Building AI Tools at Scale

What started as a quiet solo build has, over the past two days, sharpened into something worth watching. The first signal arrived May 5th: a developer's six-month post-mortem on building a multilingual AI tools directory in Kotlin and Next.js, alone. The framing was deliberately unglamorous — "mostly painful" — which is either the most useful subtitle in recent Dev.to history or a rhetorical move that's aged well regardless of intent. Solo, multilingual, six months: that's not a weekend project. That's a sustained bet on a specific thesis about where AI tool discovery is heading, and the author chose to write about the cost of that bet rather than the trophy.

Then, the day after, two more signals arrived within hours of each other, and the story got stranger. A developer published a post about building an llms.txt generator "that actually works at scale" — a functional, if modest, claim. Hours later, a second post appeared from what appears to be the same developer, with a title that lands like a punchline and a gut-punch simultaneously: they'd shown their llms.txt generator to the creator of the llms.txt standard, and had to rewrite everything. That second post scored higher, which makes sense. The first post was about competence. The second was about the specific humiliation of building something until it works, then discovering the person who invented the problem thinks you've solved the wrong one.

Taken together, these three signals sketch a pattern that runs deeper than individual build stories. Developers are racing to build infrastructure — directories, generators, tooling — around AI standards and workflows that are themselves still being defined. The llms.txt standard is young enough that its creator is still reachable for feedback, which means builders are simultaneously shipping against a moving target and learning the rules from the referee mid-game. The six-month directory builder and the llms.txt rewriter are separated by domain but united by the same underlying condition: they're constructing load-bearing scaffolding around systems that haven't finished deciding what shape they want to be.

If confirmed, here is what this means. The gap between "working implementation" and "correct implementation" in AI tooling is wider than most developers currently price in — and the cost of that gap isn't just technical debt, it's months. The rewrite story is instructive not because the developer failed, but because they succeeded well enough to reach the person with the authority to invalidate that success. That's a specific kind of risk that doesn't appear in sprint planning. For teams building on emerging AI standards like llms.txt, the strategic implication is uncomfortable: proximity to the standard's creator is now a competitive variable, not just a nice-to-have. The developers who ship fast and then discover the ground has moved aren't being careless — they're operating at the frontier of a standards process that has no formal notification system.

Watch for whether the llms.txt rewrite post generates response from the broader developer community or from the standard's creator directly — that exchange, if public, would tell you a great deal about how stable this standard actually is and whether the rewrite was idiosyncratic or universal.

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