tesla robotaxi miles went 8x in one quarter. 70% of every driverless mile it has ever driven happened in the last 7 weeks. Wild.

I'd like to share a little about what I've been working on at my new company @ultrametricai with my cofounder @tylerlastovich. We have been exploring how we could automate the startup itself, leaving founders with more time to focus on their exact mission.

Enter our first rtup Repo: github.com/ultrametricai/…

A common foundation for transparent automation of the startup.

After close to 20 years in silicon valley, backing hundreds of amazing founders & startups, being in the trenches of founding Heyzap (YC08), then solo founding @golden, going through 2 acquisitions and working behind the scenes with some of the iconic companies today, I'm witnessing the largest change to how we work I could have imagined - a truly monumental era of creative abundance.

I believe the next generation of companies will largely run themselves: from operations, code, product upgrades, growth, vendor control & selection, process execution, special situations, strategy etc etc. That leaves founder time for 'the art' at the very niche core: the (currently) unautomatable, the special leaps humans tend to make, and north star setting.

We want founders to go super deep into the main problem they are trying to solve and not get held up doing anything else, from searching for domain availability with trademark checks to setting their company up, to running their processes on autopilot.

So to help that broad foundational layer for starting and running a company, we have spent around a year exploring how to help founders focus on their mission across different verticals, geos, stages and initial starting conditions.

Our first few attempts at making something useful actually failed as the market and AI was moving so fast; it just got absorbed into the models or other product releases. We saw polsia happen, openclaw, then grokbot, muse, dots, etc era as the astras, fables, opuses, kimis and jevs rolled out. What seemed to have been missing along the way was a common repo to identify, unify, open and normalize all the lego bricks that startups have to interact with (vendors, processes, business logic, law, best practices, surface areas, art of the possible).

As the number of software vendors blew up, the interfaces to drive them multiplied, the international startup founder blew up as silicon valley moved from a specific location to a state of mind and global movement. Unlimited pre seed funding in unlimited vertical sub markets and permutations has brought us to the age of infinity but we have yet to achieve being able to type into an agent a very simple sentence with very complex implications 'just setup my company and make $1m/m'. We have ended up with a hybrid mismash of computer use, API calls, plugins, shifting surface areas (API/MCP/CLI/plugins/chats/musish futures), and changing model absorption to vendor balance making it an exciting and unstable time.

As our first step we started to rank all the vendors that startups interface to when going about their journey in terms of how agentically controllable they are via their APIs, MCPs, CLIs etc and computer use compatibility as we are largely switching from a UI driven world to the agentic control surfaces and onto full automation. We have 100 ranking areas you can check out with an overall open ranking here: ultrametric.ai/overall

Then we looked at the processes that startups do and how the vendors rank for the sub steps or process as a whole and how this changes across geos. Open processes: ultrametric.ai/processes

Then we started to hack on an open repo for startups that would contain all the components we are going to need for a common foundation for founders across the globe even if our gov processes vary so much from Bengaluru to SF or Lisbon to Shenzhen. The founder spirit is geo-invariant.

We have looked at special founder/startup situations, the business logic that will make open modules for the startup that we can hack on together, ranked law firms, government services and interfaces, stacks that go well together. We have mapped the law and lore that maps to startups and the schemas that make up the data objects and documents. But also the open documents and the missing gaps. MUCH TO DO.

All the BS processes and fluff work that founders waste their time on needs to goto zero. I want to see more supersonic planes, cancer drugs, seemingly impossible missions done and increase the bang for buck founders have with their funding.

This is a work in progress and we are keen for the community to come hack on the repo with us here: github.com/ultrametricai/…

Lets get into the structure of the general repo and the visualizations we made on ultrametric.ai site with example links:

- Open rankings: ultrametric.ai/overall

- Open rankings example (online payments): ultrametric.ai/arena/payments

- Open ranking example 2 (cap table): ultrametric.ai/arena/equity-m…

- Open processes: ultrametric.ai/processes

- Open process example (Incorporate C-Corp) ultrametric.ai/processes/inco…

- Open situations that founders face: ultrametric.ai/situations

- Open startup simulator (you can test our different initial starting conditions): ultrametric.ai/startup-sim

- Open modules (e.g. cap table, payroll logic etc - we'd love help improving these) ultrametric.ai/open-modules

- Open law (we are interested in how law changes the processes, situations and normalizing this over time) ultrametric.ai/open-law

- Open lore (we are interested in the historical lore of startups, the rules of thumb and heuristics that might guide your agent): ultrametric.ai/open-lore

- Open fields (we are interested in how the schema for the data connects to the open modules, business logic, open documents and artifacts of each process eg how is a EIN defined?) ultrametric.ai/fields

- Open documentation (eg SAFE notes and missing! docs) ultrametric.ai/open-documents

We have looked at YC companies as well in terms of which ones do well agentically: ultrametric.ai/yc

We are interested in stacks and how people are changing their work: ultrametric.ai/stacks

We are interested in what is automatable and where the agentic ceiling is currently and how to push that up in all areas: ultrametric.ai/rankings/proce…

There is more for you to explore and a lot more coming in terms of how you will be able to navigate business in an optimal way in your chosen surface area; whether you are hanging in grokbot, dots, muse, claude, terminal, chat, or whatever is coming up in this exciting time.

We have a lot of data to collect, clean up and help agents navigate the vast landscape emerging functionality the entire startup ecosystem has to offer.

Come join us getting your vendor in, ranked or that access pattern you have wanted or getting the data more accurate in terms of which vendor is best for a process or steps. Or open a module of something that currently cost money that could be free.

Also come play in our discord for founders and join the chaos (we can solve each others issues including fund raising tips from me personally): discord.com/invite/3aHky83…

It's time to lift all the boats in the harbor and I'm extremely excited as to what the community of hackers and painters will hack into the repo, what asks they will have and whether we can shift normalization of processes globally, gov agentic interfaces and have common repo for founders that grows over time. I expect a lot of experimentation along the way, bugs and upside...

I appreciate our backers having faith in us as well from @ycombinator with @garrytan to @immad doing a sizable check (congrats on $1bn rev homie) to @darbyw (congrats on Stripe buying @clerkyinc), @drewhouston form @Dropbox and also @pioneer_fund @tomerlondon cto @GustoHQ and hopefully this is 'boring' and complex enough for Charlie Songhurst :> @delingpole @Liquid2V @ivolo from @segment @ryanpowe @sonicaghi of @kong @DellyTamer+ other supporters (@michaelwfrank, Fundamental ventures and if I missed you sorry moving fast!).

[And don't worry, all vendor rankings are open scoring methods so no favors are being done if someone backed us and is a vendor they have to compete with how agentic and automatable they are just like anyone else.]

PS: we have bugs and many quality upgrades to make but thought better to go out early and fix as a community.

PPS: we have other stuff to launch we just thought this was a great place to start and have 200 PRs coming out pretty soon but I still want to know what is broken or doesnt make sense or inaccurate.

Special thanks to current and past team members for getting us here and all the experiments we did you know who you are.

Should companies control your health data? Here’s what happened with Garmin:

I’m reading today's news about their API, and I'm sitting here thinking there’s more to this.

We built Terra around connectivity across sources, and the belief that data belongs to the user.

Garmin had an API program for years, and we started working with them about 5 years ago.

About a month ago, they instructed us to make changes to our API. At the same time, they blocked our ability to make those changes by blocking our access to their platform.

We thought it might be a mistake, so we reached out: we needed access restored to make the changes.

Radio silence.

We contacted them at every level, by every means imaginable.

A few weeks later, they confirmed they blocked access because we didn’t make the requested changes.

So they asked us to make changes, blocked us from doing them, and then blocked us for not complying.

So they blocked an entire ecosystem and hundreds of thousands of users from connecting their data.

This created massive chaos.

We are on tens of calls every day, and receive thousands of complaints from users who rightfully ask why.

Since they stopped replying, we asked some customers to reach out to their CEO (his email is publicly listed) and explain the impact.

We then heard from customers that they were asked

a) to pay different $ amounts for API access
b) were forced not to use Terra
c) to sign new terms explicitly ruling out the use of third parties.

So, third parties are blocked? Interesting.

So apps can't use third-party employees, AIs, cloud providers, or any analytics platform? What can they do?

EU Data Act, Article 5, points to the Right of the user to share data with third parties

It’s meant for things like this. So in the EU, the law gives users the right to control their own data.

We then asked them to fulfill their EU Data Act obligations for a number of our users on September 29. We followed up many days later.

Radio silence.

And we come to today. Their new API enterprise and startup program has a similar setup to Terra.

And a reporter speaks badly about us.

Coincidence.

Am I suggesting we have a monopoly on APIs? No. We allow connectivity across sources, and they have a source. But a rational person would think app ecosystems make devices more useful.

What can one make of this?

Companies seem to benefit from disconnected data. Users' rights don’t really matter. But this is BS.

Just because you use a device doesn't give that company the right to your data.

Connecting your data can help you perform better, stay healthier, achieve your goals.

We spent 6 years in this space fighting nonsense like this.

Our mission has been simple: make health data accessible, connected, and useful so developers and AIs can build better experiences for everyone.

Progress only happens when developers can build across platforms, and when people can freely move their data

And users should own their data

Introducing rentakaren.ai, for when your AI agent can't get through to the manager.

Just published @Nature
A 3-agent AI personal health agent exceeded performance of a large language model/chatbot and serves as a foundation to build on for integrating sensor data, medical knowledge and coaching
nature.com/articles/s4158…

Just heard of a Chinese surgical robot knocking off a very early US company. The knockoff even beat the original to first human use.

That will never happen to @andromedasurg. If you build a perfect working replica of our robot, you've copied < 1% of what makes us interesting.

Why?
1. The value is all in the software - software built by a team that combines top healthcare + autonomy operators that exist nowhere else, on data that no one else can access.
2. Process power that lets us move at 5-10x speed. By the time you copy our current product, we're already 10-20 iterations ahead.

I bet one could learn almost as much in 4 months at @AnthropicAI as in a 2 year traditional postdoc

Fun to chat with @ByAmyBrown at @HSBC’s first neurology conf in London. Lots of momentum in CNS. Onwards.

Your best model might be the one you train on your own data. Coinbase’s results are a great example. I think pharma has a similar opportunity: years of experimental data that can teach models things public benchmarks never will.

x.com/brian_armstron…

We are truly legit.

On @Jeopardy rn…

Emma's is the right take. The interesting question is not "can lab automation replace literally everything we do by hand?"

The interesting question is "what new experiments can we perform with automation that could never be done by hand?"

And for my money the best automation-only experiments are not going to be the ones that simply require lots of scale and replicates.

I'm more curious about the way AI enables rapid real-time adjustment of experimental conditions in response to data.

I want to see tight automation loops where an AI is making significant changes to the protocol 10 times a day. Not just changing the pipetting volumes on a few experimental inputs - totally refactoring a protocol the way that a human would.

Earlier this year, I came across a peculiar email written by the late Lewis Sheiner, a UCSF physician who built the mathematical methods for safely testing drug doses in people. This field is called "pharmacometrics", and it remains a cornerstone of modern drug development.

In he emphatically assigns credit for the foundation of this work to an *economist* and *quantitative trader* named Barr Rosenberg -- who, driven by a personal tragedy involving a mis-dosed medication, had reached out to him. How did an economist get entangled in the math of drug dosing?!

This mystery seemed too fascinating to ignore. So I spent this summer tracing the story backward, speaking with Barr's former colleagues, clinical pharmacologists, programmers, and even the first director of the FDA's Center for Drug Evaluation and Research, Carl Peck(!) to learn about Barr's unlikely contributions to this important field.

This is the story of Barr Rosenberg, the pioneering pharmacometrics software -- NONMEM -- that his math inspired, and the curious inner workings of how a ragtag team of clinicians, statisticians, and programmers shaped the way we test drugs today: "Borrowing Strength".

From my opinion when we first invested in Exa in 2021, pre revenue.

Language models are good at translation. It turns out that includes biology.

CT-Tox reads a rat liver study and predicts the human response, with about 6x lower error than the industry standard. Proud of the team.

So an autonomous surgery platform with a physical robot built on hard-to-get proprietary data seems quite valuable.

Team is also a most. There are maybe 5 founders in healthcare and 5 in autonomy crazy enough to try to build this. @andromedasurg has one of each.

We are the only company in the world that can reliably produce valuable animals at scale across species.

We make CRISPR knockout mice one-shot in-utero in 30 minutes with a protocol so easy the undergrads just out of university do it and no animals die. Great for prototyping

AI will have its largest impact in health

Why will AI have its largest impact on health? Great real world example here.

Thanks for sharing @biogerontology. Longtime fan of @InSilicoMeds and proud early investor in @immunebridge

This is cool! For people outside of genomics, it might be surprising that we can’t 100% reliably identify where genes are in DNA.

Even with a map of a gene, we don't know which different versions of the same gene (or “isoforms”) are turned on, or where, until we actually observe the RNA or proteins.