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 ›
In the rapidly evolving field of software development, code stylometry analyzing unique stylistic signatures of programmers plays a crit-ical role in authorship attribution and cybersecurity. Recent advancements in artificial intelligence, particularly Large Language Models (LLMs) like GPT-3.5 and GPT-4, have introduced new dimensions to this field, challenging traditional stylometry techniques. This study investigates the effectiveness of LLM... 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 ›
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 ›
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 ›
Teaching Computers to Train Together: Building a Distributed Training Platform Across Multiple GPUs…
How I built a lightweight federated machine learning system using PyTorch to distribute training across multiple machines Read more ›
Learn about the five-dimensional design space in modern LLM serving, including tensor, pipeline, expert, data, and context parallelism 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 ›
The General Process of How a Neural Network Processes Information in Its Forward Pass Phase 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 ›
Powerful LLMs will be deployed at global scale in the next few years, and will dominate the Internet, and increasingly, ordinary life. As of mid-2026, there is no coherent vision for how knowledge professionals, or ordinary people, will be able to harness these LLMs for large productivity increases, or how they will handle cybersecurity and cognitive security. I propose a goal of creating Guardian Angels (GA): digital twin LLMs which are personalized with the goal of providing not the stereot... Read more ›
Last Updated on June 22, 2026 by Editorial Team Author(s): Alpha Iterations Originally published on Towards AI. Build a Hybrid RAG System with FAISS, BM25, LangGraph and Claude Sonnet Model Combine semantic search and keyword search into one powerful document Q&A app using Claude Sonnet 4.6 API, step by step tutorial Hybrid Retrieval (Image by Alpha Iterations, Created using ChatGPT) Non members read here for free. Introduction With the rapid advancement of Large Language Models and vector em... Read more ›
Payment data pipelines fail in ways that ruin a payments engineer’s week, and the failures rhyme. The dashboards froze. Fraud scores arrived after the transaction had already cleared. Settlement reports came in stale. Nobody slept. The frustrating part is that the same data architecture had run fine for years. So, what changed? The honest answer is that batch thinking does not survive contact with real-time payments. A lot of banks built their data foundations in an era when nightly jobs were... Read more ›
In this paper, a novel test-time scaling law for physical artificial intelligence (AI) agents is introduced. This scaling law enables physical AI agents to reason with their world models to generalize in unforeseen scenarios at test time. The derived scaling law is grounded in the first principle of active inference, which equips agents with the general objective to survive in the real world, under which their specific task objectives are subsum... Read more ›
Microservice Architecture is a distributed system design approach in which an application is decomposed into small, independently… Read more ›
Learn key machine learning concepts like overfitting, gradient descent, and transfer learning through familiar characters and scenes from… Read more ›