Author: Visual Studio Code - Bewertung: 1x - Views:4
Every model you use runs on tensors. But what actually is a tensor, where does DOOM fit in, and why does it matter when you are trying to get Copilot to optimize your code? Anthony Shaw breaks down how machine learning models work under the hood and what knowing that changes about how you write prompts that actually get results.
To learn more, please check out these resources:
* https://aka.ms/VSCode/Learn
𝗦𝗽𝗲𝗮𝗸𝗲𝗿𝘀:
* Anthony Shaw
* Burke Holland
𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻:
This is one of many sessions from the Microsoft Build 2026 event. View even more sessions on-demand and learn about Microsoft Build at https://build.microsoft.com
LIVE165 | English (US)
Broadcast Stage
#MSBuild
Chapters:
0:00 - Exploring Model Example – Harrier Text Embedding Model
00:03:59 - Discussion on Onyx being Turing complete and capable of running complex logic
00:04:31 - Reference to Doom running on Windows since 1995
00:06:26 - Audience poll concludes option 2 (CPU emulator) is correct
00:08:43 - Discussion on Excel interpreting machine code
00:10:37 - Explanation of Nectron representing a RISC CPU and RAM as tensor
00:13:56 - Scaling out challenges due to Doom being single‑threaded
00:14:00 - Best practices for coaching AI agents – importance of benchmarking
00:16:44 - Session conclusion and closing remarks praising the learning experience