Just your daily reminder if you're looking for jobs at startups you should bug your VC friends! I have way more visibility now than I did as a candidate across sectors / stages it's one of the (IMO) few ways VCs can actually be helpful...

We just did our first 100M ARR quarter 🚀

I joined @etched this past year to work on a mission I care deeply about: democratizing AI to unlock world-class education, healthcare, and intelligence for billions of people. To do so, we need hardware that is much more power and compute-efficient to serve inference at scale. Our first-gen product, powered by LVI and CSM, is just a sneak peek of what's to come, and I'm very excited to see what's possible with this new pareto frontier!

Congrats to my friends & the team at @Etched! Awesome progress solving one of the most important bottlenecks in AI.

Reminder to founders - take lots of photos and videos of the team and product as you’re building. It may seem awkward or cringe but someday you’ll wish you had documented more of those early moments.

Great conversation @ypatil125! Human capital and token capital compounding together is the entire game. This is the positive-sum future we need to build to benefit everyone.

"There should be as many models in the world as firms in the world." Satya and I dig into when to own vs. rent your intelligence, why every company should be building and climbing its own private evals, and what makes for a stable frontier.

I’m always saying this

Today's AI models train once. We don't work that way. We learn continuously, forget what doesn't matter, and retain what does. That gap is what @dan_biderman and @realJessyLin are closing at @EngramLab. AI that never stops learning, with memory that lives inside the model instead of bolted on as an afterthought. In our latest Training Data episode we get into why memory is the next frontier: why the brain forgets on purpose, why RAG is a band-aid, and what becomes possible when a model is always training. 00:00 Introduction 00:59 Always Training Explained 01:51 Beyond Context Windows 03:29 Ngram Product Overview 04:34 Adapters And Training Signals 05:32 Internalize Vs Externalize 06:49 Compute And Token Savings 08:19 Teams First Then Individuals 08:51 Memorization Vs Understanding 12:47 Dreams And Offline Digestion 14:08 Training Beats Curation 15:19 Why Everyone Needs A Model 21:44 Bitter Lesson And Architecture 24:44 RAG Killer And KV Cache 31:38 Future Of Memory And Models

We're at a breakthrough moment in AI. The most direct path to its promise is AI that accelerates its own research, and @mirendil is building exactly that. A frontier lab rebuilt from scratch around AI R&D. This team has made mind blowing progress in the short few months they’ve been working together using their own systems. @bneyshabur, @HarshMeh1a @shayan_, @tararezaeikh and the Mirendil team have a rare mix of research depth, scale experience, and the instinct to ship. Expect(ing) big things. We’re proud to have been there from day one. kleinerperkins.com/perspecti…

We're thrilled to announce that Trovy has raised our Series A and $25M in total funding! Backed by Left Lane Capital, Kleiner Perkins, DCM Ventures, and Camber Creek, we're proud to have world-class partners behind our mission. Congratulations to our co-founders, TJ Milani and Ashley Harris, and the entire Trovy team. This funding will accelerate our mission to turn every home into a financial asset. A huge thank you to our incredible investors for their support! prnewswire.com/news-releases…

Today, I’m excited to formally announce @mirendil with my amazing co-founders Harsh Mehta, Shayan Salehian, and Tara Rezaei! We’re fortunate to work with @a16z and @kleinerperkins, who led our seed round of $200M, followed by a major investment from NVIDIA, among others. Mirendil exists to accelerate science and technology, and through them, to help solve humanity's most pressing problems. Self-accelerating AI R&D is the most direct path to delivering on AI's broader promise, which is why we believe the most important application of AI is AI itself. Get this loop right, and it compounds. It fundamentally changes the rate of progress itself across all domains. We believe this capability should be democratized. It should be used to power all scientific efforts trying to innovate at the frontier. There are far more important problems—and broader ones—than any single lab can take on, so more groups should be able to pursue them. This pulls concentration of power away from a few labs: businesses and science labs can own their AI and infrastructure, keep their margins, and control their own destiny instead of ceding it all to a single AI lab. We’re a small team with a singular focus. Our founding team consists of 20 researchers and engineers from frontier institutions including Anthropic, xAI, Google DeepMind, and OpenAI, united by a passion for science and a drive to build the technologies that move it faster. If you want to build the system that builds systems, join us! @HarshMeh1a, @shayan_, @tararezaeikh

By scaling compute on user context, we reduce token spend. But it's about more than lowering cost. To develop expertise is to reduce the energy it takes to solve a problem, freeing capacity to solve harder problems yet Was great chatting about this with the amazing @LM_Braswell!

Dan and Sabri sat down with @LM_Braswell to talk about our technical direction. Thanks for having us!

Discussing @EngramLab's technical approach, the story of the Microsoft partnership, and how to remedy "tokenmaxxing" with the brilliant @dan_biderman & @EyubogluSabri --

Discussing @EngramLab's technical bets with @LM_Braswell at @kleinerperkins's Amazon room.

I'm excited to share what we're building at Engram! This team is incredible, and we're working on one of the most interesting problems in AI right now: how to build models that are tailored to each person and continually learn from experience. Come join us!

we started a company!! so, we’re tackling continual learning: what’s the learning algorithm to take arbitrary data — documents, conversations, the models’ own experience — and make better models? how do we scale compute in the same way we’ve already seen with pre-training and inference time, but scaling on the same data we see as humans, day after day with no labels, no rewards? A lot of the ingredients are out there already (rl, distillation, long-context, sparse / param-efficient architectures, etc.). our team is at the frontier of these topics, and we’re singularly focused on this. we want to understand this problem better than anyone else in the world. nobody’s solved this problem yet, but even today it’s extremely greenfield opportunity to co-develop research & useful products. in our space, how people interact with the models defines what the data distribution is - and working on this problem end-to-end, from core science to end user, gives us incredible freedom to define the problem and imagine new kinds of experiences. i expect we’ll use models that continually learn much differently than we’re using them today. it’ll feel different when the models _just know_, and build on our thinking and direction in ways we can’t even imagine. we don’t even know the queries we’re not asking, the things we would do but aren’t able to today. i’m so excited to share what we’re doing with the world in the coming months!! and the team is extremely cracked :) tackling this grand challenge and working alongside @jxmnop @EyubogluSabri @dan_biderman @MayeeChen @__howardchen @shizhehe and many others has made every day so fun. come work with us!