The 3rd edition of my book Deep Learning with Python is being printed right now, and will be in bookstores within 2 weeks. You can order it now from Amazon or from Manning. This time, we're also releasing the whole thing as a 100% free website. I don't care if it reduces book sales, I think it's the best deep learning intro around, and more people should be able to read it.

All of reality is programmable. You just have to figure out how. And the way to do that is to model it.

In the future, there will be "Latent Space Archaeologists" who investigate the model weights of the 21st century to reconstruct a long extinct culture.

America is where anything is possible

That's a great speech that captures the essence of American exceptionalism. It is not based on innate superiority, power or wealth, but rather on the fact that the outcasts of the world could set foot here, start over, reinvent themselves, achieve their potential, and shape the nation's ongoing story. This promise of a blank slate and a better future where hard work pays off is what has always made America great -- and unique.

The only long term bottlenecks are information and energy.

One way to live 400 years is to do (and experience) 5x more things per day

The jobs of the future will require high adaptability and creativity, focusing on complex problem framing rather than repetitive execution or specialized skills

This is the sort of early prediction you can make when you pay close attention to ARC-AGI scores

In retrospect... Yep

Unsurprisingly, all of the strong contenders on ARC-AGI-3 so far use this type of approach.

Even right now, many workflows are morphing into LRM-guided harnessess that manipulate symbolic programs. Which is a crude, but currently-accessible form of symbolic learning.

Does it mean LLMs / LRMs go away? Not at all. In the short term, they are still the best way to perform intuition guidance (codegen). In the long term, even if they become obsolete for reasoning itself, we will still need models of language in order to communicate with AI systems

Eventually, much of AI will converge towards intuition-guided symbolic world modeling, i.e. deep learning-guided program synthesis. It is inevitable. Symbolic modeling lets a system construct a compact, reusable, highly generalizable mental model of a problem space using minimal data.

On the pod: "Constrained Adaptive Rejection Sampling" with @ucsd_cse professor @lorisdanto. Hear how symbolic AI experts have navigated the LLM era and why the future of AI code generation depends on program synthesis and formal methods.

ARC-AGI-3 is built different, it has dumbfounded almost all regular attempts so far because it's so much harder than anything that came before. It has no rules, it's agentic and has no explicit goals, they need to be discovered. @tufalabs won the first milestone of @arcprize > There is no language built into the benchmark, but these guys "put the language back in", because in their view - it's the best way to climb up the notional "abstraction mountain" and effectively use many of the abstractions which have evolved over millions of years of language evolution. > They built a novel harness "The Duck" around a 27B open weights model (Qwen 3.6) to solve extremely challenging and novel reasoning problems that require abstraction. > This is the launch video of their winning agentic harness, "The Duck". We have also released an exclusive interview with them on MLST, just dropped. > The million dollar question is: what will @fchollet think about how they've done it, and is this a step towards AGI?

The current wave of AI technology will not lead to mass unemployment. In fact, its impact on the labor market should be minimal, consisting mostly of increasing demand for software engineers.