AI-300 Explained: Microsoft's New MLOps and GenAIOps Certification (2026 Guide)
Microsoft's new AI-300 exam replaces DP-100 and tests MLOps and GenAIOps on Azure. Here's what's asked, who it's for, and how to prepare in 2026.
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Articles on AI workflows, MCP patterns, and what we have learned building Synapse.
The thing your AI agent is missing isn't a better model
I kept upgrading the model when my agent failed. Turns out I was solving the wrong problem. Here's what a harness is and why architecture beats model quality.
Why We Built AI in a Shell Around Concepts, Not Just Chat
AI can help you move faster, but it can also make you lose the plot. The answer is not less AI. It is a learning loop where the human still reasons.
Read moreThe Complete Journey of a Prompt: How LLMs Actually Process Your Input End-to-End
Most explanations cover one piece at a time. Here's the full data flow — from your prompt to the next generated token — traced through every component in order.
Read moreWhat Is KV Cache and Why Does It Make LLM Inference Fast?
Every token an LLM generates reuses Keys and Values from everything that came before. The KV cache is what makes that reuse cheap. Here's how it works — and why inference slows down with longer context.
Read moreWhy Transformers Can't Tell Position Apart — and How RoPE Fixes It
Self-attention is blind to order. Shuffle the words in a sentence and you get identical attention scores. Positional embeddings solve this — but the way they do it determines whether your model can handle long contexts at inference time.
Read moreWhat Actually Happens Inside a Transformer Block
Attention gets all the press. But a transformer block is more than attention — there's a feedforward network that holds most of the parameters, two residual connections, and a normalisation design that determines whether large-scale training is stable. Here's all of it, in order.
Read moreWhy Your LLM Can't Think Ahead: The Token-by-Token Reality of Text Generation
Most developers treat LLMs as black boxes that write answers. The reality is stranger and more mechanical — and understanding it changes how you build.
Read moreWhy One Attention Head Is Never Enough: Multi-Head Attention Explained
A single attention pass can only ask one question at a time. Multi-head attention runs several in parallel — each head specialising in a different type of relationship. Here's what that means in practice.
Read moreThe Mechanism That Makes LLMs Actually Understand Language: Self-Attention Explained
Static embeddings can't tell 'bank' the financial institution from 'bank' the riverbank. Self-attention is how language models fix that — by rewriting each token's meaning based on what surrounds it.
Read moreYour AI Agent Has a Brain. Does It Have Hands?
Most agent failures aren't model failures — they're harness failures. Here's what an agent harness is, why it matters, and how to stop over-engineering it.
Read moreThe Wall Between You and the Model: Tokens, Encoders, and Embeddings
GPT-4 can't count letters. Here's why — and how embeddings give token IDs the meaning the tokenizer strips away.
Read moreWebMCP: Websites can now talk directly to AI agents
AI agents are terrible at browsing websites. WebMCP lets sites declare what an agent can do on a page — turning messy scraping into precise tool calls.
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The missing piece for AI coding agents: Vercel's agent-browser
AI agents can write code and run tests, but they can't see the website. Vercel's agent-browser finally closes the feedback loop.
Read moreTesting AI Agents with Vercel's Agent Browser
A practical guide to using Vercel's new Agent Browser for AI-native web automation.
Read moreDevelopers can now submit apps to ChatGPT
OpenAI is opening app submissions for review and publication in ChatGPT, creating a new ecosystem for developers.
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A better way of tool calling for AI agents
Why asking an AI to write a program is better than asking it to press buttons—and how the Model Context Protocol makes it viable.
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It asks the kind of questions senior engineers do, sketches diagrams with you, and forces you to defend every architectural choice so your ideas are ready for interviews or production reviews.