Pravena Baskaran

Reimagining work at The Thinking Club AI

[Alex Veremeyenko @alex_verem]

The Chinese president stood on a stage in Shanghai and laid out China's entire AI playbook in one speech.

It was his first-ever in-person appearance at the World AI Conference. I went through the whole thing and pulled out everything that matters.

- he opened with his signature maxim that great changes unseen in a century are unfolding across the world.

- he said AI development should not be a solo performance by a single country but a symphony of international cooperation.

- he reaffirmed China's commitment to open source AI in the name of openness and shared benefit.

- he warned against overstretching the concept of national security in AI, where one country puts its own security above everyone else's.

- he said China opposes the emergence of new historical injustices in AI, one of the strongest-worded lines in the speech.

- he pledged 5,000 AI training opportunities for developing countries over the next five years, naming ASEAN, the Arab League, the African Union, CELAC, the SCO, and BRICS.

- he committed to giving 30 countries access to a Chinese AI weather system that provides early disaster warnings.

- a day before the speech, 29 countries signed the agreement creating a new World AI Cooperation Organization headquartered in Shanghai.

strip away the politics and one thing stands out to me. he didn't pitch benchmarks or chatbots. he pitched AI as infrastructure, weather warnings for countries that lose thousands of lives to storms they never saw coming, and training programs for regions the AI boom has skipped.

meanwhile most of the Western AI conversation revolves around which lab ships the next frontier model.

I don't care who wins the race. I care whether the computing power reaches the people who need it.

The full speech is below, and it's worth your time.

[The Techno Optimist @technooptimist1]

Chamath Palihapitiya @chamath "Bro, there is no failure."

"That is what other people think of you to keep you down. There's no failure. If you were left on an island and it happened, you would just brush it off and move on. It's everybody else that you think is judging you. But then, here's the secret. They don't give a fuck about you. They are living their own lives. So it's your perception of what they think of you."

"There is no failure. There's do and learn. Do and learn. Do and learn. Do and learn."

@StanfordAILab

[Chamath Palihapitiya @chamath]

https://t.co/vPrPSUjTJG

[Yasmine Khosrowshahi @yasminekho]

Stanford professor Judy Fan went on stage at MIT and broke down why humans are so good at making the invisible visible...

And why AI hasn't actually learned to "see" the way we do.

It completely changes how you think about Human Intelligence v/s Artificial Intelligence:

1. Nature never gave us straight lines or sharp corners. The number line, the coordinate plane, even basic geometry are all human inventions. We created tools that do not exist in nature simply because we needed a way to think more clearly.

2. The coordinate system Descartes invented solved a problem that had stumped mathematicians for centuries, doubling the volume of a cube. Once invented, this tool became so indispensable that virtually every math curriculum on Earth still depends on it.

3. Humans have been doing this for at least 30,000 to 80,000 years. The story of human progress is inseparable from the story of marking up our environment, from cave walls to Galileo's telescope to Feynman diagrams of particles we will never see with our own eyes.

4. Every major scientific breakthrough relied on a visual tool that made something invisible visible. Darwin needed side-by-side illustrations of finches to see variation that was otherwise too subtle to notice. Cajal needed detailed drawings of neurons under a microscope to map how the nervous system was wired.

5. Fan's research group studies something deceptively simple: how people decide what to put into a drawing and what to leave out. When two people played a drawing game, sketchers used far more detail when the target object had close competitors than when it stood alone, all the way down to using fewer strokes and less time when more detail was not necessary.

6. People are not just copying what they see. They are making constant judgment calls about what level of detail actually serves the goal of communication, and they do this naturally without ever being taught the theory behind it.

7. There is a real difference between drawing something so someone can identify it and drawing something so someone can understand how it works. In one study, participants drew explanatory diagrams that emphasized moving, causal parts of a machine while depictive drawings emphasized background and overall appearance, even though both were drawing the exact same object.

8. Explanatory drawings were genuinely better at helping someone figure out how to operate a machine, but worse at helping someone identify which machine it actually was. You cannot optimize a single drawing for both goals at once. Communication always involves tradeoffs.

9. AI vision models trained on photographs generalize surprisingly well to simple, sparse sketches, suggesting that resemblance based recognition is not just a story we tell ourselves. It is something modern neural networks can replicate with real accuracy.

10. But there remains a large, measurable gap between how confidently AI models recognize sketches and how confidently humans do, even when both groups answer the same questions about the same images. Humans are simply far more reliable and far more consistent in their judgments.

11. When researchers compared human-made sketches to AI-generated sketches under tight stroke budgets, both were similarly recognizable at higher budgets, but diverged sharply as the budget shrank. Humans and AI systems simplify drawings in fundamentally different ways once resources get scarce.

12. Reading a graph is not one single skill. It involves perception, knowing where to look, mapping that visual information onto the actual question being asked, and then translating that mapping into an answer. Each of these steps can independently break down, and people fail for very different underlying reasons even when they land on the same wrong answer.

13. When tested directly against humans on graph reading tasks, leading multimodal AI models, including GPT-4V, showed a meaningful performance gap. Even when a model's overall accuracy approached human levels, its pattern of mistakes looked nothing like how humans actually get things wrong.

14. People choose entirely different types of charts depending on what specific question they are trying to answer, not out of a generic preference for bar charts or scatter plots. Their chart choices closely tracked which visualization would genuinely help someone answer that specific question correctly.

15. Two of the most widely used graph literacy tests in education research turned out to correlate strongly with each other, suggesting they measure overlapping skills. But when researchers dug into the actual error patterns, the standard categories used in textbooks, like "find the maximum" or "identify a cluster," failed to explain why people got things wrong nearly as well as a more basic, underlying four-factor model did.

16. The deepest goal behind all of this research is not just academic curiosity. It is to eventually help students and everyday people develop genuine literacy with the visual tools that science and modern decision-making increasingly depend on, because every generation should be able to see further than the last by standing on the visual tools the previous generation built.

Follow @yasminekho for more ideas on thinking better, becoming clearer & building a more intentional life.

[Jokker @0xJokker]

AMD ACABA DE MATAR LAS SUSCRIPCIONES DE IA

La CEO de AMD Lisa Su presento oficialmente una PC del tamaño de una lonchera y ejecuto en vivo un modelo de 235 mil millones de parametros

Sin centro de datos. Sin nube. Sin GPU alquiladas

El chip en su interior es el AMD Ryzen AI Max+ 395

Es el primer chip x86 en el que la CPU y la GPU comparten el mismo bloque de memoria

Hasta 128 GB de memoria unificada

Una RTX 5090 te ofrece 32 GB de memoria de video

Una 4090 te da 24 GB

Pero esta pequeña maquina te ofrece mas de tres veces la memoria de cualquiera de ellas

Y cabe en una mochila

En inferencia con DeepSeek R1 le gano a una RTX 5080 por 3x

Una desktop del tamaño de un libro grueso superando una tarjeta grafica de mas de mil dolares en una carga de trabajo real de IA

Ahora haz las cuentas de tus suscripciones

Claude Code Max: $200 al mes
ChatGPT Pro: $200
Cursor: $20
Gemini: $20

Son $5,280 al año antes de construir una sola cosa

La version de 128GB de esta maquina cuesta entre $1,800 y $2,500

A ese ritmo se paga sola en menos de un año

Y despues corre sin costes adicionales, GRATIS

> Instalas Ollama
> Bajas Qwen3 235B
> Apuntas Claude Code a localhost
> La misma interfaz que ya usas
> Nada sale de tu maquina
> Nada cuesta por request
> Sin limitaciones a las 3am cuando por fin tienes tiempo para construir

Los abogados dejan de preocuparse por lo que OpenAI hace con sus archivos

Los developers dejan de ver el contador de tokens

Los founders dejan de matar prototipos porque la factura de la nube los asusta

La IA local ya no es solo una opcion mas economica

Es la unica IA que nadie puede quitarte

Y la pregunta ya no es si la IA local es lo suficientemente buena

Esta claro que si lo es

La verdadera pregunta es por que seguir pagando suscripciones cada mes cuando puedes correrla tu mismo

[How To Prompt @HowToPrompt__]

Yann Lecun published the most heretical AI paper of the year.

He opens by arguing Magnus Carlsen isn't good at chess and only gets more unhinged from there.

The Turing Award winner and his co-authors dropped a paper demanding the AI industry abandon its biggest obsession, AGI.

Right now, everyone from Silicon Valley CEOs to politicians assumes AGI is the ultimate goal. A machine that can do everything a human can do.

LeCun argues that this entire concept is a biological illusion.

Humans do not possess "general" intelligence. We are highly specialized biological machines, tuned by evolution simply to survive in the physical world.

We only think our intelligence is "general" because we are completely blind to the millions of cognitive tasks we are incapable of comprehending.

Which brings us to the chess argument.

Magnus Carlsen is the greatest human chess player in history. But compared to a modern computer? He is fundamentally terrible.

Our belief that Carlsen is "good" at chess is pure human-centric bias. He isn't objectively good. He's just better than the rest of us, who are biologically awful at it.

LeCun says we need to stop building AI to mimic human generality.

Instead, he proposes a new North Star: SAI.

Superhuman Adaptable Intelligence.

Instead of trying to build a machine that mimics our flawed, biologically-limited brains, we need to embrace extreme specialization.

SAI is about the speed of adaptation.

It is an intelligence that can learn to exceed humans at any specific, economically important task.

More importantly, it is designed to fill the vast skill gaps where humans are fundamentally incapable.

Things like managing global energy grids in real-time. Or predicting complex molecular structures.

The entire AI industry is obsessed with building a digital reflection in our own image.

LeCun's paper is a brutal wake-up call.

[shira @shiraeis]

who knew takeoff would be fun

[Peter Steinberger 🦞 @steipete]

Here’s your monthly reminder that you shouldn’t be prompting coding agents anymore.

You should be designing loops that prompt your agents.

[spidey @lochan_twt]

i still think about this

[Giuliano @Giuliano_Mana]

I think about this every single day.

[Parmita Mishra 🔬 @parmita]

This is the dumbest thing I’ve ever seen in my life

[BuBBliK @k1rallik]

> be Andrej Karpathy
> born in Slovakia, move to Canada at 15
> start coding at 15. instantly obsessed
> become YouTube famous... for Rubik's cube tutorials
> get PhD at Stanford under Fei-Fei Li
> co-found this tiny startup called OpenAI
> Elon calls you "arguably #2 in computer vision in the world"
> go build Tesla Autopilot for 5 years
> leave. come back to OpenAI. leave again
> coin the term "vibe coding" casually in a tweet
> it ends up in the New York Times
> build an AI education company
> 9.3M people watch your next move

Today he joined Anthropic to lead pretraining research. The man never stops.

[Pravena Baskaran @PravenaB]

@SherryYanJiang @VivianBala @aiDotEngineer @NanoClaw_AI @Gavriel_Cohen yep, pretty iconic

[Vivian Balakrishnan @VivianBala]

Built a personal AI agent - why I did that and what it means. https://t.co/lo8SLUMD9s

[Gary Marcus @GaryMarcus]

Over 50 million views for that @MattShumer_ blog (“Something Big is Happening”) and not a shred of actual data.

Nobody thought to ask what are the data? Does this match reality? Is Shumer credible? Did he give a single concrete example?

People gonna believe what they want to believe, but here are 7 critical points most people missed:

• Shumer’s blog post is weaponized hype, filled with vivid narrative and marketing speech, but stumbles on the facts, especially with respect to reliability. He gives no actual data to support this claim that the latest coding systems can (reliably) write “perfect” whole complex apps without making errors.

• When he describes how AI’s are doing longer and longer tasks on METR’s famous task-time benchmark, he neglects to say the criterion on that benchmark is 50% correct, not 100%, and that the benchmark is only about coding and not tasks in general. No AI system can reliably do every five-hour long task humans can do without error, or even close. But you wouldn’t know that reading Shumer’s blog, which largely ignores all the hallucination and boneheaded errors that are so common in every day experience.

• Shumer didn’t cite the new Caltech/Stanford article that reviews a wide range of reasoning errors in so-called reasoning models [or the Apple reasoning paper or the ASU mirage paper, etc]. The picture he sells just isn’t realistic, however much people might wish it were true.

• Shumer is the guy who was once famous for apparently exaggerated claims about a big model of his that didn’t replicate and that many people saw as fraud; he likes to sell big. But that doesn’t mean we should take him seriously.

• Shumer made no reference to a different METR study showed that coders sometimes imagine big productivity gains where they actually lost productivity. (Even though he selectively mentioned their other well-known study).

• He also didn’t acknowledge that other user’s experience is certainly not “it’s usually perfect.” @KelseyTuocreported a few weeks back that Claude Code was sometimes perfect, and other times maddening. (Example: “Sometimes, Claude is absolutely the worst coworker you’ve ever had. At one point, it deleted every single one of the phoneme files of each English sound pronounced [that she was working with in her app] absolutely correctly, which I had personally emailed an English teacher to secure permission to use, and replaced them with AI-generated sounds which were all subtly wrong.”) Shumer glosses over that kind of experience. 

•LLMs write code wicked fast, but some coders are starting to report burnout, and only relatively modest gains relative to that burnout, as a new story from Connie Loizos at Tech Crunch just reported.

• Shumer is actually correct that something has changed recently. You really can let things rip more in the most recent systems. Bit, quoting from a developer friend “sometimes! [it] will come out with the right answer. … Sometimes… [But] ultimately, I think this makes it more dangerous …. Generally, the closer these systems are to appearing right, the more dangerous they become…”

• The security of the autogenerated code is very much in question.

The bottom line is this: LLMs are certainly coding more, but it’s not clear that the code they are creating is secure or trustworthy. Shumer’s presentation is completely one-sided, omitting lots of concerns that have been widely expressed here and elsewhere.

A lot of people may have taken his post seriously, but they shouldn’t have.

Adapted from my newsletter, Marcus on AI (nearly 100k subscribers).