Justo. Es quizás el aspecto más importante a prestar atención en el desarrollo de los próximos modelos.
INNOVATION
-
Sam Altman Summed Up by The New Yorker According to Gary Marcus
By
–
Sam Altman in a nutshell, @newyorker: [Image] [Translated from EN to English]
→ View original post on X — @garymarcus, 2026-04-06 15:45 UTC
-
AI and LLMs: Accelerating Scientific Discovery and Knowledge Generation
By
–
NEW VIDEO on DOTCSV! On the main channel! And with one of the most important topics that can be discussed… Is AI accelerating science?
Can an LLM generate new knowledge?
… Today we talk about the BIG LOOP -
Agent-Powered Neuroscience Research Corpus with Open-Source LLM
By
–
Implemented @karpathy 's obsidian idea and am now letting my agent build out a corpus around my neuroscience research. Obviously still needs vetting for informational accuracy, but certainly interesting to see the web grow! All using huggingface.co/DJLougen/Harm… with @NousResearch Hermes, ~35 t/s on my 3090.
→ View original post on X — @scobleizer, 2026-04-06 15:38 UTC
-
Building Open-Source Frontier Agents Dataset Initiative
By
–
RT @ClementDelangue: We keep saying we want open-source frontier agents. Fine. Then let’s build the dataset. @badlogicgames, creator of P…
→ View original post on X — @huggingface, 2026-04-06 15:37 UTC
-
Automatic cars eliminate need for physical buttons
By
–
My car is completely automatic. Buttons make very little sense in that world.
-
AGIALPHA Tends Towards Infinity
By
–
#AGIALPHA => ♾️ [Translated from EN to English]
→ View original post on X — @montreal_ai, 2026-04-06 15:29 UTC
-
OSGym: Infrastructure for Training Computer Use Agents
By
–
So why did the researchers develop OSGym? Training computer use agents is both a modeling problem & an infrastructure challenge: • Each task needs a full OS (GUI, apps, browser, etc.) • Scaling → massive CPU/RAM/disk costs • Random crashes from real software environments • Thousands of parallel runs required It's a key bottleneck, though it's not often discussed. [Translated from EN to English]
→ View original post on X — @mit_csail, 2026-04-06 15:25 UTC
-
MIT CSAIL Introduces OSGym for Training Computer Agents
By
–
How do you train AI agents that can use computers like humans? 🧵 MIT CSAIL researchers introduce "OSGym": scalable OS infrastructure to improve the capabilities of computer use agents. It introduces large-scale training made possible by extensive infrastructure optimization: bit.ly/47JprPd [Translated from EN to English]
→ View original post on X — @mit_csail, 2026-04-06 15:25 UTC
-
Invention Machine: Monetization Through Personal Use Rather Than Sale
By
–
An invention-automation machine starts doing things to innovation that have never been done before—operating at the substrate, where invention compounds faster than it can be understood. #AGIALPHA François Chollet (@fchollet) If you build an automation machine, the way to monetize it is to sell it to as many people as possible — anyone who has tasks to automate. But if what you build is an invention machine, then the best way to monetize it is to use it yourself. — https://nitter.net/fchollet/status/2032440942108848424#m [Translated from EN to English]
→ View original post on X — @montreal_ai, 2026-04-06 15:24 UTC