Harnesses get dismissed as just scaffolding, just prompt engineering, and not real research. But that couldn’t be farther from the truth. The same model weights that score 30% on ARC-AGI score 95% with a better harness. So we gathered a group of researchers and founders working at the frontier to do a deep dive into the state of harnesses. We’ll cover how we got to this point, the case for making your harness as expressive as possible, and what YC learned building an agent for every employee in the company.
Chapters:
00:00 – Why harnesses matter
04:27 – Building an auto-researcher by accident
07:13 – A five minute history of harnesses
13:56 – Self-improving harnesses
17:22 – Tonight’s speakers
18:35 – Seth Karten: Prime Agent, a self-improving RLM harness
21:50 – Context as an L1, L2, L3 cache
24:51 – From Turing machine to von Neumann computer
28:33 – Messaging between agents
30:04 – ARC-AGI results
33:09 – Emulator Bench and GPU kernels
37:30 – Jon Saad-Falcon: OpenJarvis, personal AI on personal devices
38:26 – How far behind are local models
39:21 – The five primitives of a personal AI stack
42:47 – Letting cloud models optimize your local stack
43:53 – 800x cheaper than the cloud
45:58 – Josh France and Regan Bell: QM, YC’s agent harness for work
47:29 – A history of YC’s internal agents
49:24 – OpenClaw and a fleet of 50 agents
51:04 – Pulling the brain out of the sandbox
54:43 – Letting the agent choose its own sandbox and model
57:16 – The grind tool: budgets on goals
58:50 – Agents don’t understand social context
Apply to Y Combinator: https://www.ycombinator.com/apply
Work at a startup: https://www.ycombinator.com/jobs
