Michael Felber

Leap to Universality for Shared Flourishing.

[Michael Felber @michaelfelber]

@PeterDiamandis I’d invest $1B in humanity’s greatest untapped multiplier: every person’s universal capacity to explain, create, test and act. I’d build a self-correcting network linking people, problems, experiments and capital to compound progress toward individual and shared flourishing.

[Markus J. Buehler @ProfBuehlerMIT]

Paper: https://t.co/B2bwWnZ2tI

[Michael Felber @michaelfelber]

Sustainable Bamboo Plastic:

https://t.co/NpyOD9Elx5

[Jason Smith - 上官杰文 @ShangguanJiewen]

This isn't in the trial phase.

The entire China International Consumer Products Expo in Hainan, recently, used only these materials for signage, food containers, and more.

This is getting scaled for mass use.

[Conjecture Institute @ConjectureInst]

Turing and Godel were replying to one of the questions that David Hilbert had asked in 1900.

Turing and Godel both addressed the problem and got the right answer.

Turing was focused on what could be done by a physical object...

Godel was focused on what can't be done by any physical object.

~Conjecture Institute Advisor @DavidDeutschOxf with @GadSaad

[Brett Hall @ToKTeacher]

Namely: we can explain reality with ever increasing fidelity and so come to change and control ever more of it.

There's books on this! "The Beginning of Infinity" explores the most fundamental consequences. And this book explores more still. Get it at: https://t.co/sfZ8l8VWT7

[Ian Miles Cheong @ianmiles]

Demis Hassabis: If you know the structure of a protein, the real question becomes—where will your drug bind, and what will it actually do?

That’s where the next wave of AI comes in. Not just predicting structures, but modeling interactions, outcomes, and real biological impact. At Isomorphic Labs, this is already happening—with 17 active drug programs and partnerships with giants like Eli Lilly and Novartis. The goal? Scale that to 100.

This is a fundamental shift in how medicine gets built. Instead of slow, expensive trial-and-error in wet labs, AI allows researchers to run thousands of hypotheses in silico—hundreds to thousands of times more efficiently. The wet lab becomes validation, not exploration.

Drug discovery is turning into a computational problem. Faster cycles, smarter predictions, and potentially massive breakthroughs in human health.

[Peter H. Diamandis, MD @PeterDiamandis]

https://t.co/rXQ9oopPzJ

[ₕₐₘₚₜₒₙ @hamptonism]

Perplexity just became the the first Al company to truly go head-to-head with the Bloomberg Terminal...

Using Perplexity Computer (with no local setup or single LLM limitation), it was able to build me a terminal with real-time data to analyze $NVDA using Perplexity Finance:

[Eric Topol @EricTopol]

Progress!

[Google Gemini @GeminiApp]

Today, we’re releasing a significant upgrade to our specialized reasoning mode, Gemini 3 Deep Think.

Deep Think is built to drive practical applications, enabling researchers to interpret complex data and engineers to model physical systems through code.

With the updated Deep Think, you can turn a sketch into a 3D-printable reality. Deep Think analyzes the drawing, builds the complex shape, and generates a file so you can create the physical object with 3D printing.

This is rolling out now to Google AI Ultra subscribers. Select the "Deep Think" option in the tools menu to get started.

Learn more here: https://t.co/MMGMgDtoK8

[Kevin Kelly @kevin2kelly]

Together with author Dan Pink, I have started a new podcast series called Best Case Scenarios. Each episode asks an expert to give us their best possible good news scenario in the next 25 years. What happens if everything goes right? What is the best case scenario for say, energy, transportation, biotechnology and brain science? Those are the subjects of our first four episodes. These scenarios are not predictions, but visions of what we can aim for in order to make them real. Get them on your podcast app. https://t.co/iQNMJRxWmb

[Michael Felber @michaelfelber]

@drmichaellevin Prescient 🦞

[Demis Hassabis @demishassabis]

AlphaGenome is our latest & most advanced genomics model published in @Nature today including making the model & weights available to academic researchers. Can’t wait to see what the research community will do with it. Congrats to the team on our newest front cover! #AI4Science

[Brett Hall @ToKTeacher]

@SahilBloom There need be no “goal”.

But if one wants to make progress don’t aim for “the truth” - aim to correct errors and create good explanations. Generate objective knowledge.

Why? Those things are all possible. Finding some Platonic, perfect Truth isn’t. Nor is it even desirable.

[Big Think @bigthink]

How evolution works in 54 minutes | Full Interview with Sean B. Carroll @SeanBiolCarroll

0:00 What if life is built on chance?
1:02 How life works: the staircase of evolution
1:22 Mutation and selection
4:45 Icefish evolved antifreeze
7:03 Speciation
9:40 The fossil record and the DNA record
11:47 Common misconceptions about evolution
13:36 How our bodies work: the staircase of self-defense
14:11 Our immune systems
16:36 Hypermutation
17:22 Immunological memory
19:45 Antibody genes and DNA
20:46 How do we make 10 million antibodies?
23:27 How cancer works: the staircase of mutation
25:15 Why does cancer risk increase with age?
26:36 150 gene mutations that drive cancer
26:59 Cancer drivers and breaks
28:03 Cancers in children vs adults
29:47 3 factors that contribute to cancer growth
33:27 Life emerging from chance
35:29The untold story of Alfred Russel Wallace
37:01 Theory of special creation
42:36 Natural selection
47:02 Archaeopteryx
49:36 Wallace and Darwin’s relationship
50:35 The Darwin-Wallace Theory

[Srishti @srishticodes]

Stanford just made a $200,000 AI degree free.

No application.
No tuition.
No “elite access”.

Stanford released its actual AI/ML curriculum on YouTube.
Not a PR-friendly intro.
Not “AI for the public”.

This is the real thing.
The same lectures shaping people working on frontier models.

What just became public:

Deep Learning (CS230)
→ https://t.co/DUtL9MO6Y7

Transformers & LLMs (CME295)
→ https://t.co/gN57biwLsE

Language Models from Scratch (CS336)
→ https://t.co/GnH11pPBdW

ML from Human Feedback (CS329H)
→ https://t.co/X9nxEX6PNg

Computer Vision (CS231N)
→ https://t.co/oBxKKWZP22

LLM Evaluation & Scaling
→ https://t.co/1tDpw9ArTq

The uncomfortable truth:

The degree isn’t the scarce asset anymore.
Execution speed is.

Top schools know this.
That’s why they’re publishing the playbook.

👉 Bookmark this.
Comment the first lecture you’ll actually watch.

[Michael Felber @michaelfelber]

@ylecun @demishassabis Because people are “universal explainers” (@DavidDeutschOxf ), generality seems to mean our unbounded-in-principle capacity to create ever better explanations, enabling ever better approximations of reality, while recognizing irreducible uncertainty.

[Ivan Zhao @ivanhzhao]

https://t.co/fxHYR6S29D