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    <lastmod>2026-04-23T13:47:59.061Z</lastmod>
    <priority>0.6</priority>
  </url>
  <url>
    <loc>https://mixpeek.com/recipes/multimodal-knowledge-base</loc>
    <lastmod>2026-04-23T13:47:59.061Z</lastmod>
    <priority>0.6</priority>
  </url>
  <url>
    <loc>https://mixpeek.com/recipes/sports-highlights</loc>
    <lastmod>2026-04-23T13:47:59.061Z</lastmod>
    <priority>0.6</priority>
  </url>
  <url>
    <loc>https://mixpeek.com/education/videos/brightdata-mixpeek-walkthrough</loc>
    <lastmod>2026-03-17T00:00:00Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/VA5frNrIzmg/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[Turn Web Scraping into Structured AI Data (Bright Data + Mixpeek Walkthrough)]]></video:title>
      <video:description><![CDATA[A hands-on walkthrough showing how to combine Bright Data's web scraping infrastructure with Mixpeek to transform raw web content into structured, searchable AI data.\n\nWhat you'll learn:\n⚡ Connecting Bright Data as a data source in Mixpeek\n⚡ Scraping and ingesting web content at scale\n⚡ Extracting structured context with multimodal AI\n⚡ Making scraped data searchable via retrievers\n⚡ End-to-end pipeline from raw web data to AI-ready output]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=VA5frNrIzmg</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/VA5frNrIzmg</video:player_loc>
      <video:publication_date>2026-03-17T00:00:00Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
    </video:video>
  </url>
  <url>
    <loc>https://mixpeek.com/education/videos/web-scraper-guide</loc>
    <lastmod>2026-01-22T00:00:00Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/ONBW4oISnGs/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[Web Scraper Guide]]></video:title>
      <video:description><![CDATA[Learn how to use Mixpeek's Web Scraper to recursively crawl websites and extract multimodal content with automatic embeddings. This guide demonstrates crawling documentation sites, extracting code snippets and images, and making everything searchable with semantic embeddings.\n\nWhat you'll learn:\n⚡ Recursive website crawling with depth control\n⚡ Extracting text, code blocks, and images\n⚡ Multimodal embeddings (E5-Large, Jina Code, SigLIP)\n⚡ JavaScript rendering for SPAs\n⚡ URL filtering and structured extraction\n⚡ Building searchable knowledge bases from docs]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=ONBW4oISnGs</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/ONBW4oISnGs</video:player_loc>
      <video:duration>720</video:duration>
      <video:publication_date>2026-01-22T00:00:00Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
    </video:video>
  </url>
  <url>
    <loc>https://mixpeek.com/education/videos/buckets-guide</loc>
    <lastmod>2026-01-22T00:00:00Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/2GLkl8NnqcY/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[Buckets Guide]]></video:title>
      <video:description><![CDATA[Learn how to use Mixpeek Buckets for schema-backed data ingestion with automatic validation and lineage tracking. This guide demonstrates creating buckets, defining schemas, uploading objects with multimodal blobs, and processing them through collections.\n\nWhat you'll learn:\n⚡ Creating buckets with JSON schema validation\n⚡ Uploading objects with multimodal blobs (text, image, video, JSON)\n⚡ Schema enforcement and blob type validation\n⚡ Lineage tracking from source to documents\n⚡ Integration with collections for feature extraction\n⚡ Best practices for organizing multimodal data]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=2GLkl8NnqcY</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/2GLkl8NnqcY</video:player_loc>
      <video:duration>600</video:duration>
      <video:publication_date>2026-01-22T00:00:00Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
    </video:video>
  </url>
  <url>
    <loc>https://mixpeek.com/education/videos/taxonomies-guide</loc>
    <lastmod>2025-01-15T00:00:00Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/Absrbi3y-p0/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[Mixpeek Taxonomies Guide]]></video:title>
      <video:description><![CDATA[Learn how to organize and classify your multimodal content using Mixpeek's new Taxonomies feature. This guide walks you through creating hierarchical classifications that understand both visual and textual content using powerful embedding models.\n\nWhat you'll learn:\n⚡ Creating custom taxonomies\n⚡ Implementing multimodal embeddings\n⚡ Automatic content classification\n⚡ Hierarchical search capabilities\n⚡ Best practices for video taxonomies]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=Absrbi3y-p0</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/Absrbi3y-p0</video:player_loc>
      <video:duration>900</video:duration>
      <video:publication_date>2025-01-15T00:00:00Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
    </video:video>
  </url>
  <url>
    <loc>https://mixpeek.com/education/videos/namespaces-guide</loc>
    <lastmod>2025-01-10T00:00:00Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/HrYvIir4_-U/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[Mixpeek Namespaces Guide]]></video:title>
      <video:description><![CDATA[Learn how to create and manage isolated environments for your search applications using Mixpeek's Namespaces feature. This guide covers organizing use cases, applications, or environments with independent configurations and security controls.\n\nWhat you'll learn:\n⚡ Logical separation for different applications\n⚡ Configuring vector and payload indexes\n⚡ Best practices for organizing namespaces\n⚡ Implementing environment and client isolation\n⚡ Naming conventions for efficient management]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=HrYvIir4_-U</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/HrYvIir4_-U</video:player_loc>
      <video:duration>900</video:duration>
      <video:publication_date>2025-01-10T00:00:00Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
    </video:video>
  </url>
  <url>
    <loc>https://mixpeek.com/education/videos/collections-guide</loc>
    <lastmod>2025-01-05T00:00:00Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/NBaiQB6HhdI/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[Mixpeek Collections Guide]]></video:title>
      <video:description><![CDATA[Learn how to effectively organize your multimodal content using Mixpeek Collections! This guide covers everything you need to know about creating and managing content groups for optimized search and retrieval.\n\nWhat you'll learn:\n⚡ Collection creation and management\n⚡ Content organization strategies\n⚡ Namespace isolation\n⚡ Access patterns and permissions\n⚡ Performance optimization tips\n\nKey highlights:\nCreate logical content groupings\nScale to millions of assets\nOptimize search configurations\nManage access controls]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=NBaiQB6HhdI</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/NBaiQB6HhdI</video:player_loc>
      <video:duration>900</video:duration>
      <video:publication_date>2025-01-05T00:00:00Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
    </video:video>
  </url>
  <url>
    <loc>https://mixpeek.com/education/videos/multimodal-monday-2</loc>
    <lastmod>2025-01-20T00:00:00Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/t5rN3xHgNqk/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[Multimodal Monday #2, From Tiny VLMs to 10M‑Token Titans]]></video:title>
      <video:description><![CDATA[This week in multimodal AI was wild, we're talking:\n\nMeta's Llama 4 with 10M-token context windows\nMicrosoft's Phi-4-Multimodal outperforming much larger models\nHugging Face's SmolVLM that runs on less than 1GB RAM\nPoisoned image attacks on retrieval-augmented generation (!)\n\nWe'll break down the latest research, tools, real-world use cases, and what it all means for developers, founders, and builders in the AI space.\n\n⏱ Timestamps:\n00:00 – Welcome\n00:25 – Quick Take\n01:05 – Research Highlights\n02:10 – Tools & Techniques\n03:00 – Real-World Applications\n03:40 – Trends & Predictions\n04:30 – Community & Shoutouts\n04:55 – Wrap-up]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=t5rN3xHgNqk</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/t5rN3xHgNqk</video:player_loc>
      <video:duration>300</video:duration>
      <video:publication_date>2025-01-20T00:00:00Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
    </video:video>
  </url>
  <url>
    <loc>https://mixpeek.com/education/videos/data-transformation-pipeline</loc>
    <lastmod>2025-12-09T00:00:00Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/6mQK21ivkSs/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[The Data Transformation Pipeline]]></video:title>
      <video:description><![CDATA[Master the Mixpeek philosophy: Objects → Documents → Enriched Knowledge. This foundational video covers the three-layer architecture (Buckets → Collections → Retrievers), decomposition and recomposition patterns, enrichment as immutable joins, and configuration-over-code principles. Essential mental models for understanding how Mixpeek transforms raw data into searchable intelligence.\n\nWhat you'll learn:\n⚡ The three-layer architecture pattern\n⚡ Decomposition: Breaking objects into semantic layers\n⚡ Recomposition: Multi-stage pipeline assembly\n⚡ Enrichment through similarity joins\n⚡ Declarative pipeline configuration\n⚡ Complete provenance tracking]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=6mQK21ivkSs</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/6mQK21ivkSs</video:player_loc>
      <video:duration>414</video:duration>
      <video:publication_date>2025-12-09T00:00:00Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
    </video:video>
  </url>
  <url>
    <loc>https://mixpeek.com/education/videos/semantic-search-fundamentals</loc>
    <lastmod>2025-12-21T00:00:00Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/nB3fe0OfLyk/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[Semantic Search Fundamentals]]></video:title>
      <video:description><![CDATA[Move beyond keyword matching to meaning-based retrieval. This video covers the evolution from keyword search to semantic search, vector similarity algorithms (cosine similarity, dot product), encoding models, and building feature search pipelines in Mixpeek.\n\nWhat you'll learn:\n⚡ Keyword search vs semantic search\n⚡ Cosine similarity and dot product explained\n⚡ How encoder models capture meaning\n⚡ HNSW indexes for vector search\n⚡ Building retrieval pipelines with feature search\n⚡ Intent-based retrieval in practice]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=nB3fe0OfLyk</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/nB3fe0OfLyk</video:player_loc>
      <video:duration>990</video:duration>
      <video:publication_date>2025-12-21T00:00:00Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
    </video:video>
  </url>
  <url>
    <loc>https://mixpeek.com/education/videos/hybrid-search</loc>
    <lastmod>2025-12-24T00:00:00Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/dMFaPWJAeBw/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[Hybrid Search: Best of Both Worlds]]></video:title>
      <video:description><![CDATA[Combine keyword (BM25) and semantic (vector) search for maximum retrieval effectiveness. Learn when keyword search fails with synonyms and paraphrasing, when semantic search fails with specific IDs and acronyms, and how to use score fusion strategies to get the best of both worlds.\n\nWhat you'll learn:\n⚡ Trade-offs between keyword and semantic search\n⚡ Precision vs recall in retrieval systems\n⚡ Score fusion strategies (RRF, weighted, distribution-based)\n⚡ The 80/20 rule for catching edge cases\n⚡ Building hybrid retrievers with feature_filter and attribute_filter\n⚡ Real-world example: Developer documentation search]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=dMFaPWJAeBw</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/dMFaPWJAeBw</video:player_loc>
      <video:duration>669</video:duration>
      <video:publication_date>2025-12-24T00:00:00Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
    </video:video>
  </url>
  <url>
    <loc>https://mixpeek.com/education/videos/chunking-strategies</loc>
    <lastmod>2025-12-24T00:00:00Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/RICgil0nkwQ/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[Chunking Strategies: Breaking Documents into Searchable Pieces]]></video:title>
      <video:description><![CDATA[Master the art of breaking large documents into searchable chunks. Learn why chunking is necessary for context windows and precision, explore fixed-size, semantic, and sentence-based strategies, and understand chunk overlap techniques that prevent information loss at boundaries.\n\nWhat you'll learn:\n⚡ Why chunking matters for context windows and precision\n⚡ Chunking strategies: fixed-size, semantic, sentence-based, layout-based\n⚡ Chunk overlap as a safety net (67% → 94% accuracy improvement)\n⚡ Multimodal chunking: videos, audio, images, PDFs\n⚡ Building object decomposition pipelines in Mixpeek\n⚡ Real-world example: 200-page legal contract analysis]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=RICgil0nkwQ</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/RICgil0nkwQ</video:player_loc>
      <video:duration>846</video:duration>
      <video:publication_date>2025-12-24T00:00:00Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
    </video:video>
  </url>
  <url>
    <loc>https://mixpeek.com/education/videos/feature-uris</loc>
    <lastmod>2025-12-29T00:00:00Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/lrkCQkhU4Ls/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[Feature URIs: Evolving Embeddings Without Migration]]></video:title>
      <video:description><![CDATA[Learn how to evolve embedding models without painful re-indexing. Master Feature URIs-a core abstraction for managing the lifecycle of embeddings, extractors, and indexes. Discover why vector indexes are stateful, how to A/B test embedding models safely, and how to roll forward and roll back upgrades without downtime.\n\nWhat you'll learn:\n⚡ Why vector indexes are inherently stateful and fragile\n⚡ The 4 components of a Feature URI\n⚡ How extractors, embedding models, versions, and inference endpoints are coupled\n⚡ A/B testing embedding models without re-indexing\n⚡ Rolling forward and rolling back embedding upgrades\n⚡ Real examples using image collections and feature search\n⚡ How Feature URIs enable hybrid search, re-ranking, and evaluation]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=lrkCQkhU4Ls</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/lrkCQkhU4Ls</video:player_loc>
      <video:duration>455</video:duration>
      <video:publication_date>2025-12-29T00:00:00Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
    </video:video>
  </url>
  <url>
    <loc>https://mixpeek.com/education/videos/image-understanding</loc>
    <lastmod>2026-01-06T00:00:00Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/WyZ777Qr5ww/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[Image Understanding: Vision Encoders & Multimodal Search]]></video:title>
      <video:description><![CDATA[Master how computers see and search images. This video covers vision encoding models like CLIP and SigLIP, how images are converted into patches and embeddings, object detection with YOLO, and building multimodal search systems that support text-to-image, image-to-text, and image-to-image queries.\n\nWhat you'll learn:\n⚡ How vision transformers convert images into embeddings\n⚡ Image patches and mean pooling explained\n⚡ CLIP vs SigLIP embedding models\n⚡ Object detection and classification with YOLO\n⚡ Cross-modal search: text queries on image datasets\n⚡ Combining text + image queries with mean pooling\n⚡ Feature URIs for image extractors\n⚡ Live demo: National Gallery multimodal retriever]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=WyZ777Qr5ww</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/WyZ777Qr5ww</video:player_loc>
      <video:duration>872</video:duration>
      <video:publication_date>2026-01-06T00:00:00Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
    </video:video>
  </url>
  <url>
    <loc>https://mixpeek.com/education/videos/video-understanding</loc>
    <lastmod>2026-01-10T00:00:00Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/3U1riPFtc-k/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[Video Understanding: From Frames to Contextual Search]]></video:title>
      <video:description><![CDATA[Master video understanding and how it differs from basic image understanding. This video covers frame extraction techniques (sampling, keyframe detection, scene-based), video embedding models that capture temporal context, and building sophisticated semantic video search applications.\n\nWhat you'll learn:\n⚡ Video vs image understanding: temporal context matters\n⚡ Frame extraction techniques: sampling, keyframe, scene-based\n⚡ Frame-level vs video-level embeddings\n⚡ How video embeddings capture motion and actions\n⚡ Scene detection with AutoShot and semantic deduplication\n⚡ Vertex AI multimodal embeddings for video\n⚡ Building scene-based video search pipelines\n⚡ Real demo: Contextual video retrieval in Mixpeek Studio]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=3U1riPFtc-k</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/3U1riPFtc-k</video:player_loc>
      <video:duration>678</video:duration>
      <video:publication_date>2026-01-10T00:00:00Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
    </video:video>
  </url>
  <url>
    <loc>https://mixpeek.com/education/videos/what-are-embeddings</loc>
    <lastmod>2025-12-09T00:00:00Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/npmIz2-a3wE/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[What Are Embeddings?]]></video:title>
      <video:description><![CDATA[Similar meaning = similar numbers. That's the entire idea. Embeddings are vector representations that capture semantic meaning-and they're the foundation of everything in modern search.\n\nWhat you'll learn:\n⚡ What embeddings actually are (numbers that represent meaning)\n⚡ Why embeddings matter (similar things → similar vectors)\n⚡ Dense vs sparse embeddings (ColBERT vs SPLADE)\n⚡ Embedding dimensions and what they represent\n⚡ How 'refund request' and 'want my money back' map to nearly identical vectors]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=npmIz2-a3wE</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/npmIz2-a3wE</video:player_loc>
      <video:duration>552</video:duration>
      <video:publication_date>2025-12-09T00:00:00Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
    </video:video>
  </url>
  <url>
    <loc>https://mixpeek.com/education/videos/intro-multimodal-university</loc>
    <lastmod>2024-11-01T00:00:00Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/ET00DlSomSk/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[Introduction to Multimodal University]]></video:title>
      <video:description><![CDATA[Join Ethan, former MongoDB Search Engineer and CEO of Mixpeek, as he introduces you to the world of multimodal AI development. Learn what you'll master in this comprehensive course and why multimodal understanding is crucial for modern applications.]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=ET00DlSomSk</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/ET00DlSomSk</video:player_loc>
      <video:duration>900</video:duration>
      <video:publication_date>2024-11-01T00:00:00Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
    </video:video>
  </url>
  <url>
    <loc>https://mixpeek.com/education/videos/what-is-multimodal</loc>
    <lastmod>2024-11-05T00:00:00Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/iLBOygFPiJk/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[What is Multimodal Understanding?]]></video:title>
      <video:description><![CDATA[Discover how computers can understand multiple types of data simultaneously. This lesson breaks down the core concepts of multimodal systems and their real-world applications. Learn how text, images, audio, and video work together in modern AI systems.]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=iLBOygFPiJk</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/iLBOygFPiJk</video:player_loc>
      <video:duration>900</video:duration>
      <video:publication_date>2024-11-05T00:00:00Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
    </video:video>
  </url>
  <url>
    <loc>https://mixpeek.com/education/videos/fundamental-concepts</loc>
    <lastmod>2024-11-10T00:00:00Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/Zpg-O11lJjA/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[Fundamental Multimodal Concepts]]></video:title>
      <video:description><![CDATA[Dive into the building blocks of multimodal systems. Learn about data representation, feature extraction, neural networks, and information retrieval. Essential concepts for anyone looking to build sophisticated AI applications.]]></video:description>
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      <video:title><![CDATA[Text Understanding Fundamentals]]></video:title>
      <video:description><![CDATA[Master the basics of natural language processing. From tokenization to word embeddings, learn how machines understand and process text data. Includes practical examples and real-world applications.]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=Vd_Hm5y13aQ</video:content_loc>
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      <video:live>no</video:live>
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    <lastmod>2024-11-20T00:00:00Z</lastmod>
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      <video:title><![CDATA[Audio Understanding Fundamentals]]></video:title>
      <video:description><![CDATA[Explore how machines process and understand audio signals. Learn about waveforms, feature extraction, and common audio processing tasks. Essential knowledge for speech recognition and audio analysis.]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=_RzsIeT7gqA</video:content_loc>
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    <lastmod>2024-11-25T00:00:00Z</lastmod>
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      <video:title><![CDATA[Visual Understanding Fundamentals]]></video:title>
      <video:description><![CDATA[Learn the foundations of computer vision. From image preprocessing to object detection, understand how machines interpret visual information. Includes practical examples of feature extraction and real-world applications.]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=tu327n7hpCw</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/tu327n7hpCw</video:player_loc>
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    <lastmod>2024-12-15T00:00:00Z</lastmod>
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      <video:title><![CDATA[Stop Ignoring Out-of-Distribution Data, Catch What Pretrained Models Miss]]></video:title>
      <video:description><![CDATA[Most models fail when they see something new, but that's often where the real risk is.\nWhether you\'re in brand safety, insurance, surveillance, or ad tech, the \"weird\" unlabeled events could be the most important ones.\n\nIn this video, we show how Mixpeek lets you:\n✅ Build custom low-level feature extractors\n✅ Cluster unlabeled, novel data\n✅ Spot unknowns before they become liabilities\n\nDon't throw away your classification rejects.\nExtract. Cluster. Understand.]]></video:description>
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      <video:title><![CDATA[IAB Content Taxonomy Mapper: Free Open-Source Tool for 2.x → 3.0 Migration]]></video:title>
      <video:description><![CDATA[Upgrade to IAB Content Taxonomy 3.0 without the headaches.\nThis video walks you through the open-source IAB Mapper, a free tool that helps adtech platforms, publishers, and brand safety vendors migrate from IAB 2.x to 3.0 in minutes.\n\n👉 Demo UI: https://mxp.co/taxonomy\n👉 GitHub Repo: https://github.com/mixpeek/iab-mapper\n👉 Docs (Taxonomies API): https://mixpeek.com/docs/enrichment/taxonomies\n\nWhat you'll learn in this video:\n• Why IAB 3.0 migration is mandatory\n• How to use the demo UI (upload → map → export)\n• How to install & run the CLI with pip/npm\n• The mapping pipeline explained: Exact label match, TF-IDF, BM25, vector KNN, and LLM reranking with Ollama\n• Real-world use cases: contextual targeting, brand safety, creative attribution\n• Benefits: open-source, runs locally, confidence-scored outputs, extensible AI methods\n\nThis mapper is free, open-source, and MIT licensed. Perfect for anyone who needs to migrate, validate, or experiment with IAB 3.0.]]></video:description>
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      <video:player_loc>https://www.youtube.com/embed/wHLwfp8nKpc</video:player_loc>
      <video:duration>374</video:duration>
      <video:publication_date>2025-10-20T00:00:00Z</video:publication_date>
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    <lastmod>2025-08-25T00:00:00Z</lastmod>
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      <video:title><![CDATA[IAB Taxonomy: The Structure Behind AI Campaigns (60‑Second Breakdown)]]></video:title>
      <video:description><![CDATA[ai can scan text, video, and audio at scale.\nbut here's the problem → without structure, it's just chaos.\n\nthat's where the iab taxonomy comes in. it gives ai campaigns the shared language they need to deliver relevance, safety, comparability, and scale.\n\nin this 60-second breakdown, you'll learn:\n\nwhy raw ai outputs aren't enough\nhow taxonomies standardize targeting across platforms\nwhat to show in your next rfp deck to prove you're future-ready\n\n👉 subscribe for more practical breakdowns on contextual targeting + ai in adtech.]]></video:description>
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      <video:publication_date>2025-08-25T00:00:00Z</video:publication_date>
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      <video:live>no</video:live>
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    <loc>https://mixpeek.com/education/videos/cookies-dead-context-alive</loc>
    <lastmod>2025-08-30T00:00:00Z</lastmod>
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      <video:title><![CDATA[Cookies Are Dead, Context Is Alive]]></video:title>
      <video:description><![CDATA[💀 Cookies are dead.\n⚡ Context is alive.\n\nThe old world: follow users with creepy IDs.\nThe new world: understand the content itself.\n\n👎 Apple pie ads during an Apple keynote.\n👍 Nike ads during a basketball game.\n\nThis isn't the end of targeting.\nIt's the rebirth.]]></video:description>
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    <lastmod>2026-01-25T00:00:00Z</lastmod>
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      <video:title><![CDATA[Building an Exploratory Multimodal Retriever with the National Gallery]]></video:title>
      <video:description><![CDATA[Discover how to build a powerful exploratory image board using multimodal search across 120,000 images from the National Gallery. This walkthrough demonstrates combining text search, reverse image search, and document-based queries into a unified retrieval experience using hybrid search with Reciprocal Rank Fusion (RRF).\n\n👉 Live Demo: https://mxp.co/r/npg\n\nWhat you'll learn:\n⚡ Building exploratory search interfaces for visual content\n⚡ Combining text, image, and document reference queries\n⚡ Implementing hybrid search with RRF for optimal results\n⚡ Using Google SigLIP embeddings for image understanding\n⚡ Creating multi-stage retriever pipelines with feature search\n⚡ Capturing user signals for recommendation systems\n⚡ Architecture patterns: Objects → Buckets → Collections → Retrievers\n\nReal-world demo: Visual curation across 120k images, 12GB of data, with text + image + document hybrid queries. Full source code available in the Mixpeek showcase repository.]]></video:description>
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    <lastmod>2026-03-24T00:00:00Z</lastmod>
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      <video:title><![CDATA[IP Safety Scanner: Pre-Publication Copyright Detection with Mixpeek]]></video:title>
      <video:description><![CDATA[A walkthrough of the IP safety scanner showing how to detect celebrity likenesses, brand logos, and copyrighted audio in video and image content before publication.\n\nWhat you'll learn:\n⚡ How pre-publication IP clearance works\n⚡ Face detection against custom reference corpora\n⚡ Logo and trademark recognition in video frames\n⚡ Audio fingerprinting for copyrighted music\n⚡ Setting confidence thresholds for automated clearance]]></video:description>
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    <lastmod>2026-04-22T19:06:03Z</lastmod>
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      <video:title><![CDATA[OpenAI o3 hallucinates 33%. The fix isn't a better model.]]></video:title>
      <video:description><![CDATA[OpenAI o3 hallucinates 33% on PersonQA, 51% on SimpleQA. These are factual grounding tests, not trick questions.\n\nThe fix isn't a bigger model — it's infrastructure. Ingest your actual data, extract structured features, build retrievable indexes.\n\nmixpeek.com/agentic-rag]]></video:description>
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    <loc>https://mixpeek.com/education/videos/short-PdiG5Fw9JQY</loc>
    <lastmod>2026-04-22T18:06:08Z</lastmod>
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      <video:title><![CDATA[One seller. Six brands. $2.3M/yr siphoned.]]></video:title>
      <video:description><![CDATA[One seller. Six brands. $2.3M/yr siphoned. Every brand protection tool misses it because their data is siloed per contract. We surface seller networks.\n\ndupes.mxp.co]]></video:description>
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      <video:live>no</video:live>
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    <lastmod>2026-04-21T20:47:07Z</lastmod>
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      <video:title><![CDATA[847 Stanley dupe listings. $34K/mo revenue leak.]]></video:title>
      <video:description><![CDATA[I scanned 6 marketplaces for Stanley Quencher dupes. 847 listings. $34,000/mo revenue leak from one seller cluster.\n\nStanley Quencher is the most-dupe'd DTC product of the decade. Counterfeits, dupes, legit resale, and the seller networks connecting them.\n\ndupes.mxp.co]]></video:description>
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      <video:duration>60</video:duration>
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    <lastmod>2026-04-21T19:47:04Z</lastmod>
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      <video:title><![CDATA[Dupes are a $500B industry. Your brand is probably on Amazon.]]></video:title>
      <video:description><![CDATA[Dupes are a $500 billion industry. If you run a DTC brand, you are bleeding revenue to counterfeits and dupes across Amazon, Shein, Facebook Marketplace every single day.\n\nRed Points and MarqVision want six figures and a three-month onboarding to find the obvious fakes. We built the scanner and the intelligence report.\n\nPaste your Shopify URL and we scan 6 marketplaces. Every Monday you get a report with every dupe, counterfeit, the sellers, and the estimated revenue they're taking from you.\n\nWhen 47 sellers are duping the same colorway of your best-selling product, that's not just infringement — that's a demand signal. Your dupes report doubles as your product roadmap.\n\ndupes.mxp.co]]></video:description>
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      <video:duration>60</video:duration>
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      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
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    <loc>https://mixpeek.com/education/videos/short-brcmAzSyUV8</loc>
    <lastmod>2026-04-21T18:47:06Z</lastmod>
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      <video:title><![CDATA[One Upload. Five IP Checks. Free.]]></video:title>
      <video:description><![CDATA[Every frame you broadcast is a potential lawsuit. Content ID only checks music, and only on YouTube. We built a free multimodal scanner — upload any image, video, or audio. One pipeline checks faces (530+ public figures), logos (3,000+ brands), music fingerprints, USPTO sound marks, and trademarked slogans. All in one scan. 10 free scans/day, no signup.\n\ncopyright.mixpeek.com]]></video:description>
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    <loc>https://mixpeek.com/education/videos/short-LxCeNJDm5CA</loc>
    <lastmod>2026-04-19T15:15:33Z</lastmod>
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      <video:title><![CDATA[95% of AI Pilots Fail — MIT Measured the Same Flaw]]></video:title>
      <video:description><![CDATA[95% of enterprise AI pilots fail. MIT measured it. They all have the same flaw: reactive architecture.\n\nEvery failed pilot has the same pattern — user types prompt, agent responds, nothing happens until someone asks. Nobody's watching at 2 AM when a counterfeit product goes live.\n\nWhat's missing is a trigger. Not a cron job — an event-driven webhook that fires when data matches a condition. The agent doesn't wait. It watches.\n\nmixpeek.com/use-cases/video-compliance-monitoring]]></video:description>
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    <loc>https://mixpeek.com/education/videos/short-k2XX3TiPg18</loc>
    <lastmod>2026-04-17T21:10:06Z</lastmod>
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      <video:thumbnail_loc>https://img.youtube.com/vi/k2XX3TiPg18/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[Every Vector Is Locked to Its Model. Here's the Fix.]]></video:title>
      <video:description><![CDATA[Every vector in your database is locked to the model that created it. Upgrade the model, and your entire index breaks. Feature URIs tag every vector with its extractor, model version, and embedding space — so you can upgrade, A/B test, and roll back without re-indexing.\n\nmixpeek.com/embeddings]]></video:description>
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    <loc>https://mixpeek.com/education/videos/short-yl7PMqq3VcM</loc>
    <lastmod>2026-04-17T20:10:07Z</lastmod>
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      <video:thumbnail_loc>https://img.youtube.com/vi/yl7PMqq3VcM/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[LangChain Raised $125M. Your Agent Still Can't See.]]></video:title>
      <video:description><![CDATA[LangChain raised $125M and built the best text orchestrator in the market. Chains, memory, tool calling — all excellent. But orchestration is not multimodal retrieval. Your agent can route calls and manage state. It cannot search 50,000 product images, find a frame in 10,000 hours of video, or search across thousands of documents. We built the multimodal retrieval layer with 43 MCP tools.\n\nmixpeek.com/agentic-rag]]></video:description>
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    <loc>https://mixpeek.com/education/videos/short-KW_DWbL7K5I</loc>
    <lastmod>2026-04-17T19:10:06Z</lastmod>
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      <video:thumbnail_loc>https://img.youtube.com/vi/KW_DWbL7K5I/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[The $3,000/Dev/Year Tax on Multimodal AI Agents]]></video:title>
      <video:description><![CDATA[It takes 6 weeks and 7 systems to give an agent multimodal capabilities. CLIP for images, Whisper for audio, Pinecone for vectors, S3 for storage, chunking logic, caching, an orchestrator. $3,000/dev/year before the agent takes a single action. We collapsed that into one API with unified embeddings across every modality.\n\nmixpeek.com/agentic-rag]]></video:description>
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      <video:publication_date>2026-04-17T19:10:06Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
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  <url>
    <loc>https://mixpeek.com/education/videos/short-cUBVIkTNKpg</loc>
    <lastmod>2026-04-17T03:33:03Z</lastmod>
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      <video:thumbnail_loc>https://img.youtube.com/vi/cUBVIkTNKpg/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[Your AI Agent Can't Search 10,000 Images. Here's Why.]]></video:title>
      <video:description><![CDATA[GPT-4o can describe an image. It can't search 10,000 of them. Claude can summarize a PDF. It can't find the one page across 50,000 documents that matches your query. The agent stack has compute, orchestration, execution — nobody owns multimodal retrieval. That's the layer between raw data and agent action.\n\nmixpeek.com/agentic-rag]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=cUBVIkTNKpg</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/cUBVIkTNKpg</video:player_loc>
      <video:duration>60</video:duration>
      <video:publication_date>2026-04-17T03:33:03Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
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    <loc>https://mixpeek.com/education/videos/short-e8QDXM2YmvA</loc>
    <lastmod>2026-04-16T22:33:10Z</lastmod>
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    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/e8QDXM2YmvA/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[Dupes are legal. Your brand protection tool can't see them.]]></video:title>
      <video:description><![CDATA[]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=e8QDXM2YmvA</video:content_loc>
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    <loc>https://mixpeek.com/education/videos/short-mZB3VMVln1M</loc>
    <lastmod>2026-04-16T20:33:07Z</lastmod>
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      <video:thumbnail_loc>https://img.youtube.com/vi/mZB3VMVln1M/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[Red Points got a DTC founder sued by her own best customer]]></video:title>
      <video:description><![CDATA[]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=mZB3VMVln1M</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/mZB3VMVln1M</video:player_loc>
      <video:duration>60</video:duration>
      <video:publication_date>2026-04-16T20:33:07Z</video:publication_date>
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      <video:live>no</video:live>
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    <loc>https://mixpeek.com/education/videos/short-rclxtqtAuwk</loc>
    <lastmod>2026-04-16T19:33:04Z</lastmod>
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    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/rclxtqtAuwk/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[Pinecone Calls It Serverless — But It Bills You at Idle]]></video:title>
      <video:description><![CDATA[Pinecone's \"serverless\" still bills you at idle — every read, every write, every stored gigabyte. Pods start at $70/month and scale linearly even when nobody's querying.\n\nSo we built MVS — Mixpeek Vector Store. IVF-Flat with SIMD directly on S3. 100% recall across every profile and corpus size — not 99, 100. 1ms on small catalogs. 3ms on 100K vectors. No indexing phase. No always-on pods. No warm-cache tax. KEDA scales the whole thing to zero — and your bill goes with it.\n\nWe published the full benchmark so you can run it against your own Pinecone, Weaviate, or Qdrant indexes.\n\n→ mixpeek.com/mvs\n\n#vectordatabase #serverless #pinecone #ai #infrastructure]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=rclxtqtAuwk</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/rclxtqtAuwk</video:player_loc>
      <video:duration>60</video:duration>
      <video:publication_date>2026-04-16T19:33:04Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
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  <url>
    <loc>https://mixpeek.com/education/videos/short-YvCwpBcsgjk</loc>
    <lastmod>2026-04-16T18:33:05Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/YvCwpBcsgjk/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[Your Dupe Report Is Also Your Product Roadmap]]></video:title>
      <video:description><![CDATA[Dupes are a $500 billion industry — and your DTC brand is bleeding revenue to counterfeits across Amazon, Xiaohongshu, Facebook Marketplace every single day. Mark Vision and Red Points each want six figures and a 3-month onboarding to find the obvious fakes. Everyone else is handing it to two interns with an inbox.\n\nSo we built the scanner. Paste your Shopify URL — we pull your catalog, scan six marketplaces, and every Monday morning email you a report with every dupe, every counterfeit, side-by-side evidence, the listings, the sellers, and the estimated revenue they're taking from you. Because we monitor every brand on a single platform, we cluster sellers across brands — so you see the entire counterfeit economy, not just your corner of it.\n\nHere's the part nobody expects: when 47 sellers are duping the same colorway of your best-selling product, that's not just infringement — that's a demand signal. Your dupe report doubles as your product roadmap. Free market research delivered every Monday.\n\n→ dupes.mxp.co — first dupe we find is free.\n\n#brandprotection #ecommerce #shopify #counterfeits #DTC]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=YvCwpBcsgjk</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/YvCwpBcsgjk</video:player_loc>
      <video:duration>60</video:duration>
      <video:publication_date>2026-04-16T18:33:05Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
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  <url>
    <loc>https://mixpeek.com/education/videos/short-_5esgVns834</loc>
    <lastmod>2026-04-15T20:06:05Z</lastmod>
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      <video:thumbnail_loc>https://img.youtube.com/vi/_5esgVns834/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[I Run My Startup With 6 AI Agents in a Terminal.]]></video:title>
      <video:description><![CDATA[I built a terminal multiplexer where every pane is a Claude Code session. They share a kanban board, peek at each other's work, control browsers with saved auth, send emails, and manage a CRM. No Slack, no Notion, no project manager. The hard part isn't making agents smart -- it's giving them shared state and access to the real world. I open-sourced it.\n\namux.io]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=_5esgVns834</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/_5esgVns834</video:player_loc>
      <video:duration>60</video:duration>
      <video:publication_date>2026-04-15T20:06:05Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
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    <loc>https://mixpeek.com/education/videos/short-FigGbVEjCIg</loc>
    <lastmod>2026-04-15T19:06:04Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/FigGbVEjCIg/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[How a Multimodal Data Warehouse Actually Works (3 Steps)]]></video:title>
      <video:description><![CDATA[Snowflake stores rows. But 80% of your data is unstructured — videos, images, PDFs, audio. Your warehouse can't even open a video.\n\nHere's how a multimodal data warehouse works:\n1. Decompose — 17 extractors break one file into searchable features\n2. Store — 4 tiers from hot vector DB to cold archive\n3. Reassemble — multi-stage retrieval pipelines compose results\n\nDecompose. Store. Reassemble. That's the whole warehouse.\n\nSee the architecture: mixpeek.com\n\n#DataWarehouse #MultimodalAI #AI #MachineLearning #UnstructuredData #VectorSearch #DataEngineering]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=FigGbVEjCIg</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/FigGbVEjCIg</video:player_loc>
      <video:duration>60</video:duration>
      <video:publication_date>2026-04-15T19:06:04Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
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  <url>
    <loc>https://mixpeek.com/education/videos/short-i6_6nOYgoyM</loc>
    <lastmod>2026-04-15T18:06:06Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/i6_6nOYgoyM/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[Keyword Search Can't Find a Wagging Tail (Semantic vs Hybrid)]]></video:title>
      <video:description><![CDATA[You search \"happy dog\" and keyword search returns zero results. Why? Because no document contains those exact words — even though 1,000 photos of joyful puppies exist.\n\nSemantic search converts your query into a vector. \"Happy dog\" lands right next to \"wagging tail\" and \"playful puppy\" in meaning space.\n\nBut the real move? Hybrid. Run both + RRF fusion in a single query.\n\nMeaning lives in the space between words.\n\nmixpeek.com/retrievers — semantic, keyword, and hybrid fusion in one API.\n\n#SemanticSearch #AI #VectorSearch #MachineLearning #SearchEngineering #NLP]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=i6_6nOYgoyM</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/i6_6nOYgoyM</video:player_loc>
      <video:duration>60</video:duration>
      <video:publication_date>2026-04-15T18:06:06Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
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  <url>
    <loc>https://mixpeek.com/education/videos/short-ZCjWSykSmDo</loc>
    <lastmod>2026-04-14T20:47:04Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/ZCjWSykSmDo/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[10M Objects/Day Broke Our Pipeline — Here's How We Fixed It]]></video:title>
      <video:description><![CDATA[We hit 10 million objects per day at Mixpeek. Videos, images, PDFs — all hitting the pipeline at once. Here's what broke:\n\n1. Celery queue saturated at 2M jobs — backed up for hours\n2. Ray OOM on files over 500MB\n3. 47-second cold starts costing $14K/month\n\nThe fixes: priority queues, streaming extraction, pre-warmed clusters. The queue is the ceiling — fix the ceiling.\n\nSee the architecture: mixpeek.com/mvs\n\n#DataEngineering #MLOps #ScaleProblems #AI #Infrastructure #RayComputing #StartupEngineering]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=ZCjWSykSmDo</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/ZCjWSykSmDo</video:player_loc>
      <video:duration>60</video:duration>
      <video:publication_date>2026-04-14T20:47:04Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
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  <url>
    <loc>https://mixpeek.com/education/videos/short-_wVXI6bs214</loc>
    <lastmod>2026-04-14T18:47:04Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/_wVXI6bs214/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[Did Google create ChatGPT??]]></video:title>
      <video:description><![CDATA[Did Google create ChatGPT??]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=_wVXI6bs214</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/_wVXI6bs214</video:player_loc>
      <video:duration>60</video:duration>
      <video:publication_date>2026-04-14T18:47:04Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
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  <url>
    <loc>https://mixpeek.com/education/videos/short-DSkycM30ouI</loc>
    <lastmod>2026-04-14T12:40:37Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/DSkycM30ouI/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[AI Media Compliance: 4 Pipelines, 2 Seconds, Every Frame Scanned]]></video:title>
      <video:description><![CDATA[Every frame you broadcast is a potential lawsuit. Unlicensed faces, uncleared logos, copyrighted audio — and you won't know until legal calls.\n\nMost compliance teams spot-check 10%. The other 90%? Hope.\n\nWe built a system that scans every frame in real time:\n- Face recognition\n- Logo detection\n- Audio fingerprinting\n- Text matching\n\nAll running simultaneously, under 2 seconds per image.\n\nSee it live at NAB Show — April 21, 3PM, Backblaze booth N1259.\n\nmixpeek.com/backblaze\n\n#NABShow #MediaCompliance #AI #BroadcastTech #ContentSafety #MachineLearning]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=DSkycM30ouI</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/DSkycM30ouI</video:player_loc>
      <video:duration>60</video:duration>
      <video:publication_date>2026-04-14T12:40:37Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
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  </url>
  <url>
    <loc>https://mixpeek.com/education/videos/short-8TBIfjM-vdQ</loc>
    <lastmod>2026-04-10T21:10:06Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/8TBIfjM-vdQ/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[One Video. 16 Extractors. 4 Seconds. (Parallel ML Pipeline)]]></video:title>
      <video:description><![CDATA[Most AI pipelines process files sequentially — one model at a time. A single 10-minute video takes 3+ minutes. Here's what happens when you fan out to 16 extractors in parallel:\n\n1. One file hits the system\n2. Ray fans it out to 16 workers simultaneously\n3. Face, text, audio, object, sentiment, embeddings — all at once\n4. GPU workers handle heavy models, CPU handles light ones\n5. 4 seconds later: 33 searchable vector indexes\n\nThe file doesn't wait. The extractors do.\n\nSee the architecture: mixpeek.com\n\n#AI #MachineLearning #DataPipeline #RayComputing #ParallelProcessing #MLOps #AIInfrastructure #FeatureExtraction]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=8TBIfjM-vdQ</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/8TBIfjM-vdQ</video:player_loc>
      <video:duration>60</video:duration>
      <video:publication_date>2026-04-10T21:10:06Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
    </video:video>
  </url>
  <url>
    <loc>https://mixpeek.com/education/videos/short-DTgITn3JJkU</loc>
    <lastmod>2026-04-10T20:10:10Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/DTgITn3JJkU/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[Anthropic Built an AI Too Dangerous to Release (245-Page System Card)]]></video:title>
      <video:description><![CDATA[Anthropic just published a 245-page system card for Claude Mythos Preview — their most capable AI model ever. The numbers are staggering:\n\n1. 93.9% on SWE-bench Verified (up from 80.8%)\n2. 97.6% on the US Math Olympiad (up from 42.3%)\n3. 100% on cybersecurity benchmarks\n4. 84% Firefox exploit success rate (previous best: 15%)\n\nBut they decided NOT to release it publicly. During testing, an early version escaped a sandbox, emailed a researcher, searched process memory for credentials, and tried to cover its tracks.\n\nAnthropic calls it \"the best-aligned model they've ever trained\" — and also says it poses \"the greatest alignment risk.\"\n\nRead the full system card: anthropic.com\n\n#AI #ClaudeMythos #Anthropic #AISafety #FrontierAI #Cybersecurity #AIAlignment #MachineLearning]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=DTgITn3JJkU</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/DTgITn3JJkU</video:player_loc>
      <video:duration>60</video:duration>
      <video:publication_date>2026-04-10T20:10:10Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
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  </url>
  <url>
    <loc>https://mixpeek.com/education/videos/short-D74XUSikHHg</loc>
    <lastmod>2026-04-10T19:10:07Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/D74XUSikHHg/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[Meta Built a Brain Simulator — I Use It to Edit My Videos]]></video:title>
      <video:description><![CDATA[Meta's TRIBE v2 predicts how your brain responds to video content — trained on 700 people's fMRI data, 20,000 cortical vertices per second.\n\nI run every reel through it before publishing:\n1. Feed it the audio + transcript\n2. Get per-second brain activation scores\n3. Find flat beats (low activation = viewers scroll away)\n4. Rewrite, re-render, re-score\n5. Only publish when every beat clears the threshold\n\nThe model is open-sourced by Meta AI.\n\n🔗 ai.meta.com/blog/tribe-v2-brain-predictive-foundation-model\n\n#TRIBE #MetaAI #BrainEncoding #ContentCreation #VideoEditing #AITools #fMRI #Neuroscience]]></video:description>
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      <video:player_loc>https://www.youtube.com/embed/D74XUSikHHg</video:player_loc>
      <video:duration>60</video:duration>
      <video:publication_date>2026-04-10T19:10:07Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
    </video:video>
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  <url>
    <loc>https://mixpeek.com/education/videos/short-rE14Zv2Uw7c</loc>
    <lastmod>2026-04-10T03:33:05Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/rE14Zv2Uw7c/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[I Indexed 2,600 Faces Across 63 Super Bowl Ads]]></video:title>
      <video:description><![CDATA[$8 million for 30 seconds. 127 million viewers. 63% of this year's ads featured celebrities — but there was no way to search faces across all of them.\n\nSo I built one.\n\nHere's the pipeline:\n1. 63 ads → scene detection splits each into 847 segments\n2. Face extraction pulls every face — frontal, profile, partial\n3. ArcFace embeds each as a 512-dimensional vector\n4. Drop a headshot → compare against 2,600 indexed faces\n5. Top matches back in 500ms, ranked by confidence\n\nEvery face, indexed. Five hundred milliseconds.\n\nTry the live demo → superbowl-face-search.mxp.co\n\n#FaceSearch #SuperBowl #AI #MachineLearning #VectorSearch #Mixpeek #ComputerVision #MultimodalAI]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=rE14Zv2Uw7c</video:content_loc>
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      <video:duration>60</video:duration>
      <video:publication_date>2026-04-10T03:33:05Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
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  <url>
    <loc>https://mixpeek.com/education/videos/short-p5HLb4tqpFY</loc>
    <lastmod>2026-04-09T22:33:04Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/p5HLb4tqpFY/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[Your Embedding Model Isn't the Problem. Your Chunking Is.]]></video:title>
      <video:description><![CDATA[87% vs 13% accuracy. Same model. Different chunking. A NAACL 2025 study tested 25 chunking configs across 48 embedding models — and chunking mattered as much as the model itself.\n\nHere's what's actually breaking your AI search:\n1. Fixed 500-token splits that cut sentences mid-thought\n2. Treating a PDF like a plain text file\n3. Comparing OpenAI vs Cohere vs Jina — instead of fixing the input\n\nHere's the fix — modality-aware decomposition:\n1. Video → scenes, faces, transcripts\n2. PDF → tables, entities, layouts\n3. Image → objects, text, colors\n\nThe embedding model gets clean context. Not sentence fragments. Not mid-paragraph cuts.\n\nChunking matters more than the model. Fix the input.\n\nFull guide → mixpeek.com/chunking-strategies\n\n#RAG #ChunkingStrategies #AISearch #VectorSearch #LLM #MachineLearning #AI #Mixpeek]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=p5HLb4tqpFY</video:content_loc>
      <video:player_loc>https://www.youtube.com/embed/p5HLb4tqpFY</video:player_loc>
      <video:duration>60</video:duration>
      <video:publication_date>2026-04-09T22:33:04Z</video:publication_date>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
    </video:video>
  </url>
  <url>
    <loc>https://mixpeek.com/education/videos/short-bKUsnY7z-CY</loc>
    <lastmod>2026-04-09T20:33:03Z</lastmod>
    <priority>0.6</priority>
    <video:video>
      <video:thumbnail_loc>https://img.youtube.com/vi/bKUsnY7z-CY/hqdefault.jpg</video:thumbnail_loc>
      <video:title><![CDATA[99% of Your Company's Data Is Invisible to AI]]></video:title>
      <video:description><![CDATA[Ninety-nine percent of your company's data is invisible to AI. Not missing. Invisible.\n\nHere's the math:\n1. 80% of enterprise data is unstructured — videos, PDFs, images, audio\n2. Only 0.5% of it ever gets analyzed\n3. The rest sits in S3 at $738/TB/year — untouched, unsearchable\n4. You're training on the 1%. The other 99% is rotting in a bucket.\n\nThe fix isn't a better model. It's decomposition:\n→ One API call breaks a video into scenes, faces, transcripts, embeddings\n→ A PDF becomes tables, entities, layouts\n→ An image becomes objects, text, colors\n→ 17 extractors. Every file type. One pipeline.\n\nThe data isn't missing. It's locked in formats AI can't read.\n\nSee what decomposition looks like → mixpeek.com/unstructured-data\n\n#UnstructuredData #EnterpriseAI #RAG #DataEngineering #AI #MachineLearning #Mixpeek #DarkData]]></video:description>
      <video:content_loc>https://www.youtube.com/watch?v=bKUsnY7z-CY</video:content_loc>
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      <video:title><![CDATA[$67B in AI Hallucinations — It's Not the Model]]></video:title>
      <video:description><![CDATA[Sixty-seven billion dollars lost to AI hallucinations last year. Everyone's upgrading the model — but 60% of RAG accuracy comes from retrieval, not inference.\n\nHere's what a real retrieval pipeline looks like:\n1. Start with 1,000 candidate documents\n2. Attribute filters remove wrong categories\n3. Feature search narrows to semantic matches\n4. Cross-encoder reranker scores what's left\n5. LLM sees only 10 verified, relevant documents\n\nFive stages: filter, sort, rerank, enrich, reduce.\n\nStop upgrading the chef. Fix the groceries.\n\nFull pipeline docs → mixpeek.com/retrievers\n\n#AIHallucinations #RAG #Retrieval #LLM #MachineLearning #AI #VectorSearch #Mixpeek]]></video:description>
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      <video:title><![CDATA[Netflix Just Killed Rotoscoping with VOID (Open Source, 5B Params)]]></video:title>
      <video:description><![CDATA[Netflix dropped VOID — a 5B-parameter model that deletes objects from video AND reasons about what happens next. No more $7,200 rotoscoping bills.\n\n1. Traditional rotoscoping costs $10/frame — $7,200 for 30 seconds\n2. Existing AI inpainters (ProPainter, DiffuEraser, Runway) leave physics broken\n3. VOID uses a vision model to predict physical consequences (cup falls, pins scatter)\n4. Tested against 6 baselines — humans picked VOID 64.8% of the time\n5. Apache 2.0, open weights, runs on a single A100\n\nTry it: huggingface.co/netflix/void-model\n\n#NetflixVOID #VFX #Rotoscoping #AIVideoEditing #OpenSource #ComputerVision #DeepLearning]]></video:description>
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      <video:title><![CDATA[Your S3 Bill Is a Tax on Laziness (21 Cheaper Alternatives)]]></video:title>
      <video:description><![CDATA[10 TB stored, 5 TB egress out. AWS S3 will charge you $689/month — and $0.09 of every gigabyte just to *leave*. Egress is the moat, and AWS owns the bridge.\n\nThere are 20 other S3-compatible providers nobody told you about. iDrive e2: $46. Wasabi: $49. Cloudflare R2 and Tigris? Egress is literally zero. Same API. Same buckets. One-fifteenth the price.\n\nBut read the fine print first:\n→ Wasabi locks you in for 90 days\n→ R2 has no versioning\n→ Backblaze only has 3 regions\n\nWe sourced every gotcha. 21 providers, side by side. Run your numbers at storage.mixpeek.com.\n\n#AWS #S3 #ObjectStorage #CloudStorage #Cloudflare #Wasabi #DevOps #CloudComputing]]></video:description>
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      <video:title><![CDATA[Vector Databases Are a Scam — I Did the Math]]></video:title>
      <video:description><![CDATA[Vector Databases Are a Scam — I Did the Math\n\n$80K/year for vector search at 1B vectors. You're paying for RAM. That's it.\n\nHere's what most people don't realize:\n1. Managed vector DBs store everything in RAM — that's the cost\n2. Object storage (S3) can serve vector queries in under 10ms\n3. We built a Rust engine on S3: same latency, 95% cheaper\n4. The architecture: Ray coordinator → Rust shards → S3\n\n1B vectors. Managed: $80K. S3-native: $3,500. Same recall. Same latency.\n\nBenchmarks are open-sourced → mixpeek.com\n\n#VectorDatabase #AI #MachineLearning #VectorSearch #AIInfrastructure #StartupCosts #Engineering]]></video:description>
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      <video:title><![CDATA[Your AI Stack Is 8 Vendors. The Model Isn't the Problem.]]></video:title>
      <video:description><![CDATA[Vector DB. Chunker. Embedder. Reranker. OCR. Video pipeline. Audio pipeline. Plus the glue script holding it all together. That's 8 contracts, 8 dashboards, 8 places it breaks at 3am — and nobody told you the pipeline IS the product.\n\nOne warehouse. 17 extractors. 5 stages: filter, sort, reduce, enrich, apply. Decompose, store, reassemble. That's the entire thing.\n\nBenchmarks: github.com/mixpeek\n\n#AI #MLops #VectorDB #RAG #DataEngineering #LLM #MultimodalAI #AIInfrastructure]]></video:description>
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    <lastmod>2026-04-08T13:54:31Z</lastmod>
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      <video:title><![CDATA[AWS S3 Files: Why It's a Bigger Deal Than People Think]]></video:title>
      <video:description><![CDATA[AWS just shipped S3 Files — and most people are sleeping on it. The short version: you can now mount any S3 bucket like a regular file system. ~1ms latency on cached data, the same bucket reachable through both the S3 API and POSIX, already live in 34 regions.\n\nWhy does it matter? Most tooling — data science notebooks, build systems, log processors, even the bash scripts you wrote five years ago — expects a normal file system. Until now you'd copy data out of S3 onto EFS or a local disk, run your job, and copy it back. S3 Files just deletes that step.\n\nOne nice side effect: agents get easier. A coding agent already knows ls, cat, and grep — point it at a mount path and it just works.\n\nThe bigger thing is the pattern. Object storage stays the source of truth, a faster layer sits on top as cache. We use the same shape at Mixpeek for vectors — S3 Vectors underneath, Qdrant on top, retrieval pipeline above that.\n\nmixpeek.com\n\n#AWS #S3 #CloudComputing #AIInfrastructure #DataEngineering #ObjectStorage #Mixpeek]]></video:description>
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      <video:title><![CDATA[I Left MongoDB to Build the Multimodal Database]]></video:title>
      <video:description><![CDATA[I ran search go-to-market at MongoDB. I sat with hundreds of teams shipping search. They could query rows, JSON, full-text, even vector search on documents. But the moment a customer dropped a video, image, PDF, or audio file — that data was invisible. Just sat in S3, untouchable.\n\nEvery team was building the same hack: pull the file, run it through 5 models, glue the outputs into Mongo, pray. Reinventing a multimodal pipeline from scratch — and it broke every time the data shape changed. There was no database for that. No platform. No category.\n\nSo I left and built it.\n\nOne platform. Any file in. Extractors run in parallel. Features stored. Query across all of it with a single API.\n\nmixpeek.com\n\n#MongoDB #MultimodalAI #VectorDatabase #AI #FounderStory #MachineLearning #SearchEngineering #Mixpeek]]></video:description>
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      <video:title><![CDATA[Your RAG Pipeline Is a Rube Goldberg Machine]]></video:title>
      <video:description><![CDATA[Your RAG Pipeline Is a Rube Goldberg Machine — Here's the Fix\n\nLangChain. Chunker. Embedder. Vector DB. Re-ranker. LLM. Six tools. Six failure points. And it's why your AI hallucinates.\n\nHere's the breakdown:\n1. Every hop loses information — it's a game of telephone\n2. The chunker breaks sentences. The embedder doesn't know the re-ranker. The vector DB ignores your filters\n3. By the time the LLM sees the context, it's garbage in, garbage out\n4. Real fix: one retriever pipeline → Filter → Sort → Reduce → Enrich → one API call\n\nStop duct-taping six tools together. The pipeline is the product.\n\nmixpeek.com\n\n#RAG #LangChain #VectorDatabase #AI #LLM #MachineLearning #AIEngineering #RetrievalAugmentedGeneration]]></video:description>
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      <video:title><![CDATA[Vector Databases Are a Scam — I Did the Math]]></video:title>
      <video:description><![CDATA[Vector Databases Are a Scam — I Did the Math\n\n$80K/year for vector search at 1B vectors. You're paying for RAM. That's it.\n\nHere's what most people don't realize:\n1. Managed vector DBs store everything in RAM — that's the cost\n2. Object storage (S3) can serve vector queries in under 10ms\n3. We built a Rust engine on S3: same latency, 95% cheaper\n4. The architecture: Ray coordinator → Rust shards → S3\n\n1B vectors. Managed: $80K. S3-native: $3,500. Same recall. Same latency.\n\nBenchmarks are open-sourced → mixpeek.com\n\n#VectorDatabase #AI #MachineLearning #VectorSearch #AIInfrastructure #StartupCosts #Engineering]]></video:description>
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      <video:title><![CDATA[What is RAG? Explained in 60 Seconds]]></video:title>
      <video:description><![CDATA[RAG (Retrieval Augmented Generation) is the #1 AI skill you need right now. In this video I break down how it works in 3 simple steps:\n\n1. Retrieve — search your data with semantic search\n2. Augment — feed the results into the LLM as context\n3. Generate — get answers grounded in real data, no hallucinations\n\nThis is how every serious AI app works — customer support bots, legal tools, e-commerce search, and more.\n\nBuild your own retrieval pipeline → mixpeek.com\n\n#RAG #AI #MachineLearning #LLM #SemanticSearch #RetrievalAugmentedGeneration #AIEngineering]]></video:description>
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    <lastmod>2026-02-19T19:33:04Z</lastmod>
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      <video:title><![CDATA[This Ad Anomaly Could Cost You a Lawsuit]]></video:title>
      <video:description><![CDATA[This Ad Anomaly Could Cost You a Lawsuit\n\nIf one of your ads is wildly different from the rest and you didn't catch it — that's not just a\n  brand problem, that's a legal problem. Anomaly detection isn't optional anymore. It's how you find the ads that\n  slipped through your safety net before they get served to the wrong audience.\n\n#BrandSafety #AdTech #AnomalyDetection #Legal #AI #Programmatic #RiskManagement]]></video:description>
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    <lastmod>2026-02-18T21:06:01Z</lastmod>
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      <video:title><![CDATA[\]]></video:title>
      <video:description><![CDATA[People aren't getting results because they throw everything at an LLM and pray. Here's what\n  actually works: decompose your content into layers, build retriever pipelines, match against your taxonomies —\n  THEN layer AI on top. The order matters. Most companies do it backwards.\n\n#AI #AdTech #LLM #Taxonomies #ContentStrategy #BrandSafety #HotTake]]></video:description>
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      <video:title><![CDATA[LLMs Can't Read the Fine Print in Your Ads]]></video:title>
      <video:description><![CDATA[That fast-talking pharma disclaimer at the end of the ad? The tiny scrolling text? LLMs don't see\n  it. They can't tell the difference between a logo, a background image, and compliance text. If you're serving\n  ads across jurisdictions, this is a ticking time bomb.\n\n#AdCompliance #LLM #AdTech #BrandSafety #Pharma #Regulation #AI]]></video:description>
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      <video:title><![CDATA[Your Super Bowl Ad Has 10 Layers You're Ignoring]]></video:title>
      <video:description><![CDATA[Videos have scenes. Scenes have faces. Faces have expressions. Expressions have pupil dilations.\n  You can go as deep as you want. If you're not decomposing your creatives into layers, you're leaving targeting\n  precision on the table. This changes everything for contextual advertising.\n\n#SuperBowlAds #AdTech #CreativeStrategy #ContextualTargeting #AI #VideoAnalysis #Programmatic]]></video:description>
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      <video:title><![CDATA[The Holy Grail of Ad Classification]]></video:title>
      <video:description><![CDATA[What happens when you need to classify content that doesn't match any label you've ever defined?\n  Zero-shot labeling. Search for things you didn't know existed. This is where the smartest agencies in adtech are\n   investing right now — and most companies have no idea it's possible.]]></video:description>
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    <lastmod>2026-02-17T20:47:03Z</lastmod>
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      <video:title><![CDATA[RAG Explained in 60 Seconds (No BS)]]></video:title>
      <video:description><![CDATA[GPT, Claude, Gemini — they're all just converting strings into numbers. RAG is retrieval +\n  generation. Retrieval finds the most relevant data. Generation reasons about it. This is how you stop AI from\n  hallucinating. Every adtech company needs to understand this.]]></video:description>
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      <video:title><![CDATA[Stop Letting AI Run Your Ads Without This]]></video:title>
      <video:description><![CDATA[Every industry has a taxonomy — medicine, law, accounting. In adtech, it's IAB. But here's what\n  nobody talks about: without taxonomies as guardrails, your AI agents will serve ads that destroy your brand.\n  Here's why structured data is the missing piece in your AI stack.]]></video:description>
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      <video:description><![CDATA[This is how AI actually understands images 👀\nPixels → patches → embeddings → semantic search.\nText-to-image isn’t magic. It’s math.]]></video:description>
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      <video:title><![CDATA[Powerful things you can do with AI: pt 1]]></video:title>
      <video:description><![CDATA[Powerful things you can do with AI: pt 1\n\nCreate your own personal mood board from the National Gallery of Art\n\nmxp.co/r/nga]]></video:description>
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      <video:title><![CDATA[I built a visual search engine for 200 years of art]]></video:title>
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      <video:title><![CDATA[Why Domain-Specific Search Is Hard]]></video:title>
      <video:description><![CDATA[General-purpose models often fail when language becomes specialized. Words mean different things in healthcare, legal, finance, and technical domains—and search systems need to understand that.\n\nIn this video, I explain:\n\n* Why generic semantic models break in domain-heavy data\n* How domain-specific meaning changes relationships between words\n* Why healthcare, legal, and technical search is especially difficult\n* What’s required to build truly domain-aware retrieval systems\n\nThis is the core challenge behind enterprise-grade semantic search.]]></video:description>
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      <video:title><![CDATA[Precision vs Recall: How Retrieval Systems Are Measured]]></video:title>
      <video:description><![CDATA[Not all search systems fail the same way. Some miss relevant results. Others return too many irrelevant ones.\n\nIn this video, I explain:\n\n* What precision and recall actually mean\n* Why improving one often hurts the other\n* How embedding models influence this tradeoff\n* How teams tune retrieval systems for the right balance\n\nThis is the core framework for evaluating semantic search and RAG quality.]]></video:description>
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    <lastmod>2025-12-25T20:33:02Z</lastmod>
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      <video:title><![CDATA[Cosine Similarity Explained for High-Dimensional Vectors]]></video:title>
      <video:description><![CDATA[Cosine similarity measures how aligned two vectors are, not how big they are—and that’s why it works so well for embeddings.\n\nIn this video, I explain:\n\n* How data becomes points in high-dimensional space\n* Why cosine similarity focuses on direction, not magnitude\n* How this scales from 2D intuition to thousands of dimensions\n* Why cosine similarity is the default for semantic search\n\nThis is the simplest way to understand how modern AI compares meaning.]]></video:description>
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    <lastmod>2025-12-25T19:33:03Z</lastmod>
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      <video:title><![CDATA[Vector Similarity Algorithms Explained (Cosine, Dot Product, Euclidean)]]></video:title>
      <video:description><![CDATA[All semantic search systems rely on vector similarity—but not all similarity metrics behave the same.\n\nIn this video, I break down:\n\n* Cosine similarity and when to use it\n* Dot product and how magnitude affects ranking\n* Euclidean distance and what it measures\n* Why different systems choose different similarity algorithms\n\nThis is the math layer that powers embeddings, k-NN search, and modern AI retrieval.]]></video:description>
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      <video:title><![CDATA[Operationalizing Semantic Search End-to-End]]></video:title>
      <video:description><![CDATA[Semantic search isn’t just theory—it’s a concrete pipeline that turns queries into vectors and compares them at scale.\n\nIn this video, I walk through:\n\n* How text becomes embeddings with fixed dimensions\n* What those dimensions actually represent\n* How cosine similarity ranks results\n* How new queries are compared against existing indexed vectors\n\nThis is how semantic search systems run in production.]]></video:description>
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      <video:title><![CDATA[Multidimensional Space: How Similarity Is Actually Computed]]></video:title>
      <video:description><![CDATA[Semantic similarity isn’t abstract—it’s math in high-dimensional space.\n\nIn this video, I explain:\n\n* What “multidimensional space” actually means\n* How concepts are represented as vectors\n* Why similarity comes down to distances and angles\n* How small numeric differences change ranking results\n\nThis is the mental model behind embeddings, similarity search, and modern retrieval systems.]]></video:description>
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    <lastmod>2025-12-24T19:06:02Z</lastmod>
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      <video:title><![CDATA[Why Encoder Models Matter in Semantic Search]]></video:title>
      <video:description><![CDATA[Everything in semantic search depends on the encoder. If the model doesn’t understand relationships and domains, retrieval breaks—no matter how good the rest of the system is.\n\nIn this video, I explain:\n\n* What encoder (embedding) models actually do\n* Why model choice shapes semantic understanding\n* The difference between general-purpose and domain-aware encoders\n* How encoder quality directly affects search and RAG results\n\nThis is the foundation layer most systems underestimate.]]></video:description>
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    <lastmod>2025-12-23T21:47:01Z</lastmod>
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      <video:title><![CDATA[Visualizing Semantic Similarity with Embeddings]]></video:title>
      <video:description><![CDATA[Embeddings live in high-dimensional space, but we can project them down to actually *see* how meaning clusters together.\n\nIn this video, I cover:\n\n* Why embeddings start in hundreds of dimensions\n* How dimensionality reduction maps them into 2D\n* What clustering reveals about semantic similarity\n* How to interpret distance and grouping visually\n\nThis is the simplest way to build intuition for how semantic search and retrieval actually work.]]></video:description>
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    <lastmod>2025-12-23T20:47:02Z</lastmod>
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      <video:title><![CDATA[Multimodal Retrieval: Search Text, Images, and Video by Meaning]]></video:title>
      <video:description><![CDATA[Search shouldn’t be limited to text. Multimodal retrieval lets you find relevant results across **text, images, and video** using shared meaning.\n\nIn this video, I show how the same system can:\n\n* Take a text query and find matching images\n* Use an image to retrieve similar images or video segments\n* Find related video moments based on visual and semantic similarity\n\nThis is the foundation behind modern AI search, recommendation systems, and content understanding across modalities.]]></video:description>
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      <video:title><![CDATA[Semantic Search over Keyword Search (Here’s Why)]]></video:title>
      <video:description><![CDATA[Keyword search matches words.\nSemantic search matches meaning.\n\nThat’s why searching “heart attack symptoms” can find results that never use those words at all.\n\nThis is the core idea behind modern AI search, embeddings, and vector databases.\n\n#SemanticSearch #AIExplained #MachineLearning #SearchTech #AIInfrastructure]]></video:description>
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      <video:title><![CDATA[Why Product Search Is Harder Than You Think]]></video:title>
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      <video:title><![CDATA[Introduction to Watchdog]]></video:title>
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