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Plain-English explainers for core AI concepts, model terms, prompting patterns, and the practical skills people need to use AI well.
By Jason FutrillCurated insights on what's new in AI.
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Vibe coding is a style of AI-assisted software development where you describe what you want in plain language and an AI tool writes or edits the code. It is useful for prototypes, MVPs and learning, but generated code still needs review, testing and security checks.
May 25, 2026 · 16 min read
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An AI agent harness is the runtime and control layer around an AI agent. It connects the agent to tools, context, memory, permissions, workflows, logging, evaluation and human approval so the agent can complete tasks safely and reliably.
Tokenmaxxing is the habit of pushing AI token usage as high as possible, often through long prompts, coding agents, parallel workflows and internal usage leaderboards. Used well, it can encourage serious AI experimentation. Used badly, it turns productivity into an expensive token bonfire.
A transformer model is an attention-based neural network architecture behind many modern LLMs. It helps models weigh relationships between tokens in context, which is why GPT-style systems, BERT-style systems and other AI tools can handle prompts, examples and language patterns so flexibly.
A beginner-friendly explainer of AI model training, datasets, training examples, pattern learning, and why model quality depends so heavily on data quality.
RAG connects an AI model to external documents, databases, files, or knowledge bases so it can retrieve relevant context before generating a more useful answer.
A plain-English guide to AI agents, including how agentic AI can plan steps, use tools, observe results, complete tasks, and differ from a chatbot that mainly responds in conversation.
A beginner-friendly explainer of AI prompts as the instructions, context, examples, constraints, and output format given to tools like ChatGPT, Claude, Gemini, and other AI assistants.
A clear practical guide to the hierarchy of AI, machine learning, deep learning, and generative AI, with examples, comparisons, limitations, and common misconceptions.
A beginner-friendly explainer of reinforcement learning from human feedback, how reward models work, and why human preference data can improve AI model behaviour.
A beginner-friendly explainer of AI evals, test cases, grading methods, and the pre-deployment measurement habits teams use to make AI systems more reliable.
A beginner-friendly explainer of how AI image generators turn prompts into images, how diffusion-style generation and editing work, and where text-to-image AI is useful or limited.
GPT stands for Generative Pre-Trained Transformer: a transformer-based language model style that is broadly trained before use and generates text from context. GPT is usually an LLM, but it is not the same as all AI, all generative AI, or every transformer model.
A practical guide to open source AI versus closed AI models, including open weights, proprietary models, transparency, cost, safety, and business control.
AI inference is the process of using a trained model to generate answers, predictions, labels, images, recommendations or other outputs from new input.
A beginner-friendly explainer of machine learning as pattern learning from data, including how ML differs from fixed-rule software, how models train on examples, common use cases, main types, benefits and limitations.
A plain-English guide to multimodal AI, including how models process text, images, audio and video together, where multimodal systems are useful, and why high-stakes outputs still need human review.
A context window is the active token budget an AI model can use for a request or turn. It explains why ChatGPT can seem to forget details in long conversations and why large documents need careful context management.
