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Automatic context rotation for Claude Code (no manual steps)

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Automatic context rotation for Claude Code (no manual steps)

AI coding agents break when the context window fills up — they lose state, hallucinate, or auto-compact shreds the context you built up. I built a 3-hook pipeline that rotates before that happens, with a dry-run replay you can run locally (no LLM/API keys). Quick demo: - https://github.com/Vinix24/vnx-orchestration/tree/master/dem... How it works: ┌─────────────────┐ │ PreToolUse hook │── checks context % every tool call │ (≥65% → block) │ └────────┬────────┘ ▼ ┌─────────────────┐ │ Agent writes │── structured ROTATION-HANDOVER.md │ handover file │ (task state, files, progress, next steps) └────────┬────────┘ ▼ ┌─────────────────┐ │ PostToolUse │── detects handover → atomic lock │ launches rotator│ → vnx_rotate.sh (nohup, detached) └────────┬────────┘ ▼ ┌─────────────────┐ │ Rotator │── /clear via tmux → waits for SessionStart │ injects resume │ → pastes continuation prompt └─────────────────┘ Why 65%? It’s ~15 points before auto-compact (~80%), so there’s enough headroom to write a clean handover without racing compaction. I analyzed 5 projects attempting similar fixes — none have a full detect → handover → clear → resume → verify loop. Repo: https://github.com/Vinix24/vnx-orchestration Docs: https://github.com/Vinix24/vnx-orchestration/blob/master/doc...

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor
92%92% 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.
Hacker NewsMay not resonate with HN audience · Strong signals: pipe, io · Missing: https docs, excited, just released
48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
40%40% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
25%25% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
20%20% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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