Introduction What is Trooper The real problem: context loss on fallback The solution: three-layer context compaction Anchor : The first two turns of the conversation are always preserved. These establish the original intent and set the tone. SITREP : The middle turns get compressed into a structured summary called a SITREP. It extracts intent, entities, open loops, recent actions, and resolved ite
Clandestine network smuggling Starlink tech into Iran to beat internet blackout
You've found a contributor whose work you depend on. The maintainer of a package you use, a developer who fixed something for you upstream, the author of a CVE you need to coordinate with. You have their GitHub username. You'd like their email. You'd think this would be a GET away. It isn't. Here's why — and what it actually takes to find one. GET /users/:login returns an email field. For the vast
You just got back a 300-line API response. Somewhere inside three levels of nesting is the email field you actually need. So you write a loop, then another loop, then a conditional — and now you're maintaining brittle traversal code that breaks every time the API schema shifts. There's a better way: JSONPath. JSONPath is a query language for JSON, similar to how XPath works for XML. Instead of wri
Most teams do not need another way to paste SQL into a chat box. They need a safer way for AI tools to answer real questions from live data. PostgreSQL often already holds the context: accounts, subscriptions, events, product usage, operational state. The problem is not that the data is missing. The problem is that every useful question still becomes a handoff. Without a governed access layer, the
Why Does Switching Embedding Models Make Such a Huge Difference? In the first four articles, we built the RAG pipeline, tuned parameters, and mastered chunking strategies. But there's one question we haven't dived into: After your documents are chunked, how do they become vectors? This process is called Embedding. It transforms human-readable text into machine-computable vectors. The choice of E
I've spent roughly two years debugging production AI systems for engineering teams that have already shipped, with production traffic, real users, and real cost surfaces. Different stacks (LangChain, LlamaIndex, vanilla SDK calls, custom agent harnesses), different audiences (B2B SaaS, internal tools, consumer features), and different scales. But the failure modes are remarkably consistent. Here's
🌟 مراجعة كورس Teaching AI Fluency من Anthropic كلنا نتحدث عن الذكاء الاصطناعي، لكن كم منا يعرف كيف يُعلِّمه بشكل صحيح؟ أنهيت للتو كورس Teaching AI Fluency من Anthropic، وكان مختلفاً عن كل ما توقعته. الكورس لا يتحدث عن "كيف تستخدم الذكاء الاصطناعي" بل عن كيف تُصمّم تجارب تعليمية حوله — وهذا فرق جوهري. حلقة التفويض والحرص (Delegation-Diligence Loop): متى تثق بالذكاء الاصطناعي ومتى تتحقق. حلقة