Engram
topic on 2 shows · 7 statements across 1 episodes · said 12 times in 3 episodes since 2026
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2026 12 mentions in 3 episodes 4 per episode
- The AI Memory Problem: Why Long Context Isn’t Enough — Dan Biderman, Engram Co-founder & CEO
- Thinking Machines’ First AI Model, California Loses $3.2B to Texas, TSMC Adds $100B | Diet TBPN
- Next 100x in AI: Inference, Networking, & Self-Optimizing Models — Philip Kiely & Ali Taha, Baseten
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7 statements about Engram, every show
Biderman: Hard engineering tasks will require test-time gradient updates
“We think that eventually part of the solution for very hard tasks in, in science and engineering and defense and all that stuff will involve some form of gradient based updates during during doing these long horizon tasks.”
Biderman: Engram works with Harvey on large file systems
“And so, for example, these are the kinds of things we work with Harvey.”
Biderman: Engram aims to give every user personalized, continually-learning weights
“Our ambition in the long term, ah, is that. Every person has a model, or a part of the model, or a set of weights that, that represents their knowledge, their expertise, learns from them, that the more time they spend with the model, the better it gets for the…”
Biderman: PC hardware will soon run near-trillion-parameter models locally
“And in the long, long term, I do think these things will actually run on people's devices, and we're seeing right now the new hardware on personal computers is already, ah, you know, soon approaching the ability to run inference on close to trillion parameters…”
Biderman: Engram trains models to decide what to memorize vs keep in notes
“The way to work on it is to train models both, to train models to manage it themselves, and that's an active area for us. Have the model know, like, without any explicit supervision signal to determine this kind of stuff I can pull from my brain, and that kind…”
Biderman: AI solutions will rely on model routing, not single monolithic models
“So I think routing will be part of the solution there for sure, and I think Many people, not just myself, say this solution is multi-modal. It's not Engram taking over. There's one model, and you teach it things, and you can close Stargate. That's not our appr…”
Biderman: Personalized AI adapters require hot-swapping millions of endpoints at inference
“And if you truly believe that we can get to the level where we have those kinds of parameter efficient adapters for every person and team, you suddenly think about deployments that involve millions of different endpoints stored in different places that need to…”