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A Coding Implementation to Train Safety-Critical Reinforcement Learning Agents Offline Using Conservative Q-Learning with d3rlpy and Fixed Historical Data

agentic ai, ai, AI (Artificial Intelligence), AI Agents, Artificial Intelligence, Editors Pick, Machine Learning, Technology, Tutorials

In this tutorial, we build a safety-critical reinforcement learning pipeline that learns entirely from fixed, offline data rather than live exploration. We design a custom environment, generate a behavior dataset from a constrained policy, and then train both a Behavior Cloning baseline and a Conservative Q-Learning agent using d3rlpy. By structuring the workflow around offline […]

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How to Build Advanced Quantum Algorithms Using Qrisp with Grover Search, Quantum Phase Estimation, and QAOA

ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Machine Learning, Staff, Technology, Tutorials

In this tutorial, we present an advanced, hands-on tutorial that demonstrates how we use Qrisp to build and execute non-trivial quantum algorithms. We walk through core Qrisp abstractions for quantum data, construct entangled states, and then progressively implement Grover’s search with automatic uncomputation, Quantum Phase Estimation, and a full QAOA workflow for the MaxCut problem.

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How to Build Multi-Layered LLM Safety Filters to Defend Against Adaptive, Paraphrased, and Adversarial Prompt Attacks

ai, AI (Artificial Intelligence), Artificial Intelligence, Context Engineering, Editors Pick, prompt engineering, Security, Staff, Technology, Tutorials, Uncategorized

In this tutorial, we build a robust, multi-layered safety filter designed to defend large language models against adaptive and paraphrased attacks. We combine semantic similarity analysis, rule-based pattern detection, LLM-driven intent classification, and anomaly detection to create a defense system that relies on no single point of failure. Also, we demonstrate how practical, production-style safety

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How to Build Memory-Driven AI Agents with Short-Term, Long-Term, and Episodic Memory

agentic ai, ai, AI (Artificial Intelligence), AI Agents, Artificial Intelligence, Editors Pick, Staff, Tutorials

In this tutorial, we build a memory-engineering layer for an AI agent that separates short-term working context from long-term vector memory and episodic traces. We implement semantic storage using embeddings and FAISS for fast similarity search, and we add episodic memory that captures what worked, what failed, and why, so the agent can reuse successful

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A Coding and Experimental Analysis of Decentralized Federated Learning with Gossip Protocols and Differential Privacy

ai, AI (Artificial Intelligence), AI Infrastructure, Artificial Intelligence, Editors Pick, Staff, Technology, Tutorials

In this tutorial, we explore how federated learning behaves when the traditional centralized aggregation server is removed and replaced with a fully decentralized, peer-to-peer gossip mechanism. We implement both centralized FedAvg and decentralized Gossip Federated Learning from scratch and introduce client-side differential privacy by injecting calibrated noise into local model updates. By running controlled experiments

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A Coding Deep Dive into Differentiable Computer Vision with Kornia Using Geometry Optimization, LoFTR Matching, and GPU Augmentations

ai, AI (Artificial Intelligence), AI Infrastructure, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine Learning, Staff, Technology, Tutorials

We implement an advanced, end-to-end Kornia tutorial and demonstrate how modern, differentiable computer vision can be built entirely in PyTorch. We start by constructing GPU-accelerated, synchronized augmentation pipelines for images, masks, and keypoints, then move into differentiable geometry by optimizing a homography directly through gradient descent. We also show how learned feature matching with LoFTR

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How to Design Self-Reflective Dual-Agent Governance Systems with Constitutional AI for Secure and Compliant Financial Operations

agentic ai, ai, AI (Artificial Intelligence), AI Agents, Artificial Intelligence, Editors Pick, Staff, Tutorials

In this tutorial, we implement a dual-agent governance system that applies Constitutional AI principles to financial operations. We demonstrate how we separate execution and oversight by pairing a Worker Agent that performs financial actions with an Auditor Agent that enforces policy, safety, and compliance. By encoding governance rules directly into a formal constitution and combining

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How Tree-KG Enables Hierarchical Knowledge Graphs for Contextual Navigation and Explainable Multi-Hop Reasoning Beyond Traditional RAG

ai, AI (Artificial Intelligence), Applications, Artificial Intelligence, deep learning, Editors Pick, Knowledge Graphs, Language Model, Machine Learning, Staff, Tech News, Technology, Tutorials

In this tutorial, we implement Tree-KG, an advanced hierarchical knowledge graph system that goes beyond traditional retrieval-augmented generation by combining semantic embeddings with explicit graph structure. We show how we can organize knowledge in a tree-like hierarchy that mirrors how humans learn, from broad domains to fine-grained concepts, and then reason across this structure using

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How a Haystack-Powered Multi-Agent System Detects Incidents, Investigates Metrics and Logs, and Produces Production-Grade Incident Reviews End-to-End

agentic ai, ai, AI (Artificial Intelligence), AI Agents, AI Infrastructure, Artificial Intelligence, Editors Pick, Staff, Technology, Tutorials

In this tutorial, we design this implementation to demonstrate how Haystack enables building advanced, agentic AI systems that go far beyond toy examples while remaining fully runnable. We focus on a cohesive, end-to-end setup that highlights orchestration, stateful decision-making, tool execution, and structured control flow, demonstrating how complex agent behavior can be cleanly expressed. We

How a Haystack-Powered Multi-Agent System Detects Incidents, Investigates Metrics and Logs, and Produces Production-Grade Incident Reviews End-to-End Read More »

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A Coding Implementation to Automating LLM Quality Assurance with DeepEval, Custom Retrievers, and LLM-as-a-Judge Metrics

ai, AI (Artificial Intelligence), Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine Learning, Staff, Technology, Tutorials

We initiate this tutorial by configuring a high-performance evaluation environment, specifically focused on integrating the DeepEval framework to bring unit-testing rigor to our LLM applications. By bridging the gap between raw retrieval and final generation, we implement a system that treats model outputs as testable code and uses LLM-as-a-judge metrics to quantify performance. We move

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