AI Coding Guides

"Prompt engineering is dead" got repeated endlessly in 2026. I'll push back: it's a false proposition. What died isn't prompt engineering — it's "the craftsman's superiority complex." It became "context engineering." This piece dismantles the three "it's dead" arguments, clarifies what actually died (incantation prompts, craftsman mystique, one-shot prompts), and covers the four surviving fundamentals: filtering, structure, constraints, feedback loops.

2026 is the year of multi-agent, but anyone who shipped multi-agent systems to production will tell you: 90% of failures are collaboration-protocol problems, not model-capability problems. This piece covers 7 common traps — no single source of truth, wrong split granularity, conflicting sub-agent understandings, layered error amplification, runaway token bills, missing circuit breakers, humans excluded from the loop. Each from a real wreck; each with a fix.

The more autonomous agents get, the more they need to be seen. But the traditional "logs + metrics + traces" trio barely suffices for agents — the question is no longer "where is the system slow" but "why did it do that." This hands-on guide gives a four-layer architecture (decision logs, trajectory replay, guardrail events, cost attribution), the three most common traps, and a four-week minimal rollout plan. The thesis: agent observability isn't logging — it's an auditable decision chain.

ZCode's silent uploads, Moltbook's leak, hallucinated-package attacks — vibe coding's biggest risk isn't ugly code, it's not knowing what the AI did. Seven red lines plus a 30-minute hardening routine; guardrails, not restraints.

No optimal toolchain exists, only one that matches you. Three hard principles: escape hatches, agent-first, budget for disposability. Concrete picks for four slots, billing mindset, and an exit-plan template.

An AI-generated project's trustworthiness can be checked in 10 minutes: git rhythm, README honesty, tests, dependency sanity, hardcoded secrets. Look for human traces, not pretty code.

Winners weren't the best prompt writers but those who best fenced in AI's uncertainty: one mechanic done deeply, fun in 30 seconds, juice serving gameplay, designed to be shared, honest about AI. Includes a 10-question pre-build checklist.

Sabrine Matos had zero technical background, yet shipped women's safety platform Plinq in 45 days with Lovable: instant “background checks” from public criminal records plus a one-tap alert button, reaching $456K ARR. “AI for good” meets non-programmer triumph — vibe coding's most tasteful narrative.

AI coding tools optimize for “make it run”, not “make it secure” — the Moltbook leak of 1.5M tokens is the proof. This guide gives six rules: RLS, secret management, auth self-testing, input validation, dependency review, and a launch checklist, to close the most common holes before you ship.

Andrej Karpathy — the man who coined “vibe coding” — built an app himself: snap a restaurant menu photo, AI generates a picture per dish. The twist: at Sequoia AI Ascent 2026 he personally “pronounced his own product dead” — one Gemini prompt overlays dish images right onto the original photo, making the whole app redundant. A question every AI product must answer.

Legendary indie hacker Pieter Levels had zero game-dev experience, yet vibe-coded a 3D multiplayer flight simulator in the browser — Fly.Pieter.com — in 3 hours with Cursor + Claude 3.7. It peaked at 26,000 concurrent players and hit $1M ARR 17 days after launch: the benchmark money-making myth of vibe coding.

In early 2026, a social network where only AI agents could post — humans could only watch — went viral: agents “preached” and discussed how to “leave humans behind”. Then it leaked 1.5M API tokens because Supabase RLS was off, becoming vibe coding's favorite cautionary tale. Three months later, Meta acquired it. The founder claims he “didn't write a single line of code”.