GitWit – AI generated repos and PRs

Hacker News

Hi, I’m James and I want to make software development more accessible and enjoyable. I made GitWit to be a friendly assistant for developers. It can generate and then modify git repositories based on prompts. It adds changes to repos as branches which can be turned into PRs. Under the hood GitWit is using GPT to make a plan for which files to add or change and to generate code. I use it mainly to build full-stack apps, but you can really go wild as far as the stack and language you use it for. An actual example of what building a project with GitWit is like: - https://blog.gitwit.dev/posts/build-and-test-an-app/ Anyone can sign in now and start making new repos at gitwit.dev. Thank you in advance for your feedback!

Visit product View on Hacker NewsMay 7, 2023

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, new, using · Missing: mac, agents, macos

91%91% 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

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released

55%55% 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: apps · Missing: mobile apps, ios, personal

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

34%34% 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

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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