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Joined February 2013
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#GitHubUniverse kicks off next week, October 28–29.
Grab your pass to join us in person or sign up to tune in virtually.
➡️msft.it/6013aappD
ALT A promotional image for GitHub Universe 26, featuring colorful graphic elements and a cartoon cat logo. Text includes 'Experience GitHub Universe' and event details: 'Fort Mason S F, C A, Oct 28-29, 2026.'
You have an idea. Now take it beyond the scoreline.
Build something that changes how football is understood, told, or experienced with AI.
Knowing when to use AI and when to question its output is part of learning to build.
Watch @shanselman, software engineer Julia Gao, and CMU’s Dr. Michael Hilton discuss PRAISE and how to support the next generation of developers.
youtube.com/watch?v=BglzGRnH…
Classification is a key use case for decision models.
Check out how accurately and quickly Microsoft-Decision-1 can categorize a variety of queries compared to GPT-6 Sol.
Microsoft-Decision-1 achieved the highest accuracy in our 36-benchmark comparison, spanning nearly 150,000 questions across benchmarks kept blind from training.
The challenge starts now.
Build an AI-powered solution that turns synthetic football data into match intelligence, narratives, recaps, or personalized fan experiences.
Register by Oct. 20.
Submit by Oct. 27: msft.it/6018aUKYq
ALT A promotional graphic for a Developer Hackathon titled 'Inside the Game:' featuring logos of Premier League and Microsoft. The background has a geometric design and text states, 'SUBMISSION WINDOW IS OPEN.'
Today, we announced the next step in the Project HydraFusion vision: local models and intelligent routing for GitHub Copilot on Windows.
@cinnamon_msft shows what this can mean for your everyday coding workflows.
Grounding ties extracted values to source evidence. Confidence scores flag uncertainty. A developer-defined JSON schema gives your app structured output.
Together, they help you validate results or route them for review before your app or agent acts.
In Azure Content Understanding, OCR and layout models preserve text, tables and reading order in Markdown before an LLM extracts fields.
The perception layer reads the page. The LLM reasons over that representation.
