Fl

Flowshare: Create and discover how-to guides for everyday software

Hacker News

Flowshare: Create and discover how-to guides for everyday software

Hi HN, I created Flowshare ( https://flowshare.io/ ) to make creating step by step guides with screenshots and annotations as easy as recording a screencast. As a reader, I find screenshots and text much easier and faster to follow than watching a long screencast. As a creator, recording a screencast is much easier. Flowshare gives you the best of both worlds. You can search existing guides at: https://flowshare.io/search Here is an example of one the most popular guides: https://flowshare.io/flow/how-to-block-spam-invitations-from... I'm including a link to the chrome extension which is used to create guides: https://chrome.google.com/webstore/detail/flowshare-how-to-g... Would love to hear any feedback/suggestions.

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, including · Missing: supports, reddit linkedin, podcasting
80%80% 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: google · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: google · Missing: mobile apps, ios, personal
54%54% 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
44%44% 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 · 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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