🚨 BREAKING: Grok continues to dominate AI benchmarks, beating OpenAI's ChatGPT, Google's Gemini and others in reasoning, coding, and agentic tasks.
#1 in GPQA (Scientific Reasoning)
#1 in SciCode (Coding)
#1 in Terminal-Bench (Agentic Coding & Terminal Use)
I started co-leading Gemini API a few months ago. My focus: how the API should evolve for agentic use cases.
LLM APIs are quietly evolving from a simple request/response into a full-blown protocol with growing complexity.
Some notes 👇
Think about a gymnast or stunt person who is doing a new and dangerous trick. We usually see the perfected performance over months of intuition building. Face diving at a metal bar is inevitably going to go sideways. If we saw all the outtakes we would see hundreds of oopsies.
The great filter to study the really interesting things are. Correct me if I am mistaken:
- calc 1&2, multi variable calc, diff equations and linear algebra, discreet math
Almost every high level interesting subject and paper is based on a combinations of these 6
It is pretty…
The great filter to study the really interesting things are. Correct me if I am mistaken:
- calc 1&2, multi variable calc, diff equations and linear algebra, discreet math
Almost every high level interesting subject and paper is based on a combinations of these 6
It is pretty…
Introducing two new Gemini 2.5 models (Flash and Flash-Lite) which are more intelligent, cost effective, and token efficient. You can keep up with our latest models through `gemini-flash-latest` and `gemini-flash-lite-latest`!!
Joy of life-long learning day 36...
today I finished Calculus 1 🥳
First full course down, many more to go!
Calculus 2: 28% ⬆️ 5%
--------
Linear Algebra 1/3: 31% ⬆️0%
Introduction to probability: 68% ⬆️ 0%
Applied Calculus with Python: 32% ⬆️ 0%
Discrete Math for CS 1/5: 56%…
Joy of life-long learning day 36...
today I finished Calculus 1 🥳
First full course down, many more to go!
Calculus 2: 28% ⬆️ 5%
--------
Linear Algebra 1/3: 31% ⬆️0%
Introduction to probability: 68% ⬆️ 0%
Applied Calculus with Python: 32% ⬆️ 0%
Discrete Math for CS 1/5: 56%…
Why did OpenAI train GPT-5 with less compute than GPT-4.5?
Due to the higher returns to post-training, they scaled post-training as much as possible on a smaller model
And since post-training started from a much lower base, this meant a decrease in total training FLOP 🧵
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