Notedis a Feedback Widget for Freelancers Tired of Enterprise Pricing

Hacker News

I built Notedis - a visual feedback widget that captures annotated screenshots plus the technical context I need to debug (browser info, device details, screen resolution). Clients don't need to login or learn anything new. They click the widget, draw on what's wrong, and I can fix things much quicker with context. $20/month flat. Unlimited sites, unlimited feedback. No seats, no tiers. I kept it deliberately simple - three components: widget, dashboard, email replies. No Slack integrations, no AI features, no roadmap to enterprise. Just the core workflow that actually saves me time. Try out a free trial and let me know how I can improve it please!

Visit product View on Hacker NewsNov 21, 2025

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

71%71% 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: slack, new, email · Missing: mac, agents, macos

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: month · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

42%42% 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: plus · Missing: platform, intuitive, reviews

40%40% 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

26%26% 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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