Member of Technical Staff at @modal
San Francisco, CA
Joined September 2025
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π±π±π± this is obnoxiously cool
The result in mathematics concerns harmonic analysis.
Our models produced a proof of the local smoothing conjecture in every dimension and therefore also resolve the Kakeya conjecture and Steinβs restriction conjecture in all dimensions. The new proof is based on a novel auxiliary partial differential equation.
We obtained our AI output, and then we reached out to an expert to discuss forming a collaboration around this result. Following their recommendation, we decided to release the AI output publicly, following the same strategy for this result as our October 6th, 2026 release. We have released both an AI natural language output writeup produced by our models and a Lean proof in our public GitHub repository.
github.com/openai/math/blob/β¦
"The broader takeaway from these experiments is that if you keep your GPUs busy, for example by running several experiments at once, Spindle offers much better price-performance than closed-source APIs"
Tinker changed how people do post-training. We're big fans, and so are many of the teams we work with. But as those teams scale up, they keep raising the same two problems: per-token pricing gets expensive quickly, and youβre limited to the models and features the provider decides to support.
We think a good API deserves more than one implementation.
Today we're releasing Spindle, an open-source, Tinker-compatible training server that runs in your Modal workspace. Your existing Tinker scripts run unchanged, and because you own the server, you pay for GPU time instead of tokens.
π π π π π Raymond Tana retweeted
Tinker changed how people do post-training. We're big fans, and so are many of the teams we work with. But as those teams scale up, they keep raising the same two problems: per-token pricing gets expensive quickly, and youβre limited to the models and features the provider decides to support.
We think a good API deserves more than one implementation.
Today we're releasing Spindle, an open-source, Tinker-compatible training server that runs in your Modal workspace. Your existing Tinker scripts run unchanged, and because you own the server, you pay for GPU time instead of tokens.
Modal magic, now for training!
Performant reinforcement learning is a hard infrastructure and research problem. Weβve recently made on-demand multi-node clusters available and showed how to take advantageous of heterogeneous sampling compute. But there still exists the need for high-fidelity environments, clear and robust reward functions, and tuned training recipes that actually work.
Most teams are actually nearly there! With an agent harness deployed in production and evals/agent traces collected from that deployment, you just need an easy way to start training your data. So we built a training library that gives you the building blocks, tuned defaults, and observability you need for easy, production-grade LLM training.
Itβs available now. All it takes is a prompt.
π π π π π Raymond Tana retweeted
Performant reinforcement learning is a hard infrastructure and research problem. Weβve recently made on-demand multi-node clusters available and showed how to take advantageous of heterogeneous sampling compute. But there still exists the need for high-fidelity environments, clear and robust reward functions, and tuned training recipes that actually work.
Most teams are actually nearly there! With an agent harness deployed in production and evals/agent traces collected from that deployment, you just need an easy way to start training your data. So we built a training library that gives you the building blocks, tuned defaults, and observability you need for easy, production-grade LLM training.
Itβs available now. All it takes is a prompt.
π π π π π Raymond Tana retweeted
The secret to Modal's speed is our custom lazily loaded container filesystem, ImageFS. ImageFS is built on a massive tiered distributed filesystem, layering local NVMe disks, regional and p2p caches, and global blob stores.
When we launched VM Sandboxes this month, we wanted to make sure they could take advantage of ImageFS in the same way our gVisor containers do. To do that, we built out a new filesystem stack that tricks a VM's ext4 driver into thinking it's talking to a normal disk.
π π π π π Raymond Tana retweeted
Today we're announcing that Modal has crossed $1B in annualized run rate, as well as raised a $750M Series D at a $15.75B valuation, led by @Accel.
Since our Series C, usage has tripled and weβve grown more than 15x year-over-year.
We are deeply grateful to the developers and companies building on Modal. There's still so much more to build, and we can't think of a better time to be building software.
π π π π π Raymond Tana retweeted
Training large models with RL typically runs on a fixed homogeneous cluster of the latest accelerators for numerical stability and faster throughput. This throws away inference optimization work done for other vendors and generations, limits the capacity you can utilize, and is unnecessarily expensive.
So we trained a model on agentic coding tasks using fully asynchronous RL and mixed-precision, mixed-accelerator samplers. With off-policy correction, training stayed stable through convergence, on par with homogeneous samplers, at a lower cost.
Finally digitized notes on *algorithmic information theory* (raymondtana.github.io/ait-noβ¦) from my PhD dissertation... I think of them like the backdrop to what I really want to write about:
the connection between information theory <-> ML <-> computability theory ... more soon(ish!)
π π π π π Raymond Tana retweeted
Been incubating a new project at modal, called Latte. We just got it working end-to-end Friday.
Off the name alone, take a guess what it is.
365 days ago i moved to sf, and i thank myself every day for making the leap in this city of contradictions
Should be a good time! I'll talk about the time-travel filesystem @erikdunteman and I have been working on
Our second meetup in SF is next week and it's going to be sick:
@glcst from @tursodatabase
@haakamaujla from @agentmail
@pretermath from @modal
Join if you're around!
π π π π π Raymond Tana retweeted
Come join me today at the @databricks agentic AI night, where I'll be presenting my work on agent filesystems at Modal.
You'll see a live demo of our filesystem Overeasy (pray to demo gods π€) and our vision for durability on Modal backed by the almighty WAL-on-S3
durable (and rewindable) state is an obvious inevitability, but it's undergoing active development and requires feedback from the people who are going to most benefit from it
