Scraping JSON metadata from reverse engineered Jango API

Hacker News

Sufficiently advanced users reverse engineer undocumented APIs for fun. I've been sufficiently advanced since I reverse engineered the Jango API over ten years ago. It's been a while but I still remember using Burp Suite to trace Referer. These days I would just use Chrome Developer Tools instead. This year when some search results disappeared from the official Jango search page then I was faced with a choice. Option 1: contact Jango people and ask "Hey where are the missing search results?" Option 2: scrape all the JSON metadata from the Jango API and build my own search page that searches Jango the way I want. So I chose option 2 and went on a months long quest for Jango JSON metadata. Option 1 would have been socially awkward and completely unnecessary since I'm perfectly capable of solving problems without help from anyone. Result! Warning: technical tools only look nice to nerds. https://jango-index.ml/ Source code! The secret is to pipe the curl commands together. Isn't that right guys?? https://jango-index.ml/src/ Yup. It was worth doing. Sorry there's nothing to monetize here.

Visit product View on Hacker NewsAug 19, 2020

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2points

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

92%92% 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: user, using, code · Missing: mac, agents, macos

73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

57%57% 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 · Strong signals: month, users, way · Missing: mobile apps, ios, personal

31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

26%26% 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.

Incorrect prediction on native model

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