OS

OSS B2B Commerce Starter Built on Next.js and Medusa 2.0

Hacker News

OSS B2B Commerce Starter Built on Next.js and Medusa 2.0

Victor & I (Riqwan) built this starter as a way to showcase how you can build B2B stores on Medusa 2.0. We're both developers at Medusa (an open source commerce platform) and we've launched our 2.0 version today after a year of working on a more modular commerce architecture. We built our own workflow engine to perform actions on multiple modules with the ability to revert each step incase a failure occurs at some point in the lifecycle. Modules are encapsulated group of related logic - like product management, fulfillment, orders, promotions, etc. We've also built our own query engine based on GraphQL that you can use to query across modules or even external services - like Sanity or other CMS. Tinloof did a very cool demo of this earlier today. The whole point of our 1 year work was to make things easily customizable and be able to easily work with the plethora of external services every commerce company would have to work with. It takes less than a minute to get started locally. Give this a shot and let us know if you've got any feedback.

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: open · Missing: mac, agents, macos
80%80% 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
74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released
67%67% 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
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
30%30% 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
18%18% 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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