Bryan Lee

daily 1%. building @appliedcompute. prev Sentinel (YC), @twosigma, @harvard

[Applied Compute @appliedcompute]

We partnered with @harvey to post-train the state-of-the-art legal agent on their LAB benchmark. It surpasses Opus 4.8 Max and GPT-5.5 xhigh.

[Sahar Zadeh @sahar__zadeh]

https://t.co/45RwdiEWRa

[Applied Compute @appliedcompute]

@nvidia’s Nemotron 3 Ultra handles software-engineering tasks at a fraction of the per-task cost of frontier models. So we trained a router to send each coding task to the cheapest model that can successfully solve it, cutting inference cost while holding frontier-level quality.

The result: GPT-5.5-level pass rates on held-out SWE-bench Verified at ~25% lower cost. The oracle policy sends 72% of tasks to Nemotron 3 Ultra, and the trained router captures most of that.

[Applied Compute @appliedcompute]

Enterprise AI deployments today are frozen in time. Model capabilities stagnate in production. The problem compounds because companies aren’t static either. Every time your company improves, the model falls further behind.

The bottleneck is continual learning. How does a model do something once and improve from feedback?

The future of enterprise AI is Specific Intelligence: custom models teams own, trained on a company’s choices, interaction by interaction, using internal knowledge general models cannot access.

Applied Compute helps companies train, serve, own, and improve custom models. Thanks @apoorv03 for having @ypatil125 at MS&E 435 to talk about the future of model training.

[Bryan Lee @_brylee10]

at AC i’ve learned forward deployed work is among my favorite. a personal favorite memory was getting a high five from a customer after a day in the office and a successful prod deployment.

closely collaborating with companies and diving into the nitty-gritty of their systems to make agents work is challenging but rewarding.

it’s “full stack” in the sense it involves a eng, research, and understanding customer needs which makes each day different and gets me excited.

[Applied Compute @appliedcompute]

There is a large delta between what models can do and what they deliver in company-specific workflows. We bridge that gap through forward deployment.

In a given week, our engineers might build eval frameworks from scratch, deploy a large-scale context ingestion engine, and present results to F500 leadership. We fine-tune models on proprietary data no frontier lab has seen and optimize agent performance against real-world outcomes.

We're excited by engineers with rigor, high customer empathy, and a bias toward action in ambiguity.

https://t.co/cpdSLIFEot

[Applied Compute @appliedcompute]

https://t.co/EKdpo225SO

[Applied Compute @appliedcompute]

We partnered with @DoorDash to train a proprietary RL-powered agent that encodes internal QA standards into an automated grader, turning expert judgment into a scalable training signal. The result: a 30% relative reduction in critical menu errors and a production system now live across all US menu traffic.
https://t.co/rZFbMmdDiW

[Bryan Lee @_brylee10]

Ready to ship

[Applied Compute @appliedcompute]

Generalists are useful, but it’s not enough to be smart.

Advances come from specialists, whether human or machine.

To have an edge, agents need specific expertise, within specific companies, built on models trained on specific data.

We call this Specific Intelligence.

It's what we're building at Applied Compute.

We unlock the latent knowledge inside a company, use it to train custom models, and deploy an in-house agent workforce that reports to your team.

We work with sophisticated companies that have already captured early gains from general models, like @cognition, @DoorDash, and @mercor_ai. They’re pulling even further ahead with proprietary in-house agents that don’t need to wait for the next public model release.

Together, we are building and validating models and agents in days instead of months, achieving state-of-the-art performance on customer evals.

Our team has high density and low latency. Our founders all worked on different parts of this problem while they were researchers at OpenAI — @ypatil125 as a key member on the agentic software engineer effort (Codex), @rhythmrg as a core contributor to the first RL-trained reasoning model (o1), and @lindensli as a core contributor on ML systems and infrastructure for RL training.

Two-thirds of the team are former founders, and everyone brings a deep technical background, from top AI researchers to Math Olympiad winners.

We are backed by $80M in funding from Benchmark, Sequoia, Lux, Elad Gil, Victor Lazarte, Omri Casspi, and others. With their support, we are growing the team, scaling deployments, and bringing to market the first generation of agent workforces built on specific models.

In short:

1. We are building Specific Intelligence for specific work at specific companies.
2. That will power in-house agent workforces to support their human bosses.
3. That in turn will unlock AI’s full potential through humanity’s greatest engine of progress: thriving corporations in a free market.

[Bryan Lee @_brylee10]

@appliedcompute ready to ship

[Bryan Lee @_brylee10]

@uzpg_ can attest the product is super cool

[Bryan Lee @_brylee10]

@ycombinator congrats on the launch!

[Bryan Lee @_brylee10]

@ycombinator @trace_so @timcherkasov @K1ngArtes congrats on launch!

[Bryan Lee @_brylee10]

the scale on this gpt-5 graph is puzzling, 50 < 47?

[Bryan Lee @_brylee10]

@ycombinator @aaronchewbani @kavandoc congrats!

[Bryan Lee @_brylee10]

one thing @AnthropicAI doesn't have: https://t.co/gm1bpSf2OT

[Bryan Lee @_brylee10]

just paid for comet. never thought I'd pay for a browser

[Bryan Lee @_brylee10]

@treeeckob is the model interpretable

[Bryan Lee @_brylee10]

@ycombinator @CyberdeskHQ Congrats on launch, 🍩 king

[Bryan Lee @_brylee10]

@adisingh I love this

[Bryan Lee @_brylee10]

@dadiomov a trying period in history