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WindowsKnowHow – A video guide to Windows 10

Hacker News

WindowsKnowHow – A video guide to Windows 10

Hi everyone, I appreciate that HN is probably not my target audience but I just recently launched my own video training course on Windows 10 for beginners and non-techies and would love to get some brutally-honest but constructive feedback from the HN community (e.g. are the course videos too long, too boring, too techie, too pointless etc?). https://www.WindowsKnowHow.com There are around 10 sample videos available for free on the homepage, but you can also sign up for a free 7-day trial (no credit card required) and gain access to the entire course (60 videos) by signing up here: https://www.windowsknowhow.com/signup/free-trial-contact-for... . All fields on the sign-up form are mandatory, but feel free to enter the following dummy info for the fields below: Company Name: HackerNews; Role: Reviewer; No of users you’d like to train: 1; You will need to provide a valid email address so that I can send the login details. Thanks in advance.

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Indie HackersFits the IH revenue-focused audience · 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.
TrustMRRLess likely to generate early MRR · Strong signals: video, users · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: user, new, email · Missing: mac, agents, macos
30%30% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released
30%30% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
15%15% 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.

Incorrect prediction on native model

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