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
We debate endlessly about whether AI will ever achieve consciousness, but we forget how consciousness actually compiled in the first place. It wasn’t spawned in a vacuum; it was forged by the brutal necessity of survival. For millions of iterations over millions of years, early cognition was nothing but pure instinct and bloodlust—refined only by the fight for the right to exist. Humanity is not
FutureMe has 15 million letters in its database. They've been there since 2002. Some of them will be there in 2050. Evengood will have zero. This week I shipped The Quiet Letter — a feature where you write to your future self today, we email it on a date you pick, and we hard-delete the row from our database within 24 hours of sending it. The email is the only artifact. We don't keep a copy. Every
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 —