates tens of exaflops of public AI compute across the EuroHPC supercomputers and the national AI Factories.
- A 1 GW campus, by contrast, waits a mean of 7.6 years for grid power.
- Federated with low-communication (DiLoCo-style) training, the compute Europe already has can deliver a frontier-class model around 2028, against around 2033 for a new gigawatt campus.
Results & evidence
- A 1 GW campus, by contrast, waits a mean of 7.6 years for grid power.
- Federated with low-communication (DiLoCo-style) training, the compute Europe already has can deliver a frontier-class model around 2028, against around 2033 for a new gigawatt campus.
- Europe Has Tens of Exaflops at Home." euromesh/ ├── README.md ├── requirements.txt ├── paper/ │ ├── compute-at-home.md / .pdf the report │ ├── grid_queue_dataset.md sourced 1 GW vs 40 MW grid-connection lead times │ ├── eurohpc_substrate.md sourced EU publi...
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.