Oaky, But Why?

<p>Reading the shipping policy is useful. Checking a real order requires a different capability. We follow a tool call from the model’s proposed arguments through application-controlled validation, authorization...

<p>Brenda’s 40,000 documents are back, and this time we’re retrieving the useful ones. We explain retrieval-augmented generation, embeddings, vectors, semantic search, and chunking without turning the episode into a...

<p>How does a model become a feature in your own application? Follow a request from the browser through your server to a model provider, with APIs, SDKs, streaming, token costs, and failure handling along the way...

<p>Can we replace elaborate prompt rituals with a useful brief? Sabrina joins Parisa and Jules to discuss instructions, examples, context windows, randomness, and structured output. Brenda proposes pasting all 40,000...

<p>Is a language model a digital mind or autocomplete wearing an expensive coat? We explore tokens, next-token prediction, transformers, attention, and context to understand what happens when an LLM generates an answer...

<p>If nobody programmed every answer, where did they come from? Parisa and Jules unpack training data, models, weights, neural networks, and the difference between training and inference. Along the way, “let’s train our...

<p>Management says, “We should add AI.” Parisa has a follow-up: “To do what?” We untangle AI, machine learning, deep learning, generative AI, and LLMs—and introduce Support Assistant, our fictional company’s attempt to...

<p>Our little podcast app probably never needed Kubernetes. From static portfolios to large distributed systems, we compare infrastructure choices against actual requirements, costs, and maintenance responsibilities...

<p>Someone configured production six months ago. Nobody remembers exactly what they did. We explore how infrastructure as code makes those decisions reviewable and repeatable, why Terraform and OpenTofu keep state, and...

<p>The asterisk is doing some serious work. We explore cloud services by what they manage: compute, storage, databases, networking, containers, and more. The useful question is how much responsibility you want to...

<p>Kubernetes has a lot of nouns. Now they have jobs. We connect clusters, nodes, Pods, Deployments, and Services through one simple example: we want three replicas, but only two exist. What happens next—and what can...

<p>One container is manageable. Hundreds of containers across machines that occasionally fail create a different problem. Before introducing the vocabulary, we explain why orchestration exists—and why “Docker or...

<p>Pushing code means the code has traveled. It doesn’t necessarily mean anything is deployed. We trace a change through automated checks, image builds, registries, and production, then ask what happens when the green...

<p>Our fictional podcast app now has a frontend, an API, and a database. Docker Compose helps describe how they fit together—and Parisa discovers that familiar tools like DDEV and Lando have been doing quite a bit of...

<p>The API works. The database works. Together, they refuse to speak. We follow requests across container boundaries, decode port mappings, and discover why deleting a container can become a very unpleasant lesson about...