A React island inside a Django page needs three things: a template tag that records rather than renders, one batched call to a render sidecar, and a browser that hydrates only the IDs the server named. Shared state then lives outside React, in a small store or the URL.
Git worktrees let you run agents in parallel, but every new worktree starts cold, fights over ports and leaves junk behind. Worktrunk wraps worktrees in a few simple commands and a hook system, so one config file gives the whole team warm, isolated environments that clean up after themselves.
Server actions look like React compiler magic, but underneath they are a name mapped to a function behind one endpoint. You can build the same shape in Django, call it from React or a plain HTML form, and keep validation at the boundary.
17 lock-wait timeouts in two hours. The SELECT was doing exactly what it was supposed to. The problem was what InnoDB did silently in addition: a gap lock on the write path, invisible in logs, invisible in EXPLAIN, visible only in production.
A customer support AI agent built in stages: shadow mode first, internal notes second, auto-send only after the data earns it. A walkthrough of the architecture, the knowledge base design, and the lessons that held up.
The claude -w flag spins up an isolated git worktree in seconds, so you can keep coding while a long-running task occupies your main session. No conflicts, no context pollution, no waiting.
Last week, we deleted 16,000 lines of code, rewrote 11,500, migrated frameworks, ripped out React, and swapped our entire CSS setup with AI. It took less than 6 hours.
Conductor by Melty Labs makes parallel agent workflows practical by running multiple agents with separate tasks simultaneously. The trade-offs are real but manageable, and this is where development is heading.
Part 2 of the article about Mossie, when it was faced with scaling to include every bird in the world, complete with photos, sounds, and icons.
A small birding app with 300 manually entered species faced an ambitious challenge: scale to include every bird in the world, complete with photos, sounds, and icons. This article explores how the team used GenAI to bootstrap a comprehensive birding database from scratch.
Vibe coding or AI-assisted development? The choice isn't binary, but getting it wrong at the wrong stage will cost you. This piece breaks down when to embrace speed over architecture, when to take back control, and why the best teams don't pick sides.
AI coding agents draft code, write tests, and open pull requests autonomously. This guide covers the tool landscape, readiness signals, a phased rollout plan, and the team dynamics that make or break adoption.
AI code review catches what humans miss and misses what humans catch. This guide compares the tools, shows what each catches, and walks through setup without replacing human judgment.
Performance bugs erode trust quietly until users explode. Three pragmatic steps help you catch slowdowns early: explore real bottlenecks with Sentry, test with production-sized data, and add lightweight API load tests.
If you believe what you see on LinkedIn, startups don't need employees anymore, real founders just have agents building their companies. You write a prompt, fire off the agent, and wait for customers. In reality, you get a vague workflow that produces a mediocre demo at best.
