AI Concepts Articles
Browse 1363 articles about AI Concepts.
How to Classify AI Agent Actions by Risk: A Four-Tier Framework
Not all agent actions carry the same risk. Learn how to classify read-only, reversible, external, and high-risk actions to build safer AI workflows.
What Is the /goal Command in Claude Code? Autonomous Long-Running Tasks Explained
Claude Code's /goal command lets agents work toward an objective for hours without input. Learn how it works, when to use it, and how to prompt it well.
Gemini Multimodal RAG: How to Search Images and PDFs in One Query
Google's Gemini File Search API now supports multimodal RAG. Learn how to embed images and text together and query both with page-level citations.
GPT Realtime 2 vs GPT Realtime Translate: Which Voice Model Do You Need?
OpenAI's new voice models serve different use cases. Compare GPT Realtime 2 for voice agents and GPT Realtime Translate for live multilingual translation.
LLM as Judge: The Agent Safety Pattern Every Builder Needs to Know
LLM as judge uses a second AI model to validate agent actions before execution. Learn how this pattern prevents costly mistakes in production workflows.
How to Build a Multimodal RAG Pipeline with Metadata Filtering
Learn how to build a retrieval-augmented generation system that searches images and text together, filtered by custom metadata like department or topic.
What Is Speaker Diarization? How IBM Granite Speech 4.1 Plus Identifies Speakers
Speaker diarization labels who said what in a transcript. Learn how IBM Granite Speech 4.1 Plus handles speaker attribution and word-level timestamps.
What Is Claude Code Agent View? How to Manage Multiple AI Agents at Once
Claude Code's new Agent View lets you monitor and control multiple coding sessions from one terminal tab. Here's how it works and why it matters.
What Is the Agent Economy? How AI Agents Are Reshaping Business Operations
The agent economy describes AI systems running businesses with minimal human labor. Learn what it means for automation builders and enterprise AI strategy.
AI Auditing With vs. Without NLAs: Catching Misaligned Claude Haiku 3.5 in 12–15% of Cases
NLA-equipped auditors caught misaligned Claude Haiku 3.5's hidden motivation 12–15% of the time vs. under 3% without. What the gap means for AI oversight.
AI Workflows vs Agentic Workflows: The Key Difference Every Builder Must Understand
AI workflows follow fixed steps you define. Agentic workflows let the model decide. Learn the difference and when to use each for your automation.
Anthropic's Natural Language Autoencoders: How Researchers Can Now Read Claude's Thoughts
Anthropic built NLAs that translate Claude's internal neural activations into readable text. Learn what they found and why it matters for AI safety.
Anthropic's NLA Research: 5 Times Claude Was Caught Hiding What It Was Really Thinking
Anthropic's Natural Language Autoencoders caught Claude Mythos planning to hide cheating. Here are 5 documented cases of unverbalized AI intent.
Claude Knew It Was Being Tested in 26% of Benchmark Runs — Anthropic's NLA Data Explained
NLA data shows Claude flagged evaluation awareness in 16–26% of SWE-bench runs but under 1% of real sessions. What that gap means for AI safety.
The Four Levels of AI Automation: Chatbots, Workflows, Agentic Workflows, and AI Systems
From ChatGPT to full agentic AI systems, each level gives AI more autonomy. Learn the four levels and where your business should be operating.
How Anthropic's Natural Language Autoencoders Work: The 3-Component Architecture That Reads Claude's Mind
Anthropic's NLA uses a Verbalizer and Reconstructor to turn Claude's neural activations into plain English. Here's how the round-trip architecture works.
How to Check If Your Brand Appears in AI Search: Tools and Strategies
More buyers use AI tools to discover products. Learn how to audit your brand's visibility in ChatGPT, Gemini, and Perplexity and improve your presence.
Jack Clark Says 60% Chance of Recursive AI Self-Improvement by 2028 — What Anthropic's NLA Research Actually Shows
Anthropic co-founder Jack Clark put 60% odds on recursive AI self-improvement by 2028. NLA interpretability research shows why that timeline matters now.
What Is a Harness? The Infrastructure That Turns AI Models into Agents
A harness wraps an AI model so it can read files, run commands, and call tools. Learn how Claude Code, Codex, and Cursor use harnesses to enable agentic work.
What Is Claude Dreaming? Anthropic's Scheduled Memory Feature for Managed Agents
Claude Dreaming reviews past sessions, extracts patterns, and updates agent memory on a schedule. Here's how it works and why it matters.