It’s funny how every podcaster/public ai figure is so certain text as a Ui will go away and it’s not going anywhere.

Excellent more grounded review. A few questions:

> LLMs are emerging as a new kind of intelligence, simultaneously a lot smarter than I expected and a lot dumber than I expected

Isn't this concerning? How can we know which one we get? In the realm of code it's easier to tell when mistakes are being made.

> regular people benefit a lot more from LLMs compared to professionals, corporations and governments

We thought this would happen with things like AppleScript, VB, visual programming. But instead, AI is currently used as a smarter search engine. The issue is that's also the area where it hallucinates the most. What do you think is the solution?

The bit about o3 being the turning point is very interesting. I heard someone say that o3 (or perhaps the cheaper o4-mini) should have been called gpt-5, and that people would have been mind blown. Instead it kind of went under the radar as far as the mainstream goes.

Whereas we just got the incremental progress with gpt-5 instead and it was very underwhelming. (Plus like 5 other issues at launch, but that's a separate story ;)

I'm not sure if o4-mini would have made a good default gpt though. (Most use is conversational and its language is very awkward.) So they could have just called it gpt-5 pro or something, and put it on the $20 tier. I don't know.

Here's the source for the jagged spiky intelligence diagram:

https://x.com/colin_fraser/status/1994235521812328695

https://karpathy.bearblog.dev/the-space-of-minds/

What is current state of the art workflow when working with legacy code across multiple languages?

This would be a 100 kLOC legacy project written in C++, Python, and jQuery era Javascript circa 2010. Original devs have long left. I would rather avoid C++ as much as possible.

I've been Github Copilot (in VS Code) user since June of 2021 and still use it heavily, but the "more powerful intellisence" approach is limiting me on legacy projects.

Presumably I need to provide more context on larger projects.

I can get pretty far with just ChatGPT plus and feeding bits and pieces of project. However that seems like using the wrong tool.

Codex seems better for building things but not sure about grokking existing things.

Would Cursor be more suitable for just dumping the whole project (all languages) basically 4 different sub projects and then selectively activating what to include in queries?

personally the point about apps built on top of LLMs resonated the most. however the success of well-engineered tools for specific use cases, often with choice of model, goes to show that:

- benchmarks don't mean a lot for the frontier stuff, but can be interesting for the same series of models (smaller v/s larger). reminds me of comparing clock speeds between CPUs.

- the app layer can fill the gaps to squeeze out the most for a use case, but there is still no one-size-fits-all situation.

- often the discourse here or the perspective of people building seem disconnected from an average user. a lot of discussion in the post is irrelevant for the vast majority of users. e.g. as cool as TUI can be, it is not an interface most users would gravitate towards.

while not directly related, other modalities are more exciting, and comes thanks to applying techniques for handling text to other media forms, or in conjunction.

LLMs still need to bring clear added value to enterprise and corporate work; otherwise, they remain a geek’s toy.

Big media agencies that claim to use AI rely on strong creative teams who fine-tune prompts and spend weeks doing so. Even then, they don’t fully trust AI to slice long videos into shorter clips for social media.

Heavy administrative functions like HR or Finance still don’t get approval to expose any of their data to LLMs.

What I’m trying to say is that we are still in the early stages of LLM development, and as promising as this looks, it’s still far from delivering the real value that is often claimed.

Vibe coding is sufficient for job hoppers who never finish anything and leave when the last 20% have to be figured out. Much easier to promote oneself as an expert and leave the hard parts to other people.

Beyond graduating students, I see model labs as “accelerators/incubators” bundling, launching, and productizing observed ideas that gain traction. The sheer strength of their platforms, the number of eyes watching them, near-zero marginal costs, and seemingly unlimited budgets mean that only slow decision-making can prevent them from becoming the next Amazons of everything.