There are few moments in a lifetime to be part of something that gives you a sneak peek into the future, and I’ve been seeing sparks of something different firsthand @EngramLab since I joined earlier this year. Many exciting things to come soon!

John and Jordi from @TBPN are in the pretraining data, but most people aren't. Frontier models train on trillions of tokens and still have no idea who you are. We're fixing that. Thanks for hosting us – and banging the gong for Engram. This meant more than you'll ever know.

Great conversation and a fun way to learn about an important open AI problem!

The models we use every day are brilliant strangers. They forget your organization the moment a chat ends, then relearn it on the next query. @EngramLab fixes that. It learns your world once and reuses that memory, matching frontier systems on 1-10% of the tokens. @Microsoft, @NotionHQ, and @Harvey are already testing it within their organizations. Congratulations to the team, and hear directly from @dan_biderman (CEO and co-founder) and Sabri Eyuboglu (CTO and co-founder) with @LM_Braswell ⬇️

Thank you to @Nasdaq for supporting Engram on our launch day yesterday! Some have commented that this photo looks AI-generated. It's not. This really happened. Feel free to send this picture to your moms. We're certainly going to.

I've spent years studying how human memory works: how we learn and forget, and how our memories shape what we do. I'm thrilled to share that I've joined the founding team of Engram, which is coming out of stealth today. Now is the time to understand how to build machines with neural memories. Excited to do it with the best team for the task. If you're curious, reach out.

Today we announced our Initial Public Offering, a humble article on X. Thanks to the @NYSE for supporting us so early in our journey! And thanks to all of our lovely supporters here on X dot com for following along. More soon 🔜

it's crazy that when i tell an agent to "run XYZ", i basically expect it to read 20 files every time just to figure out what the hell XYZ is. it sounds like engram will be big for model welfare

you haven't seen? unfuckwithableintelligence.c…

Engram is making a personal AI that really learns and internalizes everything you care about. It’s a different kind of lab with an amazing team put together by @dan_biderman @EyubogluSabri @realJessyLin and @jxmnop. Very excited for what they are building!

Today AI models can't delight users because they don't remember user behaviors and preferences. Over the past 5 years I’ve gotten to know @dan_biderman, @realJessyLin, @jxmnop, and @EyubogluSabri, a research dream team. If anyone can solve the memory problem it's them.

The single biggest impediment in AI isn't absolute intelligence or coding ability. It's deeply understanding large repositories of knowledge that every person and every company has. Our repositories will explode in size, with AI agents writing much of them. This is a two-way street: we need to understand what our AI generates, and AI needs to better understand us. A breakthrough in memory is needed – gradients need to be taken, and our team is the right one to take the big swing.

There are many people building impressive things. But building something that fits how people truly think and work is much more difficult. Engram is taking it on head-on. We are building models that continuously train on user workspaces and improve with use — compressing context into weights between sessions, like human memory during sleep. More to come!

I've joined Engram! Our thesis starts with the observation: today's AI systems are brilliant strangers. Every new chat requires re-establishing your context. Memory systems help, but we believe the market-defining solution will be to encode memory directly into the model itself. That creates a new scaling axis: scaling compute on context. The team is amazingly talented, and we're working with outstanding large-scale partners such as Microsoft, Harvey, and Notion. If you're interested in RL infrastructure, Cartridges and LoRA, or pushing the limits of per-user training and serving performance at trillion-parameter scale, we'd love to hear from you.

Most times I use an agent it spends minutes rereading all my stuff – spending dollars and energy just to gather the context it needs to understand my work. But it won’t always be this way. We can train that understanding into the model – amortizing across tasks. We started Engram to build the systems required for a future where there are billions of models, each learning from users and deeply understanding their work. I’m so grateful and excited to be working on this problem with this incredible team

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!

I've spent my PhD figuring out how a model learns from data, digging through piles of Spanish math problems, fanfiction, and the depths of StackExchange. Engram opens a new dimension: how a model learns from you - your world, how it evolves, and the threads that tie it all together. Figuring out how to do this well is a wide-open research problem. 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!

finally sharing what i've been up to! left phd end of 2025 and co-founded Engram. there are a few startups in SF right making very different bets on the right way to train AI models. this is ours: people want models that learn over time, remember details, adapt and interact like a person would everyone gets a model. your model updates ~every minute. this is the world we're building. :)

"I talk to the model more than I talk to my mother and it doesn't really know who I am." I left Stanford in December because I believe that the future isn’t one giant model that knows everything about nothing, it’s one that knows everything about you. I knew I could only study and push continual learning to the limit in the real world. I’ve never worked with a team I admire this much—picking the right people is as big a bet as picking the right problem to work on. Join us!