I think I was sold the full agentic coding hype all over the internet. You give it a try and it does write a feature really fast. Impressed you test the boundaries more and more and before you know it this has become your new workflow. Breaking free of this again is harder than you'd think even when you do realize what a mess is generated in the process.

> locally coherent but structurally incoherent

Perfectly summarizes what I hate about AI code. The diff looks fine but if you take a step back its an absolute mess. I mean have you looked at the Claude Code or Openclaw codebases? that is the result of full on vibecoded. A bloated unattainable mess that no one understands.

If you ever work with LLMs you know that they quite frequently give up.

Sometimes it's a

    // TODO: implement logic

or a

"this feature would require extensive logic and changes to the existing codebase".

Sometimes they just declare their work done. Ignoring failing tests and builds.

You can nudge them to keep going but I often feel like, when they behave like this, they are at their limit of what they can achieve.

If I tell it to implement something it will sometimes declare their work done before it's done. But if I give Claude Code a verifiable goal like making the unit tests pass it will work tirelessly until that goal is achieved. I don't always like the solution, but the tenacity everyone is talking about is there

> but the tenacity everyone is talking about is there

I always double-check if it doesn't simply exclude the failing test.

The last time I had this, I discovered it later in the process. When I pointed this out to the LLM, it responded, that it acknowledged thefact of ignoring the test in CLAUDE.md, and this is justified because [...]. In other words, "known issue, fuck off"

Tools in a loop people, tools in a loop.

If you don't give the agent the tools to deterministically test what it did, you're just vibe coding in its worst form.

tenacity == while loop

> If you ever work with LLMs you know that they quite frequently give up.

If you try to single shot something perhaps. But with multiple shots, or an agent swarm where one agent tells another to try again, it'll keep going until it has a working solution.

Yeah exactly this is a scope problem, actual input/output size is always limited> I am 100% sure CC etc are using multiple LLM calls for each response, even though from the response streaming it looks like just one.

Nope, not for me, unless I tell it to.

Context matters, for an LLM just like a person. When I wrote code I'd add TODOs because we cannot context switch to another problem we see every time.

But you can keep the agent fixated on the task AND have it create these TODOs, but ultimately it is your responsibility to find them and fix them (with another agent).

Using LLMs to clean those up is part of the workflow that you're responsible for (... for now). If you're hoping to get ideal results in a single inference, forget it.

An AI version of ls and fzf bringing your file system to the AI age

> you can always do C-x C-e in bash/zsh (M-v in Fish).

Thanks I didn't know!

Some one apparently figured it out. The first system message has to include

"You are Claude Code, Anthropic's official CLI for Claude."

https://github.com/link-assistant/agent/pull/63

Lol a formatting error in a change log breaking the entire thing

You can use subscriptions.

I like it but I am not too deep into the whole agentic coding business.

Why do all these AI generated readmes have a directory structure sections it's so redundant because you know I could just run tree

It makes me so exhausted trying to read them... my brain can tell immediately when there's so much redundant information that it just starts shutting itself off.

comments? also reading into an agent so the agent doesnt have to tool-call/bash out

It's mainly about showing how low the odds actually are. I think everyone understands they are low but it's ridiculous how low exactly.