UpScraper
Backfill your product catalog without the manual grind
UpScraper launched on May 18, 2026 with 4 votes, #2,419 of 20,538 launches that month and more than 75% of that year's launches.
What it does
Tired of manually hunting down product images and specs for thousands of SKUs? UpScraper automates it from Chrome's side panel — no tab switching, no context loss. Import your product list, run multi-source image and spec retrieval in the background, review and override results, then push structured data directly to your backend via a configurable API endpoint with auth token support. Smart skip logic ignores products with verified images. Built for catalog managers. Free, no account required.
Chrome extension that bulk scrapes product images, brand logos and color-variant specs from GSMArena, Bing, DuckDuckGo, Clearbit and Apple CDN. Backfill your e-commerce catalog in minutes.
Does a similar job
all alternatives →
Smart Image Upload for Shopify2021 · apps.shopify.com · ▲101Bulk upload product images and save your time

Universal E-Commerce ScraperNov 2025 · apify.com · ▲6Automatically detect, crawl, and extract clean product data
I build a chrome extension to try on clothes before buying2024 · github.com · ▲6Hi HN, I built a Chrome extension that lets you try on clothes virtually in any e-commerce store. The entire product was built over the weekend using Cursor editor's multi-compose feature. How It Works: - Captures the product image directly from the webpage using GPT-4. - Uploads your photo to Cloudinary for AI processing. - Uses the Kolors model from Hugging Face via the Gradio API for the try-on. - Stores images in your browser cache for faster performance. To use it, you'll need to set up your OpenAI and Cloudinary API keys in the extension settings. Here is a demo video:…

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Source: Product Hunt launch ↗
Launched alongside, May 2026
the whole month →

Parallel agents, diff reviewer, and multi-model comparisons
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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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