It feels like that the rate of change of optimizations gains > rate of change at the frontier. And this swap has happening faster in the last 30-60 days.

Tonight I’m at AiOS SF talking AI on Apple devices, but... one more thing 😀 Years ago at Apple I set up a demo for a senior government official: real-time translation software for service members, running on an iPod touch strapped to a forearm. Before it started, I was alone in the room with Tim Cook. I don’t think he had ever said my name before. “So Todd, what are we showing today?” I WAS NOT READY FOR THAT. I told him all about the translation software. There was a pause that felt terrifying. Then: “That’s good, but remember to always show and explain the Apple product first.” The translation app was the cool part, but Apple sold iPod touch. That’s what I was there for. I never forgot that. I see the same thing in AI now. Companies talk about the model they’re using, and tooling, and hardware, and the customer is left wondering what the product actually does for them. That’s where I fit. After thousands of presentations to executives during 22 years at Apple, and after helping launch Apple Intelligence and AI messaging, I’m starting @DaywardAI to help companies show and explain their AI, and build things with it. A lot of it runs on hardware they own. Turning the technical into the understandable is my superpower. I’m already working with a few companies, private individuals, and artists to create amazing results from unreasonable requests, and I’m having a blast doing it! We are so so early on AI, the next few years are going to be INCREDIBLE.

How SporeLabs built Cue: a 32M model that catches promises, questions and hand-offs in a live call. Problem, data, labels, proof: SporeLabs did every step. About 28x faster than Jev's API, and it ranked promises and hand-offs more accurately.

This is why you will need both a personal and a company agent. Both firewall from each other. One interesting opportunity is a data access primitive when agents speak to each other. Anyone working on this?

Great meeting! loved the stories and the wisdom! Look forward to jamming soon.

He's been doing some amazing work on older Macs!

This on the surface appears this way, but try using the end points and you will realize that the quality & consistency of inference varies wildly. (Fp/ZDR, throughput/latency..etc) Good quality inferences is not a race to the bottom YET.

I call it the "White screen of death" problem and it has not yet been solved. They do have some of the ingredients to solve it.

It’s been a week of amazing progress but also one with the most doubt as a founder. Feels closer yet farther all at the same time.

Disappointing - you can have agentic AI running on a 💻. Understand the issues, put in the time before you rush to duck tape symptoms with regulation. We really need various voices talking to the politicians. Who's working on this? is there an org ?

I've attempted to map everything in oncology in a public website and open source repo for all. The site is trying to get all cancers, products, technologies, bottlenecks, people, startup opportunities with 1000+ ideas for upgrading the field. If you have a loved one with cancer and you are technical or can engineer, go take a look, file improvement requests or bugs or help with the open repo and make this the best open and free info resource for individuals, researchers and educational use. This should save people time, aid AI oncology projects and generate positive action. If you are not technical just complain in this thread about broken or annoying or things you want and I'll fix them live. Some of the interesting pages: Treatments: onco.cc/drugs A gallery of the molecules being used onco.cc/molecules/ And targets: onco.cc/targets/ The technologies in oncology: onco.cc/technologies/ 1100 ideas for helping oncology: onco.cc/ideas/ Bottlenecks on oncology: onco.cc/bottlenecks/ Open questions (LETS GO RESEARCH PEOPLE) onco.cc/open-questions/ Startup requests (LETS GO STARTUP PEOPLE) onco.cc/startup-requests/ Mechanics of cancer: onco.cc/mechanics Battlefronts: onco.cc/fronts/ Isotope supply: onco.cc/isotopes Key papers: onco.cc/key-papers Pipeline funnels: onco.cc/pipeline Cancer by type: onco.cc/cancers/ Institutional rankings: onco.cc/institutions/ The startups: onco.cc/startups/ Heros and heroines : onco.cc/heroes/ Key medical people: onco.cc/people/ Here is the project roadmap: onco.cc/roadmap/ Models and data sets: onco.cc/models/ There are other views as well, take a browse. Try making a PR if you have an upgrade to this on the repo here: github.com/judegomila/OnCo If you are biologically/medically minded and something is wrong, file a bug as well or say on the thread and we will get it fixed live. If this is a useful project star the repo and help get it calibrated. I've tried to add some other languages but I cannot speak them so tell me if that doesnt work well. I believe we will crack oncology and having total information dominance is key to the problem. Let the feedback flow!

"We are building prod not god" Sane stuff we can all get behind!

Who's gonna tell him that this also America first! Some amazing kids and their parents who are brown went to this school. I think I have had more than 4 folks in my family that went through this school - all amazing folks! So Bo, thank you for showing your ignorance!

This is a pretty broad question - depends on what you want to do with it. Some attributes coding vs non coding, configuration flexibility vs out of box.

👉🏼🐻

🤯how much longer before this comes down to smaller MOE models?