Automatic Speech Recognition: Build vs. Buy
Learn how to build a scalable ASR pipeline using Ray and Whisper, with batching, GPU optimization, and real-world tips from production deployments
Multimodal Monday #10: Unified Frameworks, Specialized Efficiency
Multimodal Monday Week 10: Xiaomi's 7B model outperforms GPT-4o, Ming-Omni unifies all modalities with 2.8B params, and specialized efficiency beats raw scale. The AI landscape is shifting fast.
Multimodal Monday #9: Compact Power, Creative Edge
A weekly pulse on everything multimodal—models, data, tools & community.
Multimodal Monday #8: Faster Systems, Faster Impact
A weekly pulse on everything multimodal—models, data, tools & community.
🚀 The Rise of the Dataset Engineer
Why the future of AI isn’t about bigger models — it’s about better data.
Multimodal Monday #7: Tailored Tools, Wider Reach
A weekly pulse on everything multimodal—models, data, tools & community.
Understanding Late Interaction Models in Multimodal Retrieval
Late interaction models enable precise retrieval from multimodal data like PDFs and images by comparing query tokens with token or patch embeddings—ideal for RAG, search, and document understanding.
Multimodal Monday #6: Retrieval Refined, Reach Expanded
A weekly pulse on everything multimodal—models, data, tools & community.
Multimodal Monday #5: GPT-Image Drops, Security Pops
A weekly pulse on everything multimodal—models, data, tools & community. 🎯 Quick Take (TL;DR) * OpenAI GPT-Image 1 arrives in
Video Segmentation: Unlocking Structure for Search and Analytics
Segmentation turns raw video into searchable chunks—objects, actions, scenes—boosting precision in multimodal search. It bridges unstructured content with structured queries like “man falling in warehouse,” enabling faster, more accurate retrieval across large datasets.
Multimodal Monday #4: From Pixels to Plans
Visual CoT, video gen, and color benchmarks highlight this week's multimodal AI leaps—plus tools, papers, and real-world use cases.
From Calls to Coaching: How CraftFlow Leverages Mixpeek to Extract Insights and Drive Sales
CraftFlow uses Mixpeek to extract talk patterns, auto-score calls, surface objections, and summarize key sales moments with AI-powered embeddings.
🎯 Multimodal Monday #3 — Scaling Multimodal AI: Laws, Lightweights & Large Releases
Apple’s new scaling law research redefines how multimodal models are built, while Moonshot and OpenGVLab drop powerful open-source VLMs with reasoning and tool-use.
AI-Powered Content Recommendations for AdTech
Even with data-driven targeting, most ads still miss. Contextual AI changes that—boosting relevance, clicks, and ROI without cookies.
Contextual Advertising After the Death of Cookies
As Google phases out third-party cookies, advertisers face declining performance from behavioral targeting. Learn how Contextual AI offers a privacy-safe, high-precision alternative.
🎯 Multimodal Monday #2 — From Tiny VLMs to 10M‑Token Titans
📢 Quick Take (TL;DR) * Major multimodal model releases: Meta unveiled Llama 4 Scout & Maverick – open Mixture-of-Experts models with native
Turning Frames into DataFrames: AI-Powered Video Analytics
By applying the classic group_by pattern to structured video data at index time, you can turn raw frames into searchable, analyzable DataFrames aligned with how your users explore footage.
🧠 Multimodal Monday #1 - State of the Stack
Researchers introducing new methods to replace embeddings with discrete IDs for faster cross-modal search
Semantic Video Chunking: Scene Detection
Intelligent video chunking using scene detection and vector embeddings. This tutorial covers how to break down videos into semantic scenes, generate embeddings, and enable powerful semantic search capabilities.
AI Video Tagging With Dynamic Taxonomies
AI video tagging used to mean manual review and basic object detection. With multimodal models and dynamic taxonomies, you can now automatically detect brand moments, inappropriate content, actions, moods and trending content at scale.
Building your own AI-Powered Media Asset Management System
This guide will walk developers through building a modern Media Asset Management (MAM) system with semantic search capabilities using Mixpeek's infrastructure.
Reverse Image Search with CLIP and MongoDB
AI-powered image discovery app using Mixpeek's multimodal SDK and MongoDB's $vectorSearch. Features deep learning, vector embeddings, and KNN search for advanced visual content management.
Build a Model Context Protocol (MCP) Server on S3 using Lambda, Temporal, Ray, and Qdrant
Build a scalable MCP pipeline on S3 using AWS Lambda, Temporal, Ray, and Qdrant to process and index unstructured data like video, audio, and PDFs for real-time AI search and retrieval.
Advanced Video Understanding: Mixpeek Embed and Weaviate KNN for Multimodal AI
This article demonstrates how to build a reverse video search system using Mixpeek for video processing and embedding, and Weaviate as a vector database, enabling both video and text queries to find relevant video segments through semantic similarity.