Meet the Founder Rachel St. Clair spent years solving a problem most AI teams live with daily but rarely name: the data-compute lock-in that makes building AI slow, expensive, and inaccessible. Her path here wasn't linear. PhD in Complex Systems and Brain Sciences at FAU. Postdoc at the Center for Future Mind. Computer vision systems for the Department of Homeland Security. Innovation Lab Director managing 25+ researchers. 20+ peer-reviewed papers. Work spanning compressed sensing networks, GANs, quantum ML, and bio-inspired architectures. But the throughline across all of it: the belief that AI's biggest bottleneck isn't intelligence. It's infrastructure. She founded Servamind to fix that at the architecture level — not with another tool, but with a new standard. The .serva standard. Free 1TB beta launch → coming soon! servamind.com

Meet our CTO. The person who helped us figure out how to hyperscale our stack. Austin Cook (@alignment_lab ) currently serves on the Board of Directors of the Active Inference Institute and has spent the majority of his career contributing to open-source AI, focusing on Optimization and Representation research. Those open-sourced contributions have been adopted across the industry from LAION through Intel to Nvidia as key milestones for state of the art openly accessible AI His take on what we're building: "Every model, every framework, every hardware target, they've all been operating on incompatible data dialects. .serva is the universal language they've been missing." servamind.com

Great work from GoogleResearch on TurboQuant. Strong results — 3-bit KV cache quantization, 8× attention speedup, zero accuracy loss. Solid theoretical foundations. Worth noting the distinction: quantization optimizes what happens inside the model. .serva operates at the data layer — before the model ever sees the input. .serva is universal and lossless. When downstream tasks are unknown — which they often are in general AI pipelines — you cannot know in advance what information will matter. We preserve everything and defer relevance to the learning system. We're also operating at a different layer entirely: ~44× speedup at the data layer in fine-tuning. We’ve built across any model, at any stage — pretraining, fine-tuning, inference — with no retraining required. The efficiency stack is being built from multiple directions at once. That's a good sign for the field.

Meet the researcher who designed the foundation ServaEncode and Chimera from the ground up. @PeterSutorJr is a PhD candidate in Computer Science at University of Maryland — one of the world's leading experts in Hyperdimensional Computing, with 7+ peer-reviewed papers including a publication in Science Magazine. He worked with the Army Research Laboratory under an ORAU Fellowship, collaborating on Hyperdimensional Computing and Vector Symbolic Architectures. His thesis is built on the same theoretical foundations that power .serva. His take: "I joined Servamind to make Hyperdimensional Computing the lifeblood of modern AI — to fully capitalize on efficiencies that classical machine learning cannot take advantage of." That's not a vision statement. It's already in the benchmarks: 30–374× energy efficiency. 68× compute payload reduction. Same accuracy. servamind.com

Meet the engineer who takes our research from theory to production. @VictorCavero has spent his career doing one thing really well: making complex systems actually work at scale. Embedded systems. IoT. Automotive. Military R&D. Combat-critical systems design. Before Servamind, he took a compression algorithm from research-stage into a production-grade C implementation — from scratch. That's exactly what we needed someone to do with .serva. At Servamind he owns the architecture design of our core technology — responsible for turning the encoding and compute engine into infrastructure that works in the real world, on real hardware. His take: "Obsessed with making things more efficient — there's still so much to build and explore, but better technology shouldn't come at the planet's expense." That's the Servamind ethos in one sentence. servamind.com

Procedural Memory -- Lessons from Bio to AI xitter.cf/i/spaces/1MJgNgVjvbPGL

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

Your brain doesn't retrieve memories. It reconstructs them — partially, emotionally, from fragments. No AI system does this. That gap is the whole problem.Gave a keynote today on Software, Memory & Language at @ekkolapto. Listen to our founders talk today at luma.com/bioprompting

Building multimodal AI? You know the pain: Separate pipelines for images, text, audio Format conversions eating 80% of dev time Data locked to single model architectures Serva Encoder solves this. One universal format (.serva) for all modalities. Any model. No retraining. More energy efficient. Zero accuracy loss. Sign up for our beta servamind.com/join-the-beta

🚀 Serva Encoder is launching in beta soon and we're offering 1TB free encoding to early users. One universal format for all your multimodal data. Images, text, audio, sensor streams. Encode once, use anywhere. No more pipeline chaos. No more format lock-in. Claim your 1TB → servamind.com/join-the-beta