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Context engineering is the next big thing If you have been following the AI space recently, you may have come across the term context engineering . Coined by Shopify CEO Tobi Lütke and quickly endorsed by Andrej Karpathy, the argument is that "prompt engineering" undersells what is actually going on when you get consistently good results from an AI model. "The art of providing all the context for the task to be plausibly solvable by the LLM."  - Tobi Lütke, Shopify CEO Clever

"You're an expert X." "Act as a Y" Role prompting is one of the most widely repeated tips in AI circles. The underlying idea is that if you assign the model a job title or area of expertise, it will respond more like a person who holds that role, making it more relevant, more precise, more expert-sounding. The research on whether that actually holds up is more nuanced than the advice suggests. And the nuance is important if you want to try this out yourself. What is role pro

AI, Machine Learning, Deep Learning, Generative AI, and LLMs are all terms commonly thrown around in tech writing, news and media, but they each refer to slightly different things and shouldn't be used interchangeably... even if they sometimes are. This guide explains what each one actually means, how they relate to each other, and why it matters which is which. What is AI, exactly? Artificial intelligence  is the broadest of the four terms. It describes any computer system

There's a weird phenomenon with the way AI reads documents and conversations, and it negatively impacts the accuracy of the responses we get back. AI's accuracy when recalling information located in the middle of the context window is lower than if the same information were located at the start or the end . It's known as the 'Lost in the Middle' effect , and it's a problem that researchers have studied in some depth. The consequences of this are pretty simple to understand an

Learn what a context window is, how tokens work, and why they affect your everyday AI use - explained in plain English.

Claude Code is an AI tool that runs in the terminal. For a lot of people, that sentence is enough to put them off. That would be a mistake. While it may have been designed as a coding assistant, it's really just an agent capable of managing your files, using skills, and writing and running code (all by itself) to solve whatever problem you throw at it. Claude Code is one of the more capable AI tools currently available, and you do not need a programming background to get real

Skills engineering is how we teach AI agents to handle tasks in the way we want them done. Instead of hoping the agent figures it out, we provide detailed instructions that guide its decision-making process. But not all skills are created equal. A poorly written skill wastes tokens , confuses the agent , and produces inconsistent results . A well-crafted skill makes your agent faster, more reliable, and easier to maintain. The quality of your skills directly determines your

Write Once, Use Forever. Think about the last time you explained your workflow to a new hire at work. You probably didn't just hand them a list of tools and wish them luck. You walked them through the process, explained why things work the way they do, and maybe showed them an example or two. Three weeks later, they're flying solo. AI can't do that. At least not out of the box. Every conversation starts from scratch. There's no memory of how you like things done, no recollect

AI agents are useful on their own. But out of the box, they're working in isolation — they can only see what you paste into the conversation. They can't check your calendar, search your company's files, pull live data from a platform you use, or send a message on your behalf. MCP servers change that. What is an MCP server? MCP stands for Model Context Protocol. The technical details aren't important. What matters is what it does: an MCP server is a connection between your AI

Can AI models write Manim code from natural language prompts? I tested Gemini, GPT, Claude, and Kimi to find out what works, what breaks, and why spatial reasoning is the bottleneck.

Modern AI chat tools are powered by Large Language Models (LLMs), which are sophisticated systems trained on vast amounts of text data to understand and generate human-like responses. LLMs like Claude and Gemini don't truly "understand" in the human sense, but they excel at recognizing patterns in language and predicting what text should come next based on your input. This is why the quality of your prompt matters so much. The AI model can only work with what you give it, m

And What Happened to CoT Prompting? Prefer to listen to this article instead? I used ElevenLabs TTS  to create this narration. Check them out using my affiliate link, here . "Think step by step" ...was once great prompt engineering advice, but now seems to have little to no effect. In fact, what if I told you that this technique, at best, has little effect on output quality, and at worst, increases costs, latency, and may even reduce the accuracy of your response? It decreas

Master AI-driven code mapping with this 4-step guide to navigating legacy projects. Turn complex code into clear diagrams and actionable documentation.

Do you over-rely on AI? Understand the risks of using AI for self-directed learning. Struggle productively. Reclaim your critical thinking.

Learn how to use regex pattern matching in Excel for advanced data cleaning, text extraction, and analysis.

Convolution kernels explained. Learn edge detection for images using filters and the Sobel operator. Essential computer vision concepts with Python.

Learn to build AI-powered apps using LLM APIs. This beginner’s guide covers prompt engineering, function calling, and creating custom AI tools.

Master advanced prompt engineering techniques like Chain-of-Thought, ReAct, and role-based prompting strategies to get smarter LLM responses

Unlock the math behind 22/7! Learn how continued fractions generate increasingly accurate rational approximations for irrational numbers like pi

Explore the math behind language. Learn how formal grammars shape speech, code, NLP, and the rules machines use to understand us.