Cck ClaudeCode file change tracking and auto Claude.md

Hacker News

Every Claude Code session starts fresh. You re-explain your project structure, build commands, and conventions. Every. Single. Time. I built cck to solve this. Two modes: *1. CLAUDE.md Generation* ```bash git clone https://github.com/takawasi/claude-context-keeper && cd claude-context-keeper && pip install . cck sync ``` Scans your codebase, generates CLAUDE.md. Claude reads it at session start. *2. Per-Turn Context Injection (the real power)* ```bash cck setup --cb-style cck watch --with-history & cck hook install --use-history ``` This tracks every file change in SQLite and injects recent changes on every turn: ``` [CCK] Recent changes: 15:23:45 ~ src/main.py 15:22:30 + src/utils/helper.py 15:20:12 ~ tests/test_main.py ``` Claude sees exactly what you just edited. No more "I just changed X" explanations. Built from 300+ Claude Code sessions. Zero AI calls, pure static analysis. GitHub: https://github.com/takawasi/claude-context-keeper

Visit product View on Hacker NewsDec 30, 2025

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, context, single · Missing: mac, agents, macos

81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

30%30% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

26%26% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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