Yesterday, I helped two people vibe-code solutions. Both were non-expert IT pros who can code but aren’t fluent. Person Alpha and I were on a call in the morning. Alpha needed to OCR PDF pages. I bragged, “Ten minutes. Let’s do it now!” But I was on a train with only my phone, so Alpha had to code. Vibe-coding was the only option. ...
LLMs are smarter than us in many areas. How do we manage them? This is not a new problem. VC partners evaluate deep-tech startups. Science editors review Nobel laureates. Managers manage specialist teams. Judges evaluate expert testimony. Coaches train Olympic athletes. … and they manage and evaluate “smarter” outputs in many ways: Verify. Check against an “answer sheet”. Checklist. Evaluate against pre-defined criteria. Sampling. Randomly review a subset. Gating. Accept low-risk work. Evaluate critical ones. Benchmark. Compare against others. Red-team. Probe to expose hidden flaws. Double-blind review. Mask identity to curb bias. Reproduce. Re-running gives the same output? Consensus. Aggregate multiple responses. Wisdom of crowds. Outcome. Did it work in the real world? For example: ...
How can non-programmers build apps? Claude.ai, Replit.com, Bolt.new, V0.dev, Pythagora.ai and a few other tools write and deploy code just based on a prompt. You should try them out. But how do you build the skill? Is there a tutorial?" I’m often asked. No, I can’t find a tutorial, but here is my suggestion. You probably can’t guess what’s easy or hard. e.g. “Take my picture in black & white” is FAR easier than “When’s the next lunar eclipse? ...
I’m planning to publish a 3-hour self-paced #onlinecourse. But I don’t know which topic would be more useful. One topic is #datascience tools for non-programmers. Another is a step-by-step guide to #datastorytelling for analysts. What’s more useful for you? Could you share with people, so I work on the more useful course? (Thanks 🙏) LinkedIn