Gr
Grokking Trends from Across the World
Hacker NewsGrokking Trends from Across the World
I'm happy to share a project I've been working on for the past year, available to everyone. It aggregates trends across the world from various cities and niches. It includes summaries, sentiments and sources for every trend. I recently pushed out a feature for being able to search trends. Should be useful for querying trends based on your specific interests!
Share cardActual performance
1points
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: grok · Missing: mac, agents, macos65%65% 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, podcasting56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive51%51% 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 released49%49% 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 · Missing: mobile apps, ios, personal45%45% 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, revenue13%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, crypto0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
Similar products
Hy
Hypewatching – crowdsourced, geolocated trends around the world39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Hypewatching – crowdsourced, geolocated trends around the world
Hacker News71Hy
Hypewatching – crowdsourced, geolocated trends around the world39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Hypewatching – crowdsourced, geolocated trends around the world
Hacker News4Co
Covid-19 Trends by Glimpse40%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Covid-19 Trends by Glimpse
Hacker News2Pr
Programmer Trends49%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Programmer Trends
Hacker News4OOIR31%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Trends in the sciences
Indie Hackers1$50/mobooksHa
Hackerbuzz – Simple HN trends45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Hackerbuzz – Simple HN trends
Hacker News4HighJackar21%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Discovering Trends. Validating Ideas.
Indie Hackers1aiCo
Compare Trends on HN46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Compare Trends on HN
Hacker News2RibbonGG62%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
TikTok Statistics and Trends
Indie Hackers1analyticsSh
Show HN Developer trends49%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Show HN Developer trends
Hacker News3