Desational – Removing Sensation, Fluff and Nonsense from News

Hacker News

Hello HNers, On Dec 27, 2018 I started working on this side project as a way to dive into natural language processing and machine learning. The question I had: Could I take a news article and remove all of the sensation, fluff, and non-news content from it? The result: Desational. What this project consists of the following components: - newsapi.org data source - Desationalizer source code repo for machine teaching, training, and prediction. - Hugo Static Website - GitLab pipleline corn jobs updating the site every hour I'll have more detailed blog posts about how I teach, train, and predict using machine learning, but I wanted to share here and get feedback on the project? https://desational.com

Visit product View on Hacker NewsJan 19, 2019

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2points

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Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

80%80% 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: ide, io · Missing: https docs, excited, just released

57%57% 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: mac, new, using · Missing: agents, macos, agent

47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

45%45% 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

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

Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue

17%17% 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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