> It handles complex coding tasks with minimal prompting...
I find it interesting how marketers are trying to make minimal prompting a good thing, a direction to optimize. Even if i talk to a senior engineer, i'm trying to be specific as possible to avoid ambiguities etc. Pushing the models to just do what they think its best is a weird direction. There are so many subtle things/understandings of the architecture that are just in my head or a colleagues head. Meanwhile, i found that a very good workflow is asking claude code to come back with clarifying questions and then a plan, before just starting to execute.
> Meanwhile, i found that a very good workflow is asking claude code to come back with clarifying questions and then a plan, before just starting to execute.
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.
> Even if i talk to a senior engineer, i'm trying to be specific as possible to avoid ambiguities etc.
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.
What makes you look at existing AI systems and then say "oh, this totally isn't capable of describing a problem or figuring out what's actually wrong"? Let alone "this wouldn't EVER be capable of that"?
Interesting/unfortunate/expected that GPT-5 isn't touted as AGI or some other outlandish claim. It's just improved reasoning etc. I know it's not the actual announcement and it's just a single page accidentally released, but it at least seems more grounded...? Have to wait and see what the actual announcement entails.
At this point it's pretty obvious that the easy scaling gains have been made already and AI labs are scrounging for tricks to milk out extra performance from their huge matrix product blobs:
-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).
This is the worst they’ll ever be! It’s not just going to be an ever slower asymptotic improvement that never quite manages to reach escape velocity but keeps costing orders of magnitude more to research, train, and operate….
I'm the first to call out ridiculous behavior by AI companies but short of something massively below expectations this can't be bad for openai. GPT-5 is going to be positioned as a product for the general public first and foremost. Not everyone cares about coding benchmarks.
OpenAI's announcements are generally a lot more grounded than the hype surrounding them and their stuff.
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.
> GPT-5 will have "enhanced agentic capabilities” and can handle “complex coding tasks with minimal prompting.”
this seems to be directly targeted at anthropic/claude, wonder if it leads anywhere or if claude keeps it's mystical advantage (especially with new claude models coming out this week as well).
> GPT-5 will have four model variants, according to GitHub...
i also find it interesting that the primary model is the logic-focused one (likely very long and deep reasoning), whereas the conversational mainstream model is now a variant. seems like a fundamental shift in how they want these tools to be used, as opposed to today's primary 4o and the more logical GPT-4.1, o4-mini, and o3.
I think the promise back when all the separate reasoning / multimodal models were out was that GPT-5 would be the model to bring it all together (which mostly comes down to audio/video I think since o3/o4 do images really well).
Readit News
I find it interesting how marketers are trying to make minimal prompting a good thing, a direction to optimize. Even if i talk to a senior engineer, i'm trying to be specific as possible to avoid ambiguities etc. Pushing the models to just do what they think its best is a weird direction. There are so many subtle things/understandings of the architecture that are just in my head or a colleagues head. Meanwhile, i found that a very good workflow is asking claude code to come back with clarifying questions and then a plan, before just starting to execute.
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).
Well, the problem is that the expectations are already massive, mostly thanks to sama's strategy of attracting VC.
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
this seems to be directly targeted at anthropic/claude, wonder if it leads anywhere or if claude keeps it's mystical advantage (especially with new claude models coming out this week as well).
> GPT-5 will have four model variants, according to GitHub...
i also find it interesting that the primary model is the logic-focused one (likely very long and deep reasoning), whereas the conversational mainstream model is now a variant. seems like a fundamental shift in how they want these tools to be used, as opposed to today's primary 4o and the more logical GPT-4.1, o4-mini, and o3.
> gpt-5: Designed for logic and multi-step tasks.