GLinksWWW – A student-built retro browser with a 9-slot multi-clipboard

Hacker News

Hi HN, I'm a student developer who loves retro aesthetics and privacy. A few days ago, I shared my browser "gLinksWWW" here, but I received some valid concerns about sharing files via Google Drive. Taking your advice to heart, I have now officially open-sourced the project and moved everything to GitHub. Why I built this: I wanted a browser that felt like the old days but handled modern privacy needs. 9-Slot Multi-Clipboard: My favorite feature. Copy 9 different things and paste them using Ctrl + 1~9. As a student, this saves me so much time when researching and coding. Privacy by Default: No history, no tracking. I built it because I wanted to feel safe while browsing. Lightweight & Retro: It's designed to be fast and has that nostalgic UI. Since I am still learning, the code might not be perfect, but I've put a lot of heart into it. I’d be honored if you could check out the repository, and any feedback (or a Star!) would mean the world to me. GitHub: https://github.com/rio719/gLinksWWW-browser?tab=readme-ov-fi... Releases: https://github.com/rio719/gLinksWWW-browser/releases/tag/v1.... Thank you for helping me become a better developer!

Visit product View on Hacker NewsJan 30, 2026

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

74%74% 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 · Missing: supports, reddit linkedin, podcasting

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

44%44% 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: google · Missing: mobile apps, ios, personal

33%33% 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.

Correct prediction on native model

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