Search Results for "code review"

A cryptographically verifiable code review system for the cargo (Rust) package manager. cargo-crev is an implementation of Crev as a command-line tool integrated with cargo. This tool helps Rust users evaluate the quality and trustworthiness of their package dependencies. Crev is a language and ecosystem agnostic, distributed code review system. Use reviews produced by other users.

LaReview is a developer-first, local-first code review workbench designed to transform complex pull requests or diffs into structured, high-signal review workflows powered by AI assistance. Instead of overwhelming developers with raw diffs or automated comment spam, the tool analyzes code changes and generates an intent-driven review plan that groups changes into logical flows such as authentication, API behavior, or data handling, and prioritizes them based on risk. ...

Snapshot plugin with rich features that can make pretty code snapshots for Neovim.

...The project includes macros for generating snapshots, reviewing changes, and storing inline snapshots inside source files. A companion cargo tool helps manage the review workflow, while a VS Code extension improves snapshot file editing and inspection. Insta is especially useful for command output, serializers, parsers, compilers, debug values, and tests where human review matters.

X For You Feed Algorithm is the open-sourced core recommendation system that powers the For You feed on X (the social network formerly known as Twitter), and it represents one of the first times a major social platform has published production-level ranking code for public review and experimentation. The repository contains the full pipeline that ingests user engagement and content candidate data, processes it through retrieval, hydration, filtering, scoring, and selection layers, and ultimately ranks posts to show what appears in a user’s feed. At its heart, the system uses a transformer-based model adapted from xAI’s Grok architecture to predict probabilities for various user actions (such as likes, replies, reposts, clicks, and negative signals), then combines those into a weighted final score that drives ranking.