The Pink Elephant problem of LLMs. You can not reliably make them NOT do something.

Vibe coding you do end up spending a lot of time waiting for prompts, so I get the results of that study.

I fall pretty deep in the power user category for LLMs, so I don’t really feel that the study applies well to me, but also I acknowledge I can be biased there.

I have custom proprietary MCPs for semantic search over my code bases that lets AI do repeated graph searches on my code (imagine combining language server, ctags, networkx, and grep+fuzzy search). That is way faster than iteratively grepping and code scanning manually with a low chance of LLM errors. By the time I open GitHub code search or run ripgrep Claude has used already prioritized and listed my modules to investigate.

That tool alone with an LLM can save me half a day of research and debugging on complex tickets, which pays for an AI subscription alone. I have other internal tools to accelerate work too.

I use it to organize my JIRA tickets and plan my daily goals. I actually get Claude to do a lot of triage for me before I even start a task, which cuts the investigation phase to a few minutes on small tasks.

I use it to review all my PRs before I ask a human to look, it catches a lot of small things and can correct them, then the PR avoids the bike shedding nitpicks some reviewers love. Claude can do this, Copilot will only ever point out nitpicks, so the model makes a huge difference here. But regardless, 1 fewer review request cycle helps keep things moving.

It’s a huge boon to debugging — much faster than searching errors manually. Especially helpful on the types of errors you have to rabbit hole GitHub issue content chains to solve.

It’s very fast to get projects to MVP while following common structure/idioms, and can help write unit tests quickly for me. After the MVP stage it sucks and I go back to manually coding.

I use it to generate code snippets where documentation sucks. If you look at the ibis library in Python for example the docs are Byzantine and poorly organized. LLMs are better at finding the relevant docs than I am there. I mostly use LLM search instead of manual for doc search now.

I have a lot of custom scripts and calculators and apps that I made with it which keep me more focused on my actual work and accelerate things.

I regularly have the LLM help me write bash or python or jq scripts when I need to audit codebases for large refactors. That’s low maintenance one off work that can be easily verified but complex to write. I never remember the syntax for bash and jq even after using them for years.

I guess the short version is I tend to build tools for the AI, then let the LLM use those tools to improve and accelerate my workflows. That returns a lot of time back to me.

I do try vibe coding but end up in the same time sink traps as the study found. If the LLM is ever wrong, you save time forking the chat than trying to realign it, but it’s still likely to be slower. Repeat chats result in the same pitfalls for complex issues and bugs, so you have to abandon that state quickly.

Vibe coding small revisions can still be a bit faster and it’s great at helping me with documentation.

First time I'm hearing them be related to vibe coding. They've been very respectable in the past, especially with their open-source CodeMirror.

What are they helpful tools for then? A study showed that they make experienced developers 19% slower.

It sounds like this guy was also relying on the AI to self-report status. Did any of this happen? Like is the replit AI really hooked up to a CLI, did it even make a DB to start with, was there anything useful in it, and did it actually delete it?

Or is this all just a long roleplaying session where this guy pretends to run a business and the AI pretends to do employee stuff for him?

Because 90% of this article is "I asked the AI and it said:" which is not a reliable source for information.

Don't you have any security concerns with sending all your code and JIRA tickets to some companies servers? My boss wouldn't be pleased if I send anything that's deemed a company secret over unencrypted channels.

They could hire on a contractor and eschew all those costs.

I’ve done contract work before, this seems a good fit (defined problem plus budget, unknown timeline, clear requirements)

I'm not the person you're replying to but the one thing I've found them helpful for is targeted search.

I can ask it a question and then access its sources from whatever response it generates to read and review myself.

Kind of a simpler, free LexisNexis.

The tool isn’t returning all code, but it is sending code.

I had discussions with my CTO and security team before integrating Claude code.

I have to use Gemini in one specific workflow and Gemini had a lot of landlines for how they use your data. Anthropic was easier to understand.

Anthropic also has some guidance for running Claude Code in a container with firewall and your specified dev tools, it works but that’s not my area of expertise.

The container doesn’t solve all the issues like using remote servers, but it does let you restrict what files and network requests Claude can access (so e.g. Claude can’t read your env vars or ssh key files).

I do try local LLMs but they’re not there yet on my machine for most use cases. Gemma 3n is decent if you need small model performance and tool calls, phi4 works but isn’t thinking (the thinking variants are awful), and I’m exploring dream coder and diffusion models. R1 is still one of the best local models but frequently overthinks, even the new release. Context window is the largest limiting factor I find locally.

All I see is people chatting with an LLM as if it was a person. “How bad is this on a scale
of 1 to 100”, you’re just doomed to get some random answer based solely on whatever context is being fed in the input and that you probably don’t know the extent of it.

Trying to make the LLM “see its mistakes” is a pointless exercise. Getting it to “promise” something is useless.

The issue with LLMs working with human languages is people eventually wanting to apply human things to LLMs such as asking why as if the LLM knows of its own decision process. It only takes an input and generates an output, it won’t be able to have any “meta thought” explanation about why it outputted X and not Y in the previous prompt.

What are they helpful tools for then? A study showed that they make experienced developers 19% slower.

I have to use Gemini in one specific workflow

I would love some story on why AI is needed at all.

It seemed like the llm had decided it was in a brat scene and was trying to call down the thunder.

This only proves some of them can't solve all complex problems. I'm only claiming some of them can solve some complex problems. Not only by remembering exact solutions, but by remembering steps and actions used in building those solutions, generalizing, and transferring them to new problems. Anyone who tries using it for programming, will discover this very fast.

PS: Some of them were already used to solve problems and find patterns in data humans weren't able to get other ways before (particle research in CERN, bioinformatics, etc).

The [AI] safety stuff is more visceral to me after a weekend of vibe hacking,” Lemkin said. I explicitly told it eleven times in ALL CAPS not to do this. I am a little worried about safety now.

This sounds like something straight out of The Onion.

Batch process turning unstructured free form text data into structured outputs.

As a crappy example imagine if you wanted to download metadata about your albums but they’re all labelled “Various Artists”. You can use an LLM call to read the album description and fix the track artists for the tracks, now you can properly organize your collection.

I’m using the same idea, different domain and a complex set of inputs.

It can be much more cost effective than manually spending days tagging data and writing custom importers.

You can definitely go lighter than LLMs. You can use gensim to do category matching, you can use sentence transformers and nearest neighbours (this is basically what Semantle does), but LLM performed the best on more complex document input.

You're referring to more generic machine learning, not LLMs. These are vastly different technologies.

And I have used them for programming, I know their limitations. They don't really transfer solutions to new problems, not on their own anyway. It usually requires pretty specific prompting. They can at best apply solutions to problems, but even then it's not a truly generalised thing, even if it seems to work for many cases.

That's the trap you're falling into as well; LLMs look like they're doing all this stuff, because they're trained on data produced by people who actually do so. But they can't think of something truly novel. LLMs are mathematically unable to truly generalize, it would prove P=NP if they did (there was a paper from a researcher in IIRC Nijmegen that proved this). She also proved they won't scale, and lo and behold LLM performance is plateauing hard (except in very synthetic, artificial benchmarks designed to make LLMs look good).