How Project HydraFusion reduces the cost of frontier AI

Everyone's racing to build a better model. HydraFusion optimizes the path instead, orchestrating existing models per request rather than picking just one, and reduces the cost frontier AI. Join Aashna Garg and Julia Kasper to learn how it works, and the ceiling we hit that forced the shift.

— RESOURCES —

Learn more about HydraFusion https://gh.io/ghcpdayhydrafusion
Get hands-on with the Copilot app https://gh.io/ghcpdaycopilotapp
Install the GitHub Copilot CLI https://gh.io/ghcpdaycopilotcli

— CHAPTERS —

0:00 Intro: meet Julia and Aashna
0:52 Finding Hydra Fusion in the Copilot CLI
1:35 What is Hydra Fusion?
2:01 Why model routing isn't enough
2:52 The router's two jobs
3:19 Selection has a ceiling, composition doesn't
4:04 The three workflows explained
4:56 Why the critic reviews but never edits
5:50 How two models still cost less
6:15 Demo: side-by-side cost comparison
7:31 Cascade workflow on a failing test
8:19 Critic workflow on auth changes
8:46 Session savings: 42% and 47%
9:37 How Hydra Fusion was evaluated
10:53 Why we built Checkpoint Bench
12:48 Try it and send feedback
13:38 Final word: stop being the router

#HydraFusion #GitHubCopilot #GitHub

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