Vorelios
DoFollowEngineering is bottlenecked by slow, costly simulations. We're fixing that by building foundation models that learn physics end-to-end and deliver the same results in seconds, at a fraction of the cost.
DeepMark
DoFollowDeepMark is the authentication layer for AI voice. When an AI agent speaks, we embed a compact, machine-readable ID directly into the audio, not in metadata, which gets stripped the moment a file is touched. The ID resolves to a trust record, so a bank, a call center, or the person on the line can verify in real time who the agent is.
Sentient OS
DoFollowNobody's built truly proactive AI, because it requires running inference on your entire life. In the cloud, that's insanely expensive and a privacy nightmare. But not on your own chip. Every night, Sentient's on-device LLM wakes your Mac and understands what's new in your life (email, messages, files, Granola transcripts...), entirely locally, and distills it into a knowledge base. You wake up to your work already prepared: the reply you forgot, drafted from your own context; the subscription you never use renewing tomorrow, caught; the report you promised in the group chat, ready to send. Everything is one click from firing through computer use, and nothing fires until you click. Or click your Mac notch anytime and say "finish this for me." Sidekick takes over the thing you were doing with its own cursor while you move on. On-device inference at this scale was supposed to be impossible: devices too slow, and models too dumb. So we built the stack ourselves: a custom LiteRT-LM fork (KV-cache reuse, speculative decoding, custom k-quants) that runs nightly on 8 GB Macs. The user's hardware does ~90% of the compute, and their own ChatGPT or Claude subscription does the rest, so our marginal cost is ~$0, and our servers store nothing. We soft-launched late July with one Reddit post: top of r/macapps, 2,000+ users in 48 hours, $0 marketing. Free and open source for consumers; the same engine on the work stack (Slack, Granola, Linear, Notion) becomes the enterprise business. Sentient OS raised ~$1M in pre-seed, backed by YC, Afore, and the a16z Speedrun scout fund.
Halmos Labs
DoFollowHalmos Labs builds automated biotech R&D on the principles of validity and interpretabillity. We run research, and simulations answering open questions ranging from wetlab experiment design to full cancer vaccine manufacturing.
The Agentic Data Co.
DoFollowWe design and collect audio datasets for training speech models.
Sona8
DoFollowSona8 is a voice agent that talks to every employee about how their work gets done. Consulting, transformation and corporate development teams spend a large part of every project on stakeholder interviews and stakeholder alignment, and still only hear from a small share of the people affected. Each employee gets a link and talks to the agent for about 20 minutes. The agent asks follow-up questions, for example why a step is still done by hand or who else is involved. From the conversations we build process maps and a list of the problems people described, by site and team. During the implementation, the agent goes back to the employees involved after each initiative and asks whether it was implemented and what changed. The result is a project management office that has talked to every employee. Today consulting firms buy it for the stakeholder interviews at the start of an engagement. Transformation and corporate development teams buy it to run the implementation of a change program. Over time this becomes the way a company knows how it runs. Sona8 reads the systems the company already uses, connects them to what employees said, and keeps the picture current by asking again. Before a decision, you ask what it would do across sites and teams and get an answer from the whole company. A small team steers a company-wide program that used to need a large one. And AI agents inside the company come here first to learn how the work is done before they change anything.
Qokedas
DoFollowModels are trained on the entire internet, but most of what happens on Earth is never written down. We turn those signals into training data so labs can make models better at science