Starter.place – Gumroad for Starter Repos

Hacker News

Hey HN! Starting a new project is so hard because before you actually get to building the idea itself, you have to search for tools and figure out how to piece them together. With starter.place, you can find proven starter templates/boilerplates/stacks that use the technologies you want and get to building right away. If you’ve made a starter repo you think others would find useful, you can immediately reach a wide audience without having to make your own site/app to sell it and advertise it by posting on starter.place. Just focus on building the starter all while earning from it if you so choose. starter.place is so helpful to buyers and sellers because buyers are added as view-only collaborators to the repo on GitHub, where they get continuous updates. Buyers can help drive the project by submitting issues and PRs too. Let me know what you think! And if you have a starter template but are hesitant to list it, let me know what I could do to change that. Oh and as for the app's own stack, it uses Remix, EdgeDB, and Tailwind deployed on Vercel and AWS Fargate.

Visit product View on Hacker NewsFeb 22, 2023

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Launch Intel predictions

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

69%69% 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

52%52% 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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide · 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 · Strong signals: way · Missing: mobile apps, ios, personal

29%29% 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

10%10% 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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