Loger
Turn messy product data into structured catalogs
What it does
Loger.io helps ecommerce stores turn messy product data into structured, optimized catalogs at scale. Unlike manual tools, it imports, organizes, and syncs thousands of products automatically, saving time and reducing errors. Built for modern ecommerce, it’s fast, scalable, and designed to handle growing product catalogs without tedious manual work.
Does the same job
all alternatives →- 3I30k IKEA items in flat textJan 2026 · huggingface.co · ▲55
OP here. I took the unofficial IKEA US dataset (originally scraped by jeffreyszhou) and converted all 30,511 products into a flat, markdown-like protocol called CommerceTXT. The goal: See if a flatter structure is more efficient for LLM context windows. The results: - Size: 30k products across 632 categories. - Efficiency: The text version uses ~24% fewer tokens (3.6M saved total) compared to the equivalent minified JSON. - Structure: Files are organized in folders (e.g. /products/category/), which helps with testing hierarchical retrieval routers. The link goes to the dataset…
- IBI Built a next.js Product Catalog with an open source ecommerce module2023 · product-module.medusajs.com · ▲6
I built a next.js product catalog entirely on open source modular e-commerce and Vercel's serverless functions. You own the data, the commerce logic is integrated into your project and run through your own APIs. This is a showcase for our Modular way of approaching e-commerce rather than approaching it through Shopify or commercetools APIs. You can follow the twitter thread here - https://twitter.com/RiqwanMThamir/status/1671426435813179394 Looking forward to hearing your thoughts :)
AI Bulk Listing GeneratorJul 2026 · aibulklistinggenerator.com · ▲8Turn product photos into ready-to-use product listings.
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Filleo: AI Product Builder For ShopifyMay 2026 · apps.shopify.com · ▲3Transform messy supplier data into complete product listings
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
Launched alongside, March 2026
the whole month →
Switch from ChatGPT to Claude with import memory feature
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