Until last week the public verify endpoint at GET /api/v1/verify/{signature_id} returned a single boolean: {verified: true} or {verified: false}. That works for the buyer's first-glance UX. It does not work for a regulator or a court trying to figure out what actually went wrong. An older trust-data-infrastructure design we read for context lays this out: their verify can return Valid Document, In
Building a Guarded Control Plane for Tomcat Automation I built and maintain an open-source project called InfraPilot, and I’m looking for technical feedback from DevOps, SRE, Ansible, and platform engineering practitioners. InfraPilot is a natural-language control plane for Tomcat operations. The goal is to let an operator use plain-English commands for Tomcat deployment, scaling, verification,
Separating what AI does well from what code does well
https://raw.githubusercontent.com/sanjaybk7/agentic-guard/main/docs/demo.gif If you're building LLM agents with LangGraph or the OpenAI Agents SDK, your architecture might already be vulnerable — and no runtime tool will catch it The problem nobody is talking about
المشكلة لو بتكتب عربي وإنجليزي مع بعض في أي موقع، البراوزر بيتلخبط في الاتجاه: جملة زي "مرحبا API بتاعك كويس" — كلمة API بتتعكس وتتقرأ غلط بسبب الـ Unicode Bidi Algorithm. المشكلة دي مش في موقع معين — هي موجودة في كل المواقع. حتى Claude.ai وChatGPT نفسهم بيعانوا منها. بدل ما نضبط dir="rtl" على الـ element بس، عملت BiDi parser حقيقي يقسّم النص: "مرحبا API tools بتاعك" ↓ tokenizer [arabic]
A rejection is data. Until last week we were throwing it away. If an attacker submitted a forged signature against the public verify endpoint, we returned 404 signature_not_found and that was the entire footprint. Same for a cross-org access attempt on the replay endpoint, an agent that got suspended mid-run trying to sign once more, or a probe walking through random sig_* ids. An older trust-data
We are currently witnessing a massive shift in AI development. We’ve moved past the "Chatbot" era and into the era of Agentic Systems—AI that doesn’t just suggest text, but actually executes code, moves money, and modifies databases. However, there is a fundamental architectural flaw in how most agents are built today: we are giving "Intelligence" and "Authority" to the same probabilistic model.
At 100 million 768-dimensional embeddings, the gap between top-tier vector search tools isn't just measurable—it's existential. In our 6-month benchmark across 12 hardware configurations, FAISS 1.9 delivered 4.2x lower p99 latency than Chroma 0.6, while Pinecone 1.6 cost 11x more than self-hosted FAISS for equivalent throughput. Here's what the numbers actually say. What Chromium versions are ma