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WowTo V2 – Free Video Knowledge Base Software

Hacker News

WowTo V2 – Free Video Knowledge Base Software

Several months back we introduced WowTo in its nascent stage to the HN community and received lots of feedback, suggestions and product positioning. A lot has gone into our product since then and it helped shape our product for the better. Today we are introducing v2 of WowTo.ai as video knowledge base software. Before we start explaining about it let me briefly convey on what made us create it. When we studied on what users currently use for their video help centers, we came to understand they mostly use YouTube channels. However on connecting with some of these users, we came to know their pain points on using YouTube as their primary host for support videos. * YouTube advertises aggressively which spoils the support experience for videos * Videos cannot be organised beyond a point, it is not made of organisational structure especially like support centers * Shows related videos that are not really related at all But ofcourse YouTube has many pros also, it is free and has a great social community, However when it comes to support videos, we don't think it is really the goto-place. This made us branch out WowTo into a video knowledge base platform to create, host and share support videos with ease. With Wowto you can * Create step-by-step video tutorials in a snap since the editor is designed for support professionals * Host all your videos in a YouTube like beautiful and branded video help centers. Or even create a video academy * Provide in-app video support contextually with our support video widget - after all we know video engages and explains it better in most cases. This time we have a very liberal free plan with no time limit. Go ahead and create your video help center for your product at https://wowto.ai

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
86%86% 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: user, context, using · Missing: mac, agents, macos
81%81% 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 · Missing: https docs, excited, just released
48%48% 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, month, users · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host, users · Missing: plus, intuitive, reviews
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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