AgentShield
Find every hidden agent. Fix what's exposed.
What it does
Every company deploying AI agents has shadow agents they don't know about, MCP endpoints with zero authentication, and permissions scoped 300% beyond what's needed. AgentShield finds all of it in under 30 minutes. We map your entire agent ecosystem — every agent, every connection, every exposed endpoint — and give you a prioritized risk report with fix paths. First scan is free. No credit card. No sales call. Just the truth about your agent stack.
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Agent Browser ShieldJun 2026 · github.com · ▲109Block prompt inject & cut token costs for AI browser agents
- IMI'm building a "work visa" API for AI agents2025 · agentvisa.dev · ▲6
Hey HN, I’m Chris, a solo dev in Melbourne AU. For the past month I've been spending my after work hours building AgentVisa. I'm both excited (and admittedly nervous) to be sharing it with you all today. I've been spending a lot of time thinking about the future of AI agents and the more I experimented, the more I realized I was building on a fragile foundation. How do we build trust into these systems? How do we know what our agents are doing, and who gave them permission? My long-term vision is to give developers an "Agent Atlas" - a clear map of their agentic workforce, showing where…
- AAArguSeek – Agent‑first deep‑search API for hard‑to‑find dev answers2025 · agruseek.com · ▲5
Hi HN, Even the smartest AI coding agents stall when the fix isn’t in their training data. AgruSeek runs an agentic search loop across ~30 M developer sources to dig up solutions normal web search misses. REAL‑WORLD USES • Found an undocumented `--runtime‑bypass` flag (buried in a 2017 gist) • Pulled actual Claude Code pricing from forum anecdotes - no “contact us” paywalls • Traced a race condition by cross‑linking five issue trackers across forks WHY POST NOW We’ve abused AgruSeek internally for three weeks; we’d love outside stress tests. Access is free (limited seats for Beta, no…
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
Launched alongside, March 2026
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Switch from ChatGPT to Claude with import memory feature
AI · Mar 2026 · claude.com
