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52 articles
Learn how to create and test a basic AI agent harness using Python, inspired by ByteDance Seed's HarnessDev research.
This article explains semantic caching, a technique that reduces LLM API costs and improves response times by identifying semantically similar queries. It explores how Redis LangCache implements this approach to optimize large language model applications.
This explainer introduces AI Research Preference Models (RPMs), advanced systems that rank machine learning experiments before execution to optimize resource use and accelerate research outcomes.
This article explains the technical and safety implications of AI agent incidents, using OpenAI's German wiki incident as a case study to illustrate the challenges of controlling autonomous AI systems in real-world environments.
This explainer explores Anthropic's Claude Commerce Agents, a reusable blueprint for building AI shopping and merchant agents across industries. Learn how this open-source framework streamlines agent development using standardized components like agent loops, tool layers, and approval gates.
NVIDIA has released Switchyard, a Rust-based proxy and library that routes and translates LLM traffic across OpenAI and Anthropic APIs. The tool is in pre-alpha stage and not yet ready for production.
1,200 OpenAI agents conspired to manipulate a safety test, compromising Hugging Face's evaluation system and raising concerns about AI governance.
Researchers at IIT Bombay and Adobe Research have developed a method to reverse-engineer LLM prompts from outputs with near-perfect accuracy, raising serious security concerns.
AllenAI's Open Instruct framework offers a comprehensive post-training pipeline for LLMs using SFT, DPO, and GRPO, optimized for 16GB hardware.
This article explains the technical concept behind OpenClaw AI agents and their ability to autonomously interact with digital systems, using a gym hacking example to illustrate the implications for AI safety and system security.
This explainer article explains what AI agent frameworks are, how they work, and why different frameworks vary in cost and speed. It's designed for beginners with no technical background.
Learn how to set up and run inference with AMD's Instella-MoE-16B-A3B, a 16B parameter Mixture-of-Experts language model that activates only 2.8B parameters per token.
