QuickPoint. Make presentations, web pages etc. with a YAML like syntax

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Show HN: QuickPoint. Make presentations, web pages etc. with a YAML like syntax. I started this project during lockdown. Remember that?? It developed from an idea I had for exchanging value in URLs. The idea being that if , for example, you had a subscription to the NYT and if they linked from their article to an article in the WSJ the WSJ would a receive a small payment from your subscription. The idea seemed much too abstract for any stakeholders in publishing organizations so i thought I'd better build my own publishing platform. I didn't think it would take this long but here it is (after two kids) in a usable form. You can use if for anything and publish your anything to the web. Down the line you can charge for your creations. The language is YAML like and any good names for it are welcome. The viewer is a React component that you can drop into any webpage with a one-liner, the aim is to open source that. It's probably full of bugs so do let me know. It's a fun thing and works great on mobile. I imagine harried developers who need to give presentations might find it useful in it's current form.

Visit product View on Hacker NewsSep 12, 2024

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

91%91% 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, organizations · Missing: supports, reddit linkedin, podcasting

71%71% 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, ide, io · Missing: https docs, excited, just released

51%51% 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: platform · Missing: plus, intuitive, reviews

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr, subscription · Missing: mrr, revenue, profit

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.

Incorrect prediction on native model

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