I code with AI a good bit for a side project since I need to use my work AI and get my stats up to show management that I’m using it. The “impressive” thing is learning new softwares and how to use them quickly in your environment. When setting up my homelab with automatic git pull, it quickly gave me some commands and showed me what to add in my docker container.

Correcting issues is exactly like coding with a high junior dev though. The code bloat is real and I’m going to attempt to use agentic AI to consolidate it in the future. I don’t believe you can really “vibe code” unless you already know how to code though. Stating the exact structures and organization and whatnot is vital for agentic AI programming semi-complex systems.

This is very different from my experience, but I've purposely lagged behind in adoption and I often do things the slow way because I like programming and I don't want to get too lazy and dependent.

I just recently started using Claude Code CLI. With how I use it: asking it specific questions and often telling it exactly what files and lines to analyze, it feels more like taking to an extremely knowledgeable programmer who has very narrow context and often makes short-sighted decisions.

I find it super helpful in troubleshooting. But it also feels like a trap, because I can feel it gaining my trust and I know better than to trust it.

checking the code is much harder than coming up with it yourself

That's always been true. But, at least in the past when you were checking the code written by a junior dev, the kinds of mistakes they'd make were easy to spot and easy to predict.

LLMs are created in such a way that they produce code that genuinely looks perfect at first. It's stuff that's designed to blend in and look plausible. In the past you could look at something and say "oh, this is just reversing a linked list". Now, you have to go through line by line trying to see if the thing that looks 100% plausible actually contains a tiny twist that breaks everything.

Sure we did.

"Lines Of Code" is a good one, more code = more work so it must be good.

I recently had a run in with another good one : PR's/Dev/Month.

Not only it that one good for overall productivity, it's a way to weed out those unproductive devs who check in less often.

This one was so good, management decided to add it to the company wide catchup slides in a section espousing how the new AI driven systems brought this number up enough to be above other companies.

That means other companies are using it as well, so it must be good.

Vibe coding guy wrote unit tests for our embedded project. Of course, the hardware peripherals aren’t available for unit tests on the dev machine/build server, so you sometimes have to write mock versions (like an “adc” function that just returns predetermined values in the format of the real analog-digital converter).

Claude wrote the tests and mock hardware so well that it forgot to include any actual code from the project. The test cases were just testing the mock hardware.

if you reject her pull requests, does she fix it? is there a way for management to see when an employee is pushing bad commits more frequently than usual?

But who's making money? All the AI corps are losing billions, only the hardware vendors are making bank.

Makers of AI lose money and users of AI probably also lose since all they get is shit output that requires more work.

People will upvote anything if it takes a shot at AI. Even when the subtitle itself is literally an ad.

Codestrap founders say we need to dial down the hype and sort through the mess

The cult mentality is really interesting to watch.

Keep replying! Maybe this is a good honeypot for stupid people. “I hate you!!” Lmao

My take exactly. Especially the bits about unit tests. If you cannot rely on your unit tests as a first assessment of your code quality, your unit tests are trash.

And not every company runs GitHub. The metrics he's talking about are DevOps metrics and not development metrics. For example In my work, nobody gives a fuck about mean time to production. We have a planning schedule and we need the ok from our customers before we can update our product.

The "ceiling" is the fact that no matter how fast AI can write code, it still needs to be reviewed by humans. Even if it passes the tests.

As much as everyone thinks they can take the human review step out of the process with testing, AI still fucks up enough that it's a bad idea. We'll be in this state until actually intelligent AI comes along. Some evolution of machine learning beyond LLMs.

Its early adoption problems in the same way as putting radium in toothpaste was. There are legitimate, already growing uses for various AI systems but as the technology is still new there's a bunch of people just trying to put it in everything, which is innevitably a lot of places where it will never be good (At least not until it gets much better in a way that LLMs fundementally never can be due to the underlying method by which they work)

My job has me working on AI stuff and it reminds me a lot of Internet technology back in the 90s.

For instance: I’m creating a local model to integrate with our MCP server. It took a lot of fiddling with a Modelfile for it to use the tools the MCP has installed. And it needs 20GB of VRAM to give reasonably accurate responses.

The amount of fiddling and checking and rough edges feel like writing JavaScript 1.0, or the switchover to HTML4.

Companies get a lot of praise for having AI products, but the reality isn’t nearly as flashy as they make it out to be. I’m seeing some usefulness in it as I learn more, but it’s not nearly what the hype machine says.

Those of us with eyes have already seen the ceiling of currently available GenAI "solutions," which is synonymous with early adoption problems.

The technology will evolve, and the same basic problems will exist. The article has good points about how structured acceptance criteria will need to be more strictly enforced.

It is possible to get good results, the problem is that you yourself need to have an very good understanding of the problem and how to solve it, and then accurately convey that to the AI.

Granted, I don't work on embedded and I'd imagine there's less code available for AI to train on than other fields.