waldrews

The separate lines of business need much smaller human controlling teams now, but they still need a few responsible humans each, that will make sure the separate thread never creates a problem that bubbles up to become a drain on OpenAI's leadership's time and cognitive load, which should be reserved for their critical path. It's far less risky if those secondary businesses that aren't strategic to 'make and control the leading AI platform' are not OpenAI's responsibility.

Soon enough, it'll be 'Could we not entrust our safety critical systems to those emotional error prone humans? Pretty Please?'

The property grid control is still the greatest general purpose UI of all time. Define your classes with some attributes and it just works. It was all downhill from there with web and mobile.

It's about prioritizing candidates for the costly initial audition, and breaking symmetry for the introduction, something particularly useful for people who can't just approach other at random in a social setting.

That's fine if you're doing interactive dev tasks, but we're in the large volume, cost effective, big inputs, business still with low error tolerance business, and tuned the heck out of what we can get with minimal fix cycles. Millions of cases at hundreds of thousands tokens each - after all the prefiltering by cheaper models - and the tasks still need them to do convoluted reasoning. So 'usually get it right the first time' is a big part of the cost equation.

Yup. The problem is that it's bizarrely still not in General Availability status.

Completely free flow typing is risky in terms of interpretability, but type narrowing - var a : supertype; if (a is subtype) { // a is known to be subtype }, or type case, saves boilerplate in any OOP language.

Yup, tried all variations of that. There's the advantage that you can use a lower hallucination OCR specific model for the pre-processing, at least for clean text. But for something hard like handwritten forms, applying VLM with context is less error prone than preprocessing to text.

Also - and this is bizarre - the token cost of doing that is higher, not lower, at least in Gemini world, and by a large margin. That's very counterintuitive, but a page encoded as image tokens can be smaller than same page as text, and is not meaningfully lossy on documents that are just typed text because the models are well trained on those.

We sure did. It's a great writer, better in a harness, will process lots large context, but complex reasoning with convoluted rules and low hallucination tolerance? That's still larger model territory.

It's still 'preview' and not generally available, so can't run it for US restricted workloads.

(off topic) why the headache-inducing animated background? An annoyance for all, and an actual accessibility issue for some.

Every technical topic involves AI now. Even if it's about why the ancient Greeks didn't turn their steam engines to industrial use - the AI's should be researching the details. We might as well ask which topics involve humans or use language.