SpecsMedia

AI-structured specs for electronics from stores worldwide

17 followers

AI-structured specs for electronics from stores worldwide

17 followers

SpecsMedia uses AI to identify, normalize and structure electronics sold under inconsistent names and descriptions across stores and regions. It turns scattered listings into rich specification tables, so you can tell when two offers are actually the same product and compare prices with confidence.

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Launch tags:Artificial Intelligence•Search•Data & Analytics

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Promoted

OpenAI

GPT-6 Astra Challenge

How did Astra change the scope or ambition of what you built?

Maker

SpecsMedia was already in development when Astra launched, so it expanded what we could finish before launch. We used Astra heavily for code reviews, debugging, and improving the full end-to-end pipeline that ingests messy retailer listings, resolves product identity, normalizes specifications, and turns them into structured, comparable product records. Astra’s speed and strong code-review capabilities let us tackle deeper pipeline issues and refinements we would otherwise have postponed. Instead of shipping a narrower first version, we were able to improve the system behind roughly half a million products and make our first public launch significantly more production-ready.

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2mo ago

Maker

📌

Hi Product Hunt! 👋 I’m Simeon, founder of SpecsMedia, and this is our first-ever public launch. SpecsMedia started from a simple problem - Comparing electronics across stores is much harder than it looks: - Two listings can appear to be different products when they’re actually the same model. - Or they can look identical while hiding important configuration differences. - Sometimes manufacturers use different names for identical products, in different regions. That makes price comparison unreliable unless you first solve product identity. SpecsMedia uses AI to identify, normalize and structure messy product data from stores around the world. Titles, descriptions, partial spec lists and regional naming differences are transformed into consistent, detailed specification tables. The result is that you can understand exactly what a product is, match equivalent listings across stores and regions, and compare them with much more confidence. In many cases, SpecsMedia can present a richer structured view of a product than any single retailer listing provides. We’re currently applying this pipeline across roughly half a million consumer-electronics products. GPT-6 Astra became part of the project during our final push toward launch. We’ve been using it for coding, debugging, and improving the full data pipeline — from the systems that ingest messy retailer data to the logic that helps turn it into structured product information. SpecsMedia was never launched before, so this is the first time we’re putting it in front of a wider audience. I’d really value feedback on one thing in particular: when you compare products online, what information do you most often find missing, inconsistent, or difficult to trust?

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2mo ago

Reviews

OpenAI

GPT-6 Astra Challenge

How did Astra change the scope or ambition of what you built?

Maker

Report

2mo ago

Maker

📌

Report

2mo ago