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CodeZen – A simple CLI tool to ask LLM questions about your codebase

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CodeZen – A simple CLI tool to ask LLM questions about your codebase

Hello HN! I regularly utilize LLMs for querying my codebases and have been seeking an efficient solution for that. Using ChatGPT can be quite tedious, requiring manual copying and pasting of file contents. Github Copilot Chat (beta) falls short when the queries need information from multiple files. This is why I created a tiny CLI tool named CodeZen. It lets you ask a question and sends it to GPT with the entire codebase as context. For example, you can use `codezen "write me a readme.md file"` to create an entire readme file for your project. It’s pretty bare-bones right now - only suitable for codebases small enough to fit in the LLM context. Yet, it's been quite handy for me, and I thought it might be beneficial for others too. Looking forward to hearing your feedback and suggestions Maor.

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Product HuntOn track for Day 1 leaderboard · Strong signals: context, chatgpt, tiny · Missing: mac, agents, macos
94%94% 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.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
60%60% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive
48%48% 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
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.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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