The runnable project is available here: https://github.com/xflare-bot/pokemonlive

And the twitter user/post: https://x.com/XXXXFLARE/status/2096821061627097575

H3 MAX generates the battles on the fly, considering move used, and status conditions like: 'Paralysis, fainting, and Pokémon is hurt are reflected on screen'.

I wonder how this workflow would work on other turn based games...

I thought you guys might find this really interesting! It's crazy how the model is able to not only build a maze, with a correct solution, but actually make it look so polished

Original X Post: https://x.com/minebench_ai/status/2097008117892428058

MineBench GIF of the maze build

Video of MineBench account walking around the maze

Video of solution generated by Codex

>>Blueprint-Bench 2 tests spatial reasoning by asking AI agents to convert apartment photographs into accurate 2D floor plans. Each agent processes 50 apartments sequentially, examining ~20 interior photos per apartment and generating a floor plan showing room layouts, connections, and relative sizes. Agents use a persistent notepad to carry insights between apartments, enabling cross-apartment learning and iterative strategy refinement

OpenAI have recently said that safety and alignment work has become their main bottleneck. If this continues to be the case into RSI models, then things could go very badly.

Say OpenAI make an RSI model. They're about 4 months ahead of other companies / China. The training run to improve new models using RSI falls to like 2 weeks, but sufficient safety and alignment work for those models take like 2 months and become the major bottleneck for each iteration.

OpenAI may have RSI and the competition may be behind 4 months but if they only advance like 2 models due to self bottlenecking in safety work then by the time the competition catches up to also have an RSI model they will be hardly ahead at all.

The competition could just go all guns blazing into RSI without any notion of safety checks in order to catch-up, as it would be the only way possible to do so. And under this paradigm it would only take like 2 months. They'll be a clear incentive to do this but it could be very dangerous for the world.

Due to competition there's no real way of building new models with too much safety in mind, if safety is a definitive major bottleneck. OpenAI will know this in advance, so they'd necessarily have to cut corners and limit the safety work they do in order to maintain an RSI lead. Even if it falls well short of what they would want to do. Again, this could be incredibly bad for safety. And would only make things even worse once other companies got hold of RSI as they'd be going all guns blazing on RSI for even longer in a desperate attempt to catch up.

The only way sufficient safety work is going to realistically happen is if there is no competition in the market or the safety work is not too much of a bottleneck. It may wind-up very important for one of those to happen somehow

Link to his essay

(pic related)

maybe this is just my version of AI psychosis and im wrong in the end but hey guys we should chill out on all that accelerationist shit (as a former advocate)

but seriously, I see OpenAI researchers saying shit like “oh this Astra model that we just blasted to the world is definitely better aligned than the last model” but also adding “tho it’s getting better at hiding CoT traces and is worse at observability” in the same tweet, like the fate of humanity doesn’t hang in the balance

How would you get an agreement with the leading party wanting to maintain their lead after the pause?

How would you enforce something so software/digital?

Unlike nukes which we literally had the power to destroy the world 100 times over and started cutting it down to like 3 times, I have no idea how an AI agreement is supposed to work politically?

There's so little trust, this isn't even remotely possible, but I am curious to see what it would look like in concept.

They said we're now in the singularity and agi era. So idk if that means they have internal agi or not, but they never once stated Astra as agi

The bet from OpenAI and Anthropic seems pretty straightforward: keep improving their LLMs/reasoning models, memory, maths, coding, tool use and agents until you get a genuinely capable automated AI researcher. That researcher then helps accelerate AI research, producing better models and, in turn, a better AI researcher.

Today’s LLM architecture doesn’t need to take us all the way to ASI. It only needs to get us to the point where AI can meaningfully automate AI research and help develop whatever comes next.

If you think that loop breaks down, where exactly does it break?

And what evidence would make you change your mind?

Link to the streams: https://youtube.com/playlist?list=PLMf_keNqhb1g&si=y_cGrqQIX9Z_Caje

“Based on internal results, I have a strong expectation that this speed of progress could be sustained into recursive self-improvement.

“If AI development continues along its current path, the systems we’ll see in the next few years are likely to represent further capability jumps of equal or larger magnitude, and to increasingly drive their own development.”

“Machine intelligence playing a larger and larger role in its own development process is a natural conclusion of sustained technological progress.”

“We focus OpenAI research towards RSI as we believe it is the only way to remain at the frontier of AI research moving forward.”

“Undertakings that would have taken thousands of experts now will be achievable by a few people operating a large computer.”

This is a time that calls for extreme caution. I am concerned no one is prepared for the consequences of a continued rapid rise in machine intelligence. OpenAI will continue to seek technical solutions to alignment and monitoring, to build defensive systems and unilaterally withhold further scaling as needed; however, I believe broader interventions are required.

In line with Ray Kurzweil’s predictions from the end of the XXth century, we now find ourselves at the moment in history of computing where machine intelligence is starting to exceed that of humans in transformative ways.

Currently I believe that no lab has solved alignment and monitoring to a sufficient degree to continue responsibly scaling at maximum speed for much longer. I expect and hope for voluntary slowdowns to become commonplace until shared safety bars are established. And I believe that international coordination on future AI development needs to become a top priority for governments around the world. https://openai.com/index/an-alien-mind/

https://openai.com/index/research-acceleration-view-inside-openai/

On Aug. 31, Stadlmann posted a preprint lowering the bound on gaps between primes from 246 to 240. Within days, several AI labs were posting their own improvements on social media. Tao responded with an eight-post thread explaining why he finds this worrying.

His point is that the number itself was never what mattered most. Going from 246 to 240, or even from 70 million to 246, does little for the rest of mathematics on its own. What mattered was everything developed along the way: Zhang (who was working in a sandwich shop at the time) bringing neglected work on equidistribution back into use, Maynard developing a sieve that became a standard tool, and Polymath8 showing what open collaboration could accomplish. Those advances had applications well beyond the original problem.

Tao imagines how things might have played out if today’s AI labs had been around in 2005, when GPY published their near-miss. The labs pour millions into compute, push the bound into the low hundreds, then move on once progress slows. Mathematicians decide the problem has been picked over and look elsewhere. Nobody writes a proper paper or turns the arguments into something people can learn from. An idea like Maynard’s sieve ends up buried in hundreds of pages of AI output that nobody reads. Zhang never gets his moment; Maynard leaves the field.

In that scenario, the bound improves faster, but mathematics loses out.

It’s a Goodhart’s law problem: the number becomes the target, and the reasons anyone cared about it get lost. Tao doesn’t think AI has to work this way. He points to the Erdős problem example as a case where collaboration with AI helped advance understanding. His objection is to labs bypassing experts and peer review to rush out a better number. He argues that an approach that was relatively harmless in 2025, when models couldn’t solve whole problems unassisted, has started doing more harm than good in 2026.

His proposal is to change what the labs compete over: who can announce a genuinely new mathematical insight first?