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AI-Native dependency upgrades through codemods

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AI-Native dependency upgrades through codemods

Apiweiser, is a CLI tool that will manage the dependency upgrades of your repositories. This is designed for developers and teams which struggle to keep the dependencies up to date regardless of security patches or API depreciation. It is AI-native by default where each changelog is interpreted by an AI agent and the code changes in the repository are done by a deterministic codemod generated by you local coding agent. The codemods will be stored in a local registry and can be extended and used in other repos so that the costs of code changes are reduced significantly! Apiweiser will also raise a PR in your remote git server for the code changes regarding the dependency upgrade. Apiweiser is built to take the pain out of code changes when dependencies need to be upgraded, but we know every codebase is unique. If you have a repository with tricky dependencies, please give it a spin and let us know where it breaks. We’re currently in the early stages and would love to hear your thoughts.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, coding, code · Missing: mac, agents, macos
90%90% 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.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
30%30% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
27%27% 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
25%25% 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
19%19% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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