Incredibly excited to announce that @ezrainc has been acquired by @function. Together, we're introducing the world's first $500 Full Body MRI.

A bit more about the announcement below.

The AI impact won't be felt immediately through layoffs (existing jobs are sticky), but through a collapse in hiring (jobs that don't yet exist have no stickiness).

The problem isn’t that AI is intelligent. It isn’t. The problem is that too many adults (but not all) are at once mechanistic, imitative, unimaginative, stupid, formulaic, influenceable, & gullible, arrogant beyond their skill set, & too expensive to maintain.

When my kid asks "How are babies made?", I'm going to say "A doctor combines a sperm and egg in a test tube, and then freezes it until Daddy's startup has a liquidity event"

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.

Craziest day in cyber security in a long time

Wake up.
Every profession deemed white-collar, the middle class dream, is about to be decimated by AI, at a much faster pace than anticipated, with monstrous social consequences.

Real Open AI.

🚀 Introducing DeepInfer, our in-house inference framework, now fully open source!

Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable.

🔗 github.com/deepseek-ai/De…

1/4

PSA for all Starlink lovers out there:

United has a status match for the next 48 hours. Get it while it’s hot!

promo.united.com/offers/statusm…

@KenneyNL This is bullshit, Kenney. I’m the same tech tree following optimist who moved from C to C++, from 2D to 3D, and from closed source to open source. I run a company. I write code. I contribute to published papers. I’ve upped my expectations of AI. Up yours!

I tried GLP in Jan this year as an experiment, and this was exactly my experience 👇

When I was writing Unreal Engine 1, my time was divided as follows:

- Around 30% went to seriously deep coding on hard problems: assembly language texture mappers, C++ BSP trees, data persistence, object oriented systems design, and networking.

- Around 70% was just typing and refactoring large bodies of code needed for a complete engine feature set. This was not hard, and while I was doing it my mind was mostly on the deeper coding topics, thinking and planning for what's next. It didn't feel much different from vibe coding today, except that it was just me typing code on an IBM Model M keyboard rather than prompting it and reviewing the results.

So, it has always seemed perfectly logical that we would rely on AI for that 70%, once the AI could do it with a similar quality standard as we could, which has never been perfection. It came as no surprise that, after a few years of AI model evolution, it was able to take the reigns on that 70% and free up programmers' time for more valuable tasks.

What's more of a surprise is the emergence of AI as a sort of insta-Carmack able to solve hard technical problems quite cleverly and often cleanly, and that it's able to reason about and work in complex 3D scenes both by operating the tools and by directly understanding the raw data files.

I abandoned assembly language in 1998 when out-of-order CPUs made much simpler C/C++ code faster, and never I missed it. If prompting through architectural documents, concept art, and design plans can transcend typing code and tweaking vertices, that will enable developers to spend their time on higher-level, higher value technical and creative tasks.

What matters is the end product, whether it's a game or an engine. Anything developers can do to try out more ideas and make better software and games faster is a win for everyone.

Bot is now faster and a bit more delightful.

There is a long road to a bot that "just works" (in the limit, it should be able to handle things in the physical world 🙂). More to come.

Your bot can't do what my Grok @bot can do. 👇👇👇

1) What

Grok @bot rocks

I've been using Grok @Bot as my daily driver for a few weeks now and:

- it has replaced 100% of my search-like queries on chatgpt / claude
- it has replaced 100% of my Hermes usage & automations
- it now runs 100% of my Cursor cloud agents, reviews PRs, merges for me

It also does things that other agents can't do (or can, but do it unreliably) - it has the most available connectors, it's VM is linked to my tailscale and has access to all my machines over ssh, can spin up claude code or codex agents in the terminal, can create other bots on the fly when it needs to, and my main Bot can manage other bots for me. And so much more!

What a time to be alive.

Technical moats are mostly over at this point. If you’re building something that is meant to be durable, the only moats now are:

• Proprietary data
• Network effects
• A physical component

We were able to fine tune an open weight model (Qwen3.5-9B) on proprietary internal data at @coinbase, and it out performed frontier models, at much lower cost and speed, for a specialized task (fraud prevention in this case).

I think we'll see more of this pattern inside models are amazing generalists. But increasingly you'll see specialist models out perform on more narrow tasks, either working on teams, or by themselves.

We're making it easy to fine tune models internally for our employees. We even have a skill for it in our internal harness that makes it easy to spin these up and start training them:

"/fine-tune-and-serve-llm I want to fine-tune Qwen for payments risk review. help me get started"

I imagine we'll have some backend services that could be replaced entirely by this new architecture.

2026 is 78% complete.