Tag: startups

Roge draws a needed line between a generated artifact and a product. AI can produce a convincing interface or feature demo in an afternoon, but a product has users, a market, and a problem important enough to solve repeatedly. The implementation is only one component. Distribution, onboarding, support, reliability, security, and the accumulated knowledge of what people actually need are the parts a localhost reveal cannot show.

YouTube, Google, and Instagram are imperfect tests because their defenses include enormous networks and economies of scale, not just hard-to-write code. A more portable definition is dependability over time: people can discover the thing, trust it with real work, and expect someone to keep it useful. AI lowers the cost of testing an idea, which is genuinely valuable. It does not manufacture demand, differentiation, or commitment. The gap between demo and product is where most of the work moved, not where it disappeared.

Alexander Belanger's guide is useful because it connects ordinary application decisions to the database behaviors they eventually expose: schemas, query shape, index ordering, connection pressure, autovacuum, long-running writes, and lock-aware queue work. It is practical without pretending that every slow query needs an index, and the EXPLAIN (ANALYZE, BUFFERS) habit is exactly the one to cultivate before improvising a fix.

The Hacker News thread supplies an important amendment to the word "survival": a production database also needs proven backups, restore drills, and clear recovery ownership. Tuning plans and avoiding bloat keep the service healthy; they do not recover a lost or corrupt system. The best reading of this guide is as a strong operating playbook that sits beside, not ahead of, a tested recovery plan.