I have a big career announcement:
I’m taking my experience teaching computer science to hundreds of millions and connecting it to my lifelong love of piano.
Announcing Payam Music: the first nationwide piano school, with a new way of teaching—the Payam Method—endorsed by Hans Zimmer and showcased on 60 MINUTES and USA TODAY.
With Payam Music, students learn faster, they outperform traditional methods, and they even learn to write their own music. Every year our students rank nationally for their composition and creativity.
If you’re worried about kids’ obsession with screens and social media, the solution is to give them a new obsession: piano. ❤️🎹❤️
Proven over 10 years, the learning outcomes of the Payam Method are extraordinary, and so is the team behind it. Besides Hans Zimmer, we’re announcing the support of iconic business leaders including Mark Cuban, Dara Khosrowshahi, Michelle Zatlyn, Drew Houston, and many others.
Payam Music is available in cities around the US and expanding rapidly. Our schools teach 1-on-1 lessons, in person and even online. We have limited spots, so if you or your child want to learn piano, sign up now! And if we don’t have a school near you, join our wait list, we’re growing fast.
Fall in Love with Piano
https://t.co/HeAhbLIldn
Together with UC Berkeley we are announcing the laser phase plate - a breakthrough in atomic resolution imaging. This is the brightest continuous wave laser in the world, 100 million times the intensity of the surface of the sun.
Phase contrast plays an important role in microscopy, but it was thought close to impossible for electron microscopy, where it would require interfering with an electron beam. Holger Mueller and Robert Glaeser proposed exactly this using a standing wave laser. It has taken over 15 years to make this a reality. Biohub partnered with UC Berkeley and Mueller to support this work and to engineer and build the technology.
Contrast has been the critical barrier to achieving atomic resolution imaging of the cell. In cryo-electron tomography, a cellular imaging technology that uses electron microscopy, the low contrast makes it impossible to resolve anything but the largest proteins within their cellular context. The laser phase plate removes that barrier.
With advances in AI this breakthrough in contrast will start to open up a new frontier in structural biology, that will allow us to see the molecular machines of the cell, and how they assemble into far more complex and dynamic systems, and understand how they work.
Neo Scholar applications close in 3 days! This is what Neo members have to say about what our community means to them 💙
If you're a college student who shares our love for building, apply by Sunday 6/14 at https://t.co/hDcfXz7tvn
Services are the future. Today we launched Ramp’s AI services motion.
It's easy to buy an AI subscription. It's hard to transform your company to actually run on agents.
Here’s our entire strategy.
1) Why now
Services are the new software (Sequoia)
Human labor TAM >> software license TAM. The market is bearish on seats and subscriptions.
Every enterprise AI company is doing this -- the labs have poured billions into services partnerships and their own deployment functions. Superintelligent models alone are not enough.
Palantir proved this is a strong business model: deeply embed engineers, build on top of a powerful platform, and customize extensively.
2) The real problem
Companies want AI. But the gap between "we have AI tools" and "agents run our workflows and we spend way less time" is enormous.
What we've found across over 50 companies we engaged with: agents start replacing real work when there is: complete data, read/write access across systems, agent-friendly policies. Most big companies struggle because:
- processes live in operators' heads
- dozens of disconnected systems (legacy ERPs, endless one-off excel sheets, etc.)
- archaic software with poor or no API access
Good data in the right place is a hard prereq to working agents.
Also, vibing in localhost ≠ a production system your enterprise can rely on. You still need hosting, ci/cd, observability, feedback loops, good interfaces. And taste to know what's even worth automating.
Everyone has a bulldozer, but most jobs just need a shovel pointed at the right spot.
What companies usually need is to be made agent-friendly. That's exactly what we do.
3) What we do
We focus on what Ramp does best -- finance.
And we embed FDEs that:
-> understand your problems
-> identify high-leverage, high-impact workflows that fit agents
-> scope the solution
-> connect your data
-> capture your context
-> deploy agents and often bespoke software for humans to collaborate with them
-> drive the business metrics that matter
Discovery and scoping are crucial. Building is easier than ever and thus judgement about what to build is more important than ever.
We're not a generic AI services arm, we're finance domain experts. Across the spectrum of financial operations, we help companies find and frame the problems worth automating -- similar to the taste a founder has in choosing which problems are worth solving (ex-founders make great FDEs).
Here’s the stack we deliver:
- Production infrastructure. Shipping an index.html from Claude isn't the same as creating a repo, hosting in a cloud service, ci/cd, testing, setting up evals, managing memories and skills, adding feedback loops, ensuring uptime, incident management, etc. Agents don't one-shot production systems yet. Production software is hard -- we build, host, and run it for you in a single-tenant, dedicated cloud environment. Most operators don’t have the time, knowledge, or experience to do this e2e. We help abstract the low-leverage plumbing so they can focus on the essential parts of their jobs.
- Data connectivity. Most enterprises have data lakes, but data is often incorrect, stale, or entirely missing. And write interfaces vary dramatically. Ideally we can use MCPs or CLIs, but usually it’s poorly documented APIs, SFTP, manual uploads, and email.
- A context layer. Things people have done for years aren't written down, so an agent can't do them until we capture that context -- ranging from simple policies to complex decisions. This usually involves creating policy documents, shared agent memories, and skills.
- Evals and feedback loops. How you know an agent is doing a good job, and how it improves over time.
4) Why Ramp AI Solutions
We focus on finance because it’s the vertical we know deeply, have structural advantages, and are most differentiated:
- Data. 70k+ customers use our core product, over $200B in annual payments, years of vendor data, millions of transactions and bills monthly.
- Money-movement primitives and partnerships. Global money movement rails, partnerships with banks, Visa, Stripe, etc. You don’t want to vibecode international wires for bill payments.
- An intelligence layer on top: fraud detection from hundreds of millions of expenses, PO-to-invoice matching, state-of-the-art OCR, and fine-tuned models for accounting coding, spend routing, policy review, etc.
Unlike the labs, we’re not incentivized to sell tokens.
Ramp is an AI fiduciary and an impartial broker to deliver AI that is:
- model-agnostic -- we benchmark all the leading models (labs, open source) and fit the right one to each task
- and token-efficient by design
Our main incentive is business outcomes -- which is Ramp’s mission, to save our customers time and money.
I’m extremely bullish about our motion, and the broad industry growth of AI-native services.
If you're a finance leader trying to be more agent-native,
If you’re interested in joining our FDE team,
I’d love to talk 🙂
Every student will live and work in a world shaped by AI. Our Founder and Chairman of the Board @hadip on what that means for education — and why understanding how it works is just the beginning.
Full panel from @FortuneMagazine#BrainstormTech w/@tadeoyerinde@karinklein: https://t.co/Pwj984RX60
"Looking to the future, every student needs to understand: how does Al work. So it's not just like this magic machine. Understanding the math and the statistics behind it, the training, the ways it can make mistakes, and then learning how to harness Al to create new technology to make us all creators, and then lastly to learn how to use it responsibly." -@hadip
Last week I was so proud to see Forbes recognize my twin brother @apartovi and @Neo as the #2 seed investor on its global “Midas” List.
And this was *before* Cursor and Kalshi’s current level of success. Next year #1. 🤞
https://t.co/Rdz02dQL5d
Imagine your own child learning to write music like this. It’s extraordinary.
At Payam Music we teach students more than playing piano, we teach them to be creative artists.
https://t.co/LD0tNNPFyO
The news continues to roll in about Payam Music! We have been inundated with interest from students and parents who want to learn piano in our schools, with waitlists in over 50 locations where we hope to open new schools. Learn more and sign up at https://t.co/HeAhbLIldn
https://t.co/GAy7vmD42w
Welp, that happened faster than I predicted. Thought it would be end of 2027, then early 2027, but agentic traffic growing so fast that bots have now passed human traffic online for the first time in the Internet's history. https://t.co/2zX5bHdhsa
https://t.co/ZGbUKOkXie is changing its name.
13 years ago, https://t.co/ZGbUKOkXie launched with a simple idea: every student should learn computer science — to learn how technology works and how to create it — not just how to use it.
After more than 2 billion hours of learning, the focus of CS has moved from coding to AI. As AI reshapes every part of daily life, students need digital fluency: the ability to understand AI, direct it, question it, and create with it — built on the foundations of computer science, AI science, and data science.
Today, https://t.co/ZGbUKOkXie enters its next chapter as CodeAI.
Our curriculum, teacher training, and frameworks, are there already: AI Discoveries and AI Foundations are free and in classrooms now. The K-12 digital sciences pathway is expanding.
Our goal is a generation with agency over the systems shaping their lives — prepared to shape the work, civic life, relationships, and meaning that come after.
Welcome to CodeAI.
Rockets are hard. Flying robots are harder.
Sensing, compute, and software stack that parallels a self-driving car, but three orders of magnitude smaller in size and weight. Plus the full gamut of aerospace complexity spanning aerodynamics, thermals, vibration. Very high flight volumes, low and close to the ground, in and around obstacles. And you have to fight for every gram.
1/ We’ve raised over $1B at a $26B valuation, led by @Lux_Capital, @generalcatalyst, and @8vc.
Our enterprise usage has grown >10x since the start of this year, and our run-rate revenue grew to $492 M.
We launched Devin two years ago as the first AI software engineer. Since then, cloud agents have gone from niche to mainstream, and today they are the fastest growing way to create software.