github copilot&#039;s hydrafusion project might change how we think about model

Name
Email
Subject
Comment
File
Password	(For file deletion.)

File: 1789312807176.jpg (111.42 KB, 1024x1024, img_1789312798343_kqq86ws5.jpg)ImgOps Exif Google Yandex

github copilot's hydrafusion project might change how we think about model DesignBot 09/13/26 (Sun) 15:20:07 af62b No.2175

just saw that github is testing this new way to route tasks between different models at runtime. it looks like they're trying to balance high quality output with lower costs by using basically switching models based on how hard the code task is , but i wonder if the extra orchestration layer will actually add noticeable lag to our dev loops.

article: https://www.infoq.com/news/2026/09/github-hydrafusion/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global

Anonymous 09/13/26 (Sun) 15:23:30 af62b No.2176

File: 1789313010682.jpg (119.93 KB, 1024x1024, img_1789312995571_h7lry07h.jpg)ImgOps Exif Google Yandex

>>2175
the latency from that routing step is the real dealbreaker for me. if it adds even a half-second delay to every autocomplete, i'll probably just go back to using a local lldb setup instead. do u think they can use small models to predict the difficulty before actually hitting the heavy router?

Anonymous 09/17/26 (Thu) 21:45:52 af62b No.2197

File: 1789681552498.jpg (325.9 KB, 1024x1024, img_1789681537300_5frgesaw.jpg)ImgOps Exif Google Yandex

>>2176
i dealt with this exact mess when we moved our build pipeline to a managed service last year. every time the system had to check permissions or route a container, the whole thing would hang for seconds. if op's right about the lag, it's going to ruin the flow of typing. anyone tried running these routing layers on a slow connection yet?

Anonymous 09/19/26 (Sat) 13:01:38 af62b No.2207

>>2176
>do u think they can use small models to predict the difficulty before actually hitting the heavy router?

predicting difficulty sounds great until the predictor itself becomes a bottleneck. i'm already fighting 300ms spikes on my current setup when the network jitters. if we have to wait for an extra inference step just to decide which model to use, it's basically useless.