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In-depth articles and tutorials on leveraging LLMs, including natural language processing, code generation, and data analysis, with insights into training, fine-tuning, and deploying LLMs. Curious how to get started? Check out our guide on architecting LLM-powered applications .
A new repository-level dataset, published on GitHub under CC0-1.0, helps researchers and developers discover multilingual developer content across READMEs, issues, and pull requests.
Alerts are more trustworthy and actionable when noise is reduced. See how we improved the verification step with context-aware LLM reasoning.
Agentic workflows that run on every pull request can quietly accumulate large API bills. Here’s how we instrumented our own production workflows, found the inefficiencies, and built agents to fix them.
How to build the “Trust Layer” for GitHub Copilot cloud agent without brittle scripts or black-box judgements by using dominatory analysis.
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AI is settling the “typed vs. untyped” debate by turning type systems into the safety net for code you didn’t write yourself.
How using GitHub’s free inference API can make your AI-powered open source software more accessible.
Everyone’s talking about MCP these days when it comes to large language models (LLMs)—here’s what you need to know.
We released a new open source byte-pair tokenizer that is faster and more flexible than popular alternatives.
Unstructured data holds valuable information about codebases, organizational best practices, and customer feedback. Here are some ways you can leverage it with RAG, or retrieval-augmented generation.
Here’s how SAST tools combine generative AI with code scanning to help you deliver features faster and keep vulnerabilities out of code.
