Summora – Everything, in summary

Hacker News

Hey HN! I have developed a platform for user-generated articles on any topic. The twist is that everything should be written in short form. The editor is built in such a way that an article is divided into segments (e.g. "chapters" for a book summary). Each segment then has bullet points, which define the actual content. https://www.summora.com/ Here is an example article where I amateurishly explain quantum computing: https://www.summora.com/post/clgjtw6rr0000lc082cppvzex Such platform is not a new idea, but started as a learning opportunity I just felt like building it. The UI is written entirely by hand. Writing/editing articles should be as smooth as possible (imagine Google docs), accordingly there are few "save" buttons. The technologies used are Next.js, tRPC, Prisma, Tailwind CSS and AWS S3. The code can be found here: https://github.com/bennettdams/summora I have a lot more features in mind, but haven't found time to implement them yet. Would love some feedback!

Visit product View on Hacker NewsMay 8, 2023

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

79%79% 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.

Hacker NewsStrong engagement from HN community · Strong signals: ide, 000 · 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.

Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

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

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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: google, way · Missing: mobile apps, ios, personal

38%38% 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

13%13% 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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Product Hunt+9

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