PostgreSQL Query Rewriting Techniques The previous articles in this series covered performance problems you fix by adding indexes, restructuring joins, or tuning memory. This one is about the queries where the plan is "fine" — every node is doing something reasonable — but the query itself is asking the wrong question, producing unnecessarily large intermediate results or forcing the planner dow
I have a confession: I'm a productivity app addict. Notion, Todoist, Things, TickTick, Bear, Obsidian — I've tried them all. And every single one failed me in the same way. Not because they were bad apps. But because they let me add unlimited tasks. So I'd wake up Monday morning, open my to-do app, and see 47 items staring back at me. By 9am I was already paralyzed. Decision fatigue is real. When
SQL is widely known for data querying and manipulation but systems do grow; data becomes larger; processes become repetitive and operations become sensitive. SQL has some features which enables it to be considered a fully fledged programming language. Some of the features which I discuss in this article are procedures, functions and transactions. Each of these concepts serve distinct purposes. Sto
In the gold rush of Artificial Intelligence, developers often obsess over model parameters, token limits, and inference speeds. But in the Apple ecosystem, a groundbreaking AI model is only as good as the interface that houses it. If your app delivers world-changing insights but hides them behind a keyboard or makes them invisible to VoiceOver users, it isn't a "smart" app—it’s a broken one. Build
Hi 👋, In this post we shall explore Bedrock's structured KB with this architecture: Upload CSVs to S3 > SNS Queue > Crawl data with Glue > Query with Redshift > Bedrock KB > Query with LLM. Let's do some of this with code. Let's get started. Clone the repo and switch to the project directory. git clone [email protected]:networkandcode/networkandcode.github.io.git cd structured-kb-demo/ Do a uv sync
The previous three posts covered how events flow from the SDK to the UI, how the timeline renders, and how tool cards visualize. This final post looks at SwiftWork's infrastructure — how data is stored, how state is restored, how Markdown is rendered, how code is highlighted, and how API keys are managed. These components are independent, but all essential to making the app usable. SwiftWork uses
The previous two posts covered how events flow from the SDK to the UI. This post focuses on visualizing one specific type of event: tool calls. Tool invocations are the most frequent operations in an Agent application. A typical task might call tools twenty or thirty times—reading files, writing files, executing commands, searching code. If every tool call renders as the same gray block, it's hard
Post 1 covered how AgentBridge converts the SDK's AsyncStream<SDKMessage> into [AgentEvent]. This post looks at what [AgentEvent] becomes — how TimelineView renders 18 event types, handles scroll behavior, and stays smooth when the event count gets large. TimelineView is the main body of the workspace, filling all the space between the sidebar and the input box. Its view hierarchy is shallow: Time