gongy

Eric Zhang Verified account @ekzhang1 · 2d

I’m back at @modal

Working at @thinkymachines was really something. I’ll miss my coworkers, and a lot has happened since I joined last year - all sorts of ML and production systems, witnessing what goes into a model, throwing new kernels at training and inference, scaling research infra, making interaction models fast and good, wrangling RL dynamics.

It was also my second job, so perhaps a formative experience. When I joined, I discovered I was the youngest at the company (like 60 people) and felt pretty out-of-place, surrounded by ex-OpenAI/Meta/GDM/Mistral folks. Anyway, the company reinvented itself many times, and I think with the recent model releases we can see it’s on a good path.

Thanks to @cHHillee for originally recruiting me to Thinky and my boss and various coworkers (whom I won’t dox) for teaching me very much

I saw this sunset rainbow the other day in NYC, and it’s the only time aside from July 4 I’ve seen everyone on the streets stop and stare at the sky for a bit, together. It was brief. This won’t last forever.

Anyway, hope to have more for you soon… much to build! The Modal team is very strong. I’m glad to be here.

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Modal @modal · Oct 14

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 we’ve 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.

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Modal @modal · Oct 13

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.

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SOTA Wives Verified account @SOTAwives · Oct 11

WTF is a SOTA Wife?

SOTA Wives is a podcast exploring the unfiltered side of Silicon Valley.

We built it as an antidote to the performative polish of tech Twitter.

Brought to you by @MilksandMatcha x @vivianmshen.

Episode 1 goes live on Monday, 12th October at 9 AM PT on everywhere you get your podcasts 💙

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