itay-maman

First impression(s):

1/ it took me a while to understand this is a social network. I thought it was about giving me more visibility into my sessions/token usage

2/ Unless I am missing something it should be pretty easy to game the system.

3/ I think that landing the user on a global feed of all users will help

The inherent problem with evaluating coding performance of models remains: most day-to-day coding tasks are open-ended/partially-spec'd, and as such there is huge uncertainty on how the "right" solution looks.

It's very hard to rank models' solutions on such problems, which is why they rarely appear in benchmarks (I'd be glad to stand corrected).

Even Opus 4.5 coding a C compiler from scratch - jaw-dropping as it is - doesn't tell the whole story. Most of my tasks are not that well spec'd.

Thank you! That's the one. Amazing sleuthing.

It took me several reads to distill their post to this one sentence: "Enterprise Account contracts will no longer be offered to new customers"

I'd be glad to stand corrected but AFAICT this is the only sentence that describes the change. All other say "nothing is changing in [some area]".

Trying to downplay something to that extent immediately raises suspicious that this something (the change) is much more profound that what is stated.

My first reaction: wow, incredible.

My second reaction: still incredible, but noting that a C compiler is one of the most rigorously specified pieces of software out there. The spec is precise, the expected behavior is well-defined, and test cases are unambiguous.

I'm curious how well this translates to the kind of work most of us do day-to-day where requirements are fuzzy, many edge cases are discovered on the go, and what we want to build is a moving target.

agree on that and the speed is fantastic with them, and also that the dynamics of questioning the current session's assumptions has gotten way better.

yet - given an existing codebase (even not huge) they often won't suggest "we need to restructure this part differently to solve this bug". Instead they tend to push forward.

Something that caught my eye from the announcement:

> GPT‑5.3‑Codex is our first model that was instrumental in creating itself. The Codex team used early versions to debug its own training

I'm happy to see the Codex team moving to this kind of dogfooding. I think this was critical for Claude Code to achieve its momentum.

Impressive results, but I keep coming back to a question: are there modes of thinking that fundamentally require something other than what current LLM architectures do?

Take critical thinking — genuinely questioning your own assumptions, noticing when a framing is wrong, deciding that the obvious approach to a problem is a dead end. Or creativity — not recombination of known patterns, but the kind of leap where you redefine the problem space itself. These feel like they involve something beyond "predict the next token really well, with a reasoning trace."

I'm not saying LLMs will never get there. But I wonder if getting there requires architectural or methodological changes we haven't seen yet, not just scaling what we have.