AI

AI Setup Wizard for Dev tools and libraries

Hacker News

AI Setup Wizard for Dev tools and libraries

Hi HN, I’ve been working on an open source dev tool, and I’ve realized that it is pretty hard to: 1) explain to users that they can use the tool to help with their problem 2) integrate the tool into their app To solve these problems I’ve started working on two tools: - MCP server that augments the context with the new library documentation with additional metadata for searching packages - CLI AI agent wizard that integrates the package into the app. I think specialized agent can be more reliable. You can check out the demo on the website, it’s pretty neat - you can install the tool like you would any app on your phone. It should be ready to use in around 1-2 weeks, but I wanted to ask the HN community now: what tools and libraries do you want to see supported first? You can add your prompts in the repository!

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, mcp, user · Missing: mac, agents, macos
88%88% 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
53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, 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.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
37%37% 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
15%15% 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.

Correct prediction on native model

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