Zalt Blog

Your AI is not underperforming, it is underinformed. A capable model with thin context gives confident, generic, sometimes wrong answers. The same model with rich, accurate context feels like it actually knows your business. Most failed AI pilots are context problems, not model problems.

Building complex graph runtimes that don’t melt down over time is harder than it looks. “When Graph Runtimes Stay Sane” digs into what actually keeps them stable.

AI is not deleting jobs, it is rewriting them. Every role is a bundle of tasks. AI pulls out the routine strands and the human core, judgment, trust, ownership, grows to fill the space. The people who thrive let the shape change instead of racing the machine on the part it wins.

Every item on your must-have list feels essential, which is exactly why it will sink your first version. The Vibecoder's Handbook method for scoping an MVP: find the one core job, build one thin working path through it, and put everything else on a later list.

Can you make money vibe coding? Yes, but not the way the hype says. The money is never in the coding. It is in picking a real problem, charging for it, and shipping something that lasts after launch. Here is the honest version.

New to Cursor? Here is the exact vibe coding loop I use: install, add a rules file, open Agent mode, prompt clearly, run it, and feed errors back until it works. Plus when to reach for it over a raw Claude or ChatGPT chat.

Vibe coding gets you a working prototype in days. Production is a different job entirely: real hosting, real error handling, monitoring, backups, and cost caps before real users show up. Here's the honest map of that handoff.

Vibe coding with confidence solo is one set of habits. Doing it on a team is a different job: real review for AI-written code, shared standards so five people's agents don't write five different codebases, and a clear line for which changes need a human before they ship.

Vibe coding with confidence is not a mindset, it is four habits and four tools repeated on every single change: small increments, version control, an AI that explains itself, and a second reviewer catching what the first one missed. None of it is exciting. That is exactly why it works.

Token spend in LLM apps isn't a billing problem, it's an architecture problem. If you're tuning it after launch, you're already paying for the mistake.