1d ago · 5 min read · NVIDIA's NemoClaw hit 298 points on Hacker News and stayed on the front page for over 24 hours. The comments tell the real story: agents modified production databases, one changed a superadmin password, and enterprise teams are panicking about what a...
Join discussion4d ago · 7 min read · Production APIs demand bulletproof error handling. While Go's explicit error handling gets you started, building resilient systems requires sophisticated patterns that go far beyond basic if err != nil checks. This guide covers advanced error handlin...
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Mar 12 · 24 min read · Last year, our ML team shipped a fraud detection model that worked perfectly in a Jupyter notebook. Precision was excellent. Recall numbers looked great. Everyone was excited – until we tried to deplo
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Mar 9 · 8 min read · Spent most of last winter doing something I should have done a year earlier: actually reading the TypeScript 5.x changelogs. Not skimming the headlines — reading them, then dropping each feature into a scratch project to see how it actually behaved. ...
Join discussionMar 9 · 5 min read · Most AI implementations fail at the handoff. Not the AI part — the models work fine. They fail at the moment when the AI realizes it shouldn't handle something, and needs to route it to a human or another system. I've seen this pattern repeat across ...
Join discussionMar 8 · 9 min read · It was a Tuesday morning when I opened our Datadog dashboard and saw 847 silent failures from the previous night's batch job. No alerts. No exceptions in our logs. Just a queue that had quietly eaten thousands of tokens and returned nothing useful. O...
Join discussionMar 7 · 5 min read · I've been building software for over 30 years. You'd think I'd know better by now. But here's the thing — every time we get a new AI agent demo working for a client, there's this moment. That beautiful moment where everything clicks. The LLM responds...
Join discussionMar 5 · 8 min read · Last November, my phone lit up at 2am with a Slack alert. Our content classification pipeline had been quietly running for six hours — and in that time, it had burned through $340 in OpenAI API credits and produced results that were roughly 70% garba...
Join discussionFeb 26 · 3 min read · Give a capable agent enough time and tokens, and it will work out most things on its own. I have watched this in production work. An agent navigating an API it was never explicitly told about, piecing together the right endpoints from documentation, ...
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