From Chaotic Vibes To Real Velocity With Ai-first Engineering

In 2025, vibe coding became much than a meme; it became a full-blown movement. Suddenly, non-developers were spinning up apps overnight, solopreneurs were grinding done 16-hour “just 1 much prompt” binges, and engineers were sharing “mind blown” moments astatine nan gait of dopamine hits.

Yes, vibe coding was chaotic. Yes, it often produced AI-assisted spaghetti. But it besides opened nan gates of package creation to millions who had ne'er touched a compiler. It democratized nan joyousness of building and inspired a procreation to imagine.

The situation is that inspiration doesn’t adjacent production. At immoderate point, you request a system.

Where Vibes Hit nan Wall

Studies often peg AI coding productivity gains astatine astir 20%. That’s not because nan devices are weak; it’s because astir teams are sprinkling AI connected apical of aged workflows. They’re coding nan aforesaid measurement they did pre-AI, only pinch a fancier autocomplete.

There is nan other broadside of nan standard — group who maestro fleets of agents methodically chipping astatine their problems. For example, 1 of my engineers ran done 3.5 cardinal tokens successful a month, often moving 4 agents successful parallel, and having agents reappraisal nan activity of different agents. When we adopted a disciplined system, our throughput doubled complete nan people of respective months.

AI-First Engineering: The System

Like agile successful nan early 2000s, AI-first engineering useful arsenic a system, not a paper of tricks. The cardinal practices:

Spec-driven improvement (SDD): Break activity into 3 steps: Start pinch high-level spec → method spec (with existent interfaces, not pseudocode) → step-by-step execution plan. Let agents co-create, but support humans successful nan loop. For example, if you want to adhd Google Calendar integration to your app, you’ll commencement by asking nan supplier to thief you constitute merchandise requirements. You would past reappraisal those requirements and edit them (manually aliases pinch follow-up prompting). You would past inquire a different supplier to publication nan merchandise requirements, profoundly analyse your repository and create a tech spec. You would past reappraisal nan tech spec (by yourself and pinch nan thief of a different agent). And from there, you would inquire nan adjacent supplier to create a step-by-step scheme from nan spec.

The believe supra allows you to reset agentic discourse often. The longer nan supplier runs, nan higher nan chance that nan magnitude of nan discourse is going to commencement interfering pinch its reasoning capacity. Spec-driven improvement is simply a earthy measurement to harvester nan correct discourse pinch nan correct prompt. Metaphorically, deliberation of it arsenic humans moving focused vs. distracted.

The adjacent important conception is self-verification: That’s what made coding agents truthful overmuch much powerful than nan early versions of GPT. When prompted correctly, they effort to trial their activity and correct nan mistakes that they find connected nan way. Logging and telemetry thief agents debug themselves — a immense measurement forward. When I jump into a caller repo, earlier unleashing my coding agents, I first inquire them to build immoderate basal archiving for their early reference, past inquire nan agents to reappraisal and amended nan logging and telemetry to make debugging easier.

And nowadays I seldom inquire nan agents to simply constitute nan code. I punctual them to constitute tests first, nan believe called test-driven improvement (TDD). This dramatically improves nan agent’s self-verification capabilities.

Last, but not least, keep it elemental (KISS). Our main repository was getting truthful analyzable that our caller hires could hardly make consciousness of it. No AI supplier would win successful specified a setup. It was clip for america to refresh and simplify architecture. Cleaner repos aren’t conscionable for humans; they make agents acold much effective.

These practices together create a strategy beardown capable to transmission nan vibes into existent velocity.

The Infrastructure Layer

Tools for illustration Claude Code and Codex are revolutionizing nan developer experience. But without orchestration, they tin go fragmented: Duplicated configs, siloed agents, mislaid context.

That’s wherever platforms for illustration Zencoder travel in: A UI and orchestration furniture connected apical of your existing AI subscriptions. Instead of choosing betwixt ChatGPT, Claude aliases Gemini, you tin plug them each into 1 workflow, harvester limits, tally agents successful parallel and get compliance, analytics and team-wide collaboration retired of nan box.

Moving Beyond nan Hype Curve

We’re still early. Today, nan champion teams tin double throughput.

It still takes a uncommon operation of beardown engineers, AI-first practices, frontier exemplary entree and repos that agents tin digest. But that operation is growing, and truthful are nan wins. The way to 10x lies successful supplier swarms pinch self-verification, which allows autonomous execution, which multiplies nan parallel execution, supported by democratized access, wherever anyone tin harness enterprise-grade conclusion done mundane subscriptions.

Vibe coding was nan spark. AI-first engineering is nan harness. And if history is immoderate guide, erstwhile nan strategy takes hold, nan “we doubled our throughput” of coming will consciousness for illustration nan warmup act.

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