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The future of internet? Wormholes and strongly typed URLs

Hacker News

The future of internet? Wormholes and strongly typed URLs

Worms are URLs which are allowed to pop from any text software into any other process beyond the browser. It captures the screen and extracts worm links. Method: - Continually printscreen - OCR from the capture - Process wormholes - Only allow specific worms/URL types to run automatically (for security) - this is URL typing Example: allow "imgur" worms to auto-display pictures hosted from imgur, as a display notification, or ideally (with a compatible browser's plugin) insert into the page where the worm text is situated. It would look like this: worm://image/imgur/arg1-arg2-...-argn That can only extract image type, and only images from imgur sources. This is type-safe, and you'll see the notification-image in whatever app you happened to display that text, even inside a 3D app or videogame. It is app-agnostic hypertext links. How to make it happen right now for increased business productivity: - use something like Capture2text to OCR printed-screen into copypaste (this is the hard part) - extract worm links from the copypasted every X seconds using any programming language - process those links one by one, example: display a picture on the bottom-right corner of the display, using the imgur worm link, without having to click it. I understand there were security issues but as the years go by and security progresses, this could be useful.

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: compatible · Missing: supports, reddit linkedin, podcasting
88%88% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: using · Missing: mac, agents, macos
72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
52%52% 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: video · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
22%22% 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
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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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