Trendly–A Personalized Feed to Cut Through Content Overload

Hacker News

Hi everyone! I’m excited to introduce Trendly (trendly.global), an app I think you’ll find really useful. Here’s what we’re solving: * Social media is full of fake news and lacks depth, yet many still rely on it for updates. * There’s too much content online, making it hard to find what really interests you. * Switching between platforms to get full details on a topic is tiring. Trendly ( https://trendly.global/ ) is a mobile app that curates a personalized feed with trending updates based on your interests. It lets you dive deeper by asking follow-up questions as a chat—all in one app. We’re in the early stages, so while the chat function isn’t live yet, the other features are ready: * The feed acts as a recommendation engine, curating content based on past interactions. * Delivers concise, bullet-point summaries instead of lengthy articles * Pre-generated Q&A for easy understanding Check it out and share your honest feedback!

Visit product View on Hacker NewsOct 28, 2024

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AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews

56%56% predicted probability of success on AppSumo, 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: new · Missing: mac, agents, macos

54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

38%38% 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: personal · Missing: mobile apps, ios, entrepreneurs

28%28% 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

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat, introduce · Missing: web3, crypto, cryptocurrency

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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