That is.... actually a seriously meaty article from a blog I've never heard of. Thanks for the pointer.

I am a novice programmer -- I have programmed for 35+ years now but I build and lose the skills moving between coder to manager to sales -- multiple times. Fresh IC since last week again :) I have coded starting with Fortran, RPG and COBOL and I have also coded Java and Scala. I know modern architecture but haven't done enough grunt work to make it work or to debug (and fix) a complex problem. Needless to say sometimes my eyes glaze over the code.

And I write some code for my personal enjoyment, and I gave it to Claude 6-8 months back for improvement, it gave me a massive change log and it was quite risky so abandoned it.

I tried this again with Gemini last week, I was more prepared and asked it to improve class by class, and for whatever reasons I got better answers -- changed code, with explanations, and when I asked it to split the refactor in smaller steps, it did so. Was a joy working on this over the thanksgiving holidays. It could break the changes in small pieces, talk through them as I evolved concepts learned previously, took my feedback and prioritization, and also gave me nuanced explanation of the business objectives I was trying to achieve.

This is not to downplay claude, that is just the sequence of events narration. So while it may or may not work well for experienced programmers, it is such a helpful tool for people who know the domain or the concepts (or both) and struggle with details, since the tool can iron out a lot of details for you.

My goal now is to have another project for winter holidays and then think through 4-6 hour AI assisted refactors over the weekends. Do note that this is a project of personal interest so not spending weekends for the big man.

I'm starting with Claude at work but did have an okay experience with OpenAi so far. For clearly delimited tasks it does produce working code more often than not. I've seen some improvement on their side compared to say, last year. For something more complex and not clearly defined in advance, yes, it does produce plausible garbage and it goes off the rails a lot. I was migrating a project and asked ChatGPT to analyze the original code base and produce a migration plan. The result seemed good and encouraging because I didn't know much about that project at that time. But I ended up taking a different route and when I finished the migration (with bits of help from ChatGPT) I looked at the original migration plan out of curiosity since I had become more familiar with the project by now. And the migration plan was an absolutely useless and senseless hallucination.

You're absolutely right!

Somehow it doesn't get on my nerves (unlike Gemini with "Of course").

On the contrary, I cannot use the top Gemini and Claude models because their outputs are so out place and hard to integrate with my code bases. The GPT 5 models integrate with my code base's existing patterns seamlessly.

Can you give some concrete example of programming problem task GPT fails to solve?

Interested, because I’ve been getting pretty good results with different tasks using the Codex.

NME at all - 5.1 codex has been the best by far.

I find for difficult questions math and design questions GPT5 tends to produce better answers than Claude and Gemini.

"with a straight face"

The first step in building a large language model. That's when the model is initiated and trained on a huge dataset to learn patterns and whatnot. The "P" in "GPT" stands for "pre-trained."

That’s where they take their big pile of data and train the model to do next-token-prediction.

The fundamental problem with bubbles like this, is that you get people like this who are able to take advantage of the The Gell-Mann amnesia effect, except the details that they’re wrong about are so niche that there’s a vanishingly small group of people who are qualified to call them out on it, and there’s simultaneously so much more attention on what they say because investors and speculators are so desperate and anxious for new information.

I followed him on Twitter. He said some very interesting things, I thought. Then he started talking about the niche of ML/AI I work near, and he was completely wrong about it. I became enlightened.