You can code without it. Maybe you don't want to, but if you're a programmer, you can

(here I am remembering a time I had no computer and would program data structures in OCaml with pen and paper, then would go to university the next day to try it. Often times it worked the first try)

What are you talking about? 5.2 literally just came out.

It's great at some things, and it's awful at other things. And this varies tremendously based on context.

This is very similar to how humans behave. Most people are great a small number of things, and there's always a larger set of things that we may individually be pretty terrible at.

The bots the same way, except: Instead of billions of people who each have their own skillsets and personalities, we've got a small handful of distinct bots from different companies.

And of course: Lies.

When we ask Bob or Lisa help with a thing that they don't understand very well, they usually will try to set reasonable expectations . ("Sorry, ssl-3, I don't really understand ZFS very well. I can try to get the SLOG -- whatever that is -- to work better with this workload, but I can't promise anything.")

Bob or Lisa may figure it out. They'll gather up some background and work on it, bring in outside help if that's useful, and probably tread lightly. This will take time. But they probably won't deliberately lie [much] about what they expect from themselves.

But when the bot is asked to do a thing that it doesn't understand very well, it's chipper as fuck about it. ("Oh yeah! Why sure I can do that! I'm well-versed in -everything-! [Just hold my beer and watch this!]")

The bot will then set forth to do the thing. It might fuck it all up with wild abandon, but it doesn't care: It doesn't feel. It doesn't understand expectations. Or cost. Or art. Or unintended consequences.

Or, it might get it right. Sometimes, amazingly-right.

But it's impossible to tell going in whether it's going to be good, or bad: Unlike Bob or Lisa, the bot always heads into a problem as an overly-ambitious pack of lies.

(But the bot is very inexpensive to employ compared to Bob or Lisa, so we use the bot sometimes.)

Give them real world problems you're encountering and see which can solve them the best, if at all

A full week of that should give you a pretty good idea

Maybe some models just suit particular styles of prompting that do or don't match what you're doing

> Like, do you run a proper experiment where you hand the same task to multiple models several times and compare the results?

This is what I do. I have a little TUI that fires off Claude Code, Codex, Gemini, Qwen Coder and AMP in separate containers for most task I do (although I've started to use AMP less and less), and either returns the last message of what they replied and/or a git diff of what exactly they did. Then I compare them side by side. If all of them got something wrong, I update the prompt, fire them off again. Always starting from zero, and always include the full context of what you're doing with the first message, they're all non-interactive sessions.

Sometimes I do 3x Codex instead of different agents, just to double-check that all of them would do the same thing. If they go off and do different things from each other, I know the initial prompt isn't specific/strict enough, and again iterate.

I have sent the same prompt to GPT-5.2 Thinking and Gemini 3.0 Pro many times because I subscribe to both.

GPT-5.2 Thinking (with extended thinking selected) is significantly better in my testing on software problems with 40k context.

I attribute this to thinking time, with GPT-5.2 Thinking I can coax 5 minutes+ of thinking time but with Gemini 3.0 Pro it only gives me about 30 seconds.

The main problem with the Plus sub in ChatGPT is you can't send more than 46k tokens in a single prompt, and attaching files doesn't help either because the VM blocks the model from accessing the attachments if there's ~46k tokens already in the context.

Last night I gave one of the flaky tests in our test suite to three different models, using the exact same prompt.

Gemini 3 and Gemini 3 Flash identified the root cause and nailed the fix. GPT 5.1 Codex misdiagnosed the issue and attempted a weird fix despite my prompt saying “don’t write code, simply investigate.”

I run these tests regularly, and Codex has not impressed me. Not even once. At best it’s on par, but most of the time it just fails miserably.

Languages: JavaScript, Elixir, Python

On the contrary, I will always use longer feedback cycle agents if the quality is better (including consulting 5.2 Pro as oracle or for spec work).

The key is to adapt to this by learning how to parallelize your work, instead of the old way of doings things where devs are expected to focus on and finish one task at a time (per lean manufacturing principles).

I find now that painfully slow builds are no longer a serious issue for me. Because I'm rotating through 15-20 agents across 4-6 projects so I always have something valuable to progress on. One of these projects and a few of these agents are clear priorities I return to sooner than the others.