emi

This is the year of agents in biology. What you're seeing in code is already unfolding in molecular data analysis, reorganizing workflows in basic research and drug development. Path forward is focused benchmarking + engineering scoped to specific types of assays. Just as coding agents had to reliably write JavaScript before they could build a browser, biology agents must first learn to accurately process and interpret concrete measurements, (eg. spatial assays), before they can reason about disease, drug mechanism, or patient response. Our roadmap reflects this progression: procedural skill in analysis -> emergent biological reasoning -> synthesis across data types, translational context, and realistic ambiguity. Towards systems that can eventually support expensive, high-stakes decisions in drug programs or research projects. Diffusion in biology is slower than software and needs to be thought through carefully. We work directly with the teams building measurement tech (eg. TakaraBio and Vizgen) and package assay-specific agents alongside their kits and instruments. Scientists complete sample preparation, then use these tech-specific agents to move from raw data to answers and figures. Our partners white-label our platform; we do not run a direct biotech sales motion. Now hiring rapidly across major assay categories, prioritized by which we believe will contribute most to the area under the molecular data curve over the next several years - Spatial - Single Cell - Epigenomics - Genomics - Perturbation/Screening - Diagnostics Looking for talented scientists and engineers with strong foundations in theory and deep experience in these areas to help us build scientifically accurate agents.

Wait, Wall Street panic sold SK Hynix stock because Google's TurboQuant paper compresses the KV cache by 6x?? Do they not realize how this works? If you give me 6x memory efficiency, I don't buy less VRAM. I just multiply my context window by 6 until I OOM again 😭

building with ai agents is getting expensive fast. per-token pricing makes it hard to predict cost, slows experimentation, and turns every iteration into a tradeoff. we've used agents to optimize inference pipelines to provide you with the fastest and most affordable inference out there! see below our qwen 3.5 inference against base sglang.

Magical OpenClaw experiences that use frontier models cost $300-1,000/day today, heading to $10,000/day and more. The future shape of the entire technology industry will be how to drive that to $20/month.