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I made a browser extension that exposes the hidden cost of shopping

Hacker News

I made a browser extension that exposes the hidden cost of shopping

Hi again, HN. Three weeks ago, I showcased my impulse control PWA, "Nope It," here on HN. The discussion and feedback I received were incredible, and I'm hugely grateful to this community for it. The single most valuable piece of feedback I got was about friction. Many of you rightly pointed out that in that critical 5 second window on a shopping page, the mental energy required to open a separate app is often too high. The impulse wins by default. You were right. So, I spent the last couple of weeks building the solution. Today, I’m launching Nope It for Chrome. It's a lightweight browser extension that uses JavaScript and DOM manipulation to inject a "psychological speed bump" directly onto product pages (I'm starting with Amazon, as it's the biggest culprit for me). The core of the extension is the real time "Work Hours Psychology" engine. It shows you that a $200 gadget doesn't just cost money, it costs you a full day of your life. When you see that, you can log the impulse with a single click without ever leaving the page. It then syncs everything back to the core PWA. I'm back here because your initial feedback is what created this. My main question for the HN community is, Does this frictionless, point of sale approach feel like the right solution to the problem you helped me identify? I’d love your thoughts on the UX and the overall strategy. You can grab the new extension from the Chrome Web Store here: https://chromewebstore.google.com/detail/nope-it/imamiekokcj... The main PWA is still here: https://www.nopeit.app Thanks for helping me build this.

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Actual performance

1points
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, para · Missing: supports, reddit linkedin, podcasting
89%89% 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, new, single · Missing: mac, agents, macos
69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google, para · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released
37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real time · 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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