tylers-blogger-blog

Local LLMs don't get the hype cycle love that cloud chatbots do. They're not always shiny, they sometimes take a little setup, and they won't magically know your company's "house style" on day one. But in real developer life-where you bounce between a repo, a ticket, a terminal, and a dozen tiny decisions-local LLMs are quietly doing the work that matters: reducing friction, keeping you in flow, and letting you ask "dumb" questions without social or security overhead. If you've mostly tried LLMs via a browser tab, you've experienced the "helpful when it's helpful" version. Local LLMs are different. They're closer to a power tool: predictable, always available, and customizable to your exact workflow. The productivity win isn't "smarter answers"-it's fewer context switches Developers don't lose hours because they can't think. They lose hours because the day gets shredded into micro-interruptions: swi...

Most "productivity advice" for developers is really just a shopping list: a new IDE plugin, a new task app, a new AI tool. But the real secret is protecting your focus like it's production infrastructure. Productivity isn't speed-it's fewer context switches. Every time you jump from coding to Slack to Jira to "quickly" checking a log, you pay a hidden tax: it can take 10-20 minutes to fully regain depth. Try this: schedule two 60-90 minute deep-work blocks per day. Before you start, write a one-sentence goal ("Make the failing test deterministic") and a tiny definition of done ("Test passes 20 runs locally"). Then turn off notifications, close browser tabs, and keep a scratchpad for interruptions so they don't hijack your brain. If you want a broader framework, this guide on developer productivity tactics is a solid reference. The surprising part: the best developers don't code nonstop-they design their day to make deep work ...

Let's be real: I jumped on the AI code review bandwagon thinking it'd be a magic wand. Turns out, it was more like a noisy, slightly confused roommate who kept suggesting I wear socks with sandals. The AI would flag 'inefficient' loops that were actually optimized for readability, or miss critical context about legacy systems we were touching. One time, it told a junior dev to 'remove all comments' because it thought they were 'unnecessary noise'-yikes, that comment explained a 10-year-old workaround! We spent hours chasing down these false alarms instead of shipping features. So we did the unthinkable: we turned off the AI. We focused our human energy on what mattered : architectural risks, security gaps, and clarity for the next person reading the code. We started asking 'Does this solve the user's problem?' instead of 'Does the AI like this indentation?'. The result? Our review cycle dropped from 2 days to under 1.5 days. Why? We s...