Peter Sutor Jr @PeterSutorJr

@servamind If the input data isn't figured out already, you're expending neurons on understanding it. Emulation of the correct data is always more difficult than just being given it directly.

@servamind In particular, there was a lot of very useful work in vector based techniques in ICLR 2026.

@servamind The takeaway is to keep asking if something is possible. Either it isn't or the nagging thoughts lead people down the rabbit hole of possibilities.

@servamind Odd... that name often comes up in my research too.

@servamind Welcome aboard Dave!

@servamind Awesome stuff

@alignment_lab x.com/i/spaces/1MJgN…

Enormous, imo.

Servamind @servamind

4 months ago

New lecture drop 🎓 In our latest "Learning from Bio to AI" session, Andrew Coward explores Procedural Memory—how the brain learns skills and sequences. Join us tomorrow 7pm EST for a live Q&A on X Spaces to dig deeper. 🎥 Watch now

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Encode once, compute anywhere

Pied Piper that works arxiv.org/abs/2601.09124

tr.ee/3zmxOKVpR8

Tired of pre-processing your data? Us too... Use Serva Encoder to forever pre-process any data for any AI.

Servastack means no more padding, any shape, for mixed datasets, all to one file format, all to one AI without messy reshaping. Auto-pad is here.

AI teams spend 80% of their time on data prep—not building models. We built .serva: one data format that works with any model, on any hardware.30-374× more energy efficient. 4-34× compression. No retraining needed. The bottleneck shifts from infrastructure to imagination.

Your AI team isn't slow because of your models. They're slow because 80% of their time goes to data wrangling. .serva collapses months of preprocessing into a single encoding step. Any data → any model → any hardware See the architecture: arxiv.org/abs/2601.09124

We're looking for pilot partners to validate .serva in production. If your team: Trains models regularly Fights data preprocessing bottlenecks Wants 30-374× energy efficiency without retraining Let's talk. Early partners shape the roadmap. DM us or: [email protected]

Our new website is live. servamind.com One format. Any model. Any hardware. See how .serva eliminates data chaos and slashes compute costs by 96-99%. The infrastructure is ready. What will you build?

Beta access is now open. Test .serva in your ML pipeline. See the 30-374× efficiency gains for yourself.Early users get: → Direct engineering support → Priority feature requests → Influence on roadmap Sign up: servamind.com/join-the-beta Limited spots. First-come basis.

Join us Friday at 4pm PT for a Twitter Space on human memory. We'll explore: → Different types of memory → How brain processes implement each type → What this means for building better AI systems The .serva architecture draws from biological intelligence. Hear why. Set a reminder 🔔

Watch Andrew Coward Discuss Types of Memory in our Lessons from Biology for AI series before our X space tomorrow at 4pm PST!