Real Time Bidding (RTB) - Demand Side Platform framework
Open Source Ad Serving Platform with ML-Powered CTR Prediction | Self-hosted alternative to Google Ad Manager | Python, FastAPI, PyTorch
Open-source ad fraud detection for small businesses using machine learning. Detect click fraud and bot traffic from Google Ads, Facebook Ads, and other platforms. Completely free.
Curated list of awesome Real Time Bidding ( RTB) frameworks, libraries, resources and other related to RTB things
Real Time Bidding (RTB) - Demand Side Platform ( DSP ) application based on vanilla-rtb stack
MCP server for Google Ad Manager. Manage ad campaigns, orders, line items, and creatives through AI assistants like Claude, ChatGPT, Gemini, Cursor, and VS Code. Automate your ad operations with natural language instead of clicking through the GAM UI. Built by Matious.
Code Generators and Extensions for vanilla-rtb stack
`GitHub` `Markdown` `ADINT` `Research` - ADINT: a field guide to ad-tech surveillance. How advertising data (MAID, RTB bidstream, SDK location) becomes warrantless intelligence.
🚀 Supercharge your Ads.
🔍 A curated list of ad intelligence and competitive analysis tools — ad creative search, app analytics, web traffic, and AI-powered research. Maintained by AdMapix.
Web server to track impressions and billing notifications for Mangolaa platform. Built using Groovy and Spring Boot on top of Vert.x and Apache Kafka
Enterprise-grade broadcast intelligence platform. Real-time ad verification, audio fingerprinting, and automated video evidence clipping powered by AI.
Web app to design & generate platform-specific ad creatives — React + Supabase with server-side ad rendering via Deno edge functions
High-Frequency Real-Time Bidding Engine optimized for second-price auctions
A suite of programs written in Python 3 to extract data from Index Exchange's report and CAL REST APIs and place the resultant data in a MySQL database
域名防红系统 | Domain Protection Against Google Safe Browsing Flags | Chrome红页防护 | 广告域名安全 | 无相盾 WuXiang Shield
Production-style ML pipeline for click-through rate prediction on the Avazu dataset. 32M training rows, 13M test predictions. L2-regularized logistic regression with smoothed CTR encoding, frequency encoding, and 2^22-dim feature hashing. Validation log loss 0.382 (~11.4% improvement over baseline).
Performance playground.
Ad intelligence & mobile app analytics plugin for OpenClaw — powered by AdMapix.com. Search ad creatives, analyze competitor strategies, and track app rankings across 200+ countries.
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