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Understanding the Layers of AI Observability in the Age of LLMs

ai, AI (Artificial Intelligence), AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Machine Learning, Staff, Tech News, Technology

Artificial intelligence (AI) observability refers to the ability to understand, monitor, and evaluate AI systems by tracking their unique metrics—such as token usage, response quality, latency, and model drift. Unlike traditional software, large language models (LLMs) and other generative AI applications are probabilistic in nature. They do not follow fixed, transparent execution paths, which makes […]

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How to Build a Multi-Turn Crescendo Red-Teaming Pipeline to Evaluate and Stress-Test LLM Safety Using Garak

ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Security, Staff, Tutorials

In this tutorial, we build an advanced, multi-turn crescendo-style red-teaming harness using Garak to evaluate how large language models behave under gradual conversational pressure. We implement a custom iterative probe and a lightweight detector to simulate realistic escalation patterns in which benign prompts slowly pivot toward sensitive requests, and we assess whether the model maintains

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Google AI Releases Universal Commerce Protocol (UCP): An Open-Source Standard Designed to Power the Next Generation of Agentic Commerce

agentic ai, ai, AI (Artificial Intelligence), AI Agents, Applications, Artificial Intelligence, Editors Pick, New Releases, Open Source, Staff, Technology

Can AI shopping agents move beyond sending product links and actually complete trusted purchases end to end inside a chat? Universal Commerce Protocol, or UCP, is Google’s new open standard for agentic commerce. It gives AI agents and merchant systems a shared language so that a shopping query can move from product discovery to an

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How This Agentic Memory Research Unifies Long Term and Short Term Memory for LLM Agents

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How do you design an LLM agent that decides for itself what to store in long term memory, what to keep in short term context and what to discard, without hand tuned heuristics or extra controllers? Can a single policy learn to manage both memory types through the same action space as text generation? Researchers

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A Coding Guide to Demonstrate Targeted Data Poisoning Attacks in Deep Learning by Label Flipping on CIFAR-10 with PyTorch

ai, AI (Artificial Intelligence), Artificial Intelligence, deep learning, Editors Pick, Security, Technology, Tutorials

In this tutorial, we demonstrate a realistic data poisoning attack by manipulating labels in the CIFAR-10 dataset and observing its impact on model behavior. We construct a clean and a poisoned training pipeline side by side, using a ResNet-style convolutional network to ensure stable, comparable learning dynamics. By selectively flipping a fraction of samples from

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Meet SETA: Open Source Training Reinforcement Learning Environments for Terminal Agents with 400 Tasks and CAMEL Toolkit

agentic ai, ai, AI (Artificial Intelligence), AI Agents, Artificial Intelligence, Editors Pick, New Releases, Open Source, Staff, Technology

What does an end to end stack for terminal agents look like when you combine structured toolkits, synthetic RL environments, and benchmark aligned evaluation? A team of researchers from CAMEL AI, Eigent AI and other collaborators have released SETA, a toolkit and environment stack that focuses on reinforcement learning for terminal agents. The project targets

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Meta and Harvard Researchers Introduce the Confucius Code Agent (CCA): A Software Engineering Agent that can Operate at Large-Scale Codebases

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How far can a mid sized language model go if the real innovation moves from the backbone into the agent scaffold and tool stack? Meta and Harvard researchers have released the Confucius Code Agent, an open sourced AI software engineer built on the Confucius SDK that is designed for industrial scale software repositories and long

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How to Build Portable, In-Database Feature Engineering Pipelines with Ibis Using Lazy Python APIs and DuckDB Execution

ai, AI (Artificial Intelligence), Artificial Intelligence, Big Data, Databases, Editors Pick, Machine Learning, Staff, Tech News, Technology, Tutorials

In this tutorial, we demonstrate how we use Ibis to build a portable, in-database feature engineering pipeline that looks and feels like Pandas but executes entirely inside the database. We show how we connect to DuckDB, register data safely inside the backend, and define complex transformations using window functions and aggregations without ever pulling raw

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Stanford Researchers Build SleepFM Clinical: A Multimodal Sleep Foundation AI Model for 130+ Disease Prediction

ai, AI (Artificial Intelligence), AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine Learning, New Releases, Staff, Tech News, Technology

A team of Stanford Medicine researchers have introduced SleepFM Clinical, a multimodal sleep foundation model that learns from clinical polysomnography and predicts long term disease risk from a single night of sleep. The research work is published in Nature Medicine and the team has released the clinical code as the open source sleepfm-clinical repository on

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A Coding Implementation to Build a Unified Apache Beam Pipeline Demonstrating Batch and Stream Processing with Event-Time Windowing Using DirectRunner

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

In this tutorial, we demonstrate how to build a unified Apache Beam pipeline that works seamlessly in both batch and stream-like modes using the DirectRunner. We generate synthetic, event-time–aware data and apply fixed windowing with triggers and allowed lateness to demonstrate how Apache Beam consistently handles both on-time and late events. By switching only the

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