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Categorised eBay GPU Prices

Hacker News

Categorised eBay GPU Prices

I've made a GPU comparison site: https://gpu-prices.com/US/ Yes, there was one yesterday - I got beaten to it. This one is different in that I've put a lot of work into categorisation, so you can filter down quite precisely. For example, VRAM-per-dollar, limiting to Nvidia, and a minimum performance score allow you to find good ML GPUs. It currently supports Australia, Canada, Ireland, the UK, and the US. The tech stack is Python for data pull and static site generation then Cloudflare Pages for actually serving the site. It updates three times a day, but I could increase it to ~6x before I ran into rate limits. I'm hosting it from a raspberry pi under my desk, so this setup means that if my network/power go out the data just becomes a bit stale, rather than the entire website going down, as well as preventing me from getting hug-of-deathed. I would really appreciate some advice for how to market it. I'm an eighteen-year-old nerd with absolutely no clue how to do so, and I don't want to be the sleazy person spamming their affiliate links everywhere. The general SEO advice is "write good content" - should I really make a redundant blog for SEO purposes?

Visit product View on Hacker NewsFeb 24, 2024

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91%91% 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.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

70%70% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

36%36% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% 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.

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