Readit NewsThe only thing that seems better to me is the parallel tool calling.
The fact they didn’t do this makes me think their finances are in very bad shape.
“GPT-5 is the smartest coding model we've used. Our team has found GPT-5 to be remarkably intelligent, easy to steer, and even to have a personality we haven’t seen in any other model. It not only catches tricky, deeply-hidden bugs but can also run long, multi-turn background agents to see complex tasks through to the finish—the kinds of problems that used to leave other models stuck. It’s become our daily driver for everything from scoping and planning PRs to completing end-to-end builds.”
The cynic in me thinks that Cursor had to give positive PR in order to secure better pricing...
This is a REALLY good summary of it I think. If you lose your patience with people, you'll lose your patience with AI tooling, because AI interaction is fundamentally so similar to interacting with other people
AIs are not able to write Redis. That's not their job. AIs should not write complex high performance code that millions of users rely on. If the code does something valuable for a large number of people you can afford humans to write it.
AIs should write low value code that just repeats what's been done before but with some variations. Generic parts of CRUD apps, some fraction of typical frontends, common CI setups. That's what they're good at because they've seen it a million times already. That category constitutes most code written.
This relieves human developers of ballpark 20% of their workload and that's already worth a lot of money.
I think the question to ask is what do I do as a software engineer that couldn't be done by an AI based tool in a few years time? The answer is scary, but exciting.
My theory is the willingness to baby sit and the modality. I'm perfectly fine telling the tool I use its errors and working side by side with it like it was another person. At the end of the day it can belt out lines of code faster than I, or any human, can and I can review code very quickly so the overall productivity boost has been great.
It does fundamentally alter my workflow. I'm very hands off keyboard when I'm working with AI in a way that is much more like working with someone or coaching someone to make something instead of doing the making myself. Which I'm fine with but recognize many developers aren't.
I use AI autocomplete 0% of the time as I found that workflow was not as effective as me just writing code, but most of my most successful work using AI is a chat dialogue where I'm letting it build large swaths of the project a file or parts of a file at a time, with me reviewing and coaching.
Using LLM based tools effectively requires a change in workflow that a lot of people aren't ready to try. Everyone can share their anecdote of how an LLM has produced stupid or buggy code, but there is way too much focus on what we are now, rather than the direction of travel.
I think existing models are already sufficient, its just we need to improve the feedback loop. A lot of the corrections / direction I make to LLM produced code could 100% be done by a better LLM agent. In the next year I can imagine tooling that: - lets me interact fully via voice - a separate "architecture" agent ensures that any produced code is in line with the patterns in a particular repo - compile and runtime errors are automatically fed back in and automatically fixed - a refactoring workflow mode, where the aim is to first get tests written, then get the code working, and then get the code efficient, clean and with repo patterns
I'm excited by this direction of travel, but I do think it will fundamentally change software engineering in a way that is scary.
That can be fine for a lot of general use cases, but if you’re working in specific domains like coding agents or high-precision summarization, that routing can actually make results worse compared to sticking with a model you know performs well for your workload.
"GPT‑5 is a unified system with a smart, efficient model that answers most questions, a deeper reasoning model (GPT‑5 thinking) for harder problems, and a real‑time router that quickly decides which to use"