Programmatic (and self-updating) SaaS demo videos

Hacker News

Hey guys! While building another SaaS we got fed up with recording and, re-recording demos for help-center so decided to try and automate it using Playwright. Turns out it's not that easy, but we got it to a point that's usable, and quite useful for us already. Feel free to try and send feedback. Every new account gets some free credits so you can see what it can do. Thanks! PS: Because this is heavy on infra we have a waitlist thing but I'm monitoring and will let you in within minutes/hours, just need to space people out a bit. Thanks PPS: Here's an example video made with Rundown https://www.rundown.video/011fb9da-72f7-41d1-bdce-f187f398e0...

Visit product View on Hacker NewsFeb 26, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, using · Missing: mac, agents, macos

83%83% 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, podcasting

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

50%50% 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, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue

24%24% 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.

Incorrect prediction on native model

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