AI is rewriting how humans interact with software, work, and even each other. Conduit is building an interface for direct brain-to-computer communication. Tasklet is replacing brittle SaaS workflows with autonomous agents. OpenRouter is building a routing layer for the exploding universe of AI models. And Eigen is building AI to strengthen human relationships instead of replacing them. Together, they are reimagining a future where AI more seamlessly integrates with everyday life.

Rio Popper, founder of Conduit Intelligence

For all the advances AI has made over the past few years, the way we interact with computers has hardly changed. We type on keyboards and tap touchscreens. At the leading edge, there’s speech-to-text and autocomplete, but none of it can match the speed of thought.

Rio Popper, founder of Conduit, believes that bottleneck is limiting human potential. After becoming blind at age five, she learned to navigate technology differently from everyone else. She built a motion sensing interface to join the swim team, and went on to create an unusually high-speed workflow for coding. With this tool, she found herself coding faster than many sighted engineers. These experiences made her realize how outdated modern interfaces really are.

Conduit grew from this realization – it’s a radically different interface where software operates at the speed of thought. The company is developing non-invasive neural interfaces that allow AI systems to interpret neural signals directly and translate them into action. At the foundation of the project is over 10k hours of data from thousands of unique individuals, comprising the largest neuro-language dataset in the world. Early prototypes can already infer rough semantic meaning directly from neural signals.

Meet Rio at the showcase to learn more about creating neural interfaces for the AI era.

Andrew Lee, Founder of Tasklet

AI chatbots can answer questions and generate text, but unfortunately they don’t actually do your work for you. You’re still manually tipping every other domino in a disjointed Rube Goldberg machine of SaaS apps. Today, most people spend hours every day copying information across apps, updating CRMs, triaging emails, and stitching together workflows that should be automated. Traditional automation tools have cut some of this, but they require brittle flowcharts and constant maintenance over new edge cases.

Tasklet is the alternative. Andrew Lee built it as an AI agent that connects every tool you use and actually do the work. Tasklet operates as a cloud-native AI agent that can take real action across the tools people already use. It connects through thousands of integrations, APIs, MCP servers, or even a full browser. It can generate interfaces, write and execute code, process data, and run workflows continuously in the background. The best part is how simple the interface is. It feels like typing into a basic LLM chat box, but behind the scenes it’s actually running workflows and taking actions. Knowledge work is shifting away from humans operating software to agents doing it for them, and Tasklet is emerging as the operating system to coordinate it.

If you’re interested in building the operating system for the future of work, come meet Andrew at the showcase!

Alex Atallah, founder of OpenRouter

In the words of Alex Atallah, there’s been “a Cambrian explosion of models,” and with the boom comes a constantly shifting optimization problem. There is a new release every week from OpenAI, Anthropic, Meta, Google, DeepSeek, Mistral, and open-source variants, each with different pricing, latency, reliability, and infrastructure requirements. There is no ‘best’ model for every task and regular updates make choosing one even harder. A model that’s cheapest today may be obsolete next month. One provider might be fastest for simple queries but comparatively fail on reasoning. Another may offer better privacy but higher latency. As inference costs become one of the largest operating expenses for AI-native companies, developers are spending increasing amounts of time building routing logic, benchmarking providers, and continuously reevaluating which models to use.

OpenRouter is solving the optimization problem by becoming the universal routing layer for AI. It eliminates the need for developers to hardcode themselves to a single model provider, giving developers a single API endpoint connected to hundreds of models and dozens of infrastructure providers. It intelligently routes requests based on price, speed, reliability, or performance requirements, connecting you with the right model, for the right query, at the right time. OpenRouter aims to become a sort of AWS for inference traffic – a neutral marketplace between applications and the universe of base models.

Meet Alex at the showcase for a chance to build the universal API for the AI era!

Paul Scherer, Founder of Eigen

Social media apps designed to bring people together now feel flooded with algorithmic recommendations and push engagement over genuine connection. AI companion apps and personal chatbots threaten to accelerate this fragmentation further, offering a substitute for friendship rather than strengthening your real ones. Most companies are racing to build “your AI best friend,” without thinking about the collective experiences that make us human.

Paul Scherer at Eigen is trying to build the alternative. For him, the goal is to create shared experiences that bring people together rather than silo them. He’s creating “the world’s mutual friend.” Imagine you’ve just moved into an apartment and are looking for furniture. Your ‘mutual friend’ gives you more than generic aesthetic advice. It’ll connect you with your friend that it knows is also going furniture shopping today. Looking for ways to publicize a showcase? Instead of GPT-esque bullet points on ‘when to start promotion’ or ‘what graphics to use,’ your mutual friend reaches out to your founder friend who’s already hosted ten and comes back with a real opinion. Eigen imagines technology that nudges people toward shared discovery, introduces friends-of-friends, encourages collaborative experiences, and helps people feel more connected to the communities around them. It’s an AI that understands not just information, but your relationships, trust, and taste. In a world increasingly shaped by algorithmic isolation, Eigen is betting there’s enormous demand for technology that helps people belong and grow together instead.

If building technology that makes people feel more connected sounds like your vibe, come meet Paul at the showcase!

Apply to attend our next SF Startup Showcase and meet these founders in person.

Energy systems, industrial operations, and hardware development were never designed for the scale and complexity of the AI era. Critical infrastructure is still monitored through fragmented legacy systems with limited real-time visibility. Hardware engineering continues to rely on manual workflows, and as electricity demand surges from AI infrastructure, the U.S. faces gaps in its energy supply chain. These three founders are building solutions to these foundational problems by applying modern software, AI, and industrial technology to the core physical systems that power the economy.

Xerxes Libsch, Founder of Specter

Much of the world’s critical infrastructure is monitored by a few cameras and maybe a security guard intermittently checking a monitor. There are power plants, industrial sites, oil rigs, nuclear plants, and data centers that still rely on legacy monitoring systems, leaving operators with incomplete information, delayed alerts, or costly manual monitoring. The result is intrusions, safety incidents, or equipment failures that are detected too late, if at all. Software has transformed digital systems, but the physical world remains largely unobserved.

Xerxes is taking his experience from Uber and Anduril to solve this problem with Specter, a real-time perception system for the physical world. By deploying a network of low-cost, rapidly installable sensors connected through a wireless mesh, Specter gives companies continuous visibility across their entire physical footprint. Their platform acts as a software-defined control plane, turning raw sensor data into real-time intelligence that can detect intrusions, monitor operations, and enable automated responses.

Picture a system that partners with AI to detect everything from missing safety gear on a construction site to a building fire or a fight in a crowded stadium. The result is a shift from reactive, fragmented monitoring of physical systems to software-level observability and control.

If you’re curious to learn more about bringing real-time perception and control to the physical world, come meet Xerxes at the next Founders You Should Know showcase!

Pari Singh, Founder of Flow Engineering

The world around us relies on hardware engineering, from cars and nuclear reactors to the device you’re reading this on. Yet the system it was built with is still stuck in the dark ages. Walk into one of the best hardware engineering organizations in the world (think Space X, Tesla) and you will find teams of brilliant engineers manually moving between Excel spreadsheets, CAD tools, simulation environments, PDFs, and compliance documents. It’s extremely manual. While software development has undergone a dramatic transformation through AI-assisted coding and autonomous agents, physical engineering has largely been left behind.

As a mechanical engineer himself, this problem drew Pari Singh to develop Flow Engineering. With Flow, we are entering a world where intelligent agents will not just write software, but also design mechanical systems, run simulations, validate requirements, coordinate testing, manage regulatory workflows, and integrate across the full hardware development lifecycle. The cost of software creation collapsed once engineers could delegate implementation to AI systems. The same transition is now coming for the physical world. Flow is built for this shift. It exists to eliminate the operational bottlenecks between engineering intent and execution by creating an intelligence layer across complex hardware programs. It enables hardware engineering organizations to use agents to do the vast majority of their engineering work.

The result is a future where building a rocket, humanoid robot, or energy system starts to look less like traditional hardware engineering and more like programming software. This incredible value proposition has led Flow to partnerships with companies like Rivian, and Flow is now looking to scale their engineering team drastically in the next few months.

If you’re interested in changing how the physical world is built, come meet Pari at the next showcase.

Scott Nolan, Founder of General Matter

Right now, the primary bottleneck to scaling AI is energy, and unless you’re building data centers in space, nuclear is the answer. The problem is the U.S. lacks the fuel supply chain needed to support it. While electricity demand surges from rapid expansion of AI infrastructure and the push for reliable baseload power, the U.S. still lacks meaningful domestic capacity to enrich the uranium required for today’s reactors. America depends heavily on foreign suppliers for enriched uranium. That dependency has turned nuclear fuel into a strategic bottleneck for U.S. energy security, technological competitiveness, and long-term grid reliability.

Scott Nolan founded General Matter to rebuild this missing layer of American infrastructure. The team comes from SpaceX, Tesla, Anduril, and the DOD and approaches enrichment like a modern industrial technology problem: scalable, cost-competitive, and built for the energy demands of the next several decades. General Matter has already secured major Department of Energy support, including participation in the federal HALEU enrichment program and a $900 million DOE award to expand domestic enrichment capacity.

The next era of American energy security will not just require new reactors, but a fully rebuilt domestic fuel ecosystem that is scalable, reliable, and strategically independent. By modernizing enrichment infrastructure and bringing advanced fuel production back onshore, General Matter is likely to become foundational infrastructure for the future of U.S. energy.

Apply to attend the May 20th Startup Showcase and meet these founders in person.

Join the party and land that high-impact engineering or operator role before summer kicks off. Spots are already filling up so apply now to be considered for the showcase!

Next week, the following 10 founders will take the FYSK stage:

Rio Popper, founder of Conduit

Ian Livingstone, founder of Keycard

Neil Movva, founder of Sail Research

Jake Stauch, founder of Serval

Xerxes Libsch, founder of Specter

Paul Scherer, founder of Eigen

Scott Nolan, founder of General Matter

Andrew Lee, founder of Tasklet

Pari Singh, founder of Flow Engineering

Alex Atallah, founder of OpenRouter

A huge thank you to the Digital Ocean team for sponsoring the May showcase, and Rippling for hosting us again at their beautiful office!

Want to meet these breakout founders? Apply now to our showcase and to join our FYSK community.

We’ve reached a stage where the use of AI agents is widespread. They’re being deployed across industries, already working in sensitive systems, and running infrastructure overnight to keep companies operational. The question is no longer whether agents will run enterprise operations. It’s whether they’ll do it safely, efficiently, and in a cost-effective way.

While the AI labs continue pushing to make these agents more capable, engineers actually deploying them are no longer focused on the models alone. They’re focused on the deployment challenges, like giving an agent access to the right systems without risking a security breach and running thousands of agent calls overnight without spending a fortune on inference. These three companies at our next showcase are building the answers to those questions.

Neil Movva, Co-Founder of Sail Research

Neil Movva graduated from Stanford and spent time at NVIDIA and Apple, where he watched inference bills become the ceiling on what AI agents could actually do. Agents worth building — the ones doing deep research, running overnight code generation, processing data continuously — generate enormous compute bills. So companies don’t build them, or they build stripped-down versions that miss most of the value.

The open-source model ecosystem is competitive with frontier models on most real tasks, but the infrastructure to serve them reliably and cost effectively at production scale is lagging. That’s the gap Sail Research is looking to close.

Sail runs open-source models at up to 12x the cost efficiency of comparable proprietary alternatives and their benchmark performance — 82.4 on GPQA-Diamond — competes directly with existing models. The best part? Their API is OpenAI-compatible and they support leading open source models (e.g., Deepseek, Kimi, Qwen).

Sail is working from several angles, writing CUDA to increase performance on GPUs, maximizing efficiency of inference engines, distributing work, and pivoting between spot compute and reliable compute based on availability. In 2026, agents will be good enough to make meaningful, independent progress on hard problems, given sufficient tokens. Sail research will make sure they can.

Meet Neil at our next showcase to learn more about how his team are planning to dramatically increase intelligence per dollar, and make sure no compute is wasted.

Ian Livingstone, Co-Founder of Keycard

Through his work at Cape Privacy and Snyk, Ian watched enterprises give AI agents access to their most critical systems with no coherent security layer in between. At Cape Privacy, he tackled encrypted machine learning before most teams had figured out that data privacy and AI were on a collision course. He went on to scale Snyk’s developer platform from $30M to $300M in ARR, watching firsthand as engineering teams struggled to connect services and applications together securely. These experiences have led him to his newest problem: AI agents are beginning to operate across every system and organization, and nobody has built the security layer to handle them.

Agents are starting to work in production systems and operate autonomously across organizational boundaries, but the systems for allowing access were built for humans clicking buttons — static API keys, long-lived secrets, point-and-click authentication. None of that was designed for thousands of agents spinning up to complete a task and spinning down seconds later, each needing different permissions and access. Given the quick rise of AI agents, perhaps it isn’t surprising that even Slack and Salesforce ended up with leaks tied to agent vulnerabilities.

Keycard is looking to fix this. It’s a control plane for AI agents that cryptographically verifies an agent’s identity, enforces task-scoped permissions in real time, and maintains a full audit trail — without requiring developers to become security experts to use it. Instead of locking agents to narrow workflows or keeping humans in the loop for every sensitive action, Keycard issues short-lived, context-aware credentials that update dynamically as policies change. Access can be revoked instantly. The system integrates natively with Anthropic, OpenAI, and Microsoft, and Ian’s team is already contributing to the emerging standards (MCP, WIMSE, OAuth extensions for agents) that will govern how models securely connect with external tools and agents.

Ian co-founded Keycard with Matthew Creager, his former colleague at Snyk, and Jared Hanson, the creator of Passport.js and former Chief Architect at Auth0. Investors called it “the Auth0 moment for agent access.” Keycard is backed with $38M from a16z, boldstart, and Acrew Capital.

Learn more about Ian’s vision for the trust layer of the agent economy at our upcoming showcase.

Jake Stauch, Co-founder of Serval

Jake arrived at Serval after years of watching talented IT operators at some of the best-run companies in tech spend their days manually handling helpdesk work that should have been automated years ago. At Verkada where Jake led product, his customers were IT leaders who were motivated and desperate to automate. While these IT leaders loved what Jake was able to provide, it was clear they also wanted more. They were looking for a fix to an age old problem that until now required too many resources to effectively automate: the helpdesk.

Jake and his team developed Serval as the fix. IT teams describe their workflows in plain language, and Serval produces automations that are deployment-ready, traceable, and adjustable all the way down to code. Instead of a bandaid on top of legacy systems, Serval rebuilt the entire IT service management stack from scratch, with its own ticketing, access management, and asset management baked in.

What’s happened in practice has surprised even the founders. Customers haven’t just added Serval to their existing toolkit — they’ve ripped out their old platforms entirely. Automation has spread beyond IT into HR, Finance, Legal, and Engineering, with some teams abandoning dedicated software vendors because Serval handles the job better.

Sequoia, who led the Series B, said the customer conviction they’re hearing rivals what they saw when they first backed ServiceNow.

Join us at the showcase to see how Jake and his team are reworking IT from the ground up.

There’s a pattern across these three worth noting. Sail makes ambitious agents economically viable. Keycard provides the trust layer for enterprises to actually deploy them. And Serval is where that automation finally becomes something a non-engineer can own and operate.

The success of the agentic era won’t rely on the models alone. The tools to effectively deploy agents universally is the other half. Neil, Ian, and Jake are building these now. If you’re an engineer who wants to work on the hardest parts of the problem, this is a rare chance to hear directly from the people defining it.

Apply to attend our next SF Startup Showcase and meet these founders in person.

Something interesting showed up in the FYSK nominations this month. Some of the most compelling companies aren’t just based outside San Francisco — they’re building things that only make sense to operate outside it.

Here are a few that stood out:

• Halter (Auckland) — solar-powered GPS collars that let ranchers herd cattle from their phones. It sounds like software until you realize the product lives on actual cows, across ranches in Texas, Colorado, New Zealand. This only works if you’re close to that world.

• Starcloud (Redmond) — data centers in space. They’re putting NVIDIA H100s into orbit and used solar power and the vacuum of space for cooling. They moved to be near aerospace talent — the kind clustered around companies like SpaceX and Amazon — because that’s what the company actually depends on.

• Mariana Minerals (US, distributed) — part software company, part real-world operation. Coordination might sit in SF. But mining, logistics, and processing happen across Texas, Utah, and North Dakota because that’s where the resources are.

At first we thought these nominations might be early signs of a geographical shift away from SF as the center of gravity. But the more we sat with it, the more it felt like something else entirely.

For a long time, the most valuable startups were pure software. So they clustered in SF where the talent, capital, and network all lived. That hasn’t changed. What has changed is that, for many startups, software is no longer the whole product. It’s now the layer you apply to the real world.

Which opens up a different kind of opportunity:

Some of the most valuable companies now come from combining software with the physical world — in areas the frontier labs aren’t going to touch anytime soon.

A small team can now coordinate complex, real-world systems like farms, energy, supply chains, and satellites without needing massive scale on day one. You don’t have to own everything. You can integrate, orchestrate, and iterate.

At the same time, AI tools make it possible to build from anywhere. You can build with models like Claude or Codex anywhere in the world. The ability to build great software used to be tightly clustered. Now it’s widely accessible.

So you get this split:

• New capabilities are still created in places like SF

• But the application of those capabilities is spreading outward

Many of the opportunities to solve problems in the physical world aren’t ones the frontier AI labs will pursue anytime soon. OpenAI and Anthropic are focused on building foundational models. They’re not running mining operations, managing cattle, or launching data centers in space. Which means there’s this growing set of problems that benefit enormously from modern software and AI, require deep engagement with the physical world, and are still wide open for startups. Not because they’re more “defensible,” but because they’re underserved and increasingly tractable.

There’s a talent shift happening too. SF is still unmatched if you want to work on foundational AI alongside world-class researchers, with the highest comp, at the center of it all. But not everyone wants to build at that layer. Some engineers are increasingly drawn to building things that interact with the real world — agriculture, energy, infrastructure, aerospace. Places where the problems are messier, but the impact is massive and tangible. Those opportunities don’t all live in one city.

So this isn’t “SF vs everywhere else.” SF is still the hub where companies get started, capital concentrates, and ideas collide. It’s where the foundations of AI are getting built, one model at a time. But once you start building something that touches the real world, the map expands, because the real world has geography. It has cows, energy sources, mines, factories, and launch sites. If you want to build something that actually serves those customers, you don’t start in SoMa. You start where they are.

If you’re building something outside SF — or watching something big take shape somewhere unexpected — we’d love to hear about it. We’re always looking for standout founders to feature at the next Founders You Should Know showcase.

Looking to join a high-impact, fast-growing startup? RSVP for our next showcase (yes, in SF!) — we scour the world to bring you the good stuff.

We’re excited to announce three more upcoming dinners for engineers, all in San Francisco. Same format — a small table, no cameras, no decks, just a founder and a room full of interested engineers.

Early is everything and these are three companies worth knowing now.

The founders you’ll meet

These teams are growing quickly, and early hires will have real ownership and outsized impact.

Celeste Amadon, Co-Founder & CEO of Known

Known uses voice AI to replace the swipe — onboarding users through a real conversation, learning enough to make a single curated match, and getting two people to an actual date. In beta in San Francisco, 80% of introductions led to in-person meetings. Before Known, Celeste was a Stanford student working on AI applications.

Best for: engineers drawn to consumer AI with a genuine social mission and a product people actually love

Date: April 28th

Request to join here.

Adam Yala, Co-Founder & CEO of Voio

Voio builds a unified AI reading platform for radiologists — automating report generation across CT, MRI, and X-ray with accuracy that outperforms Google, Microsoft, and Alibaba’s leading models. Before founding Voio, Adam was an Assistant Professor of Computational Precision Health at UC Berkeley and UCSF, building and clinically validating AI models for cancer detection.

Best for: engineers excited about frontier AI with direct, measurable impact on human health

Date: April 30th

Request to join here.

Jake Stauch, Founder & CEO of Serval

Serval is an AI-native IT platform that automates help desk requests, access management, onboarding, and offboarding — replacing the legacy ticketing systems and drag-and-drop workflow builders that engineers have always hated. Before Serval, Jake was the director of product at Verkada.

Best for: engineers who want to build the new operating system for the enterprise

Date: May 7th

Request to join here.

The best founder conversations don’t happen on stage. They happen at a table, off the record, where you can dig into the details that never make it into a pitch deck.

This April, we’re bringing back our private dinner series, with intimate dinners where a small group of high-intent engineers and operators sit down with founders of inflecting companies to talk candidly about the real stories behind the build. These dinners are:

• Intimate by Design: We emphasize conversation over presentation. Small groups create space for probing questions, candid discussion, and a deeper understanding of the company’s inner workings.

• For Serious Explorers: These seats are reserved for engineers and operators who are serious about their next move and want to do their due diligence thoughtfully.

• Completely Off the Record: Everything discussed at these dinners stays at the table. These conversations won’t be recorded, shared, or posted anywhere.

Our first dinner in the April series will be with Airbase.

Airbase

The entire modern world runs on radio frequency spectrum from weather satellites and 5G to encrypted military comms. Every system we rely on competes for the same finite, invisible resource.

When the U.S. rolled out 5G networks, it nearly caused a catastrophic grounding of commercial aviation. Telecom companies had spent billions for new frequencies, but because federal agencies were operating with siloed data, nobody realized until the last minute that those new signals risked jamming the radar altimeters used by passenger jets to land. The result? Mass flight cancellations, international panic, and engineers scrambling to manually deconflict the airwaves at the eleventh hour.

Spectrum coordination still runs on static databases and PDF forms — tools built decades ago, operated by brilliant people who’ve simply never had anything better. This is the bottleneck Ari Rosner and Millen Anand set out to fix.

Airbase is the software platform for spectrum licensing, coordination, and intelligence, rebuilt for the pace of modern operators. The team is backed by a16z American Dynamism and Squadra Ventures.

The founders you’ll meet

Ari Rosner, CEO

Ari was employee #2 at True Anomaly, where he served as Chief Engineer on two orbital missions and scaled the company past 200 people. Before that he was a deep tech investor at Riot Ventures and mechanical engineer at NASA JPL — with hardware currently sitting on Mars.

Millen Anand, CTO

Millen led design and deployment of hundreds of next-generation Earth imaging satellites at Planet. He built RF payloads for geostationary satellites at Boeing, and was part of the inaugural class of a16z Engineering Fellows.

Interested?

The team is looking for software and RF engineers in New York City drawn to hard infrastructure problems with real stakes.

Request to join here.

We started Founders You Should Know with a simple idea: the best career-defining opportunities don’t come from job postings on Linkedin, they come from people. From being in the right room, at the right time, with the right builders. Or, as Josh Hoeflich, who dressed up as Aaron Burr from Hamilton for intermission show, would say, you want to be in “The Room Where it Happens.” However, if you missed the show last week, you can still catch a glimpse of what happened in our recap below.

The FYSK room is really special to me, because it’s built by my people. My brother came to the showcase. My husband emceed the show. The volunteers at registration are longtime members of this community — one of them is a founder we invested in who’s been coming since day one, and he brought his cousin along. And even our performer first came to FYSK as a jobseeker.

When you come to an FYSK showcase, you’re not just attending an event — you’re stepping into a community that compounds opportunities for you. We hope that some of our community members join the founders they meet at the show. Very often, what actually happens is you meet someone… and they introduce you to someone else… and then someone else. That n+1 effect is how both careers and companies get built.

I hope that through the FYSK community, you meet people who become collaborators, co-conspirators, and maybe even lifelong friends. If you’d like us to connect you with any of the founders below, send us an email blurb to forward!

Ricursive

Ricursive Intelligence is a frontier AI lab building a recursive self-improvement loop between AI systems and the chips that power them, compressing chip design timelines from years to weeks.

Founder: Anna Goldie

Open Roles: Engineering, Research, Security

Armadin

Armadin is a frontier AI security company building proactive security models and agents to protect organizations against attacks and maintain Western superiority in cyberspace

Founder: Travis Lanham

Open Roles: Research, Engineering, Design, Product, GTM

Zipline

Zipline is the world's largest and most experienced autonomous delivery service. With more than 130 million commercial autonomous miles flown to date, Zipline is transforming access to healthcare, consumer products, and food.

Founder: Keenan Wyrobek

Open roles: Engineering, Operations, Manufacturing + 150 roles!

Raindrop

Raindrop is an AI-native observability platform that helps teams monitor, evaluate, and debug AI agents in production.

Founder: Ben Hylak

Open Roles: ML Engineering, Product Engineering, Infra, Security, DevEx

TypeSafe AI

TypeSafe AI is building a new class of large language models designed explicitly for true no-human-in-the-loop automation, not conversational assistants.

Founder: Diogo Almeida

Open Roles: AI Research, Reliability Engineering, Generalist Engineering

Radical Numerics

Radical Numerics is using AI to master the language of life - making possible new medicines, and the design of life itself.

Founder: Eric Nguyen

Open Roles: AI and Bio

Fleet AI

Fleet AI builds simulated worlds and real-world challenge environments to help understand, evaluate, and shape advanced AI systems.

Founder: Nicolai Ouporov

Open Roles: Researchers, Environment engineers

Video not yet available

Deveillance

Deveillance is building privacy hardware that stops unwanted audio recordings, starting with Spectre I, a portable device that makes nearby microphones unable to capture intelligible speech.

Founder: Aida Baradari

Open roles: Hardware, Signal Processing, Engineering

Goodfire

Goodfire is a research lab dedicated to understanding and intentionally designing AI models.

Founder: Tom McGrath

Open Roles: Machine Learning, Research Science, Platform Engineering

Sunday Robotics

Sunday Robotics is building autonomous home robots that learn useful household tasks from large-scale real-world human demonstrations.

Founder: Tony Zhao

Open Roles: Full stack SWE, Product Managers, Robotics and ML

Want to be in the room where it happens for our next show in SF on May 20th? Apply now to our community and subscribe to our newsletter to be in the know about upcoming showcases.

Every time you make a phone call, stream a video, or board a plane, you’re depending on an invisible resource. And while over the last few decades we’ve moved from phones the size of a brick to devices in the palm of your hand that can stream orders of magnitude more data, the systems managing that underlying resource have remained unchanged.

Radio frequency spectrum is the medium beneath nearly every wireless signal. There is a fixed, finite amount of it available for different use cases – physics says so. You cannot make more. The system deciding who gets to use it, and when, and how much, was designed in the 1990s, and has barely changed since.

For a while, this was fine; the devices that needed spectrum were few enough that the patchwork held. Then satellites multiplied, drones went commercial, and autonomous vehicles started talking to infrastructure. The number of wireless devices on earth began doubling in ways that made the old growth curves look quaint, and spectrum infrastructure couldn’t keep up.

Millen Anand and Ari Rosner looked at this problem and came to a clear conclusion: this isn’t a policy problem – it’s a software one.

The Founders

Millen is the kind of person who will tell you, with complete sincerity, that he finds radio frequency allocation fascinating. At Planet Labs, he led a new class of Earth-imaging satellites, and the last thing he did before leaving was watch it go up on a SpaceX rocket.

That satellite was connected to Earth for approximately one hour a day. The rest of the time it was unreachable, flying dark, unable to send or receive anything. This is because connecting satellites to Earth requires radio spectrum and ground infrastructure, both of which are expensive and scarce. The industry has accepted this constraint as the cost of doing business in space, built their operations around it, and learned to live with the lag.

His cofounder Ari Rosner came in from a different angle. Ari was employee number two at True Anomaly, a defense tech startup where he wore every hat imaginable and grew the company from a few people in a co-working space to 200+ employees and over $400M raised. He left as Chief Engineer of two of their largest orbital programs.

Where Millen’s instinct was to go deep into the technical problem, Ari’s was to translate it: to walk into a room full of non-engineers and deeply understand their pain points and the urgency of the problems they were facing on a day-to-day basis.

Together, they shared two convictions: that the spectrum problem was solvable, and that nobody was seriously building the systems required to do so.

Pitching Washington

Driven by the conviction that the spectrum problem was a solvable software challenge, they came to Washington. What they did not have, starting out, was a company name or business cards. What opened doors was their framing: rather than arriving with complaints, as most industry contacts did, they showed up as engineers who wanted to be part of the solution. And rather than slide decks and vision statements, they showed up with things that worked, hearing about a problem on Friday and returning Monday with a working prototype.

Senior leaders in spectrum cleared their calendars. What Millen and Ari could articulate, in a way no one else had, was that they had a solution that could solve this massive problem, and deliver it in the timeline required to keep up with the exponential spectrum use. The regulators have the domain knowledge. They just lacked software capable of handling the scale and complexity of modern spectrum demand.

The timing is not incidental. A December executive order directed precisely this kind of modernization, the SAT Streamlining Act calls for automated satellite licensing, and the Golden Dome initiative has identified spectrum access as a critical military constraint. For perhaps the first time, policy is pulling in the same direction as technology.

Most companies emerging from stealth arrive with a story about what they intend to do. Last week, Airbase surfaced with something more rare. Instead of talking about what they will go build and what the end-user will be able to do, they’re already doing it. They already have a U.S. Government contract. And already have end-users actively testing their software.

The goal is not to wait. The goal is to have government regulators actively using the product in their daily work, so that the vision is not a promise but a fact.

Who They’re Looking For

Airbase is a team of seven people, and each of them owns something real. There is no orientation period, no ramp-up quarter, no safe lane to travel in while you figure out what you’re supposed to be doing. You will have an impact on day one.

The team is looking for software and RF engineers in New York City to build for customers like the NTIA, the FCC, the U.S. Military, and commercial spectrum users across the land, sea, air, and space domains.

More than any technical specialty, they’re looking for people who see something that shouldn’t work the way it does and can’t help but fix it.

Apply here: https://jobs.deel.com/airbase

The next wave of AI will bring extraordinary capabilities — and some truly unsettling ones. Systems that can design biology. Devices that can record and analyze conversations everywhere we go. These capabilities are arriving faster than most people expected, and they raise uncomfortable questions about privacy, safety, and control. But alongside those technologies, a new group of engineers is starting to build something just as important: the defenses.

Aida Baradari, founder of Deveillance

The year is 2028 and you’re sitting at a cafe doing an in-person meeting. The person you’re meeting with is wearing smart glasses that look completely normal. After the conversation ends you learn that everything you said was recorded, transcribed, and stored by an AI assistant running quietly in the background. You never agreed to it. You didn’t even know it was happening.

That world isn’t a science fiction future – it’s already happening. New wearable AI devices promise to capture and summarize entire conversations automatically. As speech recognition and large language models improve, your live conversations are turning into searchable, permanent data. What used to disappear at the end of a conversation can now live forever in someone’s cloud account.

That shift is exactly what Aida Bendari, the founder of Deveillance is pushing back against. The company is building a new category of counter-surveillance technology designed to give people control over whether their conversations become data. Their first device, Spectre I, detects nearby microphones and emits targeted cancellation signals that scramble what those microphones hear. To humans in the room, the conversation sounds completely normal. To recording devices, the speech becomes unintelligible.

The challenge is in having the system detect microphones, model how sound propagates through the environment, and generate interference signals precise enough to disrupt recording devices without disrupting the people actually speaking. It’s a problem that sits at the intersection of acoustics, signal processing, hardware design, and machine learning.

As AI systems get dramatically better at listening to and analyzing the world around us, technologies like this raise a deeper question. If every device can record everything, who gets to decide when it doesn’t?

Eric Nguyen, founder of Radical Numerics

A few years ago, a group of researchers at Stanford started asking a provocative question: what if the next breakthrough in biology didn’t come from a lab experiment, but from an AI model? That curiosity led Michael Poli and Eric Nguyen to build Evo, one of the first large generative models trained directly on genomic data. Instead of predicting or analyzing DNA, Evo could actually generate entirely new genetic sequences. It could design proteins and genomes that had never existed before, launching us into a future where AI systems help invent biological tools the same way generative models create images or code.

If AI can design biology, the upside is enormous. It could accelerate drug discovery, create microbes that break down pollution, or develop new treatments for disease. But it also raises a scarier question: what happens when the ability to design biological systems becomes widely accessible? The same technology can be used to design dangerous bioweapons.

Imagine a hospital lab in NYC detecting an unusual infection. The symptoms look like a flu, but the genetic sequence doesn’t match anything in existing databases. Normally it could take weeks for researchers to understand what they’re dealing with. But if the pathogen was engineered with AI, those weeks might be the difference between a contained incident and a global outbreak.

Now imagine having an AI system that can rapidly simulate how that organism behaves—how its proteins fold, how it interacts with human cells, how quickly it spreads. Within hours it can suggest possible countermeasures, predict mutations, and identify drugs that might neutralize it.

That kind of rapid simulation is one of the motivations behind the work at Radical Numerics. If our adversaries can use generative AI to design biology, we need equally powerful systems capable of understanding and defending against it.

Training models like Evo requires pushing the limits of compute, designing new kernels and architectures, and building systems that can simulate biological processes at enormous scale. It’s the kind of problem where advances in algorithms, infrastructure, and hardware all matter at once. If you’re curious about how AI might move beyond image and text generation to model and simulate biology, this is a rare opportunity to hear from the researchers helping define that frontier.

The technologies that Deveillance and Radical Numerics are building may seem like part of a Black Mirror episode. AI systems that can design biological molecules. Devices that can silently record every conversation around us. But the tools themselves are neither good nor bad, and the future isn’t predetermined. These powerful technologies can lead to surveillance and risk, or to safety and empowerment. The founders of Deveillance and Radical Numerics are working on very different problems, but they share the same instinct: if the future looks a little scary, the best response is to start building systems that make it safer.

Apply to meet Aida Baradari, the founder of Deveillace, and Eric Nguyen, the founder of Radical Numerics at our next showcase on March 25th in SF!

In the not-so-distant future AI systems will help decide which transactions clear in financial markets, how power flows across energy grids, and which medical cases get escalated to doctors. These systems are becoming powerful enough to control real infrastructure that affects every single person on the planet. The largest AI labs are racing to build more capable models, but they’re paying much less attention to the infrastructure needed to safely deploy them. Before AI can run the systems we depend on, we need ways to understand how it thinks, test how it behaves, and secure the environments it operates in.

Diogo Almeida, Founder of TypeSafe AI

You built an AI agent that’s supposed to process customer refunds. It reads the support ticket, looks up the order, and calls the refund API. The first few run smoothly. Then a ticket comes in that mentions a partial refund. The model decides it needs a helper function called calculate_partial_refund() and confidently calls it.

That function doesn’t exist.

Instead of stopping, the agent invents the logic, calls the wrong API endpoint, and refunds the entire order. Not once, but for every similar ticket that follows. Nothing crashed. The logs look fine. The system just quietly made the wrong decision. And it made it hundreds of times. These moments reveal the real challenge of this era of AI. The models are already powerful enough to automate meaningful work. But without reliability they’re useless.

That’s the problem TypeSafe is trying to solve. Instead of optimizing models for chat or creativity, the team is rebuilding the stack from first principles to create what they call transformative AI. Their model, System1, is designed for machine-to-machine execution rather than human conversation. The focus is reliability in environments where correctness and latency are non-negotiable: real-time decision systems, compliance pipelines, large-scale automation, or autonomous agents operating without a human constantly supervising.

Rather than bolting guardrails onto probabilistic models after the fact, TypeSafe is embedding reliability directly into the model itself. If that approach works, the payoff could be enormous. AI could finally take on the tedious but essential work that keeps companies running: compliance checks, operational workflows, data reconciliation, and thousands of small decisions that currently require human oversight.

The team behind the company makes this big vision feel achievable. Diogo Almeida, one of the founders of TypeSafe, previously worked at OpenAI, where he helped develop reinforcement learning with human feedback, the technique that made systems like ChatGPT actually follow human intent. That work became one of the most important breakthroughs in modern AI. Now Diogo is asking a different question. If the last generation of models learned how to talk to humans, what does the next generation need to look like if it’s going to run real systems? If you’re curious where frontier AI might go after the current wave of LLMs, this is a rare opportunity to hear from someone who helped shape the last one and is now building what comes next.

Nicolai Ouporov, Founder of Fleet AI

Imagine an AI agent helping coordinate activity inside a busy emergency room.

A patient is rushed in after a car accident. His blood pressure is falling. The triage nurse enters vitals, lab tests are ordered, and imaging needs to be scheduled immediately. Someone has to decide—fast—whether the patient should go straight to surgery, to a CT scanner, or to trauma observation. In a real hospital, those decisions happen in seconds and mistakes can cost a life.

In a simulation environment like the ones Fleet AI founder Nicolai Ouporov is building, an AI agent can practice these scenarios safely. The system might initially make the wrong call. It might send the patient to imaging instead of escalating the case to the trauma team. But the simulation shows what happens next—the patient deteriorates, alarms trigger, and the delay becomes visible in the outcome.

Because everything is simulated, the agent can run through thousands of emergency scenarios: internal bleeding, stroke symptoms, sepsis alerts, conflicting lab signals. Each time it sees the consequences of its decisions and adjusts its reasoning.

Over time, the system begins to recognize the subtle patterns that signal a life-threatening situation and learns when to escalate immediately. The goal isn’t to replace doctors. It’s to train AI systems in realistic environments where they can develop judgment before they are ever trusted with real patients.

That kind of training becomes essential as agents start helping with complex operational work. Before we trust software to coordinate systems that affect people’s health, finances, or safety, it needs the equivalent of residency training. Simulation environments give agents a place to build that experience.

For engineers, building those environments is a pretty cool problem. You’re creating digital worlds where intelligent systems can practice. That requires combining reinforcement learning, simulation, distributed systems, and developer tooling to design environments that are realistic enough to expose failure modes but flexible enough for agents to explore and improve. It also means thinking deeply about how to generate meaningful scenarios, measure progress, and surface the kinds of mistakes that only appear when systems interact in complicated ways. As agents start operating across thousands of tools and services on the internet, the need for this kind of infrastructure will only grow. Fleet sits right at that frontier.

Tom McGrath, Founder of Goodfire

In 2012, scientists realized that a strange molecular mechanism buried inside bacterial DNA could be turned into a powerful gene-editing tool. It was a total accident that they stumbled across this. No one was looking for it. That system became CRISPR, and it transformed biology almost overnight. What’s striking in hindsight is that the mechanism had been sitting in genomes for decades before anyone understood what it did. Today something similar may be happening inside AI models. Large neural networks are scanning enormous datasets in biology, chemistry, and medicine, learning patterns humans can’t easily see. But the internal reasoning behind those discoveries often remains opaque. If the next CRISPR-like insight is already hiding inside a model, we currently have no good way to find it.

While working as a researcher at Google DeepMind, Tom McGrath spent years studying how large neural networks actually work. What he kept running into was the same problem: even the people building the most advanced AI systems in the world didn’t really understand what was happening inside them. Modern models can reason, write code, and discover new patterns in science, but their internal decision-making still looks like a black box. McGrath became one of the early pioneers of a field called mechanistic interpretability, which tries to reverse-engineer neural networks and map the concepts inside them. After helping found the interpretability effort at DeepMind, he decided the problem was too important to stay purely academic and joined forces with Eric Ho and Dan Balsam to start Goodfire.

Goodfire is building tools to help researchers and engineers see inside modern AI models and better understand how they reason. Interpretability has been one of the most important and unsolved challenges in AI for years. If we want to trust these systems with meaningful tasks, we need ways to inspect their behavior, identify when something has gone wrong, and understand why a model made a particular decision. Goodfire’s work aims to turn what is currently a black box into something closer to a system engineers can actually debug. If we turn the Internet over to thousands of agents acting on our behalf, tools that help us understand what those systems are thinking will become an essential part of the stack.

The internet is turning into a place where software acts on our behalf. Agents will read documentation, move money, route resources, escalate medical cases, and coordinate complex systems we depend on every day. But before we trust them with that responsibility, we need an entirely new layer of infrastructure: models that behave reliably, environments where they can learn safely, and tools that let engineers understand what they’re doing.

That’s the layer TypeSafe, Fleet, and Goodfire are building. If you’re an engineer interested in developing the systems that will make AI trustworthy enough to run the world’s infrastructure, this is a rare chance to meet the founders defining this future.

Get excited to meet these three founders! Apply to attend our March 25th SF Startup Showcase.

Robots work great in demos. Then you put them in someone’s house and a dog runs through the room, or the cup is a different shape, or there’s a spill on the counter that wasn’t in the training data. The gap between “lab demo” and “real world” has killed a lot of robotics companies.

Three companies are working on different pieces of this. Sunday is building home robots. Zipline flies autonomous delivery drones. Specter is building the sensing layer that lets machines understand physical environments. At the core, they’re all building autonomous systems that work outside controlled conditions.

Sunday does the dishes. Zipline flies the deliveries. Specter tries to understand the chaos. The baby, raccoon, and two-tailed dog keep generating new edge cases.

Tony Zhao, founder of Sunday

Tony Zhao spent years in robotics research at Stanford, working with Chelsea Finn and Sergey Levine, with stints at Tesla Autopilot and DeepMind. The pattern was always the same -- a robot learns to pick up a cup in the lab, and then falls apart in a real kitchen with different cups, clutter, and a sequence of tasks. The issue, he realized, wasn’t algorithms. It was data. Robots didn’t have enough real-world examples to learn from, and nobody was collecting them.

In 2024, Zhao and Cheng Chi started Sunday to fix that. They skipped simulation entirely. Instead, they built a wearable “skill capture” glove that records how people actually do household tasks: loading dishwashers, folding laundry, clearing tables. Millions of examples from real homes. Their first robot, Memo, trains on that data to do chores that have resisted automation for decades, mostly because nobody had bothered to collect the training data at scale.

The broader bet is that robotics is hitting the same scaling curve that language models hit a few years ago. Techniques Zhao and Chi developed (diffusion policies, learning from demonstrations) are starting to work well enough to make robots work in the real world. Sunday thinks the home is where people notice first. The engineering is hardware, robot learning, perception, manipulation, data pipelines, and making it all reliable in an environment where someone’s toddler just threw a plate on the floor.

Keenan Wyrobek, founder of Zipline

Keller Rinaudo Cliffton and Keenan Wyrobek didn’t start Zipline to deliver burritos and Amazon packages. Rinaudo had built a smartphone robot company (Romotive); Wyrobek helped create the Robot Operating System (ROS). When they went looking for a problem worth solving, they kept landing on medical logistics in places where roads are bad or nonexistent. A hospital in rural Rwanda runs out of blood, the nearest supply is hours away by truck, and people die waiting. If autonomous flight actually worked, you could deliver that blood in minutes.

Instead of starting in Silicon Valley, they went somewhere harder. In 2016, Zipline launched its first distribution center in Rwanda, building a nationwide system where drones fly medical supplies from centralized hubs to remote clinics on demand. Over the years the company turned that experiment into the largest autonomous delivery system on Earth—flying tens of millions of autonomous miles and completing well over a million deliveries of blood, vaccines, and essential medicines to remote clinics on demand. They had to solve weather resilience, aircraft durability across thousands of flights, fleet coordination, and years of regulatory negotiation -- all in markets where most American startups wouldn’t bother operating.

What’s different now: the US market is finally opening up. Zipline has regulatory approval to fly beyond visual line-of-sight and is deploying with healthcare systems and retailers domestically. The core tech works, which is more than most autonomy companies can say. The open problem is scaling it into urban infrastructure, which means aircraft safety over populated areas, fleet management across cities, and the software to run all of it as a real logistics network.

Xerxes Libsch, founder of Specter

Xerxes Libsch came out of Anduril, where he saw autonomous systems improving fast while the infrastructure around them lagged behind. Sensors and drones collect huge amounts of data, but most factories, logistics hubs, and energy sites still don’t have a coherent real-time picture of what’s happening across their own operations. The gap isn’t sensors. It’s the software that stitches sensor data into something useful.

Libsch and Philip Clark started Specter to build that missing layer -- a distributed sensing platform using wireless mesh networks, software-defined radios, and edge compute. Thousands of cheap sensors wired into one network, giving you a continuously updated, machine-readable view of a physical site. This is a control plane for the physical world.

Low-power wireless, edge computing, and ML have gotten cheap enough to make dense sensor networks practical, but the hard problems are still open. Long-range wireless, SDR platforms, RF modeling in messy environments, distributed systems that turn raw signals into reliable output. Specter is early. Engineers joining now are designing the architecture, not executing someone else’s spec.

The real world is the ultimate adversarial environment. Weather changes. Sensors fail. Kids leave toys in the yard and raccoons steal the laundry. For a long time that chaos kept robots stuck in controlled demos. That’s starting to change. The teams behind Sunday, Zipline, and Specter are building systems that can actually operate in the wild, which opens up a new era of super cool engineering challenges. If you’re excited about building robots that work in the real world, come meet (and join!) the founders who are defining what this future looks like.

🌟 Engineers — want to meet these three founders and seven others? Apply to attend our March 25th SF Startup Showcase.

Something fundamental is shifting in software. Software systems aren’t just tools we write and run, they’re autonomous agents dynamically operating out in the real world. Ricursive is rethinking the physical layer, creating a self-improving feedback loop where AI models design faster, more efficient, next-gen chips, which in turn enable the training of even more powerful AI systems. Raindrop is making live AI systems observable, so engineers can understand how agents actually behave once they’re deployed. Armadin is preparing for a world where AI will be weaponized, building AI adversaries to test our defenses before attackers do. Different parts of the stack, same underlying change: once software starts making decisions on its own, the way we’ve traditionally built and managed systems need to be updated.

If you’re an engineer who wants to build the infrastructure of this new world, come meet these 3 founders at our next showcase.

Below, learn more about:

1. Travis Landham’s AI pen testing company, Armadin

2. Anna Goldie’s frontier lab, Ricursive Intelligence

3. Ben Hylak’s observability platform for AI agents, Raindrop

Travis Lanham, Founder of Armadin

Kevin Mandia could have ended his founder story when Google bought his startup Mandiant for roughly $5.4 billion in 2022. Instead, he is back at the starting line with Armadin because he’s convinced the ground is about to move under everyone’s feet: “Offense is going to be all‑AI in under two years,” he says, and if that’s true, most of today’s defensive stacks are structurally too slow.

At the center of that vision is Mandia’s cofounder, Travis Lanham, a former Google Cloud security leader who helped drive technical strategy and operations for exabyte-scale security systems inside Google after its acquisition of Mandiant. If Mandia brings the strategic vision of how cyber conflict is evolving, Lanham brings the real-world engineering experience of securing the very infrastructure that defends against today’s most sophisticated attackers.

Armadin’s core premise is that if attackers are going to leverage AI agents to lead offensive attacks, your defense can’t rely on human-driven, periodic red-team exercises. Businesses need an AI-driven adversary supercharging the business of finding vulnerabilities before someone else’s agent does.

The capital structure underscores how seriously the market takes that thesis. Armadin has already raised $24M in seed funding from Ballistic Ventures, the cyber‑focused firm Mandia co‑founded. It is in discussions to raise over $100M more from firms like Accel, GV, and Kleiner Perkins at a valuation north of $600M, even before the full story of the product is public. That kind of early valuation only makes sense if investors buy two things at once: that AI‑accelerated offense is not hype, and that Mandia and Lanham are uniquely positioned to build the defensive counterpart.

Which tech savvy pup are you? Find out at our next show!

Anna Goldie, Founder of Ricursive

Anna Goldie has spent her career in places where human intuition breaks down. At Google Brain, she co-led AlphaChip, the system that compressed chip floor planning from months into hours. The RL agents she trained to design chips looked wrong, with strange geometries and unconventional clustering, but the results came back working exactly as predicted, often better than human alternatives.

AlphaChip confirmed a constraint within the industry: AI could move faster if it didn’t take so long and cost so much to translate ideas into hardware. While models improve every quarter, silicon innovates every decade, and the industry quietly agrees to treat graphics GPUs as the long-term foundation for frontier AI.

Ricursive is Goldie’s answer to that mismatch — it’s her new frontier lab built around a simple, aggressive premise: AI should be designing the chips that power AI. Goldie believes teams should be able to describe a workload, specify performance and power constraints, and receive manufacturable silicon without needing to build a massive in-house design organization. The goal is to collapse the iteration loop for custom chips until it starts to feel less like hardware development and more like deploying software.

Ricursive’s team is dense with engineers who have already shipped frontier systems at scale. Beyond Goldie’s co-founder and AlphaChip collaborator Azalia Mirhoseini, the early group includes several engineers who worked directly on AI-for-chip and physical design pipelines inside large labs and tech companies. The team raised a $300M Series A led by Lightspeed — only a couple of months after raising a $35 million seed round led by Sequoia — and are hiring engineers.

Ben Hylak, Founder of Raindrop

Ben Hylak spent years at Apple helping build visionOS, working on systems where interaction and behavior had to line up perfectly. Spatial computing forces you to think beyond the demo. Once millions of people are using something, all the strange edge cases surface and what looked solid in testing suddenly behaves very differently in the real world.

When generative AI started showing up inside real products, Ben saw that same dynamic. The demos were impressive. But as companies move agents into production, silent failures, hallucinated actions, brittle tool calls, and unpredictable edge cases are becoming real operational risks. Traditional logging and APM tools weren’t designed for probabilistic systems, so they miss the kinds of issues that matter most in AI workflows.

Raindrop is building “Sentry for AI agents,” the first true monitoring and observability layer purpose-built for agentic systems. It provides real-time alerts, semantic issue detection, deep search, and root-cause insights that allow engineering teams to discover, track, and fix problems that would otherwise go unnoticed.

Since launching last year, they’ve grown 30% month-over-month. Clay, Tolan, Framer, AngelList, Type, and others already rely on it as they scale. Tolan reduced memory issues by nearly a third and almost eliminated “lore inconsistencies.” Founders call it “irreplaceable,” “like iOS crash reports but for our AI,” and “the thing that tells me what to prioritize when I walk into the office.”

Observability for AI agents is foundational infrastructure. Every company running agents in production will eventually need it. That makes Raindrop the kind of company where early engineers will define a category, not just contribute to one.

Want to meet these founders and ones of other breakout startups? Apply to attend our March 25th SF Startup Showcase.

The tech world is currently obsessed with a seductive idea: that software has become a commodity.

This narrative suggests that because an AI assistant can rebuild a SaaS tool over a weekend, the cost of software is racing toward zero. For lightweight apps and simple tools that might be true, but this narrative falters the moment it touches the systems that actually anchor the global economy.

Nobody is going to “vibe code” the system tasked with consolidating financial reporting across a hundred legal entities and disparate jurisdictions. You cannot simply prompt your way through the labyrinthine infrastructure that feeds into SEC filings and manages tax compliance across forty countries.

While software is undeniably getting easier to write, the sheer gravity of global commerce requires something more than a clever prompt. The world’s largest organizations do not run on weekend projects; they run on battle-tested, deeply integrated systems built for durability.

For the overwhelming majority of these giants, that system is SAP: a 300-million-line codebase that’s held market dominance for 5 decades.

In a field defined by rapid iteration, the rigid nature of the global enterprise represents the final, most difficult frontier. Emma Qian, founder of Nova Intelligence, saw this immutability as the ultimate opportunity.

How A DeepMind researcher landed on software from the 1970s

Emma wasn’t necessarily looking to work on legacy systems or enterprise software when she set out to build something new after stints in AI research at Google DeepMind and Meta; she was looking to build a durable company.

SAP processes 77% of the world’s business transactions, but the economics of working with it are challenging. For every dollar a company spends on SAP software, it spends roughly seven dollars on consultants.

Over decades, companies have layered millions of lines of custom code on top of SAP, often written by consultants who left years ago. Much of it is poorly documented and barely understood. Even simple changes can take months and cost millions.

Emma saw an opportunity: the world’s most mission-critical software is trapped by broken economics and an unsolved technical debt. Alongside co-founder Sam Yang and Alexander Zeier—the co-inventor of SAP HANA—they decided to go after the problem head on.

What Nova is building

Nova Intelligence is what Emma describes as “Claude Code for SAP”: an agentic AI platform that radically accelerates how enterprises work with SAP.

I asked Emma a common question: why not build an AI-native ERP and replace SAP entirely?

“Big players have been trying to displace SAP for a long time. They haven’t been able to budge SAP at the top enterprises,” Emma says. “Even with SAP forcing everyone to migrate to S/4HANA by 2030, virtually no one is switching. Large enterprises are spending hundreds of millions on the process. For the segment we’re targeting, SAP isn’t going anywhere.”

The larger opportunity lies not in replacing SAP, but in making it work better. Early results have been dramatic: enterprises are compressing workflows that once took weeks into mere days. This potential has even caught the attention of SAP – their own venture arm is an investor in Nova.

Why SAP requires a purpose-built AI system

Building AI agents for SAP requires solving problems that simply don’t exist in modern software development. The challenge begins with ABAP, SAP’s proprietary language, which predates many of today’s programming paradigms.

In this environment, “code” doesn’t follow the traditional definition. Business logic is rarely confined to a clean script; instead, it is scattered across a complex web of ABAP programs, deep-layer configurations, workflows, and proprietary forms. Much of the system’s intelligence lives in artifacts that look nothing like traditional source code.

“We had to build a significant amount of custom infrastructure to make AI agents work well in the SAP environment,” Emma explains.

A team built for this problem

Solving this requires a rare intersection of talent: the ability to command cutting-edge AI while navigating decades of SAP complexity. To bridge that gap, Emma and Sam recruited Alexander Zeier, the co-inventor of SAP HANA and former CTO of Accenture’s SAP Business Group. They followed by bringing on Justin Kershaw, the former CIO of Cargill—one of SAP’s largest global customers.

“It’s an unusual blend,” Emma says. “We had to merge deep domain expertise with AI-native thinking.”

For co-founder Sam Yang, the appeal was the sheer difficulty of the task. “Emma is effectively the smartest person I know,” Yang says. “The SAP problem felt genuinely hard in a way most people aren’t willing to touch.”

“When we started Nova, it felt like we were getting punched in the face every day,” Yang adds. “But that friction is exactly how we built a platform that is now nearly impossible for anyone else to replicate.”

What’s next

When Emma speaks about the long-term vision, she isn’t just talking about software. She’s talking about a fundamental shift in how businesses operate.

Today, managing enterprise processes is a grueling, human-intensive endeavor. Simply understanding how a company’s own systems function—let alone changing them—requires armies of consultants and timelines that stretch from months into years. For the first time, AI offers a way for a company to truly “know” itself, providing the clarity needed to modernize and optimize operations in real time.

The ultimate goal is a layer of agents that proactively help companies run better. Achieving that would fundamentally rewrite the rules of global commerce.

Nova Intelligence is currently hiring engineers eager to tackle genuinely difficult technical problems within the infrastructure that powers the world’s largest companies.

If you are interested in building the future of the enterprise, reach out at contact@novaintelligence.com.

We kicked our 2026 showcases off with a bang last week! Our first show of the year brought together 10 breakout founders and a room packed with engineers and for an evening of breakout stories, great conversations, and lasting connections.

The timing here matters.

At FYSK, we don’t wait until the story is obvious. We introduce founders while the team is still small, the roadmap is still fluid, and the right early hires can actually shape what gets built. The engineers in the room aren’t just there to watch — they’re potential co-builders of what comes next.

We’re no stranger to early signs of success. Last year, Görkem Yurtseven pitched fal when his team was around 20 engineers with no sales org. Ten months later, the company had doubled in size and increased its valuation by 10x, and engineers in the room had the opportunity to join during that breakout phase.

When we featured Kalshi founder Tarek Mansour in New York last April, the company had just gotten approval to distribute nationwide. Since then, Kalshi raised a $1B Series E and grew trading volume more than 12x, and early engineers in the audience had the chance to build this critical infrastructure.

These aren’t outliers. This is what happens when talented engineers meet ambitious founders before the market catches on.

If you were in the room, or you’re reading this now, these moments are for you. Our shows are for people who want to join at critical moments of inflection, ask the right questions, and actually shape a company’s direction. The founders who pitched last week and the engineers who showed up to listen are at the start of something we’ll be talking about for years.

We’re grateful for the generosity and support of our wonderful cohosts at Rippling and Digital Ocean.

Not seeing the breakout startup you’ve been following at our showcases? Nominate them, and they might just be sharing their story at our next show.

Parallel

Parallel builds web data APIs for AI agents, turning web search and complex human knowledge tasks into programmable, business-ready infrastructure.

Founder: Parag Agrawal

Open Roles: Across the board

Applied Compute

Applied Compute builds Specific Intelligence for enterprises: agents that perform reliably on your workflows, learn continuously from your data, and operate in your environment.

Founder: Rhythm Garg

Open Roles: Software Engineers

Inertia

Inertia is the commercial fusion energy company creating limitless energy through proven fusion science - star power for life on Earth.

Founder: Jeff Lawson

Open Roles: All engineering disciplines

Midi Health

Midi Health is the virtual care clinic transforming women’s health.

Founder: Joanna Strober

Open Roles: Across the board

Wispr Flow

Wispr Flow is the voice-to-text AI that turns speech into clear, polished writing in every app.

Founder: Tanay Kothari

Open Roles: Software/ML Engineers, Sales, Marketing

Matter Intelligence

Matter is building superhuman perception, enabling machines and humans to see and understand the physical world at the molecular level.

Founder: Vishnu Sridhar

Open Roles: All engineering disciplines

Quilter

Quilter makes building hardware as fast and iterative as building software.

Founder: Sergiy Nesterenko

Open Roles: Software Engineers

Outtake

Outtake’s agentic AI secures modern attack surfaces with advanced search, real-time threat classification, and automated response.

Founder: Alex Dhillon

Open Roles: Across the board

Code Metal

Code Metal leverages AI for automated code translation and optimization across industries requiring precise coding standards like defense and manufacturing.

Founder: Peter Morales

Open Roles: Software Engineers, AI Engineers, Growth, Ops

/dev/agents

/dev/agents is a cloud-based operating system designed to coordinate and manage autonomous AI agents across all a user’s devices.

Founder: David Singleton

Open Roles: Software Engineers, Business Operations

Want to be the first to hear when our next event is? Apply now to our community and subscribe to our newsletter to be in the know about upcoming showcases.

The best way to get to know a founder is through a long, unfiltered conversation, where you can dig into the details that never make it into a pitch deck.

This February, we’re bringing back our private dinner series in San Francisco: intimate, off-the-record dinners where a small group of high-intent engineers and operators sit down with founders of inflecting companies to talk candidly about the real stories behind the build. These dinners are:

• Intimate by Design: We emphasize conversation over presentation. Small groups create space for probing questions, candid discussion, and a deeper understanding of the company’s inner workings.

• For Serious Explorers: These seats are reserved for engineers and operators who are serious about their next move and want to do their due diligence thoughtfully.

• Completely Off the Record: Everything discussed at these dinners stays at the table. These conversations won’t be recorded, shared, or posted anywhere.

The founders you’ll meet

These teams are growing quickly, and early hires will have real ownership and outsized impact.

Emma Qian, founder & CEO of Nova Intelligence (2/11)

​Nova Intelligence leverages multi-agent systems and research-backed models to automate SAP custom code modernization and eliminate technical debt for Fortune 500 companies. Prior to Nova Intelligence, Emma was a researcher at DeepMind.

Best for: engineers excited about solving hard problems others aren’t willing to touch

Request to join here.

Soham Mazumdar, founder & CEO of Wisdom AI (2/17)

Wisdom AI is an agentic data insights platform that transforms complex, siloed enterprise data into an autonomous AI data analyst that provides natural language answers and proactive business insights. Prior to Wisdom AI, Soham was co-founder and chief architect of Rubrik.

Best for: engineers excited to tame messy data at massive scale

Request to join here.

Aditya (JP) Jayaprakash, founder & CEO of Blacksmith (2/19)

Blacksmith is a cloud platform that speeds up software testing and deployment (CI/CD) by using high-performance, gaming-grade CPUs to unblock AI-driven development workflows. Prior to Blacksmith, JP was a software engineer at Faire.

Best for: engineers that obsess over performance and infrastructure speed

Request to join here.

Will Yin, founder & CEO of Mandolin (2/23)

Mandolin leverages AI agents to speed up access to life-saving medicine, designed for speciality drug access. Prior to Mandolin, Will was CTO and co-founder of Jupiter, a creator-driven recipe & grocery shopping platform.

Best for: operators and PMs excited about leveraging AI to save lives

Request to join here.

We’re just a week away from our January 28th startup showcase, where we’ll introduce talented engineers and operators to 10 founders building companies at critical inflection points.

Today, we’re diving deeper into the stories of three founders who will take the stage next week.

Below, learn more about:

1. David Singleton’s operating system for AI agents, /dev/agents

2. Joanna Strober’s digital health platform for women’s midlife care, Midi Health

3. Rhythm Garg’s enterprise-specific AI solution, Applied Compute

   

David Singleton, founder of Dreamer

David Singleton has an unusual instinct for timing.

In 2001, he built mobile operating systems at Symbian, recognizing smartphones would become essential before most people owned one. In the 2010s, he moved to Google to lead Android wearables, anticipating the shift from handheld devices to wearable computing. In 2018, he joined Stripe as CTO, ready to tackle payments infrastructure unprepared for the new scale of global digital transactions.

His new startup, /dev/agents, is tackling his fourth bet on a shift in the way we interact with technology: AI agents.

Singleton believes agents are standing at the same precipice smartphones, wearables, and digital payments once did: they’re certain to reshape how we interact with technology, but their full potential requires the right supporting infrastructure. Models are now sophisticated enough to reason, plan, and strategize, but they still struggle with execution. Agents can outline a trip but not reliably book the flight, propose workflows but not coordinate systems, and diagnose failures but not recover from them.

That gap is what Singleton is building toward. His new startup is developing the next-generation operating system for AI agents—software designed to make agents reliable, interoperable, and usable beyond research demos. The goal is also not just more capable agents, but more accessible ones: tools that allow people, regardless of technical background, to build and deploy agents that actually follow through.

His team raised a $56M seed round in late 2024, but the startup is still keeping a low profile. If you want to get the inside scoop directly from Singleton on what he’s building, join us at our showcase.

Joanna Strober, founder of Midi Health

Joanna Strober is a veteran health-tech investor and former founder, and yet she had never heard the term perimenopause until it happened to her. Apparently, her doctors hadn’t either. When she brought her symptoms to clinicians, she cycled through sedatives, psychiatry referrals, and quiet resignation. Not one named what was actually happening.

Her frustrating experience with the healthcare system’s blind spot clarified what she wanted to build next. Enabled by pandemic-era telehealth deregulation, she co-founded Midi Health in 2021 with an all-female team united by shared midlife experiences. The goal was simple but ambitious: turn expert knowledge into clear protocols and deliver insurance-covered care where generalists had failed.

Midi Health is a virtual clinic built specifically for women in midlife, starting with perimenopause and menopause and expanding into broader midlife care, delivered via telehealth and covered by insurance.

In three years, Midi has moved from idea to nationwide infrastructure: insurance‑covered care in all 50 U.S. states, tens of thousands of women treated, and 91% of patients reporting overall symptom improvement within two months of their first visit. The company uses internal protocols and tooling partnerships, including AI‑powered documentation that saves clinicians more than three hours a day, to make high‑touch midlife care operationally and economically viable at scale. Midi has produced meaningful clinical outcomes, like identifying previously undiagnosed osteoporosis.

Midi has raised over $100 million to date from partners at GV and Emerson Collective. The new funding will expand insurance coverage, add 150 clinicians to the roster, and scale the platform to serve more than one million women annually by 2029.

Meet Joanna Strober at our Founders You Should Know showcase to learn how you can be a part of their next phase of growth.

Rhythm Garg, founder of Applied Compute

Rhythm Garg left his role at OpenAI to build a company centered around a strong thesis: the next breakthroughs won’t come from bigger models, but from sharper ones.

His hands-on work scaling o1’s RL reasoning at OpenAI revealed the limits of generalist models on enterprise tasks. His new startup, Applied Compute, is betting that the next wave of enterprise AI won’t be powered by ever-larger generalist models, but by highly targeted systems designed to reason deeply inside a single company’s ecosystem.

Founded last year, the startup is built around what the team calls “Specific Intelligence.” The idea is straightforward but contrarian: instead of adapting generic agents to enterprise workflows, they train proprietary agents directly on a company’s data, processes, and constraints. That means owning the full stack – training infrastructure, agent frameworks, and developer tools – and embedding engineers with customers to build systems that outperform general models on internal evaluations.

The timing matters. Enterprises are less interested in demos and more interested in real impact. Applied Compute’s autonomous agents are already running in production workflows at companies like DoorDash, Cognition, and Mercor, and engineering teams report cutting deployment times from months to days. Benchmark recently raised the company’s $85 million Series B, joined by Sequoia Capital, Lux Capital, and additional strategic backers from the AI and enterprise software ecosystem.

Learn more about how Rhythm Garg and the Applied Compute team are reshaping enterprise AI deployment at our next startup showcase.

Want to meet these founders and ones of other breakout startups? Apply to attend our Jan 28th SF Startup Showcase.

Every industry has friction points: months to design a chip, hardware that never quite works together, satellite images that are mostly noise. These three founders show what’s possible when you remove those bottlenecks and unlock a new wave of innovation.

At our startup showcase on January 28th, we are introducing 10 founders building companies at inflection points. Today, meet three who have faced industry frictions firsthand and are now solving the problems at their source.

Below, learn more about:

1. Anna Goldie’s frontier lab, Ricursive Intelligence

2. Peter Morale’s verifiable code translation platform, Code Metal

3. Vishnu Sridhar’s breakthrough satellite sensors, Matter Intelligence

   

Anna Goldie, founder of Ricursive Intelligence

Anna Goldie has spent her career in places where human intuition breaks down. At Google Brain, she co-led AlphaChip, the system that compressed chip floor planning from months into hours. The RL agents she trained to design chips looked wrong, with strange geometries and unconventional clustering, but the results came back working exactly as predicted, often better than human alternatives.

AlphaChip confirmed a constraint within the industry: AI could move faster if it didn’t take so long and cost so much to translate ideas into hardware. While models improve every quarter, silicon innovates every decade, and the industry quietly agrees to treat graphics GPUs as the long-term foundation for frontier AI.

Ricursive is Goldie’s answer to that mismatch — it’s her new frontier lab built around a simple, aggressive premise: AI should be designing the chips that power AI. Goldie believes teams should be able to describe a workload, specify performance and power constraints, and receive manufacturable silicon without needing to build a massive in-house design organization. The goal is to collapse the iteration loop for custom chips until it starts to feel less like hardware development and more like deploying software.

Ricursive’s team is dense with engineers who have already shipped frontier systems at scale. Beyond Goldie’s co-founder and AlphaChip collaborator Azalia Mirhoseini, the early group includes several engineers who worked directly on AI-for-chip and physical design pipelines inside large labs and tech companies. The team raised a $35M seed round led by Sequoia and are looking to bring on exceptionally talented engineers.

Join us at our next startup showcase to hear Anna Goldie break down her vision and articulate what comes next.

Peter Morales, founder of Code Metal

Peter Morales’ story starts with a drone that couldn’t think fast enough. Asked to take machine-learning algorithms and make them run in real time on flying hardware, he was forced to obsess over deployment paths and edge constraints, not just model design. Throughout his career, this pattern repeated: shipping the same features again and again across wildly different hardware stacks.

Code Metal is his refusal to accept the process of porting algorithms and rewriting code as normal; his deployment pain has gone from a niche problem to a structural issue. The modern hardware edge is a mess: drones, sensors, robots, industrial equipment, appliances, and vehicles all ship with different chips, SDKs, OS primitives, and language ecosystems.

His new startup lets you build for any hardware without rewriting code. The platform allows you to write your algorithms once in familiar research languages like Python, Julia, and MATLAB, and then automatically turns them into code that runs efficiently on a wide range of hardware like CPUs, GPUs, FPGAs, and specialized accelerators.

Morales takes accuracy seriously: Code Metal uses formal methods to verify semantic equivalence between input and output code – it’s not a chat interface that “probably” works, but infrastructure that has to be trusted by defense primes and the U.S. Air Force.​

The team raised $35M from Accel last June; Accel’s Steve Loughlin names Code Metal the fastest-growing company in their early-stage portfolio, with “skyrocketing” demand across defense, automotive, semiconductors, and consumer electronics. Current customers include the U.S. Air Force, Raytheon, and several automotive and electronics vendors.

Hear more about Code Metal’s traction from Morales at the showcase.

Vishnu Sridhar, founder of Matter Intelligence

Vishnu Sridhar’s work is literally out of this world – his hardware has traveled millions of miles through space. He was an engineer at NASA’s Jet Propulsion Laboratory, helping build cameras and microphones that landed on Mars and gear for a future mission to Jupiter’s moon Europa. His job was making sure those instruments survived space, worked on arrival, and sent back data you could actually trust about rocks, soil, and ice halfway across the solar system.​

Designing instruments for space taught him what good data looks like, and by comparison, why much of today’s satellite imagery of Earth is “basically noise”: it’s too coarse, inconsistent, and incomplete to answer serious questions.

If you’ve ever tried to answer a hard question from satellite pixels, you understand the motivation behind his new startup, Matter Intelligence. Matter is building ultra spectral satellites and models that treat every pixel as a material fingerprint, not just an RGB color. Their sensors, designed for satellites, aircraft, and drones, capture thousands of spectral channels and feed a physics-informed “Large World Model” that can classify materials, track temperature and composition, and spot failures before they’re obvious in standard imagery. Instead of another analytics tool on top of commodity images, Matter is trying to change what the sensor sees in the first place.​

Sridhar wants geospatial systems to care about chemistry, not just shapes—so that national security teams can see hidden infrastructure, energy companies can monitor pipelines and power lines for real material degradation, and climate and agriculture models can reason directly over crops, soils, and water quality.

Matter has raised a $12M seed round led by Lowercarbon Capital to build and launch its first satellites like EARTH‑1.

Hear how Vishnu Sridhar is pulling it off at our next showcase.

Want to meet these founders and ones of other breakout startups? Apply to attend our Jan 28th SF Startup Showcase.

If you’re going to join a startup, timing matters.

In just a few weeks, we’ll be featuring 10 founders at inflection points — joining their team means you’re early enough to shape the company, and at the right time to know the bet is real.

Over the next few weeks, we’ll introduce each founder so you know who you’re meeting at the showcase, and why they’re worth paying attention to.

In this first edition, hear more about:

1. Tanay Kothari’s breakout voice tool Wispr Flow

2. Sergiy Nesterenko’s autonomous PCB design engine, Quilter

3. Alex Dhillon’s AI-driven brand defense platform, Outtake

+ 3 FYSK Community founders hiring AEs, SWEs, PMs, and more at the end of article…

Tanay Kothari, founder of Wispr Flow

Tanay used to sleep every other night at school so he could code more. It sounds implausible until you hear the rest of his story: he shipped dozens of apps as a teenager, trained 20+ hours a day for international Olympiads, and represented India at both the International Olympiad in Informatics and the International Linguistics Olympiad. It’s no surprise he’s now the breakout founder behind Wispr Flow, a tool that’s quickly winning over nearly everyone who tries it.

If you’ve used Wispr Flow, you already know what makes it special. If you haven’t—it’s a voice operating system that replaces your keyboard with high-accuracy dictation and commands. After six months, heavy users speak over 70% of their characters through Flow instead of typing them. This isn’t habit-forcing. Wispr Flow just makes intuitive sense. In fact, much of Tanay’s early investor interest came from VCs who were already using the product and had an inkling it would be hugely successful.

Tanay’s vision goes beyond productivity gains. He talks about wanting his future kids to grow up with computers that feel like people, not “glass rectangles.” In his view, keyboards and mice are transitional technologies we’ll look back on the way we look at fax machines.

Wispr has raised $81M to date with backing from NEA, 8VC, and others.

Learn more about Tanay’s journey, what’s next for Wispr Flow, and how you can join the team at our upcoming showcase.

Sergiy Nesterenko, founder of Quilter

Sergiy has learned a painful lesson about timing. At SpaceX, he built a UWB RF positioning system with sub-centimeter accuracy. He never commercialized it. Years later, Apple rolled out UWB chips in iPhones, effectively mainstreaming the same class of technology. The lesson stuck.

Born in Ukraine three months before the Soviet Union collapsed, Sergiy grew up on stories of Yuri Gagarin and Soviet engineering feats that nudged him toward hard-physics problems. He immigrated to the U.S. at 10 without speaking English, then went on to triple major at UC Berkeley in Mathematics, Physics, and Chemistry. After stints in research labs and working on the Falcon 9 and Falcon Heavy at SpaceX until 2019, an investor told him “You should start a company. I’ll give you a million bucks. Go for it.”

This time, he wasn’t going to miss the window.

Quilter uses reinforcement learning and computational physics to generate manufacturable, optimized PCB layouts directly from schematics. More than 90% of printed circuit board layout work is still done manually using tools that haven’t meaningfully improved since the 1980s. Sergiy’s core thesis is that PCB design is a trillion-dollar bottleneck underlying every hardware product and that AI can act as a “compiler” that continuously optimizes routing and layout under real-world constraints.

Sergiy is explicit that he doesn’t want to build an “AI copilot” that still forces engineers to babysit layouts; his ambition is a fully autonomous layout engine.

The approach is working. One engineer used Quilter to figure out the minimum viable board size by simply submitting the same design at different board sizes instead of manually optimizing for weeks. The company just raised a $25M Series B in October 2025 led by Index Ventures.

Learn how Sergiy’s pulling it off at our showcase.

Alex Dhillon, founder of Outtake

By the time Alex graduated high school, he’d lived in four countries, gone to class in six languages, and attended three different high schools. That kind of global perspective shaped how he thinks about problems—especially the ones that scale across borders at internet speed.

In 2023 after spending some time at Palantir, he noticed something alarming: we’d entered the “zero-cost attack era.” Generative AI makes it trivial for attackers to spin up highly convincing phishing and impersonation campaigns at scale. Organizations from creators to hedge funds were seeing fraudulent copies of their brands proliferate online. Manual monitoring and legal processes couldn’t keep up.

His core idea: if criminals are using AI at scale, defenders need fleets of AI agents capable of continuously mapping and cleaning up the threat landscape.

Outtake uses autonomous agents and legal workflows to detect and take down AI-driven scams at scale—cloned websites, phishing pages, deepfake accounts, brand impersonations. The platform operates “outside the firewall,” with bots that crawl the public internet, classify threats, and automatically initiate takedowns across registrars and platforms.

Outtake has raised $20M to date, and early customers include OpenAI.

Learn more about how Alex and his team of ex-Palantir engineers are fighting back against the zero-cost attack at our showcase.

🤝 Beyond the Showcase: Founders Actively Hiring

Want an intro to these founders? Email a blurb about yourself to rachel@foundersysk.com — if you’re a good fit, we’ll connect you.

• Josh Payne @ Coframe is hiring Account Executives and a Customer Success Manager. He’s building the world's first AI Growth Engineer, and are the only marketing technology company officially partnered with OpenAI.

• Jeffery Liu @ Assort Health is hiring Software Engineers and Product Managers for their AI Agent products. Assort is a comprehensive patient experience platform — their omnichannel AI agents eliminate lengthy hold times and inefficiencies that stand in the way of patients getting the care they need.

• Lily Clifford @ Rime is hiring a Head of Marketing to make voice AI feel more human with their on-prem and cloud text-to-speech and voice agents.

• Helen Hastings @ Quanta is hiring a Senior Product Designer and Senior Software Engineers to build out their AI-powered accounting and finance platform.

Want to meet these founders and ones of other breakout startups? Apply to attend our Jan 28th SF Startup Showcase.

We get this question a lot: “I’m an investor… I come to your showcase?” The short answer: yes, with one condition.

Our showcases are built for engineers. The room works because it’s high-signal and optimized for real conversations between founders and top technical talent. To keep it that way, we’re intentional about who’s in the room.

The rule to get into the show

If you’re an investor and want to attend the showcase, we ask that you help us get three great engineers in the room.

Here’s how it works:

• Encourage engineers to apply to the community and list you as a referral

• Once three of your referred engineers are accepted, we’ll send you a ticket

Pro tip: Share the showcase in communities you’re part of—Slack channels, alumni groups, email lists, etc.—to reach talented engineers you think would be a great fit.

The process ensures every investor in the room is helping maintain a high-quality experience. If you can’t name three engineers you’d trust in a curated technical room, this probably isn’t the right event for you. If you can, this rule should be pretty straightforward.

Sample Messaging to Use

Here’s a message you can easily copy + paste into an alum/community group:

If you’re exploring startup jobs & are looking for a high-signal way to meet founders in person, I recommend attending the Founders You Should Know startup showcase on Jan 28th in SF.

Inflecting founders share their stories and then hang around to chat 1:1 with engineers. They focus on featuring founders just as they break out: they featured Anthropic at their Series A, Cursor at their seed, Kalshi when they were at their Series B, etc. Engineers at these shows got to meet the founders + join these teams early.

Apply to join at foundersysk.com & put me down as a referral for expedited review.

Why We Do It This Way

Most startup events don’t work like this. Walk into a typical demo day or networking event and the room is often 60% investors, 30% founders, and maybe 10% engineers. The energy becomes transactional, and engineers, if they show up at all, often leave early.

We flip that ratio. Our room is majority engineers—people who build things, evaluate technical decisions, and care about how something actually works. The result is stronger conversations and a different kind of energy.

When investors help surface strong engineers:

• Founders meet higher-quality candidates

• Engineers discover companies they actually want to join

• The showcase stays high-trust and high-signal

This keeps the event aligned with its purpose: helping breakout founders meet exceptional engineers at the right moment. It’s the simplest way to preserve what makes the event work and why founders, engineers, and investors keep coming back.