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 →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 […]
Read More →You’re deep in a refactor, you hand off a task to your AI coding agent, and you walk away to get coffee. When you come back, your project directory has been restructured — but so has everything else. The agent followed a chain of reasonable-looking steps that ended somewhere you never intended. This isn’t a […]
Read More →Large language models are increasingly extensible. Whether they’re called “skills,” “plugins,” “tools,” or “MCP servers,” the core idea is the same: let an LLM invoke external code, read external data, and act on external instructions. It’s also one of the most serious attack surfaces in modern AI systems. Table Of Contents What’s the problem? Prompt […]
Read More →Forward Deployed Engineer (FDE) is the most in-demand technical role in AI right now. Not because of the title, because of the problem it solves. AI research labs are shipping capabilities faster than enterprises can absorb them. The gap between what is technically possible and what is actually running in production is enormous, and closing […]
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