I was asked in an interview about talks or public presentations I've given. Thought it might be useful to publish them here. Most of them are in Spanish, but here goes anyway: 1. Modern Data Stack @thedatapub An attempt to demistify the word "modern" by going through the cycles technology has had for analytics

Elon Musk is an engineer. Jeff Bezos is an engineer. Larry Elison is an engineer. Larry Page is an engineer. Sergey Brin is an engineer. Jensen Huang is an engineer. Turns out capitalism does reward skills and intelligence, and the richest people are indeed engineers.

Linux kernel: 37.7 million lines of code Chromium: 38.8 million lines of code

Apple Container、version 1.0がリリースされている！！！！！！！！！！ github.com/apple/container

Frog put the model in a box. "There,” he said. "Now it will not answer about cybersecurity, biology, and AI research." "But you still can open the box," said Toad. "That is true," said Frog.

Keys to shipping more: - Fewer meetings - More trust in teammates - Listen exclusively to heavy metal / hardcore / metalcore

Only got up to ~30% of fable’s context window before running out of quota

No crying in the casino.

this is my personal singularity moment this post may sound like a paid ad. I only wish. I'm concerned, more so than happy. the world is changing, and, among the scenarios where AI goes terribly wrong, inequality is the most realistic, yet, the one Anthropic seems to be the least concerned about. I'm glad OpenAI is taking the opposite stance: *personal AGI for everyone*. I think this is a commendable position in the times we live. but who am I in the queue of the bread? anyway, Fable is here, so I'll just report my first-hour experience first of all, all my pet prompts are solved. → λ-calculus puzzles → bug questions → one-shot apps all are trivial to it. I don't have anything harder other than my ongoing work so, in the last several days, I've been toying with HVM5, a new interaction net evaluator with a faster loop. after writing the first version, I left 32 GPT-5 agents working for ~20 hours each. this resulted in up to 2x speedups, but the file size increased by 2-fold and quality decreased significantly. I then simplified the whole thing into an even simpler core, and left Opus 4.8 and GPT 5.5 optimizing it for 8 hours. Opus got a legit 6% - 34% speedup in most benches. GPT got better results, but, sadly, an unusable file. I then asked Fable to optimize it. 2 hours later, it landed a 1770% speedup in one case, 100% in other 4, and 22% in average. yes, in 2 hours it outperformed me, opus 4.8 and a swarm of gpt 5.5 agents, by one order of magnitude. that could not possibly be legit. "it must be hardcoding the benchmarks" (GPT trauma). so I read its explanation and what it did was, indeed, the most high impact optimization one could try first. seems like HVM5 was wasting a lot of time garbage-collecting unused branches of pattern-match nodes. I had optimized that for static mats, but not for dynamic mats. skill issue. Fable figured how to do it for these, resulting in a massive speedup in some benches but wait, is that *correct*? I'm not sure yet, it is credible, but this is the kind of thing that is very easy to get wrong on interaction nets. the problem is, when I was ready to start auditing Fable's solution so I could tell whether it was buggy or legit, it interrupted me to tell me it had found a massive bug on the code *I* had written. ... wait, what? so... for garbage collection purposes, I stored a bit on lambda term pointers that meant "the variable bound by this lambda has been freed, so, its lambda must free whatever argument it is applied to". that's fine. yet, on duplicator nodes, I also used the same bit to mean "one of the duplicated variables was freed, so, treat this dup as a passthrough no-op". so, if a lambda entered a duplicator, it would mistake the lambda's collection bit for its own, resulting in corrupted interaction! that's a mouthful, why I'm writing this? just so you can appreciate the sheer absurdity of what just happened. I didn't ask it to find bugs. I asked it for an optimization. and even if I did ask it to find bugs, this bug is so astonishingly subtle and specific, identifying it takes mastering the domain to an extent that it beyond even me. I'd easily need hours or days to fix it, *if* I ever came across it. chances are it would just go unnoticed. and Fable found it and fixed it like it was nothing, while it was busy adding a 17x speedup to a file that neither I, nor Opus 4.8, nor a fleet of GPT 5.5 managed to barely make 2x faster. oh and there is also another tab where it is also ripping through Bend's codebase and finishing everything I had to do I don't know what to say anymore this isn't about Anthropic or OpenAI, this is about our collective future as a species. the world is changing, and we need to be aware of it, and discuss how to handle this change. receipt below . . .

If you've adopted AI at your company but haven't seen any tangible results, read this 1990 article: "The Dynamo and the Computer" by Paul David. When electricity first arrived, factories that "adopted" it barely got faster. They just swapped the steam engine for an electric one and ran everything else exactly as before: same machine layout, same workflow, same management. Electricity in, no real gains out. The most common mistake with any new technology is to drop it into the old organization and then declare the transformation done. The real leap came decades later, when each machine got its own small motor. Suddenly machines no longer had to be lined up around one central drive shaft. They could be rearranged around the actual flow of work. The productivity gains didn't come from electricity. They came from REDESIGNING THE ENTIRE FACTORY around it. AI is the same. Bolting it onto your existing process gets you a faster steam engine. The payoff comes when you redesign the work itself. (link to paper in comments)

My favourite one is “argmaxxing”.

It is easier than ever to live a comfortable life and harder than ever to live a beautiful one.

“A human being should be able to change a diaper, plan an invasion, butcher a hog, conn a ship, design a building, write a sonnet, balance accounts, build a wall, set a bone, comfort the dying, take orders, give orders, cooperate, act alone, solve equations, analyze a new problem, pitch manure, program a computer, cook a tasty meal, fight efficiently, die gallantly. Specialization is for insects.”

“Unfortunately, AI companies have an incentive to make apocalyptic statements like this. Nothing markets a technology like the suggestion that it’s too powerful to exist. But such pronouncements are making the public needlessly afraid of what is, in fact, a miracle. Electricity was a miracle. The internet was a miracle. Today, we take them for granted. Within a few years, we won’t remember why we got so worked up about AI coding agents. We’ll take them entirely for granted too. That’s how technological miracles go. In 1900, about 40 percent of Americans worked on farms. Today it’s just 2 percent. If you’d told someone in 1900 that 95 percent of farmers would lose their jobs to machines, they would have predicted mass starvation and ruin. Instead, we went on to do almost everything we now think of as modern life: We designed cars, wrote software, piloted airplanes, manufactured TVs, made movies, built skyscrapers, and launched satellites. Those last 2 percent now feed the rest of us, and they feed us better than any society in history. Fewer farmers didn’t mean less food. It meant more of everything else.”

Se acuerdan cuando en un debate presidencial del 2018 Anaya le dijo a AMLO: “ el problema no es que seas viejo, el problema es que tus ideas son viejas, el problema no es que no entiendas inglés, el problema es que no entiendes el mundo” A lo que AMLO contestó “Riqui, riquín, canallín” Y todo México se cagó de risa. ¿Aún les parece gracioso a quién le dimos el poder?

It is great that the frontier labs want to support data analytics workflows. It is the #1 thing many enterprises want now. But enterprise data is difficult. Turns out most models are bad at it! They’re ok at writing single SQL queries or Python scripts, thanks to the plethora of text to SQL and data science benchmarks, but they struggle to query, clean, and make sense of data from multiple database systems (relational and non relational). Fortunately, we released a new benchmark to help, the Data Agent Benchmark, with plans to get it into a super well-known benchmark very soon :-) stay tuned! arxiv.org/abs/2603.20576

why does claude like uv run python so much instead of just uv run?

“AI slop is really a way of expressing that it’s difficult to identify the intent behind the form.” This is by far the best definition of slop I’ve see

dude who complains model burns bajillion tokens and then prompts like “yeah uh, we should definitely check on that. tests should pass I suppose, I liked what you did the other time, let’s do that again”