5 Lessons Learned Building RAG Systems
Retrieval augmented generation (RAG) is one of 2025’s hot topics in the AI landscape. Source link
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Researchers at Physical Intelligence Introduce π-0.5: A New AI Framework for Real-Time Adaptive Intelligence in Physical Systems
Designing intelligent systems that function reliably in dynamic physical environments remains one of the more difficult frontiers in AI. While significant advances have been made in perception and planning within simulated or controlled contexts, the real world is noisy, unpredictable,…
A Coding Guide to Build an Agentic AI‑Powered Asynchronous Ticketing Assistant Using PydanticAI Agents, Pydantic v2, and SQLite Database
In this tutorial, we’ll build an end‑to‑end ticketing assistant powered by Agentic AI using the PydanticAI library. We’ll define our data rules with Pydantic v2 models, store tickets in an in‑memory SQLite database, and generate unique identifiers with Python’s uuid…
Generating and Visualizing Context Vectors in Transformers
This post is divided into three parts; they are: • Understanding Context Vectors • Visualizing Context Vectors from Different Layers • Visualizing Attention Patterns Unlike traditional word embeddings (such as Word2Vec or GloVe), which assign a fixed vector to each…
Long-Context Multimodal Understanding No Longer Requires Massive Models: NVIDIA AI Introduces Eagle 2.5, a Generalist Vision-Language Model that Matches GPT-4o on Video Tasks Using Just 8B Parameters
In recent years, vision-language models (VLMs) have advanced significantly in bridging image, video, and textual modalities. Yet, a persistent limitation remains: the inability to effectively process long-context multimodal data such as high-resolution imagery or extended video sequences. Many existing VLMs…
Atla AI Introduces the Atla MCP Server: A Local Interface of Purpose-Built LLM Judges via Model Context Protocol (MCP)
Reliable evaluation of large language model (LLM) outputs is a critical yet often complex aspect of AI system development. Integrating consistent and objective evaluation pipelines into existing workflows can introduce significant overhead. The Atla MCP Server addresses this by exposing…
Applications with Context Vectors
This post is divided into two parts; they are: • Contextual Keyword Extraction • Contextual Text Summarization Contextual keyword extraction is a technique for identifying the most important words in a document based on their contextual relevance. Source link
Quantization in Machine Learning: 5 Reasons Why It Matters More Than You Think
Quantization might sound like a topic reserved for hardware engineers or AI researchers in lab coats. Source link
Detecting & Handling Data Drift in Production
Machine learning models are trained on historical data and deployed in real-world environments. Source link
LLMs Can Now Retain High Accuracy at 2-Bit Precision: Researchers from UNC Chapel Hill Introduce TACQ, a Task-Aware Quantization Approach that Preserves Critical Weight Circuits for Compression Without Performance Loss
LLMs show impressive capabilities across numerous applications, yet they face challenges due to computational demands and memory requirements. This challenge is acute in scenarios requiring local deployment for privacy concerns, such as processing sensitive patient records, or compute-constrained environments like…