Future of Business: Mars CEO on How Business Can Be a Force for Good
A conversation the CPG leader Poul Weihrauch about sustainability and profitability.
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How We Are Testing Our Agents in Dev
Testing that your AI agent is performing as expected is not easy. Here are a few strategies we learned the hard way. The post How We Are Testing Our Agents in Dev appeared first on Towards Data Science.
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Stop building RAG pipelines like it’s 2023
Author(s): AI Rabbit Originally published on Towards AI. Agentic Era If your architecture still looks like “User Query Vector DB LLM,” you aren’t building an AI application; you’re building a hallucination engine. The “naive” RAG era where we just dumped PDFs into Pinecone and prayed for the best is officially over. Here is the technical reality of RAG in 2025.The article discusses the evolution of Retrieval-Augmented Generation (RAG) pipelines, emphasizing the transition from outdated linear architectures to more […]
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BondingAI Acquires GenAItechLab, Add Core Team Members
BondingAI acquisition of GenAItechLab.com was recently completed, including all the IP related to the xLLM technology, the material published on MLtechniques and the most recent technology pertaining to deep neural networks watermarking. GenAItechLab was founded in 2024 by Vincent Granville, a world-class leader and well-known scientist building innovative and efficient AI solutions from scratch, hallucination-free, without Blackbox or GPU, yet delivering better results faster. Doctor Granville is now Chief AI Architect, co-founder and investor at BondingAI. In the […]
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CopilotKit v1.50 Brings AG-UI Agents Directly Into Your App With the New useAgent Hook
Agent frameworks are now good at reasoning and tools, but most teams still write custom code to turn agent graphs into robust user interfaces with shared state, streaming output and interrupts. CopilotKit targets this last mile. It is an open source framework for building AI copilots and in-app agents directly in your app, with real time context and UI control. ( Check out the CopilotKit GitHub) The release of of CopilotKit’s v1.50 rebuilds the project on the Agent User […]
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Avalon Is Thanksgiving for America
Barry Levinson was one of the most successful directors in America around 1990, when he made Avalon, an immigrant Thanksgiving movie trying to sum up the transformation of the American family in the 20th century. Continue Reading…
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7 Steps to Mastering Agentic AI
As AI systems begin handling more complex, multi-stage tasks, understanding agentic design is becoming essential. This article outlines seven practical steps to build reliable, effective AI agents.
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KV Cache Optimization via Tensor Product Attention
Home Table of Contents KV Cache Optimization via Tensor Product Attention Challenges with Grouped Query and Multi-Head Latent Attention Multi-Head Attention (MHA) Grouped Query Attention (GQA) Multi-Head Latent Attention (MLA) Tensor Product Attention (TPA) TPA: Tensor Decomposition of Q, K, V Latent Factor Maps and Efficient Implementation Attention Computation and RoPE Integration KV Caching and Memory Reduction with TPA PyTorch Implementation of Tensor Product Attention (TPA) Tensor Product Attention with KV Caching Transformer Block Inferencing Code Experimentation Summary […]
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The Rational Bull Elk
I watch a lot of nature documentaries. I’m not very choosy about the animals covered, whether whales, moles, lions, ants, chameleons, blowfish, or mosquitoes. I’m even fascinated by footage of bacteria under a microscope. I’m usually immersed as I sit in front of my large-screen television, so long as I learn something about the intricacies of the species filmed in vibrant colors. What do they eat and how do they avoid being eaten? What are their life expectancies, […]
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A Coding Implementation of a Complete Hierarchical Bayesian Regression Workflow in NumPyro Using JAX-Powered Inference and Posterior Predictive Analysis
In this tutorial, we explore hierarchical Bayesian regression with NumPyro and walk through the entire workflow in a structured manner. We start by generating synthetic data, then we define a probabilistic model that captures both global patterns and group-level variations. Through each snippet, we set up inference using NUTS, analyze posterior distributions, and perform posterior predictive checks to understand how well our model captures the underlying structure. By approaching the tutorial step by step, we build an intuitive […]
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