Linden Li

@lindensli

Co-Founder @appliedcompute. Previously scaling @OpenAI, @DbrxMosaicAI, @NVIDIA

Stanford, CA

linden-li.github.io

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Linden Li
@lindensli
Apr 8
We’ve been heads down on our mission to build Specific Intelligence for the enterprise. Today we’re announcing $80M in new funding to help us get there even faster. We’re hiring across engineering and research and working to deploy AI systems that improve the more you use them.
Applied Compute
@appliedcompute
Apr 8
Article
Applied Compute Raises $80M to Help Enterprises Advance from Generalized to Specific Intelligence
Models keep getting smarter, but there's a massive gap between raw intelligence and actual productivity on specific tasks inside companies. Delivering real value requires knowing how to perform those...
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Linden Li
@lindensli
Sep 4
@turbopuffer works great when you train agents how to use it. You can find all sorts of easter eggs in seconds in the ~3k repos indexed. codesearch.appliedcompute.com
Applied Compute
@appliedcompute
Sep 4
00:14
A 35B open-weight model trained to search a precomputed index answers repo search questions at 100x lower cost than a frontier model. We partnered with @turbopuffer to train Qwen3.6-35B-A3B to find code across ~9,000 repositories. It tops the needle-in-a-haystack task outright
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Linden Li
@lindensli
Aug 28
Before you can do the more conventional big model optimizations, you have to make sure that you don't get bottlenecked by data movement, storage, and checkpoints first. Try it in AC2 private beta.
Applied Compute
@appliedcompute
Aug 28
Kimi K3 full fine-tuning is live on AC2. Our memory optimizations reduced GPUs required per training replica by ~40%. At nearly 3T parameters, Kimi forced us to rethink how we manage memory, communication, rollouts, and checkpoints. The result is a much more efficient path to
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Linden Li
@lindensli
Aug 25
We want frontier-level model customization to be something anyone can do. We're releasing the infrastructure and tooling that our research team has come to love more broadly. At the start, we focused on building reliable and robust research infrastructure. After training a bunch
Applied Compute
@appliedcompute
Aug 25
Today, we're introducing AC2, the Applied Compute Agent Cloud, to enable every team to train, serve, and improve their own frontier models.
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Linden Li
@lindensli
Aug 25
Important, very much needed update to inference performance benchmarks from @SemiAnalysis_ ! Fixed token-in, token-out is a very different inference workload from what we observe in practice (both for RL training and production inference). The inference optimization story
SemiAnalysis
@SemiAnalysis_
Aug 25
Introducing AgentX: InferenceXs new agentic inference performance benchmark. (1/2)🧵
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