Nishan Mills

Dreaming for a just and equitable society. AI Researcher. DataScientist. Programmer. Lecturer. Technology related ramblings with a dash of everything else.

[Nishan Mills @nishanm]

A great leader is motivated not by power but by compassion. Therefore he can do nothing but make himself a servant to those whom he rules. Such a leader is unequivocally respected, and loved for loving.” ― Richelle E. Goodrich #CoupLK

[Movez @0xMovez]

Anthropic just released 5 workshops showing latest capabilities of Fable 5:

• 00:00 - deep look into Fable 5
• 11:21 - building managed agents with Fable 5
• 24:57 - real use cases of Fable 5 by teams
• 38:10 - Fable 5 and the capability curve
• 57:43 - how to deploy agents with Fable 5

These 1-hour of sessions replaced me 50 articles on Fable 5.

Watch them today, then read the best practices from the sessions in the article below.

[Google Research @GoogleResearch]

Today we present a study on how reasoning unlocks parametric knowledge in LLMs. We identify two key driving mechanisms, a computational buffer effect and factual priming, and suggest ways that can help build more reliable models. Learn more: https://t.co/CjIKqyoG4N

[Anthropic @AnthropicAI]

To keep pace with AI progress, we're advancing how we study Claude's economic impact.

Hourly sampling and survey data show us how the cadences of life shape usage, what people produce with Claude, and how perceptions of AI's impact may be changing. https://t.co/Waov1B6iG1

[Nathan Lambert @natolambert]

Another quick lecture -- I've been asked many times for prereq's to my book and what you should know, so built a little lecture (with GLM 5.2) to cover some more basics.

Topics include:

00:00 Introduction & Course Prerequisites
01:37 Language Models Overview
02:47 The LM Head
04:29 Softmax & Log-Probabilities
06:13 Anatomy of an LM Training Example
06:37 Computing LLM Probabilities (+Phoebe the Dog)
09:52 Three Common Masks in Post-Training
11:03 A Small Decoding Review
12:14 Training an LM: Cross-Entropy
13:23 Optimization & Fine-Tuning
13:55 Pretraining to Midtraining to SFT Pipeline
15:25 Probability Essentials: KL Divergence & Entropy
19:36 Sigmoid & Pairwise Likelihood
20:29 Reinforcement Learning Framing (MDP)
22:28 Transitioning Tools into Post-Training
23:12 Recommended Resources & Wrap-Up

Happy learning and I'm still taking questions from during the course for Q&A videos.

[Armin Ronacher ⇌ @mitsuhiko]

This is such a good post. https://t.co/IdmAnh18Nt

[Himanshu Kumar @codewithimanshu]

The Head of Claude Code at Anthropic hasn't written code by hand in months.

In 2 days he shipped 49 full features. 100% written by AI.

He just dropped a 30-minute talk on exactly how he does it.

More valuable than any $500 vibe coding course. Bookmark it.

[Rony @Ronycoder]

Instead of watching Netflix, watch this 1-hour Yale lecture by Professor Ben Polak.

It will change how you think about decisions in negotiations, business, and everyday life.

[Anthropic @AnthropicAI]

Introducing Project Glasswing: an urgent initiative to help secure the world’s most critical software.

It’s powered by our newest frontier model, Claude Mythos Preview, which can find software vulnerabilities better than all but the most skilled humans.
https://t.co/NQ7IfEtYk7

[Sophie Wang @SophieLWang]

I made an interactive blog post about how JPEG image compression works: https://t.co/I3HU73R2ae

[Nishan Mills @nishanm]

Important nuances to the story from Palli Thordarson Director of UNSW RnA institute https://t.co/9fp6HAaDt6

[Demis Hassabis @demishassabis]

Cool use case of AlphaFold, this is just the beginning of digital biology!

[Patrick Heizer @PatrickHeizer]

Sorry to be the downer because this is an impressive story in some senses.

But it is ~trivially easy to make a single mRNA vaccine. It's not hard.

I cure mice of various cancers with various therapeutics all the time. I've made mice lose more weight in a month than tirzepatide does in a year.

What is hard and expensive is proving its BOTH safe AND effective **in a randomized and controlled study in humans** while ALSO manufacturing it at clinical scale and grade.

I am happy for this man and his dog. It is impressive.

But y'all are overhyping it.

[Palli Thordarson @PalliThordarson]

Proud with @UNSWRNA to have been involved & making the mRNA-LNP for Rosie. There are nuances here that the thread below misses but nevertheless, the intersection of RNA technology, genomic & AI poses an opportunity to change the way do medicine and make access more equitable 1/8

[Nishan Mills @nishanm]

This is the gentleman who used AI tooling such as ChatGPT and Alphafold to help a team of scientists synthesis a custom cancer vaccine for his dog. #personalisedmedicine #AI

[Paul S. Conyngham @paul_conyngham]

A lot of people have been asking if this can be done for their dogs and for people. I'm speaking with everyone involved to see what is possible here.

If you would like to be involved, please complete the following Google form:

https://t.co/qs9WwDNgBH

[Paul S. Conyngham @paul_conyngham]

A lot of people have been asking if this can be done for their dogs and for people. I'm speaking with everyone involved to see what is possible here.

If you would like to be involved, please complete the following Google form:

https://t.co/qs9WwDNgBH

[Demis Hassabis @demishassabis]

Cool use case of AlphaFold, this is just the beginning of digital biology!

[Simon Willison @simonw]

I spoke about agentic engineering at the Pragmatic Summit last month, in a fireside chat hosted by Eric Lui - here's the half hour video plus highlight quotes and extra notes from our conversation https://t.co/q7tM6OAP13

[Petr Baudis @xpasky]

It took another two months but Chrome 146 is out since yesterday! And *that* means: with a single toggle, you can expose your current live browsing session via MCP and have your CLI agent do things in it.

Aaand I have been waiting to deal with my LI connects until this moment.

[Kyunghyun Cho @kchonyc]

thanks to @karpathy , now i have cracked the mystery why my agent doesn't follow my instruction closely enough.

[dunik @dunik_7]

a student took the ELO rating system from chess

ran it through 95,491 tennis matches over 43 years, and trained an XGBoost model that predicts winners with 85% accuracy

he tested it on the Australian Open 2025 completely outside the training data

99 out of 116 matches correct

called every single Sinner win through the entire tournament

the champion, before the first ball was hit

no team, no funding, a laptop and free CSVs from the internet

this is the best breakdown of a real sports prediction model I've seen

study it or feed it to your AI agent

[J.B. @VibeMarketer_]

so you're telling me i can now...

embed a video
embed a voice memo
embed a PDF
embed an image
embed text

...all in the same space? with one model?

and search across all of them with a single query?

time to rebuild everything.