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Baler – convert code repositories into minimum number of files for LLMs
Hacker NewsBaler – convert code repositories into minimum number of files for LLMs
Hello HN, while working on a full-stack project with Claude, it got tedious for me to sync the project directory manually everytime. I wrote 'baler' over a couple of weekends to fix this. Now you can run your repository through baler and get the fewest files before interacting with an LLM. You can also tune output file's size, filter input files based on their size or line-count. Please give it a try, and I hope it makes some of your work easier.
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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, code · Missing: mac, agents, macos74%74% 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: io · Missing: https docs, excited, just released49%49% 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, podcasting41%41% 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, personal39%39% 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, revenue19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive18%18% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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