Browse PC Parts on Amazon.com without distractions

Hacker News

I've built a website where you can browse PC parts from Amazon without distractions and clutter. Most people, like myself, value more information and a clear way to find products than being bombarded with irrelevant stuff like "hey buy this item too" or "people also buy this" Sometimes you need to scroll too far down just to see actual specs. So I decided to make it simple, just a list of products and some filters to find what I need. If you value a distraction free, no clutter way to find what you need, check it out: https://findpcpart.com In the future, I plan to extend it and provide more info for each product. If you have any questions or suggestions, feel free to comment below.

Visit product View on Hacker NewsSep 11, 2026

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Indie HackersFits the IH revenue-focused audience · 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 · Missing: mac, agents, macos

55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

51%51% 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.

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

40%40% 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

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.

Correct prediction on native model

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