Meet arrowspace — an open-source library for curating and understanding LLM datasets across the entire lifecycle, from pre-training to inference. Instead of treating embeddings as static vectors, arrowspace turns them into graphs (“graph wiring”) so you can explore structure, not just similarity. That unlocks smarter RAG search (beyond basic semantic matching), dataset fingerprinting, and deeper insights into how different datasets behave. You can compare datasets, predict how changes will affect performance, detect drift early, and even safely mix data sources while measuring outcomes. In short: arrowspace helps you see your data — and make better decisions because of it.

AI coding platforms work best when you treat the AI as a junior engineer, not a replacement for your thinking. Break problems into small tasks, plan in Markdown before coding, and keep your context window lean - accuracy drops sharply past 50% capacity. Never debug in the same chat where you built the feature; the AI is biased by its own logic. For existing codebases, reference well-written code as examples. For new projects, define strict guardrails early - without them, the AI makes hundreds of arbitrary decisions that compound into a mess. The blog dives deep into all the patterns that work, the anti-patterns that silently kill your codebase, and strategies for both brownfield and greenfield projects - each illustrated with detailed diagrams. You stay the architect; the AI executes.

Marketplaces are about to get weird. With Pedro Chaves and Donné Stevenson: agents picking your house, negotiating deals, even talking to other agents for you. Less browsing. Less choice. More automation. Convenience… or giving up control?