The prompt engineering playbook for programmers

The prompt engineering playbook for programmers
about 1 hour by Addy Osmani

AI coding assistants have become essential developer tools, but their effectiveness depends largely on well-crafted prompts. Addy explores systematic approaches to prompt engineering for common development tasks, showing how to transform vague queries into precise instructions.

Don't Let Your Cache Turn into a Database — Really... Just Don't
sponsored by Volt Active Data

Ever watch a cache slowly morph into an accidental, underpowered database? This post breaks down exactly how that happens—step by step—and why it leads to technical debt, write contention, and scalability nightmares. If you're a developer working with JSR107 or cache-based architectures, read this before your architecture hits a wall.

The ingredients of a productive monorepo
18 minutes by Sean Gillespie

Sean discusses the challenges and necessary tooling for implementing a monorepo in an engineering organization. Key focus areas include building source control systems that scale with O(change) rather than O(repo) operations, implementing efficient build and test systems that only run what's needed, creating smart CI pipelines that balance throughput and correctness, and understanding that code changes in a monorepo aren't deployed atomically despite appearing so in the codebase.

Why your AI coding assistant keeps doing it wrong, and how to fix it
12 minutes by Pete Hodgson

This article presents a nuanced view of AI-assisted coding, arguing that instead of debating whether AI can write good code, we should focus on which coding tasks AI excels at and which it struggles with. It introduces the "Constraint-Context matrix" as a framework for determining AI suitability, suggesting that AI performs best on well-constrained problems with explicit context.

How databases store your tables on disk
15 minutes by DeepIntoDev

This post demystifies how databases physically store tables on disk through pages, heap files, and indexes. It explains that databases organize data into fixed-size pages within heap files, with each page containing multiple rows. It contrasts row-based and column-based storage models while detailing how indexes work as separate data structures pointing to heap locations and explores clustered indexes, which physically sort data by the indexed column.

Systems correctness practices at Amazon Web Services
15 minutes by Marc Brooker, Ankush Desai

Marc and Ankush surveys use of formal and semi-formal methods to ensure system correctness at scale. AWS employs a portfolio of approaches including TLA+, the P programming language, property-based testing, deterministic simulation, fault injection, and formal proofs.

And the most popular article from the last issue was:

• Most important tools are a pen and paper