AI Coding Guides

Nine of ten first vibe-coding projects die of scope creep. The cautionary tale of "the AI Notion+Slack" archived in week three, the "demoable in a week" rule, four knives for cutting features, and five stall-warning signals: the goal isn't big, it's done.

Losing more money with every user is the most expensive bug in AI products. The arithmetic of per-user gross margin: why you must price to the P90 user, not the average; the photo app burned alive by its own virality; three cost levers and three margin red lines.

How many "god-tier prompts" are rotting in your bookmarks? The third time you paste the same prompt, it should graduate from a one-off instruction into an installable Skill. The real difference between prompts and Skills, three upgrade signals, and a hands-on skill directory walkthrough.

After a year of AI coding, the bottleneck moved from 'writing code' to 'saying clearly what you want.' A seven-section PRD template for agents, three real bad-vs-good cases, and four advanced techniques — plus why PRD skill is replacing coding skill as the scarce ability.

The demo is 10%; shipping is the other 90%. A pre-launch checklist built for AI-generated projects: security, data, observability, performance & cost, release & rollback — because agents have three systematic blind spots, and 'shippable' judgment is yours.

Why do AI-generated interfaces always have that "flavor"? Purple-blue gradients, rounded cards, emoji everywhere. Taste isn't talent — it's rules that can be written down: lead with references, design tokens first, typography decides 80% of the look. AI has no taste, but it can execute yours.

AI now writes in a day what a team wrote in a week — but "written" doesn't mean "owned." This guide covers code reading for the AI era: get the map first, start from entry points and tests, keep a "distrust checklist." Reading code isn't nitpicking; it's being able to catch AI when it's wrong.

Context windows keep growing in 2026, but "stuff everything in" buys sky-high bills and diluted attention. This hands-on guide treats context engineering as managing three ledgers: token money (cache everything cacheable, retrieve instead of stuffing, compress history), model attention (important at both ends, structured expression, context decay, reference over copy), and your time (10-minute cap for one-offs, build assets for recurring tasks). Includes a 5-minute context checklist template.

"Solo company" is 2026's sexiest phrase, but few do the other math: true costs. Subscriptions at $200–$800/month are the small part; the real cost is "time tax" — support tax 1–2h daily, ops tax 3–5h weekly, finance & tax 1–2 days monthly, compliance & legal per incident, emotional tax 24/7. Total: 15–20 hours a week not building product. Plus three tax-reduction strategies: make it visible, accept "solo ≠ alone," budget for emotions.

Vibe coding's sweetest moment is the demo — but sign a toB contract and start delivering, and a chasm opens between demo and delivery. This piece draws four boundaries: security compliance is a hard gate (quote at 5x demo cost), maintenance liability is bottomless (never promise a free year), requirement changes must be billed (block the "quick change"), data ownership and IP go in the contract. The thesis: between "demo" and "delivery" lies a SOC 2 — and SOC 2 can't be vibed.

The coding workflow changed: the agent writes, you review. But reviewing AI code line-by-line no longer works — AI rarely writes syntax errors; its specialty is "looks done, actually isn't." This checklist gives 12 checkpoints: 3 for requirement alignment, 4 correctness traps, 3 engineering quality, 2 security red lines. The thesis: reviewing AI code isn't about finding bugs — it's about finding "things you thought it did, but it didn't."

Indie developers share an affliction: product built, hands shaking at pricing time. This piece makes an uncomfortable argument: low pricing isn't humility — it's fear of pricing your value. The math ($5/month needs 16,667 users for $1M ARR; $99/month needs 842), three inner demons ("even I think it's expensive," "Big Tech is free," "free now, charge later"), and a four-step pricing practice: replacement cost, price interviews, three tiers, one price raise six months in.