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Trendradar for Horizon Scanning and Foresight

Hacker News

Trendradar for Horizon Scanning and Foresight

This is an update to our old Trendradar ( https://news.ycombinator.com/item?id=45332076 ) # What changed * This trend radar focuses on trends in general (not only technological trends as the previous ones) * This trend radar splits individual trends into granular signals, and each signal is based one 3 or more individual sources. The idea is to provide companies a data-driven scan of the environment (horizon scanning) as a basis. With this basis, they can derive conclusions about the direction of a market. Although V2, there are still a few issues to fix, especially around self-reporting & PR sources which tend to overrepresent trends. Furthermore, we are planning on expanding it to include a more diverse set of sources (especially patents & GitHub), to catch changes even earlier. Ironically, AI has made web scraping per se cheaper, but overall it seems that the total costs (as a consequence of complexity and the sheer number of content) have increased.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
77%77% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos
51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · 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: ide, io · Missing: https docs, excited, just released
45%45% 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
16%16% 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
4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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