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 Warning Official sources only.
- Install ECC only from verified channels: the GitHub repository github.com/affaan-m/ECC, the npm packages ecc-universal and ecc-agentshield, the GitHub App, the plugin slug ecc@ecc, and the project website ecc.tools.
- Third-party re-uploads and unofficial mirrors are not maintained or reviewed by the project and may contain malware.
Results & evidence
- 211.9K+ stars | 32.5K+ forks | 230+ contributors | 12+ language ecosystems | Cross-harness agent workflows Language / 语言 / 語言 / Dil / Язык / Ngôn ngữ / Idioma English | Português (Brasil) | 简体中文 | 繁體中文 | 日本語 | 한국어 | Türkçe | Русский | Tiếng Việt | ไทย | Deu...
- 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 6.4/10 | Corroboration: 1
Signal 9.4
Novelty 5.1
Impact 2.0
Confidence 8.7
Actionability 6.5
Summary: arXiv:2606.20512v1 Announce Type: cross Abstract: LLM-based coding agents need higher-level operational knowledge about a repository (which files house which subsystems, how to.
- What happened: In this paper we show that how the guidance is produced is the decisive variable, and introduce \emph{probe-and-refine tuning}: a procedure that uses synthetic bug-fix.
- Why it matters: Engineers typically maintain \texttt{AGENTS.md} files to supply this context as instructions for coding agents, but whether they help is contested: recent studies.
- What to do: Validate with one small internal benchmark and compare against your current baseline this week.
Deep
Context
Engineers typically maintain \texttt{AGENTS.md} files to supply this context as instructions for coding agents, but whether they help is contested: recent studies disagree on whether LLM-generated guidance improves or harms agent performance.
What's new
arXiv:2606.20512v1 Announce Type: cross Abstract: LLM-based coding agents need higher-level operational knowledge about a repository (which files house which subsystems, how to run the test suite, which workflows have historically led to wrong fixes) that d...
Key details
- Engineers typically maintain \texttt{AGENTS.md} files to supply this context as instructions for coding agents, but whether they help is contested: recent studies disagree on whether LLM-generated guidance improves or harms agent performance.
- In this paper we show that how the guidance is produced is the decisive variable, and introduce \emph{probe-and-refine tuning}: a procedure that uses synthetic bug-fix probes to iteratively diagnose and patch a repository's guidance file through single-shot...
- On SWE-bench Verified across four independent trials with Qwen3.5-35B-A3B at 200 steps, probe-and-refine achieves 33.0\,\% mean resolve rate vs.\ 28.3\,\% for the static knowledge base used to initialize it and 25.5\,\% for an unguided baseline ($p < 0.001$...
- The improvement comes from coverage rather than precision: refined guidance produces evaluable patches for 14.5 percentage points (pp) more instances while per-patch precision remains statistically constant ($\sim$59\,\%, $p = 0.119$), showing that improved...
Results & evidence
- arXiv:2606.20512v1 Announce Type: cross Abstract: LLM-based coding agents need higher-level operational knowledge about a repository (which files house which subsystems, how to run the test suite, which workflows have historically led to wrong fixes) that d...
- On SWE-bench Verified across four independent trials with Qwen3.5-35B-A3B at 200 steps, probe-and-refine achieves 33.0\,\% mean resolve rate vs.\ 28.3\,\% for the static knowledge base used to initialize it and 25.5\,\% for an unguided baseline ($p < 0.001$...
- The improvement comes from coverage rather than precision: refined guidance produces evaluable patches for 14.5 percentage points (pp) more instances while per-patch precision remains statistically constant ($\sim$59\,\%, $p = 0.119$), showing that improved...
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:2504.02885v2 Announce Type: replace Abstract: Automated medical report generation (MRG) is increasingly used to reduce the burden of manual reporting and for decision.
- What happened: We introduce a perception-driven long reasoning process that precedes report generation and incorporates radiology-specific knowledge as guidance.
- Why it matters: Our experiments demonstrate that Med-R2 effectively enhances the capability of pathological features perception and diagnosis accuracy for MRG via fine-tuned LVLMs.
- What to do: Validate with one small internal benchmark and compare against your current baseline this week.
Deep
Context
arXiv:2504.02885v2 Announce Type: replace Abstract: Automated medical report generation (MRG) is increasingly used to reduce the burden of manual reporting and for decision support.
What's new
Firstly, direct SFT enables LVLMs to generate medical reports directly without an intermediate thinking process of pathological feature perception and diagnostic reasoning.
Key details
- Large vision-language models (LVLMs) hold great promise for automated MRG due to their fine-grained image-text alignment and advanced text-generation capabilities.
- Currently, state-of-the-art MRGs primarily focus on adapting pre-trained LVLMs with direct supervised fine-tuning (SFT), a fine-tuning strategy with medical image-report pairs.
- However, several factors limit the performance of these LVLMs.
- Firstly, direct SFT enables LVLMs to generate medical reports directly without an intermediate thinking process of pathological feature perception and diagnostic reasoning.
Results & evidence
- arXiv:2504.02885v2 Announce Type: replace Abstract: Automated medical report generation (MRG) is increasingly used to reduce the burden of manual reporting and for decision support.
- Computer Science > Computation and Language [Submitted on 2 Apr 2025 (v1), last revised 18 Jun 2026 (this version, v2)] Title:Med-R2: Perception and Reflection-driven Complex Reasoning for Medical Report Generation View PDF HTML (experimental)Abstract:Autom...
- Submission history From: Hao Wang [view email][v1] Wed, 2 Apr 2025 08:18:54 UTC (1,248 KB) [v2] Thu, 18 Jun 2026 12:55:35 UTC (740 KB) References & Citations Loading...
Limitations / unknowns
- However, several factors limit the performance of these LVLMs.
- This causes a potential failure to perceive pathological features and thus leads to misdiagnosis.
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 2.6
Confidence 7.5
Actionability 3.5
Summary: Taste – Zero-config session-taste packer for AI agents
- What happened: Taste – Zero-config session-taste packer for AI agents
- Why it matters: Could materially affect near-term AI workflows.
- What to do: Track for corroboration and benchmark data before adopting.
Deep
Context
Taste – Zero-config session-taste packer for AI agents
What's new
Taste – Zero-config session-taste packer for AI agents
Key details
- Taste – Zero-config session-taste packer for AI agents
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.