Agents
It's felt like harness month on Twitter. We're seeing much faster and cheaper gains on a bunch of benchmarks by focusing on harness improvem…
It's felt like harness month on Twitter. We're seeing much faster and cheaper gains on a bunch of benchmarks by focusing on harness improvement rather than model improvement. Allowing the harness to b
It's felt like harness month on Twitter. We're seeing much faster and cheaper gains on a bunch of benchmarks by focusing on harness improvement rather than model improvement. Allowing the harness to be updated during use is just another form of continual learning. The common issue with continual learning is catastrophic forgetting. But a lot of us have an 80/20 situation where most of our agent usage is in the same domain, so it's kind of wasteful to not specialize the harness for the environment it's being used in. I like the implicit bet the Exo project makes -- allowing specialization beyond just memories and skills may lower the floor, but I think it raises the ceiling more. Excited to have it in the next (not yet announced) @LaudeInstitute Slingshots batch. @AlexKrentsel is also an absurdly natural podcast guest. The motivations for the project are well explained on @swyx's recent Latent Space podcast: https://www.youtube.com/watch?v=5lFD-34dhqE Introducing Exo 🦋 – an open-source agent that can rewrite every part of itself. It learns new environments, adapts policies, even optimize costs (and play games!!) We've been hacking away at this project with @martin_casado from @a16z and @ankrgyl from @braintrust and friends.
Related
- I think you should build your own harness too. Build on primitives. Models come and go.
- Amazing article. If you own the harness you own your memories. Else you get locked into an API model which keeps memory behind APIs and you …
- 'Agents = model + harness + context' is a much better mental model than treating the LLM as the entire product. The model provides capabilit…
Source: Swyx (X) | 2026-08-19