Stop Wasting AI Coding Tokens: Choose the Right Tool

Your AI coding agent burns through context fast — but "token waste" isn't one problem, it's five. This video breaks down RTK, Serena, codebase-memory-mcp, Context Mode, Headroom, Repomix, Code2Prompt, Gitingest, chop, and squeez, and shows you how to pick the smallest stack that actually fixes your specific bottleneck.

Timestamps:

• 0:00 Hook — token waste isn't one thing
• 0:30 The five categories of context waste
• 1:28 The default stack: RTK + one structural tool
• 2:20 Shell-output compression: RTK vs chop vs squeez
• 3:16 Structural tools: Serena vs codebase-memory-mcp
• 4:29 Large tool-output: Context Mode vs Headroom
• 5:26 One-shot context packaging
• 6:10 Decision tree and recipes
• 7:16 Measure it yourself + close

Tool repositories mentioned in this video:

• RTK: https://github.com/rtk-ai/rtk
• Serena: https://github.com/oraios/serena
• codebase-memory-mcp: https://github.com/DeusData/codebase-memory-mcp
• Context Mode: https://github.com/mksglu/context-mode
• Headroom: https://github.com/headroomlabs-ai/headroom
• Graft: https://github.com/trailhq/Graft
• Graphify: https://github.com/Graphify-Labs/graphify
• GitNexus: https://github.com/abhigyanpatwari/GitNexus
• CodeGraph: https://github.com/colbymchenry/codegraph
• Code Context Engine: https://github.com/elara-labs/code-context-engine
• Repomix: https://github.com/yamadashy/repomix
• Code2Prompt: https://github.com/mufeedvh/code2prompt
• Gitingest: https://github.com/coderamp-labs/gitingest
• chop: https://github.com/AgusRdz/chop
• squeez: https://github.com/claudioemmanuel/squeez

This video is an independent comparison, not an official or sponsored release from any of the tools above. Install commands and repository names change — re-verify against the upstream repo before installing, especially for the newer tools (Context Mode, squeez, Code Context Engine).

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