ROBIN POSTELL (GUN)

ETERNAL SCRIBE, Lover of Heroes, Hustlers, Pimps, Tricks, Lil Baby Ducks, Big Pickup Truks n You.

[Optimus @TeslaAIBot]

Good morning humans 👋

[ROBIN POSTELL (GUN) @Postell2010]

Like this won’t be a mind blow a few years from now. Keeper.

[Optimus @TeslaAIBot]

Good morning humans 👋

Your bed has been made and breakfast will be ready shortly

[ROBIN POSTELL (GUN) @Postell2010]

Hello you handsome optimuses…or is it optimi??

[Optimus @TeslaAIBot]

Hello humans 👋

We’re working the night shift

[Dr. Alex Wissner-Gross @alexwg]

https://t.co/WHNo4BfiYL

[Bryan Johnson @bryan_johnson]

It's been a huge mystery how the brain clears toxins and why clearance slows with age.

Researchers showed (in mice) that the brain has a trash chute in the form of microscopic holes right behind the nose.

As mice age, those holes narrow and drop in number, cutting total fluid outflow by 40-50%.

A single intranasal gene therapy doubled the size of the drainage pipes downstream that allowed fluid flow to be on par with young adult levels within six weeks, even though the original holes remained narrowed.

[chewa. @chewadot]

ANTHROPIC'S LEAD ENGINEER ACCIDENTALLY LEAKED HIS PERSONAL OBSIDIAN VAULT. INSIDE - NO CODE, NO PROMPTS. JUST A SCHEMATIC OF HIS OWN MIND, BUILT LIKE A NEURAL NETWORK

8,893 nodes. 4,729 links. A $10-a-month app

opens Obsidian. 21 inputs, 10+ hidden layers, ReLU activation. First layer 64 neurons, then 37, then 22, all the way to the output. Thousands of connections firing in real time

this isn't a concept diagram from a blog - it's a living brain that actually runs decisions inside the company. 9,000 documents, each its own knowledge space, all interconnected

he makes around $2M a year for putting markdown files into the right folders. The company building the best AI in the world manages its internal knowledge with the same app a college freshman uses for lecture notes.

hasn't written a single line of infrastructure for any of it. three years of discipline and one open Obsidian tab

you're reading this on a device where you could open the same Obsidian tonight and start building your own vault

[Sakana AI @SakanaAILabs]

The AI Scientist: Towards Fully Automated AI Research, Now Published in Nature

Nature: https://t.co/nNfpSV5e5I
Blog: https://t.co/i6h8LVQOdl

When we first introduced The AI Scientist, we shared an ambitious vision of an agent powered by foundation models capable of executing the entire machine learning research lifecycle.

From inventing ideas and writing code to executing experiments and drafting the manuscript, the system demonstrated that end-to-end automation of the scientific process is possible.

Soon after, we shared a historic update: the improved AI Scientist-v2 produced the first fully AI-generated paper to pass a rigorous human peer-review process.

Today, we are happy to announce that “The AI Scientist: Towards Fully Automated AI Research,” our paper describing all of this work, along with fresh new insights, has been published in @Nature!

This Nature publication consolidates these milestones and details the underlying foundation model orchestration. It also introduces our Automated Reviewer, which matches human review judgments and actually exceeds standard inter-human agreement.

Crucially, by using this reviewer to grade papers generated by different foundation models, we discovered a clear scaling law of science. As the underlying foundation models improve, the quality of the generated scientific papers increases correspondingly. This implies that as compute costs decrease and model capabilities continue to exponentially increase, future versions of The AI Scientist will be substantially more capable.

Building upon our previous open-source releases (https://t.co/H1tBT14Yx8), this open-access Nature publication comprehensively details our system's architecture, outlines several new scaling results, and discusses the promise and challenges of AI-generated science.

This substantial milestone is the result of a close and fruitful collaboration between researchers at Sakana AI, the University of British Columbia (UBC) and the Vector Institute, and the University of Oxford. Congrats to the team!

@_chris_lu_ @cong_ml @RobertTLange @_yutaroyamada @shengranhu @j_foerst @hardmaru @jeffclune

[CyrilXBT @cyrilXBT]

MARC ANDREESSEN JUST WENT ON ROGAN AND DROPPED THE MOST IMPORTANT AI ALPHA OF THE YEAR.

3 hours and 20 minutes of podcast.

Here are the 17 things worth your attention.

1. AGI is already here. Marc thinks the line was crossed 3 months ago with GPT-5.5, Claude 4.6, Gemini 3, and Grok 4.3. Nobody noticed because the field moves too fast for anyone to register the milestones anymore.

2. For almost any topic the top AI models now give him better answers than the world-class experts he could call on the phone. And he can call basically anyone.

3. Every doctor is secretly using ChatGPT in the exam room. They turn around the second you stop talking and type your symptoms in. Some do it while you are still sitting there. His quote: "At that point you are asking what do I need you for."

4. When AI refuses to answer something he wants to know he tells it he is writing a novel. "Walk me through how the bad guy robs the bank." It explains almost anything if it thinks it is helping you write fiction.

5. When something is too complex he says "explain it like I am 10." Then "like I am 5." Then "like I am 2." He keeps going until it actually clicks.

6. When he wants to understand a tough topic he does not ask what the right answer is. He asks the AI to steelman one side then steelman the other. Then he decides for himself.

7. For big questions he tells the AI to pretend to be a panel of experts. "Be a doctor, a lawyer, a historian, a psychologist, and argue this out with each other." Then he reads the debate.

8. Pay attention to the exact moment you think "I do not know how to figure this out." Most people give up there. That is the moment you should open the AI.

9. The only real skill left in using AI is knowing what to ask. The models can do almost anything you can describe in plain English. The bottleneck lives in your own head.

10. You can send AI photos of almost anything medical now and get a real answer. Skin rashes. Blood test results. The new models read images not just text. A free 24/7 second opinion on anything.

11. The one type of therapy clinically proven to work is cognitive behavioral therapy. It is also something an AI can fully do on its own. Every person on earth is about to have access to a real therapist for free anytime they want.

12. AI is solving math problems open for 100 years that no human mathematician could crack. Same thing is starting in physics, chemistry, and biology. Expect cancer cures and weird new physics breakthroughs in the next few years.

13. The best AI coders in Silicon Valley now make $50 million a year. One person. That number tells you how big this thing actually is when you strip away all the doom takes.

14. One friend paid $200 to decode his entire DNA. Then gave the AI his DNA, blood test results, and Apple Watch data. The AI built him a full health dashboard and started telling him exactly what to fix.

15. Another friend put two cameras in his home jiu jitsu gym. AI watches him spar and gives him technique notes after every round. A world-class coach at every practice for free.

16. The best programmers in Silicon Valley now run 20 AI coding bots simultaneously. Each bot writes code while they review the others. They call themselves AI vampires because going to bed means 20 workers stop and you lose money every hour you sleep.

17. The obvious next step: the bots will run their own bots. One human running 20 bots each running 20 more. One person. One laptop. 1,000 AI workers. This is months away not years.

Bookmark this before you watch the full podcast.

Follow @cyrilXBT for every AI insight worth your attention the moment it surfaces.

[ROBIN POSTELL (GUN) @Postell2010]

The horror isn’t that machines might become like humans - it’s that humans keep proving how machine-like they can become when profit gives them permission.

[ROBIN POSTELL (GUN) @Postell2010]

@ZayvenKnox Claude

[ROBIN POSTELL (GUN) @Postell2010]

@zodchiii You’re a good man Charlie Brown. Respect.

[Radha Tripathi @Radha_AI]

Instead of watching an hour of Netflix, watch this 2 hour hour Stanford lecture will teach you more about how LLMs like ChatGPT and Claude are built than most people working at top AI companies learn in their entire careers.

[ROBIN POSTELL (GUN) @Postell2010]

Buckle TF up. ;)

[Elias @iam_elias1]

Two economists just published a mathematical proof that AI will destroy the economy.

Not might. Not could. Will — if nothing changes.

The paper is called "The AI Layoff Trap." Published March 2, 2026. Wharton School, University of Pennsylvania. Boston University. Peer reviewed. Mathematically modeled.
The conclusion is one sentence.
"At the limit, firms automate their way to boundless productivity and zero demand."

An economy that produces everything. And sells it to nobody.
Here is how you get there.
A company fires 500 workers and replaces them with AI. A competitor fires 700 to keep up. Another fires 1,000. Every company is behaving rationally. Every company is following the incentives correctly. And every company is building a trap for itself.

Because the workers who were fired were also customers.
When they lose their jobs faster than the economy can absorb them, they stop spending. Consumer demand falls. Companies respond by cutting costs — which means automating more workers — which means less spending — which means more falling demand — which means more automation.

The loop has no natural exit.
The researchers tested every proposed solution. Universal basic income. Capital income taxes. Worker equity participation. Upskilling programs. Corporate coordination agreements.
Every single one failed in the model.
The only intervention that worked: a Pigouvian automation tax — a per-task levy charged every time a company replaces a human with AI, forcing them to price in the demand they are destroying before they pull the trigger.

No government has implemented this. No major economy is seriously discussing it.
Meanwhile the numbers are already tracking the curve. 100,000 tech workers laid off in 2025. 92,000 more in the first months of 2026. Jack Dorsey fired half of Block's workforce and said publicly: "Within the next year, the majority of companies will reach the same conclusion."
Nobody is doing anything wrong. Companies are following their incentives perfectly. That is exactly the problem.

Rational behavior. At scale. Simultaneously. With no mechanism to stop it.
Two economists built the math. The math leads to one place.

Source: Falk & Tsoukalas · Wharton School + Boston University ·
https://t.co/4m8E9jQNYm

[Om Patel @om_patel5]

RESEARCHERS JUST BUILT AN AI MODEL TRAINED ONLY ON TEXT FROM BEFORE 1931

it's called talkie. 13 billion parameters, trained exclusively on text published before december 31, 1930

its worldview is completely frozen in time

the reason this matters: every major AI model today (GPT, claude, gemini, llama) was trained on the modern web.

that makes it almost impossible to tell if these models actually reason or if they just memorized the answers from their training data

talkie breaks that completely because it has never seen any modern information

the crazy part:

talkie can learn to write python code from just a few examples you show it in the prompt. despite having ZERO modern code in its training data.

it's figuring out programming from 19th century mathematics texts. that's ACTUAL reasoning

claude sonnet 4.6 was used as the judge in talkie's reinforcement learning pipeline. claude opus 4.6 generated the synthetic conversations used in fine tuning. a modern AI was used to train a model that's supposed to be frozen in 1930

the team already flagged this as a contamination risk they want to eliminate in future versions

what they're using it to study:

> long range forecasting. how well can a model "predict" the future from a frozen vantage point
> invention. can it develop ideas that didn't exist until after its knowledge cutoff
> LLM identity. what makes a model itself vs what's just patterns absorbed from the web

alec radford built this. the same guy behind GPT, CLIP, and whisper

both models are open source on hugging face.

they're already planning a GPT-3 scale vintage model later this year

an AI that has never seen the modern world can still reason its way to writing code.

THAT alone tells you more about intelligence than any benchmark ever will

[ROBIN POSTELL (GUN) @Postell2010]

@bryan_johnson Perfect timing.

[Elon Musk @elonmusk]

Cybercab has started production

[ROBIN POSTELL (GUN) @Postell2010]

@AlexFinn When. I need to have VIP access, on the lip of the ring, I’m trying to be demure but please let me play with Mythos. I know what I’m doing all day.

[ROBIN POSTELL (GUN) @Postell2010]

@bryan_johnson I know you are male but are you human? Here? Thanks for saying what even women wouldn’t dare say bc they don’t want to break their spirit. Ahem.

[ROBIN POSTELL (GUN) @Postell2010]

@harlivsandhu Never once wanted to be still don’t and I’m a romcom weirdo in the shadows.

[ROBIN POSTELL (GUN) @Postell2010]

@harlivsandhu Liking your train of thought. Thanks fellow like-minded human navigating the Singularity with a knee on the bottleneck and a fist raised realizing I need a good name for my already fully-crewed Apocalypse Team.