Embedding Programming Tutorials & Engineering Articles

Generative recommender (GR) systems are emerging as a powerful new approach for large-scale personalization. Instead of treating recommendation as a set of…

Explore ClickHouse AI Functions for classification, generation, translation, embeddings, semantic search, and cost controls—all directly from SQL.

Encode-prefill-decode (EPD) disaggregation is an inference optimization technique for multimodal models that separates the vision encoder stage from the prefill…

Google Cloud has natively integrated TPU support into the vLLM serving engine, allowing developers to elastically scale high-demand embedding pipelines using Google Kubernetes Engine (GKE). To handle massive 15K+ token contexts for models like Qwen3-Embedding-8B, the engineering team implemented TPU-specific optimizations such as hardware-safe tensor alignment, JAX/XLA compilation pre-warming, and a hybrid StepPool architecture for chunked prefill management. These enhancements achieve near-perfect numerical parity with reference GPU baselines, and developers can immediately leverage the open-sourced setup recipes on the AI-Hypercomputer GitHub to build their own high-throughput semantic retrieval applications.

Recommender systems (RecSys) are one of the most ubiquitous machine learning problems in the consumer internet industry yet notoriously difficult to train and…

Meta’s Generative Ads Recommendation Model (GEM), the foundation model behind ads recommendations across Instagram and Facebook, now trains at LLM scale on several thousand of the latest-gene…

Fine-tuning LLMs for financial natural language processing (NLP) is constrained by limited, imbalanced data. Real-world financial news overrepresents earnings…

As context windows grow longer, moving large model weights efficiently becomes critical to performance. A common way to address this is quantization…

This post introduces three architectural patterns designed to integrate Model Context Protocol (MCP) Apps and Agent-to-User Interface (A2UI) to solve the tradeoff between highly custom iframe environments and native, declarative rendering. By combining these approaches, developers can serve native-feeling UIs directly over MCP servers, embed complex and stateful iframe apps securely inside declarative views, or inject generative UI components into legacy systems. Ultimately, these hybrid frameworks empower engineering teams to deliver secure, performant, and brand-consistent agentic user experiences tailored to their specific project constraints.

Google has announced the general availability of Gemini Embedding 2, a unified model that maps text, images, video, audio, and documents into a single semantic space. This model allows developers to process interleaved multimodal inputs in a single request, significantly improving performance for tasks like agentic RAG, visual search, and content moderation. By supporting over 100 languages and offering features like task-specific prefixes and Matryoshka dimensionality reduction, the model provides a highly efficient and accurate foundation for building complex AI agents.

We’ve fundamentally transformed Facebook Groups Search to help people more reliably discover, sort through, and validate community content that’s most relevant to them. We’ve adopted a new hybrid r…

Co-designed hardware, software, and models are key to delivering the highest AI factory throughput and lowest token cost. Measuring this goes far beyond peak…

Meta continues to lead the industry in utilizing groundbreaking AI Recommendation Systems (RecSys) to deliver better experiences for people, and better results for advertisers. To reach the next fr…

Uber’s Rate Limiting System details the evolution of Uber's approach to managing service overload through a unified rate-limiting architecture.

Kimi K2. 5 is an advanced multimodal vision language model (VLM) developed by Kimi, optimized for various AI tasks.

The article provides a comprehensive guide on building a document processing pipeline using NVIDIA Nemotron RAG, focusing on the extraction of structured data from complex documents like PDFs.

The article discusses the collaboration between NVIDIA and Black Forest Labs to optimize the FLUX. 2 text-to-image model for NVIDIA Blackwell Data Center GPUs.

The article discusses the NVIDIA Multi-Agent Intelligent Warehouse (MAIW), an AI command layer designed to enhance operational efficiency and supply chain intelligence in automated warehouses.

This article provides a comprehensive tutorial on building a voice agent using NVIDIA's Nemotron models, focusing on retrieval-augmented generation (RAG) and safety guardrails.

How I spent my fall internship building accounting automations to save finance teams time during month-end close.

The article discusses Uber's transition from traditional keyword-based search using Apache Lucene to implementing semantic vector search with Amazon OpenSearch.

The article discusses the evolution and scaling of Uber's Delivery Search Platform, emphasizing the transition from traditional lexical search to a semantic search model that enhances user experien...

The article discusses the integration of NVIDIA Nemotron RAG with Microsoft SQL Server 2025, showcasing how this collaboration enables the development of scalable AI applications on enterprise data.

The article reflects on a decade of AI platform development at Pinterest, detailing the evolution from fragmented machine learning stacks to a unified AI platform that supports various models.

This article discusses how Uber has integrated explainability into its machine learning platform, Michelangelo, using Integrated Gradients (IG) to provide interpretable attributions for deep learni...

The article discusses the development of an AI-powered log analysis solution using NVIDIA's Generative AI reference workflows.

The article discusses the optimization of large language models (LLMs) through pruning and knowledge distillation using NVIDIA TensorRT Model Optimizer.

The article provides an in-depth exploration of the EmbeddingGemma architecture, detailing its origins, embedding generation process, and the comprehensive training methodology.

The article discusses the concept of AI sovereignty, emphasizing the importance of choice for nations in controlling AI technologies and data.

The article provides a comprehensive guide on building a Retrieval-Augmented Generation (RAG) agent using NVIDIA Nemotron, emphasizing the integration of external information to enhance text genera...

The NVIDIA RAPIDS 25.

The article discusses the recent enhancements to the Gemini Batch API, which now includes support for the Gemini Embedding model and compatibility with the OpenAI SDK.

This article discusses the integration of Google's EmbeddingGemma model with Google Cloud's Dataflow to create a scalable embedding pipeline for AI applications.