Sid Sijbrandij (@sytses) header
Sid Sijbrandij (@sytses) profile picture

Co-founder & Executive Chair of GitLab. Co-founder of Kilo Code. I love economic mobility, remote work, new cities, big art, incentive design, and curing cancer

Joined March 2009
Photo by Sid Sijbrandij (@sytses)Photo by Sid Sijbrandij (@sytses)Photo by Sid Sijbrandij (@sytses)Photo by Sid Sijbrandij (@sytses)Photo by Sid Sijbrandij (@sytses)Photo by Sid Sijbrandij (@sytses)Photo by Sid Sijbrandij (@sytses)Photo by Sid Sijbrandij (@sytses)Photo by Sid Sijbrandij (@sytses)
I'm excited to share I'll be speaking at TED2027. Facing osteosarcoma changed how I see the future of medical treatments for all patients. I'll be sharing that experience on stage. Honored to be part of a line-up of future-thinking innovators. Join me in San Diego in March conferences.ted.com/ted2027/…
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Sid Sijbrandij retweeted
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.
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We’ve just made public 125,091 DICOM radiology images from across my cancer journey (link below). The data is both longitudinal and multi-modal, dated and linked to my treatment timeline, so you can compare imaging across initial diagnosis, standard of care, first remission, “founder mode cancer care”, and second remission. It’s viewable in OHIF, the standard open-source viewer used in radiology research. Given the long history of applying deep learning to radiology, we’re hoping to see a variety of methods and models applied to my data. If you find something interesting, please reach out to [email protected], or if you know a researcher who should see this, tag them below. Thanks to Terra LaFerriere for organizing these images, and Kamil Slowikowski for building osteosarc.com into the resource it is.
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Nabiha has assembled a smart team that lowered the price of cell preservation. Certainly worth considering before you go in for cancer chemotherapy.
In partnership with Vinod Khosla (@vkhosla), I’m proud to introduce Reservoir. Billionaires have owned their cells for years. Starting today, you can too. Sign up today: withreservoir.com
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It was great discussing my cancer journey with Mike and Devan. They have deep bio and AI knowledge and it showed in the conversation.
What if the best way to fight cancer is to think like a founder? This week we spoke with Sid Sijbrandij (@sytses), co-founder of @gitlab, about his experience developing experimental treatments for his own cancer. When standard-of-care treatments could no longer control his cancer, Sid went “founder mode,” assembling a team of researchers to attack the disease with a combination of experimental treatments never before used together. Taking a lesson from his success building GitLab, he’s made his medical data open-source for researchers to continue to innovate. We discuss the science of Sid’s cancer journey and the challenge of making highly personalized cancer treatments faster, cheaper, and accessible to more patients as sequencing costs fall and AI accelerates biological discovery. Full episode here on X and YouTube (see comment). @MikeDubrovsky @dshahh Timestamps: 00:00 - Intro 01:10 - mRNA Vaccine Basics 10:20 - Costs of mRNA Vaccine Development 15:59 - Lessons from Building GitLab 19:19 - Standard of Care for Cancer 24:50 - Sid’s Life Story and GitLab 30:49 - Going Founder Mode Against Cancer 42:03 - Selecting Experimental Treatments 50:38 - How Did Sid Know Treatments Were Working? 58:08 - AI for Treatment Assistance 1:02:54 - Making Personalized Treatments Affordable 1:08:17 - Where are the Levers for Improving Drug Development? 1:10:54 - Most Promising Areas of Drug Development 1:17:14 - The Happy Accident of Sid’s Biotech Investments 1:21:07 - Reflecting on Building GitLab 1:22:46 - How Does Sid Start So Many Companies? 1:26:02 - Finding the Right People for Early Companies 1:34:19 - Will the Bahamas be the Next Singapore? 1:38:42 - Advice for Young Scientists
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Making so much progress towards OpenVax 2.0 solely bc @sytses made all all his sequencing data public. It's now becoming the unit test dataset for Varcode, Isovar, and Vaxrank, which I'm growing to work with long read & SVs (previously was mostly using murine B16 / ILMN SNVs)
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I've attempted to map everything in oncology in a public website and open source repo for all. The site is trying to get all cancers, products, technologies, bottlenecks, people, startup opportunities with 1000+ ideas for upgrading the field. If you have a loved one with cancer and you are technical or can engineer, go take a look, file improvement requests or bugs or help with the open repo and make this the best open and free info resource for individuals, researchers and educational use. This should save people time, aid AI oncology projects and generate positive action. If you are not technical just complain in this thread about broken or annoying or things you want and I'll fix them live. Some of the interesting pages: Treatments: onco.cc/drugs A gallery of the molecules being used onco.cc/molecules/ And targets: onco.cc/targets/ The technologies in oncology: onco.cc/technologies/ 1100 ideas for helping oncology: onco.cc/ideas/ Bottlenecks on oncology: onco.cc/bottlenecks/ Open questions (LETS GO RESEARCH PEOPLE) onco.cc/open-questions/ Startup requests (LETS GO STARTUP PEOPLE) onco.cc/startup-requests/ Mechanics of cancer: onco.cc/mechanics Battlefronts: onco.cc/fronts/ Isotope supply: onco.cc/isotopes Key papers: onco.cc/key-papers Pipeline funnels: onco.cc/pipeline Cancer by type: onco.cc/cancers/ Institutional rankings: onco.cc/institutions/ The startups: onco.cc/startups/ Heros and heroines : onco.cc/heroes/ Key medical people: onco.cc/people/ Here is the project roadmap: onco.cc/roadmap/ Models and data sets: onco.cc/models/ There are other views as well, take a browse. Try making a PR if you have an upgrade to this on the repo here: github.com/judegomila/OnCo If you are biologically/medically minded and something is wrong, file a bug as well or say on the thread and we will get it fixed live. If this is a useful project star the repo and help get it calibrated. I've tried to add some other languages but I cannot speak them so tell me if that doesnt work well. I believe we will crack oncology and having total information dominance is key to the problem. Let the feedback flow!
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Omanta omanta.com was part of the latest YC batch and is working to help patients with complex cancer cases. The CEO, Alfredo Gonzalez, was a bioinformatician on my team and did an amazing job. While I’m not involved with this particular company, I wish Alfredo all the best and hope they’ll be very successful. It’s great to see more options becoming available for cancer patients.
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Sid Sijbrandij retweeted
Patients with refractory cancer seeking treatment with engineered T cells (CAR T) are going to China where the cost is considerably less China $150-300K US $550-800K wsj.com/health/healthcare/pa…
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Thanks for following my cancer journey, it has been quite the ride! I’m 16 months cancer free now. I view two people as my cofounders on the journey of going ‘founder mode’ on my cancer. These are Jose M. Mejia Oneto, who was in my YC batch of W15 with Shasqi doing cancer therapies, and became my best friend! And @JAC0BSTERN who I met through Jose and who left his job in biotech to become ‘CEO of my health’. After we made progress on my disease we started creating companies to make the latest scientific advances more accessible to others. We’ve started 15 companies in the last year and a half under the Even One Ventures umbrella, see evenone.ventures/ for more information.
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Great essay by Rux. The current US phase I system is too paternalistic.
We are on the cusp of a wave of new therapies for some of the worst diseases. But the world won’t benefit unless the US fixes its drug regulatory system. My new essay for @nytimes, on how slow clinical trials are now the biggest obstacle to curing cancer. nytimes.com/2026/09/04/opini… - I interviewed dozens of researchers, especially oncologists at leading U.S. centers. A striking consensus emerged: science is no longer the main bottleneck to new cancer drugs. It is our ability to test discoveries in patients through clinical trials. - The cost of starting a Phase 1 trial in America has roughly doubled over the past decade. As a result, companies increasingly take early trials abroad: Australia’s Phase 1 trial volume has nearly doubled in a decade, driven mainly by U.S. companies. - Unfortunately, the underlying incentives are badly asymmetric: Institutions can be blamed for harms caused by moving too fast, but almost no one is blamed when patients deteriorate during avoidable delays. One doctor called the emerging system “ritualized safety over actual risk assessment.” Or as, @DavidHongMD put it: "We often forget that the biggest risk is the cancer itself." - This problem is becoming more urgent because medicine itself is changing. Sequencing, biological engineering and A.I. make increasingly personalized therapies possible. But our regulatory system was mostly built for standardized drugs tested in large populations. - @sytse, the co-founder of GitLab, shows what personalized medicine can achieve: after relapsed osteosarcoma and being told there were no options left, he pursued a highly individualized approach and has now been cancer-free for a year. But doing so required extraordinary resources and regulatory expertise. - Pierce Ogden’s father was less lucky. After molecular analysis identified a drug that might target his glioblastoma, the manufacturer agreed to provide it. But administrative barriers delayed access until it was too late. “My dad was ready to try anything,” Pierce told me. “But the system is paternalistic.” - The A.I. revolution is making this bottleneck more important, not less. A.I. relies on relevant data. Information from early-stage trials could compound with A.I. tools to achieve truly revolutionary medicines. Without the data, this is far less likely to happen. - Another important shift is that innovation increasingly comes from academic labs and small biotech companies rather than Big Pharma. These small companies find it far harder to unable to absorb delays and regulatory barriers. - Apart from cancer, China is the biggest winner from America's outdated medical regulations. China has a much faster trial system, with testing often starting a full year earlier. This allows Chinese pharmaceutical companies to experiment and improve medicines while American companies play with mice. Today, half of all drugs licensed by major pharmaceutical companies originate there, up from less than 5 percent only a decade ago. - But we don't need to copy China. The best model is Australia: lots of on-site scientific and ethics reviews, and requirements that are proportionate to small, early-stage trials. Phase 1 studies there begin roughly 6–12 months faster, without any notable increases in adverse safety events. - Operation TrialBlazer, a 2026 HHS initiative is a good start in this direction, but we need legislative action by Congress to truly make Phase I trials faster and more efficient! I want to thank everyone who helped me with this article: everyone I interviewed and the amazing editors at the Times. This is the result of a months long journey of extensive interviews and research. Special thanks go to those who came on the record. One of the features of the system is an atmosphere of fear, where practitioners are afraid to publicly come out and explain these issues. So anyone who does is a hero in my book!
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I spoke at the Khosla Ventures CEO Summit earlier this year on going founder mode on my cancer. Founders already have the instinct for operating without a playbook and having a willingness to assemble your own team when the existing one runs out of answers. I use that instinct on my companies and my treatment. Thanks to Khosla Ventures for the room to share. khoslaventures.com/ceo-summi…
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Sid Sijbrandij retweeted
On this special episode of Builders of AI, @sytses of @gitlab and @JAC0BSTERN, share how AI guided Sid's cancer treatment: from single-cell sequencing to radioligand therapy to a personalized mRNA vaccine. Sid is now 14 months cancer-free. Full episode: youtu.be/HHsuXYcBW7U
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Sid Sijbrandij retweeted
Everyone working in cancer care — and every oncologist — should read this piece. @sytses is grounded, rational, informed, and respectful to the current oncology establishment. He underwent standard of care therapies and has deep trust in his oncologist. But — he also creates space for every cancer patient to be irreverent. To keep fighting when standard of care runs out. Because he knows what’s it’s like to be fighting for your life.
My cancer case is n=1. I'm not holding it up as a model, because most of what I did is not specifically replicable or scaleable. But it is a proof of concept that fully personalized medicine, built around one patient's own biology, is viable in ways it simply wasn't a few years ago. I have lived a version of the future of cancer care that I think is worth aspiring to more broadly. Many of the tools that made this future possible for me are getting better, faster, cheaper, and more accessible every year. I think what we need next is the infrastructure to deploy these tools, and the community willing to build this infrastructure, one patient at a time. I want to help that future arrive faster. I wrote an article in The Cancer Letter cancerletter.com/guest-edito… for the physicians and researchers closest to the patients who could benefit most from this approach, who also believe in this future. If you are working towards building the systems and practices that will make personalized medicine operationally real, we want to collaborate with you too.
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My cancer case is n=1. I'm not holding it up as a model, because most of what I did is not specifically replicable or scaleable. But it is a proof of concept that fully personalized medicine, built around one patient's own biology, is viable in ways it simply wasn't a few years ago. I have lived a version of the future of cancer care that I think is worth aspiring to more broadly. Many of the tools that made this future possible for me are getting better, faster, cheaper, and more accessible every year. I think what we need next is the infrastructure to deploy these tools, and the community willing to build this infrastructure, one patient at a time. I want to help that future arrive faster. I wrote an article in The Cancer Letter cancerletter.com/guest-edito… for the physicians and researchers closest to the patients who could benefit most from this approach, who also believe in this future. If you are working towards building the systems and practices that will make personalized medicine operationally real, we want to collaborate with you too.
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So if AI have an in person meeting does it mean showing their model to each other?
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🚨 BIG NEWS: Kilo Code has been acquired by Anaconda (@anacondainc)! We've grown our agentic engineering platform from zero to a thriving open-source community of 3M developers in only 16 months. Now, we're joining Anaconda's trusted foundation to cover the full AI-native dev lifecycle. 🐍💛 Read the full article below!
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Sid Sijbrandij retweeted
This is the way now. Ramp, Stripe, CoinBase, Uber and now Sierra are running cloud agents across every company function. Sandboxes are core to running cloud agents. Superserve sandboxes are fully open-source and we work with teams closely to help you deploy your own background agents. DM me if interested.
Everyone at Sierra uses an internal agent called Pinecone to automate 90% of our coding, analytics, and busywork. I can't go back to any other way of working. Pinecone: * Runs an agentic harness in our internal cloud. * Talks to all our tools (Slack, Github, Linear, GSuite, Clickhouse, Gong, etc.) * Lets you pick any model. Especially useful this week! * Can spin up a local version of all of Sierra to test its work. * Is accessible over mobile, Slack, Github, and Web and is collaborative. @neilrahilly and team cooked on this. In this post, they share lessons learned from building and scaling Pinecone to the whole company.
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