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I brought back Airbnb categories

Hacker News

I brought back Airbnb categories

I miss airbnb categories. It was fun and I loved being able to just browse thorugh cool listings. I'm not exactly sure why they removed it. Then, a couple weeks ago, they hit me with some A/B test and I had access to a "what" freeform textbox to custom filter results. Now apparently they removed that too. At this point, I wasn't happy - so I decided to look into how to bring it back. I noticed they still use some of the categories pages for SEO. For example: https://www.airbnb.com/united-states/stays/ski-in-ski-out That gave me hope - those filters still existed somewhere. Turns out they were only removed from the UI and the search backend still supports all of them. So after a bit of googling, I figured out how it works. Airbnb's search runs on a GraphQL endpoint you can hit anonymously, and the categories map to params on the search URL that I recovered and verified one by one: - Property types: property_type_id[]=<n> (Chalet=22, Cabin=4, Treehouse=6, Castle=5), still filtering even though the UI hides the options - Category tags: kg_and_tags[]=Tag:<n> — I got the IDs by loading those leftover SEO pages and just looking into the URL for the tag (ski-in/ski-out is Tag:681). The one thing I couldn't bring back was that "what" freeform box - turns out it's a server-side AI feature flag tied to your account, not a hidden param :/ though I have a feeling this one's gonn be coming back anyways So I vibecoded the filters I cared about into this little web app. It just builds a normal Airbnb search URL and opens airbnb.com directly — nothing scraped or stored. Hopefully they bring back categories or somethig similar soon. But in the meantime, I figured I'd share this in case anyone else misses browsing through cool treehouses or mountain chalets

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, para · Missing: reddit linkedin, podcasting, created
87%87% 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: code, open · Missing: mac, agents, macos
66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, ide, io · Missing: https docs, excited, just released
54%54% 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
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal
34%34% 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
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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