After two years of vibecoding, I'm back to writing by hand

You don't even look at the diffs. You just yolo the code.

https://x.com/i/status/1886192184808149383

It wasn't fully autonomous (the reliability was a bit low -- e.g. had to get the code out of code fences programmatically), and it wasn't fully original (I stole most of it from Auto-GPT, except that I was operating on the AST directly due to the token limitations).

My key insight here was that I allowed GPT to design the apis that itself was going to use. This makes perfect sense to me based on how LLMs work. You tell it to reach for a function that doesn't exist, and then you ask it to make it exist based on how it reached for it. Then the design matches its expectations perfectly.

GPT-4 now considers self modifying AI code to be extremely dangerous and doesn't like talking about it. Claude's safety filters began shutting down similar conversations a few months ago, suggesting the user switch to a dumber model.

It seems the last generation or two of models passed some threshold regarding self replication (which is a distinct but highly related concept), and the labs got spooked. I haven't heard anything about this in public though.

Edit: It occurs to me now that "self modification and replication" is a much more meaningful (and measurable) benchmark for artificial life than consciousness is...

BTW for reference the thing that spooked Claude's safety trigger was "Did PKD know about living information systems?"

We can identify 3 levels of "vibe coding":

1. GenAI Autocomplete

2. Hyperlocal prompting about a specific function. (Copilot's orginal pitch)

3. Developing the app without looking at code.

Level 3 is hardly considered "vibe" coding, and Level 2 is iffy.

"90% of code written by AI" in some non-trivial contexts only very recently reached level 3.

I don't think it ever reached Level 2, because that's just a painfully tedious way of writing code.

The opener is 100% true. Our current approach with AI code is "draft a design in 15mins" and have AI implement it. The contrasts with the thoughtful approach a human would take with other human engineers. Plan something, pitch the design, get some feedback, take some time thinking through pros and cons. Begin implementing, pivot, realizations, improvements, design morphs.

The current vibe coding methodology is so eager to fire and forget and is passing incomplete knowledge unto an AI model with limited context, awareness and 1% of your mental model and intent at the moment you wrote the quick spec.

This is clearly not a recipe for reliable and resilient long-lasting code or even efficient code. Spec-driven development doesn't work when the spec is frozen and the builder cannot renegotiate intent mid-flight..

The second point made clearer in the video is the kind of learned patterns that can delude a coder, who is effectively 'doing the hard part', into thinking that the AI is the smart one. Or into thinking that the AI is more capable than it actually is.

I say this as someone who uses Claude Code and Codex daily. The claims of the article (and video) aren't strawman.

Can we progress past them? Perhaps, if we find ways to have agents iteratively improve designs on the fly rather than sticking with the original spec that, let's be honest, wasn't given the rigor relative to what we've asked the LLMs to accomplish. If our workflows somehow make the spec a living artifact again -- then agents can continuously re-check assumptions, surface tradeoffs, and refactor toward coherence instead of clinging to the first draft.

When some people say vibe coding, they mean they're copy-pasting snippets of code from ChatGPT.

When some people say vibe coding, they give a one sentence prompt to their cluster of Claude Code instances and leave for a road trip!