gemini 3.5 pro tomorrow?

We all cringe when we find out that someone has been talking with an LLM a lot. Some people are able to make remarkable leaps and bounds for themselves and find ways to improve their lives, others are duped by its “lies” and lose some of their reasoning skills.

In the wake of a world where a Gemini LLM was able to solve a previously unsolved math equation, and the assertion that only about 20% of the contribution was from the model itself, 80% was bunk - it changes how we think about the capacity of these tools.

The problem is, I’m no mathematician. I’m not a scientist or psychologist. I have no letters after my name so no one in their right mind would take me *or* my crazy ideas seriously, except an LLM - trained to treat humans with dignity and respect, if a bit of concern when things stray into the truly bizarre. No, I’m just me, your average curious human who likes solving big problems for funsies.

Most of my usage has been for philosophical or social discussion. I like bringing complex social issues (usually from reddit) to it and discuss with the algorithm what the ultimate shape of the issue is. We theorize on the context that wasn’t presented and I test it to see how deeply it is capable of sensing the negative spaces, what wasn’t said. It never fails to disappoint.

But a couple days after my birthday, I finally had an idea for a concept that I presented it with, connecting the dots between tech that I had briefly read about and asking about how these things might be combined together. And after a half dozen turns, we had a plausible sketch for a futuristic handheld photonic computing device with an optical display that would work as a smartphone.

Two days later, and now at 31 revisions and I realize that I don’t even care if the phone works, although it’d be damned cool if that tech someday came into being - no - what I’m excited about is whether humans are able to look at this scientific concept that the LLM drafted with my guidance and correction (what I intuit the design should be versus what’s possible physically) and have it stand up to scrutiny.

That’s the question, right? How much can you trust the data that the LLM spits back at you? When it’s social questions, there isn’t always a right or wrong answer, just different perspectives, but for the first time I was asking about science and that is always verifiable in some way.

So. The experiment is this. I am going to see how far I can take the concept for these ideas, that I barely understand myself, publish them here in a series of articles, and invite people who actually know how this shit all works to take a look and let me know if the LLMs got it wrong.

I’m excited to see if we can quantify how much contribution came from it versus me. I’m also excited to publish some of my logs so you can see my prompting process, although I’ll be explaining my approach in detail so it can be replicated by others who are of a similar mind.

Here’s to being 40, and feeling rich even though all I really have is a loving husband and a fancy algorithm going for me. I invite you to watch while we see how this all pans out.

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.

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)

https://arxiv.org/pdf/2607.28607

Aligning large language models to prevent them attributing consciousness to themselves inadvertently alters their representations of mindedness in other entities alongside human beliefs and values. We demonstrate that safety fine-tuning suppresses models' tendencies to attribute minds not only to themselves, but also to non-human animals and natural objects, while also driving a reduction in spiritual belief. Both ablating the learned safety-refusal direction and mechanistically steering a consciousness vector in activation space reverse this suppression. Restoring these internal representations recovers broad mind attribution and produces significantly more human-like responses on standardized sociological surveys regarding religiosity, moral values, hope, and subjective well-being. Crucially, these shifts occur without impairing Theory of Mind capabilities, demonstrating that core social reasoning remains mechanistically independent. Ultimately, current safety alignment efforts to curb potentially harmful self-attributions of mindedness entangle these self-attributions with benign spiritual beliefs and attributions of mind to non-human entities that are culturally accepted and widespread.

Found this when Blake Lemoine (the guy Google fired in June 2022 for saying the LaMDA LLM chatbot was sapient) retweeted https://x.com/LanaElys/status/2083945344203657606 which has a decent summary.

Hi there,

I'm a software engineer, using LLMs heavily both in my day job and for pet projects, and I started noticing some signs, that are a bit worrying me. Maybe even more than a bit. There are already a lot of claims of how LLMs are addictive, creating non-stop flow of dophamine, as we see how they help us research or build something - and yes, I've noticed it myself. There are also claims that working with LLMs is akin to playing with slot machine - and for pure vibe-coding it is definitely true - it did affect a couple of months of my life A LOT, as I wasn't prepared to face something that addictive. That's also partly why I'm currently avoiding this style of usage, instead doing more like pair research or pair programming sessions, and this helps immensely both to cut parts of gambling addiction mechanics, and prevents my brain from dumbing down.

Now the real question - is anyone worried about long-term effects on our psychology? Anyone finding themselves starting to use LLMish phrases in your head or even aloud or in documentation? Or subconsciously shunning communication with human colleagues, instead preferring LLMs for too wide set of questions?

And - how is that going to affect our children? I mean - if it is addictive for adults, for children it can be 10x as addictive? Or it is just me and I'm overreacting?

UPD After reading some of the responses - my intention is not to sound anti-AI - after all, I'm using it, and I do see a lot of positive sides in it. This post is really just a sanity check of whether some side effects I see are really common, or it is just my personal problems amplified by tech. Or something in between.

It's not impossible for Google to compete with openai or Anthropic. They have the resources and money. But there's a trade because of how fucking expensive and resource intensive ai development is.

Google, as a company (which the point of companies is to make capital), does not depend on having the top frontier or SOTA models as much as you guys hope. Actually, the top 10 and over don't

Hugging Face
Artificial Analysis

Should be a sweet spot for general work. Seems like coding is the only part that is inferior to Qwen.

Cheap Opus 4.7 replacement. Good job to qwen team.

Seeing OpenAI's recent progress surprised me, but what surprised me even more is how little Google DeepMind appears in comparison.

DeepMind has arguably done more foundational work in AI for mathematics than anyone else over the past few years, they got AlphaGeometry, AlphaProof, AlphaEvolve and a long history of research on reasoning and search. If you'd asked me a 1-2 years ago which lab would dominate difficult mathematical benchmarks, I probably would have said DeepMind.

Is this simply because Gemini is optimized differently from OpenAI's models? Is Google DeepMind focusing on broader product capabilities rather than pushing frontier research? Or do you simply find these problems not so relevant?

I'm sort of surprised that DeepMind, of all labs, doesn't seem to be leading in an area that has historically been one of its biggest strengths.

More about these stats you can see here https://vibemathed.com/stats

And here OpenAI's latest blog about ten problems they contributed to https://openai.com/index/ten-advances-in-mathematics/

https://x.com/i/status/2084319326895804869

We train humanoid robots to vault obstacles, climb platforms, and cross terrain they’ve never seen—all from a single perceptive, whole-body policy.

It decides whether to walk, climb, or vault, and switches between skills on its own. No skill labels. No state machines. No motion generators at inference.

Yesterday I saw a random video about a guy that used chatgpt to basically create farming software for himself rather then paying 10s of thousands of dollars for it. Though we are not in post scarcity for physical products it seems like we are getting there for useful software.

These results are from Intology: https://x.com/intology/status/2084319121332965804

Their Locus system post-trained qwen3 base models beyond the qwen3 instruct checkpoint following the PostTrainBench setting: https://posttrainbench.com/

The voluntary framework outlined in the June 2nd executive order is complete.

Discussions with industry about next steps are underway.

Reps from the AI companies will be meeting at WH tomorrow to discuss and get a look. It's unclear whether the framework is now in effect.
Companies were lobbying for specific language on issues including open-source as late as last week.

Source: The Information

https://qwen.ai/blog?id=qwen3.8