Target
Amazon
Walmart
eBay
Best Buy
OfferUp
Zalando
Etsy
Labelbox
Playment
Supervisely
CloudFactory
Universal Data Tool
CrowdFlower
Dataloop AI
Labeling AI
A complete solution for your training data problem with fast labeling tools, human workforce, data management, a powerful API and automation features.
Target LabelboxService goes down often. Very slow team. Slow support.
Based on our record, Target seems to be a lot more popular than Labelbox. While we know about 261 links to Target, we've tracked only 10 mentions of Labelbox. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
// Quick check - fetch raw HTML vs rendered content // If they differ significantly, you need JS rendering Const https = require('https'); Const html = await fetch('https://target.com').then(r => r.text()); Const hasAngularVueReact = /ng-app|vue|react|__NEXT_DATA__/i.test(html); Console.log('Needs JS rendering:', hasAngularVueReact);. - Source: dev.to / 3 months ago
From camoufox import Camoufox From scrapling import Selector # Camoufox: stealth browsing with Firefox fingerprint (81MB) With Camoufox(headless=True) as browser: page = browser.new_page() page.goto('https://target.com') html = page.content() # Scrapling: adaptive parsing with CSS/XPath Sel = Selector(html) Data = sel.css('.product::text').getall(). - Source: dev.to / 3 months ago
Target → Site Map → review discovered URLs # Active endpoint discovery — find what browsing didn’t reveal Ffuf -u https://target.com/FUZZ \ -w /usr/share/seclists/Discovery/Web-Content/common.txt \ -fc 404 -mc 200,301,302,403 # JavaScript analysis — find hidden API endpoints Curl -s https://target.com | grep -oE ‘src=”[^”]+.js”‘ # Review each .js file for API paths, credentials, business logic # What to document... - Source: dev.to / 5 months ago
Import requests # Single gateway endpoint handles everything Proxy = { "http": "http://user:pass@gateway.provider.com:8080", "https": "http://user:pass@gateway.provider.com:8080" } Response = requests.get("https://target.com", proxies=proxy). - Source: dev.to / 6 months ago
From playwright.sync_api import sync_playwright # Playwright uses a real Chromium engine # TLS fingerprint matches Chrome With sync_playwright() as p: browser = p.chromium.launch() page = browser.new_page() page.goto("https://target.com") # Real Chrome TLS fingerprint. - Source: dev.to / 6 months ago
Cursor's security agents primarily operate in the first dimension, catching vulnerabilities in code. That's valuable and necessary work. But as you'll see in the walkthrough below, the other two dimensions matter just as much, especially at enterprise scale. And the organizations getting the best results, like Labelbox, which cleared a multi-year vulnerability backlog by running Cursor and Snyk together, are the... - Source: dev.to / 6 months ago
Use tools like Weights & Biases, Labelbox, or Maxim’s data engine to version your datasets, track changes, and continuously add new edge cases and user feedback. - Source: dev.to / about 1 year ago
Labelbox | Remote | Frontend / WebGL, Backend, Engineering Managers | https://labelbox.com Labelbox is building the training data platform to power breakthroughs in machine learning. We provide an end to end solutions for the full AI lifecycle from creating catalogs of unstructured data all the way to building the tools for humans to label the data to teach machines. Why choose us? - Source: Hacker News / almost 4 years ago
Hey, I have currently developed a U-Net model for segmentation and I am trying to use the model assisted labeling feature on LabelBox to annotate some masks, so I can save time on relabeling. I am just wondering if anyone is familiar with this feature or can give me a step by step guideline on how to go about doing this. I went through the examples on their GitHub but I’m honestly still very confused. Any help... Source: about 4 years ago
By now, I hope you see where I'm going with this. What is MDR doing? They're creating the labelled data used to train severance chips. They get a raw download of human brains in encoded format, and go about manually labelling the different pieces based on their most basic elements. Then, based on this manually labelled data, an algorithm can be trained to create a severance chip. MDR is basically Labelbox for... Source: over 4 years ago
Amazon - Online shopping from the earth's biggest selection of books, magazines, music, DVDs, videos, electronics, computers, software, apparel & accessories, shoes, jewelry, tools & hardware, housewares, furniture, sporting goods, beauty & much more
Playment - Playment is a fully-managed solution offering training data for AI, transcription, data collection and enrichment services at scale.
Walmart - Find quality products at unbeatable prices at home, in our stores or on the go.
Supervisely - Supervisely helps people with and without machine learning expertise to create state-of-the-art...
eBay - Buy and sell electronics, cars, fashion apparel, collectibles, sporting goods, digital cameras, baby items, coupons, and everything else on eBay, the world's online marketplace
CloudFactory - Human-powered Data Processing for AI and Automation