E-

E-commerce data scraped from any website

Hacker News

E-commerce data scraped from any website

Hey HN, One year into my startup journey, I was going through pivot hell. My life changed for good when I got a friend's call who wanted to connect me to their teammate looking to scrape an Indian apparel brand. This brand wanted to scrape quite a lot of their competitors (inluding H&M and Zara type brands) from both the DTC websites and marketplaces. This project gave us enough money to be in the game and not give up. Soon, working with them, we had gone into every depth possible of scraping e-commerce sites. 1. Identifying the product links (trust me, it's tough to fetch product links from raw HTML) 2. Scraping things like available sizes, the buttons and way of presentation is different for all websites. 3. Getting the data hidden on the page behind a button. Our tech stack worked beautifully, maybe we over optimised because we had so much time. So when the engagement with that apparel brand ended, we had time to actually work on this and make it work for each and every e-commerce website that there is. I will launch it sooooon. But here is a taste of it, so sorry that I have already added sign up. I don't want to remove this now, but it's through google, so should be easy. Any feedback would be genuinely helpful to me at this point, thanks!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
88%88% 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 · Missing: mac, agents, macos
87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google, way · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, 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.
AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive
24%24% 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
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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