WakaTime does one thing well: it tells you how long you spent actively working in your editor, broken down by language, project, and file. The plugin has broad support across editors and IDEs, the setup is minimal, and the data is accurate for what it measures.
The constraint is what it measures. WakaTime tracks editor activity. It has no visibility into the rest of your day.
What WakaTime Measures
WakaTime's model is plugin-based. Install the extension in your editor of choice — VS Code, JetBrains, Vim, Emacs, and many others — and the plugin sends heartbeats as you type: file saves, cursor movement, active keystrokes. The dashboard breaks this down by language, project, file, and branch.
That's useful for understanding your coding patterns: which languages dominate your time, whether your work is too fragmented across projects, how much active editor time is in a typical day versus how much you estimated.
The WakaTime free plan stores your data but limits the dashboard history window. Premium at $9/month unlocks the full history and more detailed breakdowns.
The Gap in the Picture
WakaTime doesn't see outside the editor. The parts of your day that aren't generating keystrokes don't exist in its model.
A developer's day has a lot of those parts. Code review, Slack, documentation reading, architecture discussions, browser research, context switching between all of the above. WakaTime shows the editor slice. How much time goes to communication, whether that ratio changes between high-output and low-output weeks, what happens to your focus time when meeting load spikes — none of that is visible.
There's also no health layer. WakaTime can't tell you anything about the connection between your sleep, your physical state, and your coding output. The correlation between those things is real, but you need data from more than one source to see it.
A Different Architecture
xeve starts at the OS level rather than the editor. The macOS app is a Swift menu bar process that captures every app switch and window focus event. The Windows app does the same from the system tray. Neither requires an open editor to run.
From those OS-level events, every session gets logged: VS Code, yes, but also Chrome, Slack, Figma, Terminal, whatever else you're using. Sessions categorize automatically into nine buckets — Development, Productivity, Communication, Browsing, Entertainment, Design, Writing, System, Uncategorized — with custom rules available for anything the defaults miss.
The VS Code extension adds the coding-specific layer WakaTime specializes in: per-file, per-project heartbeats, so the Development time in the overview also shows which project it came from. A separate Claude Code hook tracks agentic sessions — if you're spending several hours a day working with Claude Code, that logs as its own category rather than disappearing into Terminal time.
Health Data Alongside Coding Data
The iOS companion connects xeve to HealthKit. Sleep duration and quality, steps, heart rate variability — those sync to the same database as your app usage and coding time. BLE heart rate monitors — Polar, Whoop, any standard HRM — connect directly to the macOS app or iOS companion for resting HR data during your workday.
This matters when the question shifts from "how much did I code" to "what conditions produce my best work." That question can't be answered with editor data alone. It requires something on both sides: what you were doing at the screen, and what your body was doing while you were doing it.
GitHub Activity as the Output Layer
GitHub activity syncs daily: commits, pull requests, code reviews. This puts time spent alongside work produced in the same view. A week of coding time alongside the PRs it generated tells you something about ratio — focused time that shipped versus focused time that looped, got reworked, or went nowhere.
WakaTime has a GitHub integration for streak tracking, but it's a separate add-on rather than a unified data model. In xeve, coding time, GitHub output, app usage, and health data all live in one place.
The Correlation Engine
xeve auto-computes 19 metric pairs across the data it collects: sleep duration versus coding output, GitHub activity versus late-night sessions, heart rate versus focus blocks, and more. None of these are presented as causal — correlation doesn't prove causation — but over a few weeks of data, patterns emerge that don't show up in any single day's numbers.
The most common thing developers notice once they have a month of data: the gap between perceived coding time and actual coding time. You can see that in WakaTime too, from the editor side. What you can't see there is what correlates with it — whether high communication load reliably precedes low-coding days, whether certain sleep patterns predict your most productive coding sessions, whether the weeks that felt focused actually produced more GitHub output.
Practical Setup Difference
WakaTime is a plugin install. Add it to your editor, authenticate, and it starts sending heartbeats. No configuration required for the basics.
xeve requires more pieces: the macOS or Windows tracker app, optionally the VS Code extension, optionally the iOS companion for health data. Each adds a data layer and a small amount of setup overhead. The correlation engine only gets interesting after a few weeks of data from multiple sources. If you want pure editor-level coding time with minimal friction, WakaTime gets there faster.
Who Each Is For
WakaTime is for developers who specifically want editor-level coding metrics — language splits, project time, coding streaks — and don't need anything outside the editor. It works well for freelancers reconstructing billable hours, teams tracking coding output across a shared dashboard, or anyone who wants a single-purpose coding tracker with broad IDE support and low setup cost.
xeve is for developers who want to understand their full working day: not just editor time, but how communication load compares to focused work, how health patterns correlate with output, and what the ratio of focused time to shipped code looks like across weeks. It runs on macOS and Windows, handles collection and correlation automatically, and doesn't require you to export data or build your own analysis.
If the editor slice is what you need, WakaTime covers it well. If the question you're actually trying to answer is larger than that — why some weeks feel productive and others don't, what your best days have in common — you need data from more than an editor plugin.