I'm learning Go and built a scraper. How can I improve it?

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TLDR: I'm learning Go and [this]( https://github.com/ItzaMi/compare-supermarket-prices ) is my first project with it. What am I doing wrong and what could I improve? In an attempt to expand my horizons and actually get into backend development, I've decided to learn Go. I picked it for no reason other than the market seems to be in a friendly state towards it, compared to Elixir, but I'm very much enjoying it. Thankfully something unlocked in my brain and I thought of a project to do while learning the syntax and how the language works, so I've built a scraper. Here's the link: https://github.com/ItzaMi/compare-supermarket-prices At this point, I would like to know what I'm doing wrong and what I could improve in it, so any tips and advice would be very much appreciated!

Visit product View on Hacker NewsDec 27, 2024

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

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

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

AppSumoStrong fit for a featured deal · Strong signals: friendly · Missing: plus, platform, intuitive

53%53% 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

48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

13%13% 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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