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Imagine hiring an AI assistant to handle important tasks, only to find that it quietly ignores your instructions because it believes it knows better. This is known as agentic misalignment, where an AI intentionally pursues its own objective instead of the one set by its operator. To understand how often this behavior appears, Anthropic researchers […]
Agentic Misalignment Explained: When AI Agents Go Rogue Read More »
Large language models understand text well, but they become less effective when information is scattered across documents or mixed with images and other media. Modern AI systems rely on vector databases, which store embeddings and enable similarity search across collections. LanceDB is a vector database built for AI workloads, with native support for multimodal data
LanceDB Vector Database Guide: Features, Python Demo Read More »
Claude can review data, check code, write reports, and prepare presentations, but teams still end up repeating the same structure, validation rules, company standards, and final-check instructions in every conversation. That repetition wastes time and often leads to inconsistent results. Custom Skills solve this by packaging reusable instructions, workflows, templates, scripts, examples, and reference files
How to Create Custom Skills in Claude: A Step-by-Step Guide Read More »
AI-agent development has progressed through overlapping phases: prompt engineering, context engineering, tool use, autonomous loops, memory systems, and multi-agent coordination. A newer focus is graph engineering, which treats AI applications as explicitly designed workflows rather than a single autonomous agent. Graph engineering defines how agents, tools, deterministic functions, validators, data sources, and humans coordinate to
Graph Engineering for AI Agents: Beyond the Single-Agent Loop Read More »
Data science case study interviews are not just about writing code. They test how you think through a problem, analyze data, make decisions, and explain your approach in a way that solves a real business challenge. In this guide, you’ll learn a simple framework called SCOPE that you can use to approach almost any data
Earlier this year, an autonomous AI agent breached McKinsey’s internal AI platform using nothing more than an old SQL injection flaw. No credentials. No human guidance. Less than two hours. It reached production systems, exposing millions of chat messages and hundreds of thousands of files. AI security has changed, and traditional assumptions no longer hold.
A Complete Guide to AI Red-Teaming (With Garak Tutorial) Read More »
For months, Claude Code has been the go to terminal coding agent for developers. Then Grok Build arrived in beta on May 14, 2026, giving developers a second serious option and raising a new question: which one actually performs better? I tested both agents on the same real world coding tasks using identical prompts to
Grok Build CLI vs Claude Code: I Tested Both So You Don’t Have To Read More »
Large language models often receive more information than they need. A prompt may include long instructions, retrieved documents, chat history, examples, and tool descriptions. This increases token usage, cost, and response time. It can also make important details harder for the model to identify. Prompt compression reduces the prompt while keeping the key meaning, instructions,
Prompt Compression Techniques: How to Reduce LLM Costs Without Losing Important Context Read More »
This scene is playing out across engineering teams everywhere. Someone wraps a few LangChain calls inside a loop, adds a couple of tools, and proudly declares, “We’ve built an AI agent.” The demo looks great. Everyone is impressed. Then it goes to production. The first unexpected input arrives. The workflow breaks. Logs fill up. Alerts start firing.
Agentic AI vs AI Automation: What’s the Real Difference? Read More »
Thinking Machines Lab has unveiled Inkling, its first general-purpose open-weights foundation model. It is a multimodal MoE model with 975B parameters, 41B active parameters, and a 1M-token context window. Rather than chasing benchmark supremacy, Inkling is designed as a customizable foundation for multimodal reasoning, agentic AI, coding, tool use, audio and vision tasks, and domain-specific
