A Haystack pipeline can be perfectly wired and still unsafe. The retriever returns documents. Every component did its job. But if untrusted text moved through the pipeline as ordinary context, the trust boundary was lost. That is the problem this post is about. Not bad Python. A valid component connection only says: this value fits the next component It does not say: this value is safe to influen
Physics has always been one of those subjects that feels like a maze of invisible forces and abstract variables. To make learning more intuitive, I recently launched Physics AI Slover, a specialized platform designed to provide step-by-step solutions and visual derivations for students struggling with mechanics, electromagnetism, and thermodynamics. Solver Mode is for that 2 AM panic when you ju
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🌟 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
Comparison: Haystack 2.0 vs. RAGatouille 0.3 for Building High-Accuracy RAG Pipelines for Developer Docs Retrieval-Augmented Generation (RAG) has become the standard for building LLM-powered tools that answer questions using private or domain-specific data. For developer documentation (dev docs) — which includes technical jargon, versioned APIs, code snippets, and structured reference material —