Frederic Lardinois writes that Harness field CTO Martin Reynolds is addressing the surge in pull requests caused by coding agents, which can increase new code volume from 1.5x to as much as 50x. To manage this "review bottleneck," Harness has launched a rebuilt Code Repository and an AI Code Review product designed specifically for high-frequency agent traffic rather than just human teams. The company's approach focuses on using a software delivery knowledge graph to provide reviewers with context quickly, helping them distinguish critical code changes from routine dependency updates.
- Coding agents can increase the volume of pull requests by 10x to 50x compared to traditional developer workflows.
- Harness rebuilt its repository service as an "AI-first" platform that is Kubernetes-based and runs across multiple clouds.
- The new AI Code Review tool integrates with existing GitHub repositories, allowing teams to use it without migrating their entire codebase.
- Coding agents can increase the volume of pull requests by 10x to 50x compared to traditional developer workflows.
- Harness rebuilt its repository service as an "AI-first" platform that is Kubernetes-based and runs across multiple clouds.
- The new AI Code Review tool integrates with existing GitHub repositories, allowing teams to use it without migrating their entire codebase.
Mashrul Haque writes about using git worktrees to run multiple LLM coding agents in parallel, each on its own branch and isolated directory, eliminating the bottleneck of sequential agent sessions. He demonstrates a 5-agent workflow with Claude Code on a .NET/Blazor project, reporting roughly 5x throughput gains over single-agent sequential work.
- Boris Cherny, Creator and Head of Claude Code at Anthropic, calls worktrees his number one productivity tip and runs 3'' 5 simultaneously
- Claude Code ships a built-in `--worktree` (`-w`) flag that auto-creates an isolated worktree per session
- Boris Cherny, Creator and Head of Claude Code at Anthropic, calls worktrees his number one productivity tip and runs 3'' 5 simultaneously
- Claude Code ships a built-in `--worktree` (`-w`) flag that auto-creates an isolated worktree per session
Open Code Review is an AI-powered CLI tool designed for automated, high-precision code reviews. Originally developed as Alibaba Group's internal assistant, the project uses a hybrid architecture that combines deterministic engineering with LLM agents to provide stable and accurate feedback. Unlike general-purpose agents, it employs smart file bundling and fine-grained rule matching to maintain context and prevent issues like position drift or incomplete coverage on large changesets.
Key features:
- AI-driven line-level review comments
- Hybrid architecture combining hard constraints with dynamic decision-making
- Support for various LLM endpoints including OpenAI and Anthropic
- Seamless integration with CI/CD pipelines and coding agents like Claude Code
- Customizable rule sets for specific project requirements
Key features:
- AI-driven line-level review comments
- Hybrid architecture combining hard constraints with dynamic decision-making
- Support for various LLM endpoints including OpenAI and Anthropic
- Seamless integration with CI/CD pipelines and coding agents like Claude Code
- Customizable rule sets for specific project requirements
A self-hosted, GitHub-compatible API server designed for agents, automation, and developer workflows. It allows existing GitHub clients to work with owned repositories by exposing REST v3, GraphQL v4, OAuth device flow, and Git Smart HTTP while utilizing real bare Git repositories and TiDB/MySQL-compatible storage for metadata.
CAID is a new multi-agent framework for software engineering tasks. It improves accuracy and speed by using a central planner, isolated workspaces for concurrent work, and test-based verification—inspired by human developer collaboration with tools like Git. Evaluations show CAID significantly outperforms single-agent approaches.
This article explains the concept of 'skills' in the context of language models, detailing how to create and use them to enhance model capabilities. It covers the file structure, YAML configuration, and integration of scripts for task automation, providing a practical guide for developers.
