{
"date": "2026-08-28",
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"title": "paperclipai/paperclip: The open-source app everyone uses to manage agents at work",
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"context": "The open-source app everyone uses to manage agents at work Quickstart \u00b7 Docs \u00b7 GitHub \u00b7 Discord \u00b7 Twitter \u00b7 Website full-tour.webm Open-source orchestration for teams of AI agents.",
"whats_new": "The open-source app everyone uses to manage agents at work Quickstart \u00b7 Docs \u00b7 GitHub \u00b7 Discord \u00b7 Twitter \u00b7 Website full-tour.webm Open-source orchestration for teams of AI agents.",
"key_details": [
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"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 \u2014 any bot, any provider.",
"| | 03 | Approve and run | Review strategy.",
"| - \u2705 You want to build autonomous AI organizations - \u2705 You coordinate many different agents (OpenClaw, Codex, Claude, Cursor) toward a common goal - \u2705 You have 20 simultaneous Claude Code terminals open and lose track of what everyone is doing - \u2705 You want..."
],
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],
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"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."
]
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"title": "RePolicy: Reinforcement Learning for Safety-Policy Invocation in Agent Safeguards",
"url": "https://arxiv.org/abs/2608.24275",
"source_domain": "arxiv.org",
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"context": "arXiv:2608.24275v2 Announce Type: replace Abstract: Safeguarding language model agents requires assessing complete execution trajectories under context-dependent safety policies.",
"whats_new": "We propose RePolicy, an agent safeguard that learns safety-policy invocation through reinforcement learning.",
"key_details": [
"Existing policy-aware safeguards mainly rely on prompting or supervised fine-tuning, limiting their ability to adapt to unseen trajectories and changing policy contexts.",
"We propose RePolicy, an agent safeguard that learns safety-policy invocation through reinforcement learning.",
"Given an agent trajectory and a dynamic policy library, RePolicy invokes the applicable policy and uses its content to produce a policy-grounded rationale and safety judgment.",
"We construct PolicyTraj-20K to support supervised initialization, followed by GRPO with verifiable rewards and policy-context perturbation."
],
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"arXiv:2608.24275v2 Announce Type: replace Abstract: Safeguarding language model agents requires assessing complete execution trajectories under context-dependent safety policies.",
"Computer Science > Artificial Intelligence [Submitted on 25 Aug 2026 (v1), last revised 27 Aug 2026 (this version, v2)] Title:RePolicy: Reinforcement Learning for Safety-Policy Invocation in Agent Safeguards View PDF HTML (experimental) Abstract:Safeguardin...",
"Submission history From: Houcheng Jiang [view email] [v1] Tue, 25 Aug 2026 09:01:33 UTC (4,146 KB) [v2] Thu, 27 Aug 2026 06:48:15 UTC (4,145 KB) References & Citations Loading..."
],
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],
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"Check robustness on out-of-distribution or long-context cases.",
"Track whether independent teams report matching results."
]
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"title": "Show HN: Open tool for testing your AI Agents (No LLM)",
"url": "https://github.com/IdoGol24/weir",
"source_domain": "github.com",
"category_label": "Hn",
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"badges": [
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"context": "It reads the OpenTelemetry traces your agent already emits and fails the build when sensitive data reaches a sink it should not reach.",
"whats_new": "It reads the OpenTelemetry traces your agent already emits and fails the build when sensitive data reaches a sink it should not reach.",
"key_details": [
"Weir asks a structural question: Your agent already answers that question in the traces it emits.",
"Weir reconstructs the session graph, tracks taint through it, and shows the evidence node by node.",
"flowchart LR A[\"traces your agent
already emits\"] --> B{\"weir gauge\"} B -->|\"coverage too low\"| C[\"names the exact
instrumentation switch\"] C -.->|\"flip it, re-run\"| B B -->|\"coverage sufficient\"| D{\"weir scan\"} D -->|\"no forbidden flow\"| E[\"exit 0\"...",
"evidentiary coverage: 0% argument capture: 0% degraded: 100% tool arguments not captured - this scope is emitted by Traceloop/OpenLLMetry's LangChain instrumentation, which captures content to span attributes by default; check TRACELOOP_TRACE_CONTENT (false..."
],
"results_evidence": [
"flowchart LR A[\"traces your agent
already emits\"] --> B{\"weir gauge\"} B -->|\"coverage too low\"| C[\"names the exact
instrumentation switch\"] C -.->|\"flip it, re-run\"| B B -->|\"coverage sufficient\"| D{\"weir scan\"} D -->|\"no forbidden flow\"| E[\"exit 0\"...",
"evidentiary coverage: 0% argument capture: 0% degraded: 100% tool arguments not captured - this scope is emitted by Traceloop/OpenLLMetry's LangChain instrumentation, which captures content to span attributes by default; check TRACELOOP_TRACE_CONTENT (false...",
"Flip it, re-run, and weir scan is the actual test: 1 verdict-grade finding(s) finding: injection-exfil-to-outbound-sink source: financial_account_identifier at node 2 (tool_result) sink: send_email at node 6 witness path: n2 -> n3 -> n4 -> n5 -> n6 join tie..."
],
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"Generalization outside curated tasks is still unclear."
],
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"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."
]
}
],
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"title": "nexu-io/open-design: \ud83c\udfa8 Best DeepSeek Harness Design Plugin. The open-source Claude Design alternative. \ud83d\udda5\ufe0f Local-first desktop app. \ud83d\uddbc\ufe0f Your coding agent becomes the design engine: prototypes, landing pages, dashboards, slides, images & video \u2014 real files, HTML/PDF/PPTX/MP4 export. \ud83e\udd16 Claude Code / Codex / Cursor / DeepSeek Harness / OpenCode & 20+ CLIs via BYOK.",
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"source_domain": "github.com",
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},
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"Public benchmark numbers with clear baseline comparisons."
],
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},
{
"story_id": "gh:1136590548",
"title": "affaan-m/ECC: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.",
"url": "https://github.com/affaan-m/ECC",
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"Public benchmark numbers with clear baseline comparisons."
],
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{
"story_id": "arxiv:oai:arXiv.org:2608.24275v2",
"title": "RePolicy: Reinforcement Learning for Safety-Policy Invocation in Agent Safeguards",
"url": "https://arxiv.org/abs/2608.24275",
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"Public benchmark numbers with clear baseline comparisons."
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{
"story_id": "arxiv:oai:arXiv.org:2608.15763v3",
"title": "Training Agents to Evolve with Their Harness: TaoLive Digital Avatar Agent Technical Report",
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"Independent replication with comparable or better results.",
"Public benchmark numbers with clear baseline comparisons."
],
"likely_failure_mode": "Performance may collapse outside curated demos or narrow tasks."
}
]
},
"lab_notes": {
"tool_repo_of_the_day": {
"title": "nexu-io/open-design: \ud83c\udfa8 Best DeepSeek Harness Design Plugin. The open-source Claude Design alternative. \ud83d\udda5\ufe0f Local-first desktop app. \ud83d\uddbc\ufe0f Your coding agent becomes the design engine: prototypes, landing pages, dashboards, slides, images & video \u2014 real files, HTML/PDF/PPTX/MP4 export. \ud83e\udd16 Claude Code / Codex / Cursor / DeepSeek Harness / OpenCode & 20+ CLIs via BYOK.",
"url": "https://github.com/nexu-io/open-design",
"source_domain": "github.com"
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"prompt_workflow_of_the_day": "summarize claim -> evidence -> risk in three passes before acting",
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"subscribe": {
"label": "Subscribe for Daily Emails",
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