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26 articles tagged #Productivity.
P.01A Q3 2026 survey of 705 developers found 42% now have AI write half their code, up from 12% a year ago. The catch: review time grew faster than coding time shrank.
P.02Spec-driven development treats a written spec, not code, as the artifact an AI agent builds from. How Spec Kit, Kiro, and BMAD differ, and when it's worth it.
P.03METR's randomized trial found AI tools made experienced developers 19% slower, then flagged its own follow-up as unreliable. What the messy data supports.
P.04AI agents made broad, shallow knowledge cheap to fake. What's getting scarcer is the depth to know when the agent is wrong. Specializing versus generalizing.
P.05One GitHub Copilot AI Credit costs $0.01. Pro includes 1,500 a month, Pro+ 7,000, Business 1,900 a seat. Completions are unlimited; chat and agents use credits.
P.06A week-by-week playbook for onboarding a new software engineer. What to cover in the first month, what most teams skip, and how to measure whether it is working.
P.07Most agency owners are the bottleneck in their own business. Here's how to build systems for client work, hiring, and operations that run without you.
P.08Standard Scrum assumes stable backlogs, but agencies face client reviews, scope talks, and projects that end. Here's the adapted sprint structure that works.
P.09Cursor's rules system encodes your team's architecture, naming, and coding standards into the AI's context, so every engineer gets consistent suggestions.
P.10Git hooks enforced by Lefthook, Husky, or lint-staged stop broken code and style violations before they reach CI. How to build a hook setup teams actually keep using.
P.11Bruno stores API collections as plain .bru text files in your git repo, no account or cloud sync needed. Here's how it compares to Postman and why teams switch.
P.12Git worktrees give you multiple branches checked out in separate directories at once. No stashing, no context switching, no losing your place.
P.13Manually compiling monthly client reports is high-effort, low-value work. Here's how to automate the data collection and drafting while keeping a human in the loop.
P.14Command-line tools are underrated as developer products, and TypeScript makes them maintainable. Parsing, prompts, output, errors, and distribution.
P.15Structured commit messages unlock automatic changelogs, version bumps, and release notes. The full setup, from commitlint hooks to shipping in CI.
P.16Show your reasoning, not just your tech stack. Here's what makes a developer portfolio stand out when every project claims AI experience in 2026.
P.17Most agencies invoice and move on. The ones that improve fastest sit down and ask what actually happened. How to run a debrief that produces changes.
P.18Most developers still run decade-old command line tools. Here's what's replaced them in 2026: faster terminals, Git UIs, shell prompts, and search tools.
P.19Every agency hits the same wall: more work than the team can handle, not enough volume for a hire. White-label partnerships are how survivors manage growth.
P.20AI makes generating and refreshing technical documentation cheap, but keeping it accurate as code changes is still a process problem, not a model one.
P.21Developer burnout has a new texture in 2026: AI productivity pressure, skills anxiety, and blurred output-identity lines. What's different, and what helps.
P.22CodeRabbit and GitHub Copilot code review changed the first pass. Here's what these tools actually catch, where they fail, and a workflow that combines both effectively.
P.23Biome is a Rust-based toolchain that replaces ESLint and Prettier with one fast binary for linting, formatting, and imports. Here is what migrating looks like.
P.24AI IDE rules files inject project-specific context into every completion. Here is how to write rules for Cursor, Windsurf, and Copilot that change generated code.
P.25uv is a Rust-written Python package manager covering dependencies, virtualenvs, and Python versions. What changed after moving eight projects onto it.
P.26Measuring developer productivity is notoriously difficult. DORA and SPACE frameworks offer research-backed approaches, but implementation is where most teams fail.