Cu

Cumul – Concatenate all files in a directory for LLMs

Hacker News

Cumul – Concatenate all files in a directory for LLMs

Cumul is a lightweight CLI tool that concatenates text files in a directory into a single file, optimized for providing context to Large Language Models (LLMs). It respects .gitignore, filters out binaries and non-text files, adds path headers, and generates a summary report. Key features: Exclude patterns via options (e.g., -e .json,.md). Custom output prefix (e.g., -p my). Built in Zig for efficiency. Install via: curl -fsSL https://raw.githubusercontent.com/xcaeser/cm/main/install.sh | bash Usage: cm [directory] outputs <directory-name>-cumul.txt. Developed to streamline LLM workflows in development. Feedback welcome.

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, models · Missing: mac, agents, macos
51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
36%36% predicted probability of success on AppSumo, 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
35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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
16%16% 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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