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Newser, utility written in go to generate a pdf with news content

Hacker News

Newser, utility written in go to generate a pdf with news content

I've gotten myself a Supernote A5X (awesome device btw) and since it doesn't have a web browser or anything I've wanted to have a way to read news on it. I've hacked together this utility in a couple of days and it works wonders for me personally so I thought it might be interesting to others. It can also be used as a noise free newspaper generator as it removes images/ads/links and other noisy stuff. https://github.com/lnenad/newser (there is a screenshot of the first page of the generated pdf) It scrapes (news) websites for content and puts it into a pdf. For me the pdf location is my dropbox supernote directory so my setup is to run this thing daily and have a fresh pdf with news whenever I want it. It's rough around the edges probably (currently added crawl support for verge, ars, engadget) but I think it's a good base so if anyone wants to contribute feel free. Some of the stuff I want to add is pictures (maybe), maybe parse the text html to include font styling and other stuff. I've tried to generalize it as much as possible so the crawling is pretty much automatic and is controlled by a config file where you define "rules" on how to parse the website.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
69%69% 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 NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos
42%42% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
35%35% 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
14%14% 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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