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dumpall — Dump project code into AI-ready Markdown

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dumpall — Dump project code into AI-ready Markdown

dumpall: The smart file aggregator for AI context and code reviews I've built a new CLI utility called dumpall that solves a pain point I've often had: needing to quickly provide a structured dump of project files to an AI for context, or to a colleague for a code review. The core idea is to follow the Unix philosophy: do one thing well. dumpall recursively reads files in a directory, respects exclusions (like node_modules or .git), and formats the output into clean Markdown fenced code blocks. Key Features: LLM-Optimized: Output is designed for easy consumption by large language models, providing clean and structured context. Clipboard Integration: Use the --clip flag to send the entire output directly to your clipboard. Smart Exclusions: Easily ignore unwanted directories and files. Versatile: Great for providing context to AI, preparing code for a review, or simply archiving a project's state. I'd love to hear your thoughts and feedback. You can try it out with npx dumpall . -e node_modules -e .git.

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos
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.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: code review, ide, io · Missing: https docs, excited, just released
53%53% 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: reviews · Missing: plus, platform, intuitive
49%49% 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
20%20% 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
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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