A claim has been making the rounds in AI coding circles: stop prompting your coding agents and start designing loops that prompt them for you. Like most things, it gets repeated far more than it gets explained. This is the practical version — what an agent loop is, why it matters, and what one actually […]
Read More →You’ve got 100 GB of PDFs, notes, and exported chat logs you’d love to query with natural language. So you reach for the standard RAG playbook: chunk everything, embed it, store the vectors in FAISS or a hosted vector DB. Then you check the index size and it’s 150–700 GB — larger than the data […]
Read More →A hands-on introduction to Flue, an experimental TypeScript framework for building server-side LLM agents. Covers the agent/harness/session model, three working examples from a simple translator to a container-backed coding agent, and an honest look at the trade-offs — so you can decide whether Flue fits your stack before you commit. Table of Contents You’ve decided […]
Read More →This post draws on research across blogs, videos, and company profiles in the AI native space. What follows is my commentary on the patterns I kept seeing — the framework, the workflows, and the thinking that separates organizations genuinely operating this way from those just using AI tools. Being “AI native” isn’t just about using […]
Read More →A 2026 study found 26% of agent skills from public marketplaces contain vulnerabilities — and 5% show patterns of deliberate malice. NVIDIA’s SkillSpector scans skills before installation using static analysis and optional LLM review. This post covers what it catches, how to run it in CI, and the blind spots you still own. Table Of […]
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