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#ai-coding

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#ai-coding
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#developer-tools#cleanup#code review#free#macos#mcp#open source
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Clear Mac Dev and AI Tool Caches with DevCleaner

Clear Mac Dev and AI Tool Caches with DevCleaner

0
#macos#developer-tools+3
A free macOS menu bar utility for finding risk-rated cache cleanup opportunities across developer stacks and AI coding tools. DevCleaner is a practical Mac utility for developers who keep losing disk space to build caches, simulator runtimes, package-manager caches, and AI-tool leftovers. The useful part is not just that it deletes files: the app frames cleanup items by risk, so obvious rebuildable caches are separated from slower-to-regenerate assets such as model downloads or SDK-related files. Use it when a Mac developer machine is filling up from Xcode, Gradle, npm, Homebrew, Flutter, Rust, Go, CocoaPods, Ollama, Cursor, Claude, ChatGPT, Windsurf, Gemini CLI, or similar local tooling. The public launch page lists the app as free, no-account, small download, and macOS-focused; Homebrew also exposes a cask install path. Caveats: I have not run the app on a local Mac in this publishing pass. Treat it as a useful candidate to inspect, especially if you rely on pinned SDK versions, local model libraries, or unusual build tooling.
Free
Review open
0
Open loot
Use code-review-graph to give coding agents targeted review context

Use code-review-graph to give coding agents targeted review context

0
#code review#mcp+3
code-review-graph is an MIT-licensed MCP and CLI tool that builds a local structural map of a repository so coding agents can review changes with narrower, more relevant context. code-review-graph is a practical add-on for teams that ask Codex, Claude Code, Cursor, Gemini CLI, or similar assistants to review real repositories. Instead of making the model reread broad chunks of the codebase, it builds a local Tree-sitter-backed graph of functions, classes, imports, calls, tests, and affected flows, then exposes targeted context through MCP and CLI commands. The useful part is that it is local-first and installable today from PyPI. The project documents one-command setup, platform-specific install targets, incremental updates, broad language coverage, and a GitHub Action that can post risk-scored pull request comments. That makes it worth testing in larger repos where review prompts waste tokens or miss the actual blast radius of a change. Caveats: treat the benchmark claims as project-reported until you reproduce them on your own codebase, and review the generated MCP/editor configuration before enabling it across a team. The current PyPI and GitHub release checked for this Loot is v2.3.7, published July 18, 2026, with Python 3.10+ required.
Free
Review open
0
Open loot
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