I just published some of my thoughts on why I don't think most of the economy will run on SOTA AI models, why we are in a transitional phase, and why hyperscalers like $AMZN, $MSFT, $GOOGL, and $META stand to gain the most in the next leg of AI.
https://t.co/YqnX4tRyP7
@TravisMrkvicka@RihardJarc Fair point. For for coding, when you run out of quota, “dumber” model generally destroys what the “intelligent” one built.
Therefore you loose trust. Feedback hasn’t been positive for 3.5 Flash. I got the impression at I/O the Google is not at the same SOTA as OpenAI Anthropic
@Recuenco Desde la ignorancia: aporta la dislexia alguna ventaja? Puedo entender lo del Asperger leve, pero no logro ver cómo la dislexia mejore alguna función cognitiva.
1/ I just finished one of the best books I've EVER read.
Comes out next week.
My friend @DavidEpstein (Range, The Sports Gene) has written a masterpiece on the virtue of constraints, of not thinking OUTSIDE the box but "INSIDE the BOX"...
As a few quick excerpts that 🤯...
@Recuenco Leído el hilo: entre la A), la B) y la C), el fracaso es en la mayoría de casos por la falta de C)?
No lo concluyes explícitamente, pero al leer la previa interpreté que había un elemento faltante más común que los otros 2… y me quedé en ascuas.
@Recuenco Esto es exactamente la estrategia Minimax de Von Neumann en teoria de juegos.
Jugar suponiendo que el rival hará la jugada que te perjudique más.
https://t.co/OsQHxL9WPj
Hace dos semanas hablábamos de Kilian Jornet y cómo entrenó a Oriol Cardona para ser campeón olímpico.
Pues bien, este es Kilian Jornet este fin de semana:
It is hard to communicate how much programming has changed due to AI in the last 2 months: not gradually and over time in the "progress as usual" way, but specifically this last December. There are a number of asterisks but imo coding agents basically didn’t work before December and basically work since - the models have significantly higher quality, long-term coherence and tenacity and they can power through large and long tasks, well past enough that it is extremely disruptive to the default programming workflow.
Just to give an example, over the weekend I was building a local video analysis dashboard for the cameras of my home so I wrote: “Here is the local IP and username/password of my DGX Spark. Log in, set up ssh keys, set up vLLM, download and bench Qwen3-VL, set up a server endpoint to inference videos, a basic web ui dashboard, test everything, set it up with systemd, record memory notes for yourself and write up a markdown report for me”. The agent went off for ~30 minutes, ran into multiple issues, researched solutions online, resolved them one by one, wrote the code, tested it, debugged it, set up the services, and came back with the report and it was just done. I didn’t touch anything. All of this could easily have been a weekend project just 3 months ago but today it’s something you kick off and forget about for 30 minutes.
As a result, programming is becoming unrecognizable. You’re not typing computer code into an editor like the way things were since computers were invented, that era is over. You're spinning up AI agents, giving them tasks *in English* and managing and reviewing their work in parallel. The biggest prize is in figuring out how you can keep ascending the layers of abstraction to set up long-running orchestrator Claws with all of the right tools, memory and instructions that productively manage multiple parallel Code instances for you. The leverage achievable via top tier "agentic engineering" feels very high right now.
It’s not perfect, it needs high-level direction, judgement, taste, oversight, iteration and hints and ideas. It works a lot better in some scenarios than others (e.g. especially for tasks that are well-specified and where you can verify/test functionality). The key is to build intuition to decompose the task just right to hand off the parts that work and help out around the edges. But imo, this is nowhere near "business as usual" time in software.
@joaoqalves_es@sdepablos Ya se llevo 100M$ de su primer exit. No creo que sus motivos sean meramente económicos individuales, sino de tener medios para poder desarrollar su visión.
@TheGoodKnowmad Estamos ampliando la deuda técnica clásica en deuda cognitiva. Es como si el Copilot se hubiese convertido en el verdadero piloto, y el humano en copiloto que se deja llevar a la deriva.
Short musings on "cognitive debt" - I'm seeing this in my own work, where excessive unreviewed AI-generated code leads me to lose a firm mental model of what I've built, which then makes it harder to confidently make future decisions https://t.co/KUqQXDVNiS
𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗧𝘄𝗶𝗻 — 𝘁𝗵𝗲 𝗔𝗜 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗯𝘂𝗶𝗹𝗱𝗲𝗿.
No setup. Secure. Infinitely scalable.
We just raised a $𝟭𝟬𝗠 𝘀𝗲𝗲𝗱.
After a beta with 𝟭𝟬𝟬,𝟬𝟬𝟬+ 𝗮𝗴𝗲𝗻𝘁𝘀 𝗱𝗲𝗽𝗹𝗼𝘆𝗲𝗱, we’re now opening to everyone.
RT and comment “Twin” — first agents on us. 👇