Dan Lewis

We raised a $27M Series A to replace the spreadsheets and human duct tape behind $100 trillion in global assets. Fund administration is the invisible backbone of private equity and venture capital - and it’s broken. Why? Financial data is scattered, stale, and locked inside legacy providers. Books take forever to close. Basic questions about your own fund take days to answer. So we rebuilt the general ledger, waterfall engine, investor portal, and portfolio management from scratch. One single source of truth for your firm. Our AI agents read emails, propose journal entries, and extract portfolio updates in seconds. Our CPAs review every output. Today, we administer $15 billion in assets - and we’re just getting started. Every fund CFO keeps getting asked: how will you adopt AI? Now you have an answer. Run your firm in real-time with @hanoverpark. –- Excited to partner with Jake Saper at @emergencecap @peterjhebert at Lux, @chadbyers/@pratyushbuddiga at Susa and CFOs at the largest private equity firms to forge this future.

generative user interfaces at the speed of thought. you can now build "tab autocomplete" for every app. ultra-fast inference @cerebras & your components render by the @tambo_ai agent.

From a mall booth to a $3B fintech: Matt Oppenheimer’s startup lessons from a 15-year journey with Remitly geekwire.com/2026/from-a-ma… via @GeekWire

The math on this project should mass-humble every AI lab on the planet. 1 cubic millimeter. One-millionth of a human brain. Harvard and Google spent 10 years mapping it. The imaging alone took 326 days. They sliced the tissue into 5,000 wafers each 30 nanometers thick, ran them through a $6 million electron microscope, then needed Google’s ML models to stitch the 3D reconstruction because no human team could process the output. The result: 57,000 cells, 150 million synapses, 230 millimeters of blood vessels, compressed into 1.4 petabytes of raw data. For context, 1.4 petabytes is roughly 1.4 million gigabytes. From a speck smaller than a grain of rice. Now scale that. The full human brain is one million times larger. Mapping the whole thing at this resolution would produce approximately 1.4 zettabytes of data. That’s roughly equal to all the data generated on Earth in a single year. The storage alone would cost an estimated $50 billion and require a 140-acre data center, which would make it the largest on the planet. And they found things textbooks don’t contain. One neuron had over 5,000 connection points. Some axons had coiled themselves into tight whorls for completely unknown reasons. Pairs of cell clusters grew in mirror images of each other. Jeff Lichtman, the Harvard lead, said there’s “a chasm between what we already know and what we need to know.” This is why the next step isn’t a human brain. It’s a mouse hippocampus, 10 cubic millimeters, over the next five years. Because even a mouse brain is 1,000x larger than what they just mapped, and the full mouse connectome is the proof of concept before anyone attempts the human one. We’re building AI systems that loosely mimic neural networks while still unable to fully read the wiring diagram of a single cubic millimeter of the thing we’re trying to imitate. The original is 1.4 petabytes per millionth of its volume. Every AI model on Earth fits in a fraction of that. The brain runs on 20 watts and fits in your skull. The data center required to merely describe one-millionth of it would span 140 acres.

Gemini 3.1 Pro is now at the top of the APEX-Agents leaderboard. Gemini jumped from 18.4% to 33.5% on Pass@1 in just 90 days. It also completes 5 tasks that no model has ever been able to do before. @GeminiApp shows how quickly agents are improving at real knowledge work. It can turn hundreds of documents, spreadsheets, and emails in Google Workspace into a client-ready deliverable. This rapid progress is evidence that we will be able to saturate any benchmark. The bottleneck is measuring all economic value.

your app's UI has entered the chat. just register your UI components and your APIs. build an agent that speaks your interface in minutes 👇

A cool demonstration of physics. The truck is moving forward at 80 km/h. The guy is catapulted in the opposite direction at 80 km/hr.

if you are thinking about joining a team, ask them for their ai token usage graph. if it's not exponential, reconsider.

build an agent that speaks your UI. your charts. your forms. your seat maps. multi-turn, streaming, interactive. introducing tambo 1.0, the open-source generative UI toolkit for react.

Someday, we'll look back on these chats fondly lol

Now available in Copilot Studio for those using our platform to build agents and workflows. @Microsoft

Solves the "but will it work for my business?" problem. One of the best teams out there. Congrats @ypatil125, @rhythmrg, and @lindensli !

We’ve raised our $350M Series C at a $10B valuation from @felicis, @benchmark, and @generalcatalyst. Just 2 years after starting, Mercor is paying $1.5 million per day to experts in our marketplace. We’re creating a new category of work in the AI economy, where software engineers, bankers, lawyers, and other professionals earn based on their experience while advancing the frontier of AI. While most new categories take time to build momentum, we’ve broken every growth record. For comparison, in their first 2 years: - Uber paid out just over a $1 million to drivers - Airbnb paid out $10 million to hosts We are unlocking human potential in the AI economy.

Today we’re expanding Microsoft 365 Copilot with the addition of Anthropic’s Claude models. Customers can now use both OpenAI and Claude — starting in Researcher and Copilot Studio, and coming to more experiences soon. Our multi-model approach goes beyond choice. It's all about bringing the best AI from across the industry to Copilot, tuned for work and tailored to every business. Read more: microsoft.com/en-us/microsof…

@marketplaceshq @convoyteam I love talking marketplaces. Thanks for having me on!

Miss our group chat with @daniellewis the other week? This was a great chat where we got to learn about Dan's experience starting @convoyteam, some of his key learnings, we discussed how AI is impacting marketplaces, & more. The recording is now live! everythingmarketplaces.com/c/dan-lewis-co…

@saranormous Code names (both word and picture versions)

newish fun board games that don’t take a huge amount of time to learn to play, and can be finished in 2H?

Grok 4 looks very strong. Importantly, it has a mode where multiple agents go do the same task in parallel, then compare their work and figure out the best answer. In the future, the amount of intelligence you get will just be based on how much compute you throw at it.

we just talked to hundreds of people building with ai across: - fintech - edtech - b2b saas - web3 - dev tools - healthtech - hrtech - legaltech 80%+ were building ai assistants. 50% of them had already built some form of generative ui into their assistants. most weren’t happy with what they built — and many were excited to see how we had solved those problems (and more). the other half hadn’t built it yet, but were pumped by how simple we made it to get started. we are accelerating the future of gen UI. thanks @michael_milst, @heyavi_, @alecf for giving back to back demos for hours straight :)

The real AI unlock was realizing we could give AI Agents access to all the tools and data relevant to particular work. The main thing that matters then is having models be insanely good at reasoning and many general skills, and then add the right context at inference time.