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Waterbucket – A dynamic design tool for product data catalogs

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Waterbucket – A dynamic design tool for product data catalogs

Hi HN, I'm excited to share what I think is the best interface for designing dynamic product data feed catalogs. We've been working for the last 4 years to build this tool. Problem: Every ecommerce platform outputs a product data feed for automated advertising on platforms like Meta, Klaviyo, TikTok, Google, etc. The images are usually bare product shots on white backgrounds that don't grab attention. Solution: Waterbucket is middleware between the ecommerce platform and the ad platform. Our platform ingests the feed, provides the tools to creatively transform the feed, and outputs a new feed to the ad platform. At the heart of the platform is a rules engine. You can design a template and use rules to apply it to different attributes/products from your data feed. For example, say you have a sale 20% off men's shoes. You can design a template for that sale that works like a frame/overlay on the images. Then you can map that template to all the men's shoes in your feed. Waterbucket differs from something like Canva because the feed is dynamic and updates daily to reflect price or other attribute changes automatically. One of the best use cases is calling out prices. Sale price, percent off, dollars off, or BNPL prices. Price is what we all use to decide if we're interested in something. Modern advertising algorithms optimize for attention and price grabs attention. Please have a go with the demo environment and let me know what you think.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
80%80% 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, new · Missing: mac, agents, macos
64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist
56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: platform, interface · Missing: plus, intuitive, reviews
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal
44%44% 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.

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