Just curious, do you think LLM makers are deliberately adding training data for your pelican test by now?

What's your definition of interesting?

I'm half way through writing a typescript to native code translator (via .Net) compiling a large enough subset of current code with a lot of help from GPT5 and Codex CLI. It has completely blown me away.

I'd like to give you a concrete example which stood out (from by now, dozens). I wanted d.ts files from the .Net Standard Libs. One immediately obvious problem is that .Net would allow classes/interfaces to be redefined if the generic type arity is different. For example, there can be SomeClass<int> and SomeClass<int, int> which are completely separate. TypeScript of course, wouldn't allow this - you can have one with all types defined, but it'd obviously be a mess.

I was stuck with (quite ugly): const users = new List_1<User>(...); instead of const users = new List<User>(...);

So GPT comes up with this:

  declare const __unspecified: unique symbol;
  type __ = typeof __unspecified;

  // Your arity-anchored delegates exist elsewhere:
  //   import("internal/System").Action_0
  //   import("internal/System").Action_1<T1>
  //   import("internal/System").Action_2<T1, T2>
  //   import("internal/System").Action_3<T1, T2, T3>
  //   ... up to 17

  export type Action<
    T1 = __, T2 = __, T3 = __, // ... continue through T17 = __
  > =
    [T1] extends [__] ? import("internal/System").Action_0 :
    [T2] extends [__] ? import("internal/System").Action_1<T1> :
    [T3] extends [__] ? import("internal/System").Action_2<T1, T2> :
    /* next lines follow the same pattern … */
    import("internal/System").Action_3<T1, T2, T3>;

This lets me write:

  const a: Action<number> = (n) => {};        // OK (void)
  const f: Func<number, string> = (s) => 20;  // OK (string -> number)

A human could come up with this, of course. But doing this at scale (there are many such problems which crop up), would take a lot of effort. Btw I'm using Claude for the grunt work (because its faster), but GPT5 is doing all the architecture/thinking/planning/review.

not correct. are you using high reasoning? i am using codex everyday.

The GPT5 family is the most intelligent model IMO. It's the only model that I have found that can refactor complex code and have the result be better than what came before it.

Charging developers $200/month for Claude Code and getting to a billion in ARR sounds like a pretty great business to be in to me, especially with this growth rate:

> Claude Code is reportedly close to generating $1 billion in annualized revenue, up from about $400 million in July.

https://techcrunch.com/2025/11/04/anthropic-expects-b2b-dema...

That's a very long-winded way of saying "it was subsidized so it could capture a large market segment, and now that's stopping", which is what SV companies have done since checks notes forever.

Not saying that you are completely wrong, but you could try to rephrase this to make a better conversation.

I agree that many new model versions are worse than the previous. But it is also related to base rules of the model - they try to please you and manipulate you to like them, way too much.

The Chinese open source models don't have this problem -- and they're state of the art!

They announced it on Twitter yesterday: https://x.com/OpenAIDevs/status/1986861734619947305 and https://x.com/OpenAIDevs/status/1986861736041853368

> GPT-5-Codex-Mini allows roughly 4x more usage than GPT-5-Codex, at a slight capability tradeoff due to the more compact model.

> Available in the CLI and IDE extension when you sign in with ChatGPT, with API support coming soon.

I noticed the same thing with -mini. It can be even slower than the full fat version. I'm guessing their infra for it is very cost-optimized to help them offer it at such a low price