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#GitHub

All collected and published items, ordered by time. Picks are a selected subset.
First-party

Direct announcements from model vendors, companies and research institutions on their official sites, blogs and accounts.

10/14Wed
  1. GitHub Blog · AI & ML68

    GitHub’s model for secret detection: Finding more secrets with less noise

    GitHub explains how its secret scanning pipeline combines regex candidate detection with a fine-tuned ModernBERT classifier to distinguish real credentials from lookalikes using surrounding context. Compared with the previous LLM pipeline, offline evaluation showed 13% higher precision and 19% higher recall; a customer A/B experiment showed 11.5% higher repository owner-weighted acceptance. Median post-push scan execution time fell 86%, and serving costs dropped more than 98%, making it practical to extend generic secret detection to push protection.

    Why it matters: GitHub’s pipeline illustrates a transferable design pattern for narrow classification tasks, combining deterministic candidate generation with a compact contextual model trained on realistic hard negatives.

10/13Tue
  1. GitHub Blog · AI & ML49

    How to Teach GitHub Copilot About Your Codebase

    GitHub’s beginner guide explains four ways to tailor Copilot to a project: custom instructions for repository conventions, agent skills for repeatable procedures, custom agents for broader specialized tasks, and MCP servers for connecting to external tools and data. It recommends starting with a convention you often explain and adding it to `.github/copilot-instructions.md`.

10/8Thu
  1. GitHub Blog · AI & ML60

    GitHub launches ModernBERT-based secret detection model and expands push protection

    GitHub released a ModernBERT classifier fine-tuned with Microsoft Applied Sciences that uses code context to identify secrets without a fixed format. It can assess a batch of candidate secrets in 2 milliseconds and is said to more than double the number it can block.

    Why it matters: GitHub uses nine quarters of data to show the imbalance between secret leaks and manual remediation, and describes a model that moves detection earlier to the push stage.

10/6Tue
10/2Fri
  1. GitHub Blog · AI & ML22

    AI is changing how developers work; GitHub highlights three skills to strengthen

    GitHub says developers need to strengthen three skills in the AI era: learn to direct AI agents rather than merely use them, do not trust an AI's first answer, and use time saved with AI to tackle bigger problems. It points to GitHub Copilot's built-in Rubber Duck agent, which uses a second model to critically review plans, code, and tests and catch issues the first model missed.

9/26Sat
9/25Fri
  1. GitHub Blog · AI & ML38

    GitHub Copilot's canvas: when chat is no longer the right AI interface

    The GitHub Copilot app has introduced canvas, a full-stack mini-app that runs inside the app without a browser shell, can communicate in both directions with the Copilot agent, and can call third-party APIs or run code locally. The company argues that chat is a general-purpose fallback, while a canvas can be a better fit when users have a specific task. For example, a SQLite canvas can operate on a database directly instead of having an agent repeatedly spend tokens on actions such as “stage and commit.”

9/24Thu
  1. GitHub Blog · AI & ML22

    How the GitHub Copilot app renders enormous pull requests

    The GitHub Copilot app rebuilt its pull-request view to render huge PRs smoothly, including one with 2,200 files, more than one million changed lines, and over 400 inline comments. It separates document height into deterministic code geometry and dynamic-block geometry: code line heights are calculated precisely in advance, while dynamic blocks such as comments are anchored by stable keys to a file, line, and side, measured lazily, and adjusted only slightly to avoid scroll jumps.

9/17Thu
  1. GitHub Blog · AI & ML78

    GitHub uses Copilot to rewrite the Copilot runtime in Rust

    Using the GitHub Copilot app and Copilot CLI, GitHub completely rewrote the Copilot agent runtime from TypeScript into more than 800,000 lines of production Rust. AI agents wrote most of the code, merged incrementally into main across 128 pull requests rather than in a single cutover.

    Why it matters: Drawing on firsthand experience, the author reviews the 800,000-line Rust rewrite and shares agent-orchestration and review practices that can be reused.

9/12Sat
9/11Fri
  1. GitHub Blog · AI & ML38

    Getting started with the GitHub Copilot app: using the diff, terminal, and browser panels

    The GitHub Copilot app includes diff, terminal, and browser panels, letting users review, run, and preview AI-agent code changes without leaving the app. The diff panel marks additions in green and deletions in red, and supports accepting changes, commenting, or asking Copilot to revise them. The terminal can run project commands directly and supports switching between multiple windows.

9/5Sat
  1. GitHub Blog · AI & ML71

    GitHub previews Project HydraFusion for multi-model orchestration

    GitHub has released a research preview of Project HydraFusion, enabled in GitHub Copilot CLI with `/experimental` and selectable through `/model`. Usage is billed per token at each model's standard rate.

    Why it matters: GitHub reports quality and cost comparisons across three orchestration modes and three benchmarks, helping readers weigh multi-model orchestration for coding tasks.

9/4Fri
  1. GitHub Blog · AI & ML38

    Getting started with the GitHub Copilot app: running multiple agents at once

    The GitHub Copilot app supports multiple agent sessions at once, each in a separate Git worktree with its own context and no interference from others. Users can switch between sessions and resume where they left off. The sessions view shows each session's title and progress. The tutorial uses the tailspin-toys repository to run three tasks at once: a funded sort, an accessibility review, and tests.

6/30Tue
  1. Google Research76

    Google releases TabFM, a zero-shot foundation model for tabular data

    Google has released TabFM, a zero-shot foundation model for classification and regression on tabular data. It recasts table prediction as an in-context-learning problem, producing predictions in a single forward pass without manual training, tuning, or feature engineering.

    Why it matters: The source details TabFM's architecture and TabArena results, helping readers assess whether zero-shot table prediction can replace traditional tree-model workflows.