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Sabri Eyuboglu (@EyubogluSabri) reports that when he uses an agent it often…

2026-06-23 · 17:07 UTC ·@EyubogluSabri ·1 min read

Brief

Sabri Eyuboglu (@EyubogluSabri) says current agents waste minutes and dollars repeatedly rereading user context; he advocates training that understanding into models so the cost is amortized across tasks. He and his team founded Engram (EngramLab) to build infrastructure for billions of user-personalized models and promoted the project on 2026-06-23.

Why it matters

Sabri Eyuboglu (@EyubogluSabri) reports that when he uses an agent it often spends minutes rereading his files, incurring extra dollars and compute energy to gather context (tweeted 2026-06-23).

Key details

He argues that instead of repeated rereading, we should train models to internalize user-specific context—'amortizing' that understanding across tasks to save time and cost.
He and his team launched Engram (EngramLab) to build systems for a future with billions of personalized models that learn from users; public messaging includes 'Introducing Engram: Scaling compute on your context.'

Cleaned source text

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

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