GPT Repo Loader – load entire code repos into GPT prompts

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I was getting tired of copy/pasting reams of code into GPT-4 to give it context before I asked it to help me, so I started this small tool. In a nutshell, gpt-repository-loader will spit out file paths and file contents in a prompt-friendly format. You can also use .gptignore to ignore files/folders that are irrelevant to your prompt. gpt-repository-loader as-is works pretty well in helping me achieve better responses. Eventually, I thought it would be cute to load itself into GPT-4 and have GPT-4 improve it. I was honestly surprised by PR#17. GPT-4 was able to write a valid an example repo and an expected output and throw in a small curveball by adjusting .gptignore. I did tell GPT the output file format in two places: 1.) in the preamble when I prompted it to make a PR for issue #16 and 2.) as a string in gpt_repository_loader.py, both of which are indirect ways to infer how to build a functional test. However, I don't think I explained to GPT in English anywhere on how .gptignore works at all! I wonder how far GPT-4 can take this repo. Here is the process I'm following for developing: - Open an issue describing the improvement to make - Construct a prompt - start with using gpt_repository_loader.py on this repo to generate the repository context, then append the text of the opened issue after the --END-- line. - Try not to edit any code GPT-4 generates. If there is something wrong, continue to prompt GPT to fix whatever it is. - Create a feature branch on the issue and create a pull request based on GPT's response. - Have a maintainer review, approve, and merge. I am going to try to automate the steps above as much as possible. Really curious how tight the feedback loop will eventually get before something breaks!

Visit product View on Hacker NewsMar 17, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: context, using, code · 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: friendly · Missing: plus, platform, intuitive

50%50% 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

45%45% 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 · Strong signals: way · Missing: mobile apps, ios, personal

42%42% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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