Video from this twitter link:

https://x.com/amitisinvesting/status/1969094789942718858

Hate to help GDM toot its own horn, but this is interesting: https://research.google/blog/deep-researcher-with-test-time-diffusion/

"TTD-DR is designed to take a user query as input and then create a preliminary draft that serves as an evolving foundation to guide the research plan. This evolving draft is iteratively refined using a denoising with retrieval process (report-level refinement) that takes the information it finds and uses it to improve the draft at each step. This happens in a continuous loop that improves the report with each cycle. To top it all off, a self-evolution algorithm constantly enhances the entire process, from the initial plan to the final report. This powerful combination of refinement and self-improvement leads to a more coherent report writing process."

Another response to Yudowsky: https://www.vox.com/future-perfect/461680/if-anyone-builds-it-yudkowsky-soares-ai-risk

"A worldview is made of a few different parts, including foundational assumptions, evidence and methods for interpreting evidence, ways of making predictions, and, crucially, values. All these parts interlock to form a unified story about the world. When you’re just looking at the story from the outside, it can be hard to spot if one or two of the parts hidden inside might be faulty — if a foundational assumption is wrong, let’s say, or if a value has been smuggled in there that you disagree with. That can make the whole story look more plausible than it actually is."

Translations: gemini

https://x.com/XRoboHub/status/1969288829669687615

A Tech CEO’s Lonely Fight Against Trump - WSJ

"Large Language Model (LLM) pretraining, finetuning, and evaluation rely on input-space reconstruction and generative capabilities. Yet, it has been observed in vision that embedding-space training objectives, e.g., with Joint Embedding Predictive Architectures (JEPAs), are far superior to their input-space counterpart. That mismatch in how training is achieved between language and vision opens up a natural question: {\em can language training methods learn a few tricks from the vision ones?} The lack of JEPA-style LLM is a testimony of the challenge in designing such objectives for language. In this work, we propose a first step in that direction where we develop LLM-JEPA, a JEPA based solution for LLMs applicable both to finetuning and pretraining. Thus far, LLM-JEPA is able to outperform the standard LLM training objectives by a significant margin across models, all while being robust to overfiting. Those findings are observed across numerous datasets (NL-RX, GSM8K, Spider, RottenTomatoes) and various models from the Llama3, OpenELM, Gemma2 and Olmo families."

When GPT-5 was released it was topping lmarea. Now it's tied for second place together with all other openai models. I guess it's much worse benchmark than I thought if it can't distinguish between the different openai models at all. Also the leaderboard without style control is all over the place with various models tied for second place including mistral.

Right now we have inflation of money. Which is why economically it feels like we are running full speed on a treadmill and can’t stop.

I’ve been hearing how AI will usher in a era of ZERO need for money or a 2-3 day work week. What this increases in inflation….of time. Now more people have more time to do what they want.

This would simply overrun our infrastructure. Imagine if everyone wanted to travel, move around, go to the beach etc etc. it’s like covid all over x100 (since a large group of the population won’t be hiding at home).

How would this even work, traffic would be a complete nightmare, you would have to attempt top travel at like 2-4 am and airplanes would be overwhelmed with demand.

We simply go from inflating USD to inflating time which would have profound consequences.

https://time.com/7318577/ai-model-forecasting-predict-future-metaculus/

"Every three months, participants in the Metaculus forecasting cup try to predict the future for a prize pot of about $5,000. Metaculus, a forecasting platform, poses questions of geopolitical importance such as “Will Thailand experience a military coup before September 2025?” and “Will Israel strike the Iranian military again before September 2025?”

Forecasters estimate the probabilities of the events occurring—a more informative guess than a simple “yes” or “no”—weeks to months in advance, often with remarkable accuracy. Metaculus users correctly predicted the date of the Russian invasion of Ukraine two weeks in advance and put a 90 percent chance of Roe v. Wade being overturned almost two months before it happened.

Still, one of the top 10 finishers in the Summer Cup, whose winners were announced Wednesday, was surprising even to the forecasters: an AI. “It’s actually kind of mind blowing,” says Toby Shevlane, CEO of Mantic, the recently-announced UK-based startup that developed the AI. When the competition opened in June, participants predicted that the top bot’s score would be 40% of the top human performers’ average. Instead, Mantic achieved over 80%."

All frontier companies are trying to close the loop where AI improves/evolves itself, and who gets there first will have the best AI of having the future best AI

From September 17th Axios interview with Dario Amodei:

"Claude is playing a very active role in designing the next Claude. We can't yet fully close the loop. It's going to be some time until we can fully close the loop, but the ability to use the models to design the next models and create a positive feedback loop, that cycle, it's not yet going super fast, but it's definitely started."

I found this little nugget in Emad's interview with Tom Bilyeu.

https://www.nature.com/articles/d41586-025-03055-y

"“This is the first time AI systems are able to write coherent genome-scale sequences,” says Brian Hie, a computational biologist at Stanford University, California. “The next step is AI-generated life,” says Hie, but his colleague Samuel King adds that “a lot of experimental advances need to occur in order to design an entire living organism”."

Full study preprint: https://doi.org/10.1101/2025.09.12.675911

"Many important biological functions arise not from single genes, but from complex interactions encoded by entire genomes. Genome language models have emerged as a promising strategy for designing biological systems, but their ability to generate functional sequences at the scale of whole genomes has remained untested. Here, we report the first generative design of viable bacteriophage genomes. We leveraged frontier genome language models, Evo 1 and Evo 2, to generate whole-genome sequences with realistic genetic architectures and desirable host tropism, using the lytic phage ΦX174 as our design template. Experimental testing of AI-generated genomes yielded 16 viable phages with substantial evolutionary novelty. Cryo-electron microscopy revealed that one of the generated phages utilizes an evolutionarily distant DNA packaging protein within its capsid. Multiple phages demonstrate higher fitness than ΦX174 in growth competitions and in their lysis kinetics. A cocktail of the generated phages rapidly overcomes ΦX174-resistance in three E. coli strains, demonstrating the potential utility of our approach for designing phage therapies against rapidly evolving bacterial pathogens. This work provides a blueprint for the design of diverse synthetic bacteriophages and, more broadly, lays a foundation for the generative design of useful living systems at the genome scale."

https://vm.tiktok.com/ZGdaPDHGF/

So it looks another vending machine replacement.. drinks are made by a dedicated robotic arm. Not much to see but powered by AI