One AI agent answering a question is useful. Five agents that divide a complex task, pass state to each other, and act on live enterprise systems is a meaningfully different category of system. It also carries a meaningfully different category of operational problems. Multi-agent orchestration is the architectural pattern that makes the second case coherent. But a lot of teams prototype multi-agent systems in a weekend and then spend months figuring out why production is unpredictable, expens... Read more ›
James Holland from the Office of the CTO at Palo Alto Networks shares insights from having attended around 14 Black Hat events, focusing on proactive threat detection and zero-day threat analysis\. Learn how network operations centers identify emerging threats without relying on specific CVE knowledge, how firewalls provide critical visibility for zero-day attacks, and the essential role of XDR and EDR platforms in incident response and timeline reconstruction\. Discover how Black Hat researc... Read more ›
A short learning path from a weekend project: I indexed my personal markdown notes (~800 chunks), tried a few local embedding models, stored the same vectors in four different backends, and wired up simple RAG. Not a production guide — just the basics, with honest results from a corpus small enough to reason about. The idea, without the jargon pile Keyword search looks for shared words. Vector search converts text into a list of numbers (an embedding), treats that list as a point in space, an... Read more ›
Matt Fitzpatrick, CEO of Invisible Technologies, joins Bloomberg Intelligence’s Mandeep Singh on this episode of the Tech Disruptors podcast to discuss the use of reinforcement learning by frontier model providers for training, as well as the company’s enterprise business. They explore reinforcement learning from human feedback (RLHF), agentic AI and self-improvement, the evolution of large language models, coding agents and contact centers. Read more ›
LeetCode for Machine Learning. Practice ML coding problems with a real Python execution environment. Read more ›
There's a moment in almost every RAG project where someone asks the question that decides your next two years of ops work: "Do we actually need a vector database, or can Postgres just do this?" It's a better question than it sounds, because the honest answer isn't "use Pinecone" or "use Postgres." It's "it depends on numbers you probably haven't measured yet": how many vectors, how aggressively you filter, how much you care about the absolute ceiling of queries per second. Most teams pick bas... Read more ›
The NVIDIA CUDA Core Compute Libraries (CCCL) provides delightful and efficient abstractions for CUDA developers in C++ and Python. It features: This post introduces a new group of functionality in… Read more ›
Learn key machine learning concepts like overfitting, gradient descent, and transfer learning through familiar characters and scenes from… Read more ›
This is day 8 of building a neural network from scratch in python. Yesterday we said that learning is just a loop: the network makes a… Read more ›
If you've ever typed "let's think step by step" into ChatGPT and watched the answer quality jump, you've already used chain-of-thought prompting without knowing it. That phrase isn't magic — it's a deliberate technique backed by peer-reviewed research. What It Is Chain-of-thought (CoT) prompting instructs an AI model to reason through a problem step by step before delivering its final answer. Instead of predicting a response in one leap, the model generates a sequence of intermediate reasonin... Read more ›
Learn about the five-dimensional design space in modern LLM serving, including tensor, pipeline, expert, data, and context parallelism Read more ›
Amazon SageMaker AI provides fully managed real-time inference hosting for machine learning models. You deploy a model to a SageMaker endpoint backed by one or more compute instances, and SageMaker handles provisioning and scaling. SageMaker supports multiple endpoint architectures. This post focuses on the two most relevant to generative AI workloads with detailed observability: Single-model endpoints (SME) and Inference component (IC) endpoints. Read more ›
NBC Nightly News anchor Tom Llamas shares his career advice for Gen Z, work-life balance philosophy, and why success starts with hustle. Read more ›
Most tutorials on AI agents stop at chat interfaces and RAG pipelines. This one doesn't. This guide walks through building a production-grade AI agent that can read on-chain data, interact with smart contracts, and execute DeFi operations — using LangChain's agent framework, ethers.js, and a set of custom tools you'll write from scratch. By the end, you'll have an agent that can: Query wallet balances and token holdings Read state from any smart contract via ABI Simulate and execute token swa... Read more ›
When deploying a new VMware Cloud Foundation (VCF) 9.1 Fleet, users specify either a Simple or High Availability (HA) deployment model along with the desired deployment size: Small, Medium or Large. Unlike components such as NSX Manager, VCF Operations and VCF Automation, where deployment size and availability are configured independently, VCF Management Services (VCFMS) determines […] Read more ›
Data engineering underpins modern analytics, business intelligence, and digital transformation. Reliable data pipelines are critical for… Read more ›
With the rapid expansion of massive multilingual corpora, Multilingual Information Retrieval (MLIR) has emerged as a critical technology for global information access. MLIR enables users to retrieve semantically relevant documents from multilingual text collections using a single-language query. However, recent multilingual dense retrieval models often exhibit a strong preference for documents in the same language as the query. This leads to sev... Read more ›
The main focus of machine learning (ML) is making decisions or predictions based on data. There are a number of other fields with significant overlap in technique, but difference in focus: in economics and psychology, the goal is to discover underlying causal processes and in statistics it is to find a model that fits a data set well. In those fields, the end product is a model. In machine learning, we often fit models, but as a means to the end of making good predictions or decisions. Read more ›
Anthropic says it wants oversight. It’s getting something else. Read more ›
From pretraining to RLHF/GRPO — every algorithm hand-written in pure PyTorch. Read more ›