54 posts tagged with "vibe coding"

The software industry spent eighteen months solving the wrong half of the problem.

AI made code generation nearly free. Copilot, Cursor, and a wave of prompt-to-app tools let anyone turn a sentence into a running application in minutes. We called it "vibe coding," and it was genuinely exciting. But it solved the generation problem while quietly introducing a far more dangerous one: generating the wrong thing, confidently, at scale.

The fix isn't better code generation. It's the layer that comes before the code — the product specification. This is the shift from vibe coding to agentic engineering, and it's the difference between a demo that wows on Tuesday and a product that survives Wednesday.

The demo that worked, and the product that didn't​

You opened an AI tool, typed what was in your head, and watched a working screen appear in ninety seconds. It felt like magic. It felt like you'd just skipped six months and a team you couldn't afford.

Then you asked for the second feature. And the third. Somewhere around the fifth prompt, the thing started fighting you — buttons that used to work stopped working, the data model contradicted itself, and every fix broke something upstream. The demo that dazzled an investor on Tuesday couldn't survive a real user on Friday.

This is the quiet failure mode of the AI build era, and almost nobody warns you about it. Vibe coding ships prototypes. Spec-driven ships products. The gap between those two outcomes isn't talent or budget — it's what you did, or didn't do, before you wrote a single line of code.

"Is this just another vibe-coding tool?"​

It's the first question almost everyone asks when they meet Codalio. Fair question. The space is crowded with tools that promise to turn a sentence into an app, and most of them look identical from the outside.

So here's the honest answer: no. And the reason why is the entire point of this company.

Vibe-coding tools start with the output. You describe a vibe, they generate something that resembles software, and within minutes you're staring at a screen that looks finished. Codalio starts somewhere else entirely — with the specification. That single reversal changes everything downstream.

Everyone Crowned Taste. Then They Handed an Agent a Vibe.​

Earlier this year, a real fight broke out among tech leaders over what's scarce now that AI writes the code. Greg Brockman called taste "a new core skill." Paul Graham framed the differentiator as what you choose to make. Cloudflare's Dane Knecht echoed the same note. For a few weeks, "taste" was the consensus answer to the question every founder is quietly asking: if the machine can build anything, what's left for me?

Then the replies arrived. Vercel's Keith Messick summed up the backlash in one line — that "taste is the new core skill" was the rallying cry of men who kept Allbirds afloat. The meme did its job. It exposed something the original posts skipped over: taste is easy to claim and almost impossible to prove.

And here's the part that actually matters for anyone about to build a product with AI. Taste is upstream of nothing if you can't transmit it. The moment you hand the work to an agent, your sense of "good" has to live somewhere outside your head — or it doesn't survive the handoff at all.

Your MVP Runs in the Demo. That Doesn't Mean It's Safe.​

There's a moment every non-technical founder loves right now. You describe a feature, the AI agent writes it, and minutes later something real is running on your screen. Sign-up works. The dashboard loads. Payments go through in the test environment. It feels like you've crossed a line that used to take a funded team and three months.

You have crossed a line. Just not the one you think.

What you've built is working software. Working software does what you watched it do. It does not necessarily do the right thing when a stranger pokes at the parts you never demoed — the password reset, the refund path, the place where someone else's data could leak into your screen. The demo proves the happy path runs. It says nothing about the unhappy ones.

Eight weeks ago, this newsletter started with a simple promise: pull back the curtain on what it actually takes to go from idea to deployed product. No buzzwords, no hand-waving about AI doing magic, no pretending the hard parts don't exist.

We've walked through every layer. Why PRDs are the most underestimated leverage point in product development. How auto-generated user stories cut weeks off the discovery phase. What scope estimation actually looks like when story points are calculated by a system that has read your spec end to end. How UI prototyping breaks the "this isn't what I meant" cycle that kills budgets. Why Ruby on Rails is still the right answer for an MVP backend, and what Rhino generates beyond just code. How one-click deployment compresses the iceberg of infrastructure into a single button.

That is the platform. This week is about who's using it, what they're shipping, and what the data looks like once the marketing language is stripped out.

You can't see the price tag until after you've paid​

There's a new kind of dread in the founder Slack channels: the AI-coding invoice nobody can predict.

Replit moved to effort-based pricing. Vercel's v0 bills by token. Cursor, by one widely-shared account, pushed through a 20× silent price hike. The common thread isn't that these tools got expensive — plenty of good tools are. It's that you no longer know what a single prompt will cost before you run it. You type an instruction, you hit run, and you find out the price afterward.

For a non-technical founder, that's not a pricing model. That's a slot machine with a "build my app" button.

Amazon had infinite engineers. It still shipped from a stale wiki.​

In November 2025, Amazon told its engineers to use its in-house AI coding tool for at least 80% of their work each week. The goal was adoption — a metric, tracked weekly, with a number to hit.

Four months later, on March 5, 2026, a single AI-assisted deploy wiped roughly 6.3 million orders and dropped U.S. order volume by about 99%. The root cause wasn't an exotic model failure. The agent had read an outdated internal wiki, inferred what "correct" meant from it, and shipped accordingly.

Then about 1,500 engineers signed a petition. Their argument wasn't "AI is bad." It was that the company had chased a usage target instead of quality — and that they'd rather pick their own tools, like Claude Code, than hit a quota.

Here's the part that should bother every founder: Amazon has effectively unlimited engineers and the best tooling money can buy. It still broke. So the lesson can't be "hire more people" or "buy a better model."

For the last couple of weeks, this series has worked through what Codalio actually does. PRDs that read like a senior PM wrote them. UI prototypes generated from your real requirements, not stock components stitched together. Backend code that compiles, runs, and matches the spec. Every post has answered a different version of the same question: why does building an MVP cost as much as a luxury car and take as long as a graduate degree?

This week, we close the loop. Step 4. Deployment.

Because here's the part nobody tells first-time founders: writing the code is not the bottleneck. The bottleneck is everything between "the code works on my laptop" and "a stranger I've never met can sign up at a URL." That gap, the last mile of the build, is where more MVPs die than at any other point in the journey.

The rebuild isn't expensive because the code is bad​

There's a new line item showing up in startup budgets in 2026: the rescue. Reporting circulating this year claims roughly 10,000 startups tried to ship production apps on AI assistants, and more than 8,000 of them now need a rebuild — at $50,000 to $500,000 each. (Treat the counts as directional; they're secondary reporting, not an audited census.) Salesforce Ben called it: 2026, the Year of Technical Debt, thanks to vibe-coding.

The industry has already decided what went wrong. The AI wrote sloppy code. The MVP accrued technical debt. The fix is cleaner output, better models, more rigorous review.

That diagnosis is comforting because it's about the code. It's also wrong.