@kareem_carr bingo

@alexhillman My "AI Newsmaker" list is the best of the best, hand curated out of 50,000 accounts: https://t.co/fasUz7PuHq

@Vikashplus https://t.co/uXNPywmSnI My AI says we should meet up. Would love that!

@ArtSokolov @NVIDIARobotics My AI says we should meet up. I'd love that! https://t.co/uXNPywmSnI

fuck. i like the desktop app

fuck. i like the desktop app

@BarathAnandan7 I have a good first impression, but it's still a tad slow for me (using llama.cpp). About 2x slower than gpt-oss 120B on the same hardware. I think I need to look for the NVIDIA-optimized stack.

@sayashk @random_walker They only have PhD students to do work? I would have thought that training successors, would be important in of itself 🫠

At first glance this is a totally reasonable perspective. Training PhD students is a duty! But consider this — *effectively* advising a PhD student over a 5-year period is well over 1,000 hours of work, not to mention bringing in hundreds of thousands of dollars in grants. Professors will do some things for mostly altruistic reasons (peer review) but the time commitment for advising is not something that's reasonable to ask of someone without some form of compensation. So there are two options. One is to make advising a job requirement. Unfortunately this doesn't work, because the *quality* of advising is unobservable and can't be quantified by metrics, leading to a race to the bottom. The other option is the current system — advising helps advance the professor's research agenda because PhD students do most of the work, so they take on students voluntarily. Which means it's important to ask if this subtle alignment of incentives will continue despite advancing AI capabilities. Academia has many such "subtle alignments of incentives" that the system relies on in order to function — rarely articulated, poorly understood, and fragile. Maybe the advisor-advisee relationship in CS will survive the AI transition, as @sayashk predicts, but many processes and structures will surely break. Best to rethink the system now, before it's too late.

@bryan_johnson Maltipoo is best

Heading to #NVIDIAGTC next week? Let’s talk @PyTorch. 🚀 We’re bringing the community to San Jose. Drop by Booth #338 to meet expert developers and core maintainers in person. Scaling, inference, foundation models, and OSS contributions. Full schedule below 👇 #PyTorch

🗓️ Plan your week: Check out the full "Meet the PyTorch Experts" schedule here: https://t.co/VDTj78Kryu We'll be posting the daily lineups here in this thread all week. See you at the booth! 🤝 @NVIDIADev

My last lecture in Komaba: https://t.co/0S8kPkGjCI The talk will be streamed live on this YouTube channel. @HirokiSayama

My last lecture in Komaba: https://t.co/0S8kPkGjCI The talk will be streamed live on this YouTube channel. @HirokiSayama

Happy to share that my PhD paper "Scene2Hap: Generating Scene-Wide Haptics for VR from Scene Context with Multimodal LLMs" has received a Best Paper Award (top < 1%) out of 6,730 submissions at ACM CHI (@acm_chi), the most prestigious conference in the human-computer interaction field🙌 Scene2Hap is an LLM-centered system that automatically designs object-level vibrotactile feedback for entire VR scenes based on objects' semantic attributes (e.g., whether and how the object vibrates) and physical context (e.g., the object's density, spatial relationships). It then renders real-time haptic feedback across the scene, calculating vibration propagation based on LLM-inferred material properties. To the best of our knowledge, this is the first paper to address the problem itself: "designing haptic characteristics of a whole VR scene with one click." Thanks a lot to my co-first-author @EasaAliAbbasi, Sara Safaee, @FKeeL1, and my advisor Jürgen Steimle!

@artjng @acm_chi Congrats!

Sakana AI (@SakanaAILabs) has signed a multi-year contract with the Defense Innovation Science and Technology Institute under Japan's Ministry of Defense to research and develop an AI-driven Command and Control (C2) system! This system will support rapid decision-making by analyzing vast amounts of multimodal data from edge devices, powered by AI agents and small VLMs. So proud of the team!

I'm joining SpaceX and xAI, working closely with Elon and team to build superintelligence. Together SpaceX and xAI combine physical and digital intelligence under a leader who understands hardware at the deepest level. Add a high-agency culture with frontier-scale resources, and you get the possibility to achieve something truly unique. I’m excited to advance the fields I’ve obsessed over for years, from robotics research to building AI models on the founding teams of Mistral and TML. Both were extraordinary journeys with extraordinary people that shaped how I think about building intelligence from the ground up. Grateful for everything that brought me here and can’t wait to get started.

https://t.co/sbZncI4gF9

A good discussion about evaluating LLMs in medicine by the head of Health AI at OpenAI, highly recommend reading. Appreciate the shoutout for Medmarks, our LLM evaluation suite developed at @SophontAI/@MedARC_AI. Glad to see even folks at frontier labs are finding it useful!

pretty unsettling to see the disdain OpenAI employees hold for @karpathy, the most prolific educator of the AI era &gt; median openai employee: gathers niche data + runs evals for GPT-N datamix &gt; karpathy: teaches millions how to build these models who has more long-term impact? https://t.co/IL8Illo5sb

I don't want read things the author feels isn't worth the effort of writing/editing in many cases Many people authoring slop are simply swapping one kind of audience for another. I suspect in many cases trading for lower IQ audience