How I use AI

How I use AI
23 minutes by Ben Stolovitz

AI tools have changed dramatically in just 12 months. Coding agents now write over 90% of the code, handling features and bug fixes while humans focus on architecture and quality. AI search, tutoring, and transcription work well, though writing assistance remains mediocre and AI art feels like a net negative when used commercially. The technology is powerful, but how we choose to use it still matters most.

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Updating a vector database is no simple thing
8 minutes by Doug Turnbull

Doug explains the challenges of updating vector databases in production. He explores how HNSW handles inserts, updates, and deletes, highlighting the trade-offs between search speed and write performance. He also compares different database approaches and recommends testing with real production traffic rather than relying on simple benchmarks when choosing a vector database.

Message queues
13 minutes by Alex Ewerlöf

Message queues sit between services that produce work and services that do the work, letting each side run at its own pace. When traffic spikes, the queue absorbs the load instead of crashing the system. If a worker fails mid-task, the message is redelivered rather than lost. Choose a queue for async tasks, bursty traffic, or when reliability matters, but avoid them when you need fast, synchronous responses.

Little things
6 minutes by Paul W. Homer

Good coding habits matter more than most people think. Paul suggests setting up your environment and foundations before writing core logic, and always answering open questions early rather than assuming. Use lots of small, well-named functions instead of comments, keep loops minimal, and never sacrifice readability for cleverness. When something feels wrong or repetitive, stop and find a smarter approach.

We replaced Redis with MySQL for inventory reservations—and it scaled
11 minutes by Emilie Noel

Shopify replaced Redis with MySQL for inventory reservations during checkout, using MySQL 8's SKIP LOCKED feature to handle concurrent purchases without overselling. The key design stores one row per inventory unit instead of one row per item, reducing lock contention at scale. The biggest surprise was that the real bottleneck was database connection usage from unrelated checkout code, not the reservation queries themselves. Fixing that, plus a configuration update, cut primary database reads by 50% and kept CPU well under 50% during peak traffic.

And the most popular article from the last issue was:

• Getting good at AI takes time and practice