LLM

Stop Building Scaffolding for LLMs — They’re Already Doing It Themselves

Developers spend weeks building in-memory mapping layers to prevent LLM overload, but the models already generate their own Python code to handle large files. The real bottleneck is our failure to trust the LLM’s emergent problem-solving. Stop over-engineering — let the model self-orchestrate.

I Asked an AI to Judge My Hacker News Comments. The Real Lesson Wasn’t About Me.

A developer built a web app using Fable 5 to analyze HN comment histories. While the model delivered eerily accurate personality assessments, the creator discovered trivial coding errors in the app itself—cache bugs, outdated APIs—proving that even top-tier LLMs need human review. The real lesson isn’t about vanity; it’s about the gap between AI’s perceived omniscience and its practical fallibility.