Artificial Intelligence On 8 May 2026, Thariq Shihipar, an engineer on the Claude Code team at Anthropic, published a manifesto that’s been circulating ever since: drop markdown, switch to HTML for your specs, implementation plans and reports. The piece is clever, well written, and structurally an act of strategic marketing dressed up as personal experience. On a narrow subset (throwaway editors, interactive playgrounds, prototypes with sliders), the author is right; on everything else, his blanket enthusiasm conceals six blind spots. Token cost waved away, semantic HTML flipped into presentational HTML, loss of reviewability, the maintenance paradox that traps the user inside the production loop, reading that gets skimmed rather than scrutinised, an attack surface opened up by indirect prompt injection. An analysis of a poorly disguised strategic signal, plus a pragmatic decision matrix for sorting out where HTML deserves to be adopted and where markdown remains, by design, the right pivot format.
Artificial Intelligence Every session, Claude Code reopens your codebase like a visitor who has never set foot in it. It unfolds the README, fires off its greps, opens whole files to reconstruct an architecture it had already pieced together the day before. Understand-Anything, an open-source project released in March 2026, offers another path: index the codebase as a local knowledge graph, versioned like a lock-file, queried on demand by the agent instead of being re-read on every turn. This isn’t a token optimisation, it’s an architectural shift that extends the “RAG is dead, long live the Agent” thesis right into the code itself. Multi-agent pipeline, comparative economics, the limits of a project just six months old: what this break changes for Claude Code and for your workflow.
Artificial Intelligence Converting a technical PDF into RAG-ready markdown isn’t just a matter of running LlamaParse over it. On a 51-page chapter of the Funktionsrahmen MSS60, the Siemens documentation for the engine control unit of the BMW M3 E92 and M5 E60, I discovered that the interesting work begins after the automatic parsing. The semantic decisions that really condition retrieval quality, preserving the source language rather than translating, breaking dense tables into individual subsections, supplementing diagrams with French prose and pseudo-code, no parser will make them for you. This LLM-assisted work is done only once per document, its marginal cost per query is zero, and its upfront investment pays for itself in user-hours from the very first week of use. Field notes from two evenings of conversion that turned my understanding of what really makes a RAG pipeline worthwhile on its head.
Artificial Intelligence We spent two years debating the model. GPT-5 versus Claude 4.5, Gemini versus Mistral, million-token context versus prefix cache. Meanwhile, the elephant in the room stayed invisible: 90% of production RAG pipelines are swallowing a mush of badly extracted pixels, then act surprised when the LLM hallucinates. The problem isn’t in the brain. It’s in […]
Artificial Intelligence If you’re using an LLM with your own documents (or thinking about it) you’ll run into RAG, Retrieval-Augmented Generation. The premise sounds simple: feed your files to the model so it answers better. In reality it’s a pipeline of seven technical decisions, where each link determines whether your AI answers from your data or invents with confidence. This guide covers the full mechanics with the 2026 benchmarks, the concrete tools (NotebookLM, ChromaDB, LangChain), and the traps I hit building my own pipelines. From theory to a working Python pipeline in 30 lines.