Snackwyze- Bite sized clickthrough demos instantly

Hacker News

Hey Guys, Neeraj here - founder of Snackwyze (https://snackwyze.com), the easiest, fastest and the most affordable way to create, share and track clickthrough demos for your software. Personally, I have been in Sales and marketing for over 10 years and worked at various companies /various sizes including Cisco. And in those many years, I never found an easy way to create clickthrough demos for sales and marketing use cases. So I decided to build a platform for the same. My buddy and I built a chrome extension that records your screen and converts every single click into a step-by-step clickhthrough demo that you can easily share, embed, download and then track from the Snackwyze platform. I'd love for you guys to try it out and let me know if you find it useful. If you are looking to create product tours/deos for your website then I'd be delighted to help you build as well. Thank you! Cheers

Visit product View on Hacker NewsSep 13, 2021

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting

92%92% 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: single · Missing: mac, agents, macos

79%79% 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, io, including · Missing: https docs, excited, just released

47%47% 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 · Strong signals: platform · Missing: plus, intuitive, reviews

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

19%19% 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.

Correct prediction on native model

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