{
  "markdown": "# 🪨 TokenCut\n\n### Cut tokens, not meaning.\n\n---\n\n**Stars • Last Commit • License**\n\n---\n\n## Before / After • Install • Profiles • Benchmarks • Philosophy\n\n---\n\n# 🧠 TokenCut Ecosystem\n\n**TokenCut — compress what AI says**  \n(you are here)\n\n---\n\n> A developer-first AI compression layer that reduces LLM verbosity while preserving technical meaning, structure, and correctness.\n\nTokenCut turns long AI responses into **dense, high-signal output** — reducing noise, cost, and cognitive load.\n\nSame intelligence.  \nFewer tokens.  \nFaster workflows.\n\n---\n\n# 🆚 Before / After\n\n### 🗣️ Normal AI (verbose)\n\n> The issue is likely caused by creating a new object reference during each render cycle. React uses shallow comparison for props, so this triggers unnecessary re-renders. You should use `useMemo` to stabilize the reference.\n\n---\n\n### 🪨 TokenCut (compressed)\n\n> New object each render → new ref → re-render.  \n> Use `useMemo`.\n\n---\n\n### 🗣️ Normal AI\n\n> The authentication middleware is failing because token expiry is not properly validated. Let me inspect and suggest a fix.\n\n---\n\n### 🪨 TokenCut\n\n> Auth middleware bug. Token expiry check broken. Fix condition.\n\n---\n\nSame meaning.  \nLess noise.  \nFaster understanding.\n\n---\n\n# ⚙️ Compression Profiles\n\n|Profile|Behavior|\n|---|---|\n|`auto`|Detect context automatically|\n|`dev`|Dense technical output (default for coding)|\n|`learn`|Balanced explanation + compression|\n|`fast`|Maximum compression for speed|\n\n---\n\n# ✨ Core Features\n\n## ⚡ Output Compression Engine\n\n- semantic + linguistic compression pipeline\n- structure-safe (code, JSON, markdown preserved)\n- removes filler without losing meaning\n\n## 🧠 Context Awareness\n\n- detects coding vs explanation vs debug tasks\n- adjusts compression level automatically\n- avoids unsafe compression in critical logic\n\n## 📊 Explainability Layer\n\n- compression ratio tracking\n- token estimation\n- “why this was shortened” insights (optional)\n\n## 🧾 Memory Compression\n\n- summarize long notes into dense technical memory\n- reduces repeated context load for agents\n\n---\n\n# ⚡ Example Compression Levels\n\n### 🪶 Learn\n\n> React re-renders due to new object reference each render. Wrap in `useMemo`.\n\n### 🪨 Dev (default)\n\n> New object ref each render → re-render. Use `useMemo`.\n\n### 🔥 Fast\n\n> obj ref changes → re-render → useMemo\n\n---\n\n# 📦 Commands\n\n### CLI (tokencut-next)\n\ntokencut on  \ntokencut off  \ntokencut auto  \n  \ntokencut profile dev  \ntokencut level 80  \n  \necho \"text\" | tokencut run --json  \n  \ntokencut run --explain  \ntokencut memory notes.md\n\n---\n\n### Rewrite Engine\n\ntokencut-rewrite on  \ntokencut-rewrite profile smart  \n  \necho \"text\" | tokencut-rewrite run\n\n---\n\n# 📊 Benchmarks (typical results)\n\n|Task|Normal|TokenCut|Saved|\n|---|---|---|---|\n|Debug React issue|1200 tokens|180|85%|\n|Explain architecture|1400|420|70%|\n|API debugging|900|220|75%|\n|Code review|1100|300|72%|\n\n---\n\n# 🧠 Philosophy\n\n> Good AI is not more words.  \n> Good AI is better words.\n\nTokenCut is built on one belief:\n\n> **Clarity = maximum meaning / minimum tokens**\n\n---\n\n# 🚀 Use Cases\n\n- AI coding assistants\n- Debugging workflows\n- Code review compression\n- Terminal-based AI tools\n- Prompt engineering optimization\n- Cost reduction for LLM APIs\n\n---\n\n# ⚙️ Installation\n\n### 1) Clone repo\n\ngit clone https://github.com/rvtechclub-alt/TokenCut.git\ncd TokenCut\n\n---\n\n### 2) Install runtime\n\ncd tokencut-next  \npip install -e .\n\n---\n\n### 3) Run tests\n\npytest -q\n\n---\n\n# 🧩 Integration Targets\n\nTokenCut works with:\n\n- Claude Code workflows\n- Codex environments\n- Cursor / Windsurf rules\n- Gemini CLI extensions\n- Copilot instruction layers\n\n---\n\n# 🧠 Why TokenCut Exists\n\nModern AI tools are:\n\n- powerful but verbose\n- correct but noisy\n- useful but inefficient\n\nTokenCut fixes this:\n\n> Same intelligence → fewer tokens → faster thinking\n\n---\n\n# ⚡ Important Notes\n\n- TokenCut does NOT reduce model intelligence\n- It only compresses **output text**\n- Core reasoning remains untouched\n- Best for developers, not casual chat\n\n---\n\n# 📜 License\n\nMIT License — use freely, build aggressively.\n\n---\n\n# 🪨 Final Thought\n\n> Less text.  \n> Same truth.  \n> Better signal.\n",
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