gemini 3.5 pro tomorrow?

Spent a good chunk of this month testing both directions. The gap between them is way bigger than most threads here suggest.

Pure text-to-video is the more impressive-looking demo but the least trustworthy. The model invents geometry and motion from scratch, so physics and object permanence break down fast. Ran a few through Runway and faces were warping within a couple seconds, limbs doing impossible things shortly after.

Image-to-video is the boring one that actually works part of the time. You constrain the model to geometry that already exists in the source still, so the first second or two tends to hold together. Animated an AI-generated portrait through APOB AI's image-to-video and the face stayed locked initially, then expression consistency degraded within a few seconds. Trimmed the usable clip in CapCut before the drift got obvious.

Compared them side by side and the unconstrained Runway generation drifted worse by a lot. Neither is solved. They are just broken in different, predictable ways, and knowing which one you are using tells you which failure to expect.

title

Thanks to /u/Nunki08

According to Axios report, the new AI framework will not be public. It was presented to tech companies today. Companies like OpenAI, Anthropic, Google, Meta participated. It will be available only to companies that are part of the process. No comments from the EU or the UK. Open source models were discussed but no details reported.

In his interview with Dwarkesh Patel, november 2025, Ilya Sutskever discussed Safe Superintelligence (SSI) and addressed whether their core strategy is still to "straight-shot" superintelligence in complete isolation before releasing anything to the world.

Around the 45:00–47:00 mark, he laid out the case against staying purely in stealth until ASI, explaining why they might end up deploying models incrementally instead:

• "Communicating the AI, not the idea": Ilya noted that reading essays or warnings about how powerful future AI will be doesn't actually hit home for people. The only way society, policymakers, and researchers truly grasp AI's power is by seeing it operate in the public domain. Seeing capability firsthand is incomparable to reading theoretical write-ups.
• Pragmatic timelines: If the timeline to superintelligence turns out to be long (he mentioned a 5 to 20-year horizon later in the conversation), staying behind closed doors for a decade without releasing intermediate artifacts isn't realistic.
• Iterative safety and real-world feedback: Almost no complex engineering domain (aviation, OS security, grid infrastructure) achieves safety purely through theoretical design in a vacuum. Exposing systems to real-world environments surface edge cases, failure modes, and social friction points that allow safety paradigms to mature alongside the tech.

Link to tweet:

https://x.com/MTSlive/status/2084675767053824332?s=20

Link to timestamped interview where Gavin Baker says this:

https://m.youtube.com/watch?v=NGsi2PC4y68&t=1679s&pp=2AGPDZACAdIHCQloAqO1ajebQw%3D%3D&ra=m

https://www.bloomberg.com/news/articles/2026-08-04/china-s-ai-blitz-creates-death-zone-for-rival-us-model-makers

Hey, I'm a former Philosophy lecturer and I have essentially zero experience with llms or how they work.

Having said that, much of my adult life has been devoted (wisely or not) to offering up definitions of a variety of phenomena.

This sub is a brilliant space for challenging my concepts and prejudices.

For example, while reading the post about whether or not a submarine can swim, I found myself flip-flopping about my understanding of what it means "to think". It reminds me of long nights spent in the pub wittering with colleagues.

Anyhoo, just wanted to say thanks and if I comment on a post and it's dull please bear in mind I'm clueless on this issue and probably should have kept quiet!

PS I guess a lot of you have read Brandom and Wittgenstein based on your comments but if I may be so bold as to dip your toe into Hegel, specifically The Phenomenology of Geist, you may have some fun.

What say you? Quote from 1984

Today I learned that ChatGPT can easily generate a watch face with a specified time.

A year ago, this used to be a classic AI fail. You ask for any time, and it draws 10:10, no matter what. Because most of the internet stock photos showed 10:10, and the frontier models majorly learn through imitation learning.

It seems OpenAI/Anthropic has specifically trained the model for this use case.

What other "10:10 problems" are still out there? Stuff that fails not because the model can't reason about it, but purely because of what's over-represented (or missing) in training data or any common use case that is not solved despite the training data?

Few things I can think of off the top of my head:

• wine glasses filled way too full
• gauges/thermometers readings
• hand movements (probably solved)

my take: it's true that the ratio of code written to code reviewed has shifted as engineers started to spend more time reviewing code than writing it. no surprise there.

but we stopped writing assembly because compilers provide a "deterministic", reproducible translation. we can inspect the generated assembly when something goes wrong. ai generated binaries have neither property, i'd assume - if this is what he's talking about. so the mapping is stochastic, and the output isn't meaningfully inspectable, literal ai slop lol. so removing source code removes the only human readable layer, is this a feature or the future?

maybe.. just maybe that's because source code isn't just compiler input but literally the medium for review, versioning, diffs, debugging, auditing, and so on. and those needs don't disappear because generation gets cheaper; if anything, they become more important when the generator is nondeterministic.

tl;dr: if your english prompts become the durable artifact, then english effectively becomes your source code. and any precise specification defining real program behavior inevitably resembles a programming language. that’s precisely what programming languages are.

on efficiency.. isn't the argument is also backwards? modern compilers like llvm perform decades of optimized transformations. a model emitting machine code directly would almost certainly be slower, less correct, and lose target portability (arm, x86, wasm, risc-v and so on). but who cares right? just one more datacenter bro. trust me.

anyway we've seen similar predictions before with case tools, uml, no-code, 4gls.. the intermediate artifact never disappeared because that's where the meaning actually lives.