OpenAI's new GPT-5 models announced early by GitHub (opens in new tab)

RooCode supports various modes https://docs.roocode.com/basic-usage/using-modes

For example, you can first use the Ask mode to explore the codebase and answer your questions, as well as ask you its own about what you want to do. Then, you can switch over to the Code mode to do the actual implementation, or the model itself will ask you to switch to it in other modes, because it's not allowed to change files in the Ask mode.

I think that approach works pretty well, especially when you document what needs to be done in a separate Markdown file or something along the lines of it, that can be then referenced if you have to clean the context, like a new refactoring task for what's been implemented.

> I find it interesting how marketers are trying to make minimal prompting a good thing, a direction to optimize.

This seems like a good thing, though. You're still allowed to be as specific as you want to, but the baseline is a bit better.

They do that because IMHO the average person seems to prefer something to be easy, rather than correct.

Sure - but you're being specific about the acceptance criteria, not the technical implementation details, right?

That's where the models I've been using are at the moment in terms of capability; they're like junior engineers. They know how to write good quality code. If I tell them exactly what to write, they can one-shot most tasks. Otherwise, there's a good chance the output will be spaghetti.

> There are so many subtle things/understandings of the architecture that are just in my head or a colleagues head.

My primary agentic code generation tool at the moment is OpenHands (app.all-hands.dev). Every time it makes an architectural decision I disagree with, I add a "microagent" (long-term context, analogous to CLAUDE.md or Devin's "Knowledge Base").

If that new microagent works as expected, I incorporate it into either my global or organization-level configs.

The result is that it gets more and more aligned with the way I prefer to do things over time.

There is a definite skill gap between folks who are using these tools effectively and those who do not.

There will always be people that describe a problem, and you'll always need people actually figuring out what's actually wrong.

-Reasoning, which is just very long inference coupled with RL

-Tool use aka an LLM with glue code to call programs based on its output

-"Agents" aka LLMs with tools in a loop

Those are pretty neat tricks, and not at all trivial to get actionable results from (from an engineering point of view), mind you. But the days of the qualitative intelligence leaps from GPT-2 to 3, or 3 to 4, are over. Sure, benchmarks do get saturated, but at incredible cost and forcing AI researchers to make up new "dimensions of scaling" as the ones they were previously banking on stalled. And meanwhile it's all your basic next token prediction blob running it all, just with a few optimizing tricks.

My hunch is that there won't be a wondorous life turning AGI (poorly defined anyway), just consolidating existing gains (distillation, small language models, MoE, quality datasets, etc.) and finding new dimensions and sources of data (biological data and 'sense-data' for robotics come to mind).

e.g. if you look at Altman's blog of "superintelligence in a few thousand days", what he actually wrote doesn't even disagreeing with LeCun (famously a nay-sayer) about the timeline.

I doubt it can even beat opus 4.1