Hi,
I did this partly out of curiosity and partly because I was lazy: “let’s see what happens if I ask GPT-5 to build a small Chimpaigo-like demo from scratch.” The goal was simple: one HTML5 page that shows a question + answers, a players list, bots that play, and the flow question → answers → results → next question. If it worked well, the plan was to later hook it to the real Chimpaigo server.

At first it was kind of magical. GPT-5 gave me HTML, CSS and JS that looked fine and was playable in minutes. The usual “wow” moment. But when I asked it to add structure and make the thing a bit more real, the cracks started to show.

What breaks when you raise the bar

• Forgets things. It keeps things coherent for a while, but as you add features it starts forgetting variable names, agreed structures or even functions it already made. You end up repeating specs, which kills time.

• Fix one, break two. You change something to fix a bug and suddenly other parts that worked are broken. Not kidding: every fix seemed to create collateral damage.

• Bad at architecture. Ask it to split code into modules or files and you get pieces that work in isolation but don’t fit together: missing imports, implicit dependencies, inconsistent contracts.

• No care for edge cases. It handles happy paths, but strange inputs or odd flows fail. It won’t write meaningful tests and the auto-docs are shallow.

• Documentation is lightweight. What it writes as “docs” often doesn’t match the actual decisions in code. Another dev would struggle.

After a few iterations I realised the time I spent fixing and re-aligning the generated code was approaching — and sometimes exceeding — the time it would’ve taken me to build a small, clear skeleton myself.

Do I regret using the AI? Sort of.

It’s great for quick prototypes and testing UI ideas. If you want a visual mock or something to play with fast, it’s brilliant. But for anything that needs coherence over time, my workflow now is:

• Define the architecture and data contracts first (names, JSON, endpoints).

• Build a minimal skeleton myself (state management, file structure).

• Use the AI to generate specific components or helper functions.

• Version early and add basic tests from the start.

Short: GPT-5 speeds up the start, but it doesn’t replace planning and discipline once the project grows.

Final take — skeptical but hopeful

Right now, vibe coding is useful and interesting, but immature for mid/large projects because of context limits and flaky coherence. That said, I’m optimistic: with better session memory, persistent context and tools that enforce contracts, this will get much more viable. It’s not sci-fi — it’s a matter of time and better tooling.

If you want to try the demo I used for this experiment:
https://www.chimpaigo.com/play/index.html

Anyone else let an AI “do it all”? What did you end up doing by hand?