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The Playback Press

Hacker News

The Playback Press

I just launched a site to share ‘books’ of code playbacks. These are guided walkthroughs of some code as it evolves with a narrative from the author. The narrative can include text, whiteboard-style drawings, images, and videos. https://playbackpress.com/ I have been using these in my programming-focused CS courses for a couple of years. I always give an anonymous survey at the end of the semester and the feedback has been very positive. Students prefer them to traditional programming books (no surprise) and to videos (I was a little surprised). Next, I want to make these available to others. I am hoping to shake up the traditional programming book publishing market. I’m looking for feedback on the medium of playbacks and the likelihood of these replacing books/tutorials/videos. If you are an enthusiastic supporter, I am also looking for new authors and partners/cofounders.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
76%76% 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, using, code · Missing: mac, agents, macos
55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
54%54% 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: video, way · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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