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On 2026-06-23 17:03:08 UTC @realJessyLin announced a new company (branded…

2026-06-23 · 17:03 UTC ·@realJessyLin ·1 min read

Brief

Engram (announced by @realJessyLin on 2026-06-23) is a new startup focused on continual learning: creating algorithms that continuously learn from arbitrary unlabeled data (documents, conversations, the model’s own experience) and scale compute across that ongoing data stream. The founders plan to combine RL, distillation, long‑context and sparse/parameter‑efficient architectures, call the problem unsolved and greenfield, and are recruiting collaborators from their named team.

Why it matters

On 2026-06-23 17:03:08 UTC @realJessyLin announced a new company (branded Engram/EngramLab) to solve continual learning: algorithms that ingest arbitrary data—documents, conversations, models’ own experience—and improve models day-to-day with no labels and no external rewards.

Key details

  • They plan to build on existing ingredients (reinforcement learning, distillation, long‑context models, sparse/parameter‑efficient architectures) and explicitly aim to scale compute across continual data the way pretraining and inference scaled, calling the space “extremely greenfield.”
  • The founding team includes @jxmnop, @EyubogluSabri, @dan_biderman, @MayeeChen, @__howardchen, and @shizhehe; the post invites collaborators and hires and claims the team is ‘extremely cracked’ while promising public updates in the coming months.
Cleaned source text

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!

Engram (@EngramLab)

Article

Introducing Engram: Scaling compute on your context

We’re Engram. We’re building AI that learns from you and deeply understands your work.

Today’s AI models don’t understand what you do. Not really. Everything models know comes from their training –

— https://nitter.net/EngramLab/status/2069465879696576844#m