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I made a few screencasts on Git
Hacker NewsI made a few screencasts on Git
Hi Guys! I have made a few screencasts on Git that are available on SitePoint Premium. https://www.sitepoint.com/premium/series/git-like-a-pro This comes off a book that I wrote on Git (Jump Start Git). Some of the videos are available for free, or you can gain access by starting a 14 day trial. This is the first time I have created screencasts or video tutorials of any kind, so I would request some feedback on the same! Thanks in advance! :)
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Did not reach leaderboard
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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting66%66% 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.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal49%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 · Missing: https docs, excited, just released40%40% 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 · Missing: mac, agents, macos38%38% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue15%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, crypto3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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