Qu

Quickly submit links to HN from any app on your Android device

Hacker News

Quickly submit links to HN from any app on your Android device

I enjoy submitting interesting articles to HN when I run across them; however, I've found the submission process to be a bit tedious at times. To combat that, I've been using a browser extension[0] that will quickly send the link and title to HN. Unfortunately, most Android browsers don't support extensions, so submitting links to HN has required me to copy-paste the URL, manually select the page title, and then paste that into HN as well. To combat this, I've created a small tool that I call Send HN. It leverages the system share APIs so that you can click the share button in your browser, feed reader, social media app, or really anything, and have what you're looking at sent directly to HN. The app even queries the URL to extract and prefill the page title! This app is a little different than most apps you've used. Since my goal was to make sharing links to HN as dead-easy as possible, there is literally no UI in the app. When you install it, it won't show up in your app drawer; you'll only see it as a share target. I may add a barebones UI in the future, but for now it has none. Disclaimer: I actually know practically nothing about dedicated Android app development, so I used Llama 3.3 70B to write pretty much all the code. It's actually quite satisfying for a small project like this to use an LLM to write and refine the code. The app is GPLv3 and available on GitHub: https://github.com/LorenDB/SendHN [0]: https://github.com/doublemarket/hnpopup

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, using, code · Missing: mac, agents, macos
92%92% 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: created · Missing: supports, reddit linkedin, podcasting
83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: llama, 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
41%41% 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
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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