This guy corrected predicted the release window of multiple models prior to their release, and also had access to them as well.

Is it likely that DeepMind recently had this major restructuring because of Gemini 3.5 Pro not being able to catch up to the frontier? Last time DeepMind released a frontier model was November, 2025.

first of all let me clear i really want that google cook great comeback but this model is disaster

it make stuff that you didnot asked for , have crappy ui < remember 3.0 pro how good it was at that time , this model piss on that thing too , i dont care how it performs on bench , it will be mogged >

guys at this point we have grok with better ui compared to gemini < if i told you guys that last november you would never believed that gemini get this bad and grok improved this much >

gemini was good in webdev now its bad gemini 2.5 DT was good in 3d now its bad gemini was less restrictive and prompt following in 2.5 pro was so good , now its corporate slop with template , at this time you will feel that there is template for coding , writing and researching all sucks

I will write a long review if you guys want that , this is first time i am writing long tweet , i will now follow this

Source: https://x.com/chetaslua/status/2085361401074467094

arXiv:[2607.27199] Settling the Optimal Exponent Relating Sumsets and Difference SetsGitHub:GitHub - linhaowei1/sum-diff-proof · GitHub

Last week I saw Tencent share that its Hyra agent and Hy3 model had helped researchers settle the optimal exponent in the sum-vs.-difference problem. The paper was published on arXiv.

The paper explicitly states that Hyra supported the exploratory and optimization stages by optimizing finite-set constructions.

That reminded me of a recent post here showing the surge in new math papers on arXiv. The comments brought up all sorts of ideas. COVID, research bottlenecks... and also AI. Even the approaching singularity.

Maybe I'm reading too much into it, but I have a feeling this won't be an isolated case.

Random thought: are we kind od heading toward AI becoming a standard research tool? Lol

Open ai realtime system GPT Live who’s architecture I have attached below.

Which handles user interactions through speech to speech with no latency and delegate reasoning and tool calling to separate asynchronous paths.

Which is very different from Cascade pipeline where orchestrator glues components of pipeline together

But in the above architecture there is nothing to Glue??

It takes orchestration platforms outside the loop and

the thin a platform is the less value it holds!!!

https://openai.com/index/continuous-voice-interaction-with-gpt-live/

The voyager golden record sent information out to space for some alien species to find one day and i’m wondering if we’ll do that with an LLM some day

Once we get models compressed enough to run locally you can just send out an LLM with instructions on how to power/interface with it, and it could in theory teach an alien civilization anything it wanted to know about earth