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Easy Laptop Finder full laptop list

Hacker News

Easy Laptop Finder full laptop list

Last time I posted my laptop finder idea ( https://news.ycombinator.com/item?id=39653971 ) I got some great feedback. I made some adjustments to the algorithm being used to display recommendations, filtered out some lower-quality brands, and tried adding more sanity checks to the data coming from the Amazon API (surprisingly not great quality!). Importantly, I added an entirely new “advanced” section ( https://easylaptopfinder.com/?advanced ), which is essentially just a filterable table of all the laptops on the site and the attributes I’m using from the Amazon API. I figure this is a nice interface for power users (and seems to work really well for another great site, diskprices.com). I’d be interested in what folks think of this new view (or the quality of the original finder itself). Also let me know if you have any questions about how this was implemented. Thank you for taking a look and I appreciate any feedback!

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, using · Missing: mac, agents, macos
66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface, users · Missing: plus, platform, intuitive
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
10%10% 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.

Incorrect prediction on native model

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