The art of simplicity

UI generation and background coding agents are two of the "AI" tools that really changed my way of working. Together, they close a gap that's been annoying me for a while: the coding agent still needs someone to describe what the UI should look like, and that someone is usually me, typing a wall of text into an issue and hoping for the best. Google Stitch generates UI screens (HTML/CSS, Tailwind, Flutter, SwiftUI, whatever…) from a prompt or a sketch. The GitHub Copilot coding agent picks up an issue and produces a pull request in the background, without you sitting in the editor. In this post we look at how to connect the two through MCP, so the coding agent stops guessing at layout, spacing and colors, and starts reading an actual design spec. Here's how to wire it up, and where it still needs a human in the loop. My first approach: screenshots in the issue body My first attempt was to design something in Stitch, paste a screenshot into a GitHub issue, and ...

We run Dependency-Track for vulnerability analysis across our projects. One day, the OSS Index integration stopped working. No API calls, no analysis results, just this in the logs: An error occurred decrypting the OSS Index API Token; Skipping [projectName=Balansen - BatchConsole, vulnAnalysisLevel=PERIODIC_ANALYSIS, projectUuid=14ba5633-58c2-45fe-9f04-e4ab0b28375e, projectVersion=DEV] javax.crypto.BadPaddingException: Given final block not properly padded. Such issues can arise if a bad key is used during decryption. BadPaddingException is Java's polite way of saying: "I tried to decrypt this with a key, and it's the wrong key." The API token itself was fine. The key used to decrypt it wasn't. The wrong assumption Our first instinct was to re-enter the OSS Index credentials in the UI and assume a fat-fingered token was the culprit. That didn't help. The error came back on the next analysis run, right after we restarted the container for an unrelated...

Dimension tables don't stay still. A customer moves city, a product gets reclassified, a salesperson switches regions. The question is never whether this happens, it's what you do with the old value once it changes. That question has a name: Slowly Changing Dimensions (SCD). If you haven't already, it's worth reading my first post about what SCDs actually are and the full set of types first — this post assumes you already know the difference between Type 1 and Type 2 and want to get straight to implementing Type 2 in Fabric. Until recently, implementing Type 2 in Fabric meant either building a Dataflow Gen2 with a chain of merge steps, or writing a PySpark notebook against Delta tables. Both work. Both also mean you're maintaining custom logic per table, forever. Fabric's Copy job now has SCD Type 2 built in as a write method. No merge statements, no derived columns for surrogate keys, no alter-row logic. You pick a write method from a dropdown. This pos...

Being new to data warehousing, I never heard about the term "Slowly Changing Dimensions" before. It sounded like academic jargon. It isn't. It's one of those concepts that, once it clicks, explains half the weird design decisions you'll see in any reporting database. This post is the explainer I wish existed before I had to learn it the hard way: by breaking a report Facts and dimensions, the short version Data warehouses generally split data into two kinds of tables. Fact tables hold the things that happened: an order, a sale, a support ticket, a click. They're typically just numbers and foreign keys — quantity, amount, a reference to which customer, which product, which date. Dimension tables hold the descriptive context around those facts: who the customer is, what the product is called, which region a salesperson covers. A fact on its own is nearly meaningless. "47 units, customer 1182, product 309" tells you nothing until you jo...

The GitHub Copilot SDK just shipped a new feature: optional memory configuration on session create and resume. Here is what it does, and how it is different from persisted sessions. The wrong mental model first When I heared "session memory" my first thought was "persisted sessions" — the ResumeSessionAsync flow that lets you reload an existing session by ID and continue where you left off. That is not what this is. Persisted sessions are about durability of the conversation itself: close the app, reopen it, pick up the thread. Memory configuration is something different. What memory configuration actually does Memory is a feature of the Copilot runtime that lets the agent read and write facts across turns — a kind of long-running knowledge store that the agent can consult and update during a session. Think of it as the agent's notepad, not the conversation log. The new MemoryConfiguration type exposes a single Enabled flag today. You opt in per se...

The GitHub Copilot CLI keeps getting more capable. One of the newer additions is the ability to schedule prompts, either on a repeating interval or as a one-shot delayed action. Let me walk you through both approaches. Two ways to schedule The /every command schedules a prompt to run repeatedly at a specified interval, while /after schedules a one-shot prompt to run once after a specified delay. Both commands are still experimental. They are only available if you have used the /experimental on slash command, or the --experimental command-line option first. Use the following slash command to enable experimental mode in your session: /experimental on Recurring prompts with /every Use /every when you want Copilot to repeat a task on a cadence during your session. /every 10m Run the test suite and summarize any new failures /every 1h Check for new comments on my open pull requests A number with no suffix is interpreted as minutes — so /every 30 remind me to check for...