Why write code if the LLM can just do the thing? (web app experiment)
I spent a few hours last weekend testing whether AI can replace code by executing directly. Built a contact manager where every HTTP request goes to an LLM with three tools: database (SQLite), webResponse (HTML/JSON/JS), and updateMemory (feedback). No routes, no controllers, no business logic. The AI designs schemas on first request, generates UIs from paths alone, and evolves based on natural language feedback. It works—forms submit, data persists, APIs return JSON—but it's catastrophically slow (30-60s per request), absurdly expensive ($0.05/request), and has zero UI…
In plain words
This web app experiment tests whether large language models can replace traditional code by handling every HTTP request directly. The app uses an LLM with access to a SQLite database, HTML/JSON response generation, and feedback storage to power a contact manager without pre-written routes or controllers. The system automatically designs database schemas, generates interfaces, and adapts based on user input. While functional—forms submit, data persists, and APIs return results—the approach is impractical at current speeds (30-60 seconds per request) and costs ($0.05 per request), with inconsistent user interfaces. It demonstrates the technical feasibility of AI-driven applications but highlights performance and cost as current barriers.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
I spent a few hours last weekend testing whether AI can replace code by executing directly. Built a contact manager where every HTTP request goes to an LLM with three tools: database (SQLite), webResponse (HTML/JSON/JS), and updateMemory (feedback). No routes, no controllers, no business logic. The AI designs schemas on first request, generates UIs from paths alone, and evolves based on natural language feedback. It works—forms submit, data persists, APIs return JSON—but it's catastrophically slow (30-60s per request), absurdly expensive ($0.05/request), and has zero UI consistency between requests. The capability exists; performance is the problem. When inference gets 10x faster, maybe the question shifts from "how do we generate better code?" to "why generate code at all?"
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