CrabTrap: An LLM-as-a-judge HTTP proxy to secure agents in production
CrabTrap:用大型語言模型當保鑣的 HTTP 代理,保護生產環境中的 AI 代理
Brex has open-sourced CrabTrap, a security tool that uses large language models as a judge to monitor and control AI agents in production environments. The HTTP proxy sits between your application and external services, using an LLM to evaluate whether each request from an AI agent is safe before it goes through. Think of it as a bouncer at the door of your AI system—it watches what your agent is trying to do and blocks anything suspicious. This addresses a real problem: as companies deploy autonomous AI agents to handle tasks like API calls or data access, they need guardrails to prevent agents from making mistakes or being exploited. CrabTrap lets you define policies and have an LLM enforce them in real-time, making it easier to run AI agents safely without constant manual oversight.
OpenAI is hosting a livestream event. Details about the specific announcements, product launches, or demonstrations will be revealed during the broadcast.
The last time OpenAI did an unannounced livestream, they dropped GPT-4 Turbo and changed pricing overnight
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ChatGPT Images 2.0
OpenAI is launching ChatGPT Images 2.0 with major upgrades to image generation capabilities. Watch the livestream announcement at https://openai.com/live/
OpenAI is positioning this as a direct competitor to established image generation tools, suggesting they're confident enough to challenge the current market leaders
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The "just wait 6 months" argument from 2025 survived exactly one iteration
Throughout 2025 the standard response to any complaint about an LLM was some version of "just wait 3-6 months, the next generation will handle this effortlessly." The argument was everywhere. Every limitation was temporary, every missing capability was a few iterations away, every autonomous agent demo was a preview of imminent reality.
It's April 2026 now and worth checking how that held up.
On r/ClaudeAI this week there's a long thread about Opus 4.7 where multiple users argue it's a regress
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Mistral Medium 3.5 on AMD Strix Halo: Painfully Slow (Plan for Overnight Runs)
Someone actually tested Mistral Medium 3.5 on AMD's new Strix Halo chip, and the results are... not great. For a 48k-token prompt with 4k thinking tokens, it took about 2 hours just to get an answer about code architecture. Yeah, you read that right—two hours. The takeaway: if you want to run this locally on Strix Halo, queue it up before bed. The technical setup involved heavy optimization (Q5_K_XL quantization, GPU acceleration with -ngl 999, cache reuse), but even with all that tuning, it's still a crawl. Not exactly the "instant local AI" dream, but hey, at least it works.