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Online OPML editor to manage subscription lists

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Online OPML editor to manage subscription lists

I made a small online editor to manage OPML[1] files easily. There are a lot of smart online editors for formats like JSON, but while OPML is supported by most subscriptions services, the experience of editing/managing it is poor outside of what each platform support. The pain of managing the file independently increases as the size of the OPML file grow larger. It's rather simple at the moment but the goal is to provide shortcuts for common desired tasks to make the experience of editing OPML much smoother and streamlined. The editor is a static website that runs locally in your browser, it doesn't require an account, nor do any tracking. Let me know your feedback or if you've any pain points around OPML GitHub repo: https://github.com/imdj/opml-editor Website: https://opml.imadij.com/ [1] - https://en.wikipedia.org/wiki/OPML

Visit productView on Hacker NewsJun 21, 2024
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
49%49% 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
47%47% 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
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: tasks · Missing: mac, agents, macos
37%37% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · 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 · Strong signals: smart · 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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