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 release of the Open Startup Repo: github.com/ultrametricai/ult… 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/ult… 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/payment… - Open ranking example 2 (cap table): ultrametric.ai/arena/equity-… - Open processes: ultrametric.ai/processes - Open process example (Incorporate C-Corp) ultrametric.ai/processes/inc… - 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-document… 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/proc… 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/3aHky836q… 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.

An urgent notice from the Railway team blog.railway.com/p/security-…

I've spent years in China building robots. I can tell you this playbook won't work: Pre-seed $: Invent the ultimate actuator. Seed $$: Figure out mass production. The hard part isn't inventing a beautiful topology. It's process engineering. The real delta between the US and China is the ability to execute the enormous amount of physical x mental work required to iterate thousands of times on product designs and manufacturing processes. Partly because much of the tacit knowledge / management systems has left the US, but also because of the sheer human labor required. This isn't something you can solve by buying traditional automation lines either. It requires: 1. Ideation driven by human intuition. Walking up and down the line, talking to assembly workers, and developing hypotheses about what and where improvements can be made. Raw human hours and attention spent understanding a specific manufacturing process. 2. Large batches of experiments across multiple variables. Coordinating experiments that can easily involve 50+ people, issuing instructions, and tracking the execution and results of each experiment. 3. Purpose-built test design to identify key metrics. For example, a change in gear hobbing profile might require accelerated aging tests on a dyno, while a change in bearing tolerance might require NVH testing. 4. Data + human intuition-driven analysis. Interpreting test results and deciding whether to implement a process change or run the next set of experiments. 5. Strict process control. Reissuing work instructions to the line and ensuring every process is followed to the letter. The best manufacturers in Asia, from Foxconn to TSMC, have built their competitive advantage on systematically turning thousands of physical experiments into better manufacturing processes. Their moat isn't just production scale. It's the organizational machinery that makes manufacturing learn and improve faster than anyone else. I don't think most people in SF clock this. Say you're manufacturing actuator gears in-house in the US. After you've made your first article, will you hire a team of 10 process engineers to coordinate the 50 technicians running concurrent experiments with designs, processes, and materials? To understand wear patterns across every pin in a cycloidal drive after 10,000 hours? To reduce backlash from 15 arcmin to 5, then to 1? Just to keep pace with China's progress over the next few years? Do you have the skill set and management systems in place to do this? That's why our approach isn't just to automate manufacturing. It's to automate the process of learning how to manufacture better. Automate simulation and design optimization. Automate assembly and physical testing. Automate the design and execution of process experiments. Feed real-world experimental data back into simulation to continuously improve sim-to-real accuracy. The goal is to turn what currently takes 50 engineers coordinating thousands of experiments into a closed-loop system that continuously learns and improves. Then the same robotic line mass-manufactures the product. PS: Coming back to actuator design, many actuator designs are already commoditized in China. The project in the video is open source, and CNC machining cost in China is just $27 (watch the video). I hope people understand this reality. I'll link more examples in the replies.

In July we open-sourced a dynamic graph of everything a child learns in primary school. 1,590 concepts, 3,221 dependencies. Now we’re turning every concept into interactive experiments kids can play and learn from. First one, age 7: heat and water. Everything that used to be a boring textbook can now be an engaging experience, potentially tailored to every child. Kids education just got a massive upgrade.

Introducing Muse Wonder, our new family of frontier models with state of the art performance for personal and professional agentic work.

Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.

Our AI found a bug that let anyone mint infinite XRP, with $94B at risk. It had been hiding in one of the most audited codebases in crypto for nearly a decade, and earned us the maximum $250K bounty. Here's how it went unnoticed, and how we found it.

me to our GTM guy: do enterprises want benchmarks him: no, the fast ones have already benchmarked their internal use cases. the slow ones are copying the fast ones. he's onboarded a quarter of the fortune 500 already 🥵

Today we’re launching a library of Amex-curated, ready-to-use skills in Perplexity Computer to eligible U.S. @AmexBusiness Small Business Card Members. The skills are pre-built workflows that handle everyday business tasks, like forecasting cash flow or generating marketing campaign. Small business owners already do the work of whole teams. They can put AI to work by choosing a task in Computer and adding details about their business, without having to write instructions from scratch. Learn more: pplx.ai/amex

Excited to announce that we've raised $2.65M from @Accel and Google's @AIFuturesFund to re-imagine how enterprise software is maintained. Just this year, @getdodgeai has helped teams running SAP, Oracle Fusion, JD Edwards, and more save more than 26,000 hours previously billed by outsourced firms. This round was co-led by @Accel & Google's @AIFuturesFund, with participation from NewBuild Venture Capital, @AntlerGlobal, Schema Ventures, and angels from the SAP ecosystem. For the better part of the last 3 decades, enterprises have adopted mission-critical software called an ERP to track finances, logistics, discounts, inventory, and more. All of these systems become super customized, and there's an entire industry that implements them: the system integration industry. After you go live, the problem doesn't stop there. As the company evolves, you want these systems to change with it. At the same time, you also have to firefight all the daily operational incidents that happen within these systems. What ends up happening today is that enterprises end up spending more than $600 billion a year just maintaining ERPs! The traditional solution for enterprises has been to partner with these large outsourcing companies and throw 20 to 50 "consultants" at the problem. With always-on agents becoming a reality today, we think there is a better solution that combines both the functional expertise of the humans who have been holding the industry together with long-horizon agents that capture context across systems and custom processes. Dodge AI is already working with dozens of enterprises, most of whom are publicly listed companies across the world, to show them a better way. Excited to partner with @prayanks, @JRSilber, @nitinsharma1, @aarthir, @rohini_c, and more. Back to work now!

A lot of construction work is still done by hand simply because the machines to automate it don’t exist. That’s become a bottleneck for building things like solar farms, data centers, and other critical infrastructure. @cosmic_robotics is building those machines. Their robots have already installed 11,000 solar panels, and they just signed a major contract with the largest construction company in the country. They’re building the construction robots we’ll need on Earth today, and eventually on the Moon and Mars. @JamesAEmerick

Decades of machining knowledge live in hands, not manuals. It's the kind of nuance that takes years to learn, and it's the backbone behind our ships, rockets, and airplanes. There's still time left in the fourth quarter. Time to build. Randy's story, live here at Neuramill.

also important news we fixed the writing