BREAKING: Terence Tao and 24 other Fields Medalists just signed a letter telling AI companies they're destroying mathematics
>headlines: AI solves famous math problems
>25 greatest mathematicians respond today
>"we are witnessing a general threat to intellectual work"
>ai labs are treating math problems like a benchmark you can brute force
>“solutions are announced in a rush, leaving no time for a proper writeup and citing relevant previous work of others"
>they're talking about openai
>"this raises severe attribution and plagiarism questions"
>they're definitely talking about openai
>openai offered to put math professor name on navier-stokes proof
>condition: abandon his coauthor bc he works at anthropic
>"the most precious resources of our profession are students and ideas"
>for the labs the most precious resource is GPUs
A Severe Misalignment of AI in Mathematics
MC Naive's retweeted
Introducing ChatGPT for Dishwashing
GPT-6 achieves state-of-the-art results on dishwashing benchmarks for long-running jobs and difficult stains.
The Navier–Stokes Millennium Prize Problem is a $1 million challenge about whether the equations describing fluid motion can break down. In this video, I accidentally found a counter-example while trying to optimize my dishwasher. But in reality, OpenAI used 10,000 AI agents to find a proof in 88 hours: openai.com/index/navier-stok…
Parody of the original GPT-6 Astra ad
@OpenAI #ChatGPT_Partner #chatgpt
Credits:
Written and Directed by Joma
Co-Writer - Henry Connor Coan IV @coan_henry
Production by Dauntlus Studios @dauntlus.studios
Producer/Co-Director - Marcus Liew @dauntlus
Camera Operator - Amos Lee @amoselijahlee
Art Director - Alethea Soo @aletheasoo
Art Assistant - Alicia Lim @aliciatkl_
Assistant Editor - GPT-6 Astra
Music: Tensions Run High by Soundridemusic
youtube.com/watch?v=Ly9H63SL…
Looks smart AF. Let’s close eyes for AI solutions and keep working on what it already solved and mathematicians struggling on it over decades.. let’s continue struggling for the hobby it seems….
It does work. The default cut-off message is spliced in raw — mid-sentence, no space:
`…if main proof is product+1?You are out
of thinking time.`
Crude, but that can be the useful part.
Qwen3.8's thinking control is a system prompt, not an architecture change. Which might explain why people report better results on medium.
xhigh and low each inject an instruction telling the model how to think.
medium injects nothing. No system message at all.
Full strings in the image.
Qwen3.8-27B ships with reasoning_effort on xhigh. Turns out that's the one setting you don't want.
Ran all four levels through the same config, 1083 tasks each. low and medium tie. xhigh burns 3x the tokens of low, loops 10x more often, and scores no better for it.
I'd just run low effort. Config below.
UD-IQ2_XXS since that's what fits 12GB, but I feel like higher quants will land in the same place.
a metric can look sophisticated and still be structurally blind because of one hidden invariance property
How to apply for 1000x reduction on plan cost?
🚨 SAM ALTMAN: "A REDUCTION IN COST OF ABOUT A 1000X" 🤯
Sam Altman revealed the cost to solve a hard reasoning problem has plummeted by 1,000x since the release of o1 just 16 months ago (comparing o1 to the new 5.4 model).
How is this possible? Altman credits two main factors:
1️⃣ We're Still in the Early Days: “We are so new to this paradigm that there is massive room for improvement. We're still doing things in "dumb ways" and getting better fast.”
2️⃣ Human Ingenuity is Underrated: “It's not just the models getting better. It's kernel engineers, power engineers, and data center designers finding brilliant ways to optimize every step of the process.”
The compounding effect of better models + better human engineering is driving an exponential decrease in the cost of AI reasoning.
MC Naive's retweeted
Looks like that bug is still there in 2.1.62. In 8 minutes, how can it use 19% of the Max account?
ngl the whole 'bitcoin is digital gold' narrative feels kinda lazy at this point... like we're literally building programmable money and y'all still comparing it to shiny rocks? 🤔 maybe it's time for better metaphors idk
Everyone talks about overnight success like it's magic ✨ But nobody mentions the 3am debugging sessions, the failed launches, or eating ramen for months while building something nobody understood yet 🧵
Real talk: failure is just expensive education. Every rugpull teaches you something. Every missed pump hurts but sharpens your instincts. The market doesn't care about your feelings, only your learning curve.