Dev Patel
@devpatelio
@ucberkeley • @novaskyai • prev ai and npus @AMD • startups @healthenginecal
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🚀 Excited to release our new paper: “Barbarians at the Gate: How AI is Upending Systems Research” We show how AI-Driven Research for Systems (ADRS) can rediscover or outperform human-designed algorithms across cloud scheduling, MoE expert load balancing, LLM-SQL optimization,…
If you're interested in health-tech and AI, come by to our #SFTechWeek mixer w. @HealthEngineCal X @a16z at 1 Alumni House on UC Berkeley Campus! @Techweek_ partiful.com/e/iXX4LXhPrYPl…
The Tinker API recently released by Thinking Machines will have a big impact on how people think about post-training and inference systems. To allow more people to experiment with Tinker like systems and run it on their own hardware, we started SkyRL tx 🧸, an open source project…
Tinker support from @thinkymachines on SkyRL!
Introducing SkyRL tx 🧸, an open-source project to implement the Tinker API. The SkyRL team is excited about the Tinker API and the opportunities of using a single canonical interface that unifies training and inference. SkyRL tx lets you run a Tinker-like service locally today,…
.@UCBerkeley, @Cal_Engineer, @BerkeleyDataSci, @Berkeley_EECS, and @BerkeleySky have always been ahead of the curve. Think @OpenAI and @SkildAI. Now we’re teaming up with @BerkeleySky in their first-ever VC partnership. $500,000 founding contribution, plus access to the builders…
Source: Stripe is in talks to buy back shares from its VC backers at a $106.7B valuation; Sequoia bought $861M worth of shares in 2024 at a $70B valuation (@danprimack / Axios) axios.com/2025/09/23/str… techmeme.com/250923/p19#a25… 📫 Subscribe: techmeme.com/newsletter?fro…
📸 Captures from our Meraki UofT Kick-Off Social
Introducing our co-hosts for this season 🫶 (@krupaad @_richapandya @kennykgguo @achettimada @ambiguousNull)
so excited for this season !!!
Introducing our co-hosts for this season 🫶 (@krupaad @_richapandya @kennykgguo @achettimada @ambiguousNull)
a question we've gotten many times since the early days of verifiers was: "can i train on just 1 GPU?" and sadly, the answer was no... until now, thanks to the SkyRL integration from @tyler_griggs_ and team, which unlocks colocated training + inference :)
Scaling agentic simulations is hard, so in collaboration with @anyscalecompute we wrote up our experience using Ray for agent sims—featuring an end-to-end RL example with SkyRL! Check it out here: anyscale.com/blog/massively…
📈‼️✊🏾
We’ve raised over $400M at a $10.2B post-money valuation to advance the frontier of AI coding agents. The round was led by Founders Fund with other existing investors including Lux, 8VC, Neo, Elad Gil, Definition Capital, and Swish VC all doubling down. We’re also joined by new…
SkyRL x Environments Hub is live!! Train on any of 100+ environments natively on SkyRL today ➡️ github.com/NovaSky-AI/Sky… This was super fun to work on, the @PrimeIntellect team crushed it, go OSS!
Introducing the Environments Hub RL environments are the key bottleneck to the next wave of AI progress, but big labs are locking them down We built a community platform for crowdsourcing open environments, so anyone can contribute to open-source AGI
pretty awesome work!
I spent the past few months building JAXformer: One of the first open source guides on how to scale modern transformers in JAX. Trained entirely on TPUs, it supports distributed ML, Ray tokenization, MoE, n-D parallelism and end-to-end inference. Here’s how to do it:
Highly recommend poking through the Environments Hub, it's a lot of fun. Stay tuned for SkyRL integration... ✈️
Environments Hub launched a week ago, and we’ve already crowdsourced 100+ environments. Ranging from theorem proving, kernel generation, scientific qa, browser-use, and more. Every environment contributed shifts the balance of power towards open-source AI. Some highlights:
come throughhhhhhhhh
introducing tiny-tpu, a tiny TPU, supporting both training and inference, ENTIRELY on chip! here is an overview of how I got started and how this was built:
I spent my summer building TinyTPU : An open source ML inference and training chip. it can do end to end inference + training ENTIRELY on chip. here's how I did it👇:
i remade tiny-tpu to support both inference and training! we successfully tested our architecture on the classic XOR problem. here's what i learned throughout the process:👇
as the unemployed friend on a monday afternoon, i spent the past two months building a TPU without any prerequisite knowledge in digital logic, ASIC design, or verilog. here are my coolest unlocks so far:
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