Ai

Aidev, ask GPT-4 to modify files in your repo

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Aidev, ask GPT-4 to modify files in your repo

Hey, I got inspired by GPT Repo Loader and built aidev over the weekend, asking it to build a big chunk of itself as a meta-experiment. I was curious to find the current limits of how far I can push this AI coding stuff. Here's an example of a prompt that I believe represents the very limit of what GPT-4 can handle: > We're working on support for paths in only/include patterns. The match func has a bug: the dir argument is an absolute path, but we need a relPath, and we compute it incorrectly via filepath.Join. Instead, we need to compute a relative path against directory of .aidev file that this pattern came from. But that information is lost! > Let's kill the idea of merging TreeConfigs completely. Instead, save a pointer to a parent TreeConfig loadConfig. Change ShouldIgnore to build and process a list of applicable TreeConfigs: builtin config, a list of configs for the directory, and override config. ...Overall, though, I did not enjoy using aidev for coding. Definitely did not save me time. There is a part that I've enjoyed, though: having aidev write documentation. In a real project, you would definitely want to edit those yourself, but having a draft seems beneficial. The README of aidev is unedited AI output, so you can judge the quality yourself.

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Product HuntOn track for Day 1 leaderboard · Strong signals: using, coding · Missing: mac, agents, macos
89%89% 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
84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
39%39% 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
33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% 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.

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