whatpulse.org

A key counter is a tool that tallies how many keys you press and which ones, without recording the text you type. WhatPulse, which has been counting keystrokes since 2003, is one example: it tracks keystrokes, mouse clicks, applications, websites, uptime, and bandwidth, then turns those counts into a picture of where your time goes. This is useful if you want to understand your typing volume, habits, and peak hours — and it is not the right tool if you need a record of what was actually written.

Counting input is not the same as logging content

The distinction matters because it determines what the tool can and cannot tell you.

• A key counter measures volume and pattern. It knows you pressed a key, which key it was, and when. WhatPulse describes its approach as tracking activity, never content.
• A content logger records meaning. That would mean capturing the actual words, which is a different category of tool with different privacy implications.

WhatPulse frames itself as "built for curiosity, not surveillance" and describes its website tracking as domain-level — it shows where browsing time goes without tracking page content. The same logic applies to the keyboard: you get counts and distributions, not a transcript.

What a key counter can actually show you

WhatPulse breaks keyboard data into several views:

• Key usage — which keys you press most often.
• Typing history — how your output changes over days, weeks, and longer periods.
• Keyboard heatmaps — a visual map of where your fingers land.
• Peak typing hours — when during the day you type the most.
• Application breakdowns — how typing is distributed across the software you use.
• Layouts and long-term trends — how habits shift when you change keyboard layout or over months of use.

On its own, this answers questions like "how much do I actually type?" and "when am I most productive at the keyboard?" Most people only have a rough guess; a counter replaces the guess with a number.

Where the keyboard stream fits in the bigger picture

Keystrokes are one of four streams WhatPulse combines into a single view:

Because the streams share a timeline, you can see that a spike in keystrokes came from a particular application, or that heavy mouse movement coincided with a specific site. The keyboard count alone is a number; combined with the other three, it becomes a story about attention and effort.

Practical uses

• Understanding your own habits. Compare typing volume across days to see whether your output matches your perception.
• Comparing layouts. If you switch keyboard layouts, long-term trends show how the change affects your key distribution.
• Grouping activity into projects. WhatPulse lets you sort activity into profiles for each project, client, or context, keeping a running tally of time and input behind each one. That turns billable hours, side projects, and focus blocks into measured figures rather than estimates.

What to check before choosing a key counter

• Does it count or record? Confirm the tool's stated policy on content. WhatPulse states it tracks activity, never content, and tracks websites at the domain level.
• Which platforms are supported? WhatPulse runs on Windows, macOS, and Linux.
• Is there a free starting point? WhatPulse offers a free plan with no credit card required, and lists a separate pricing page for premium options — check that page for current terms rather than assuming what premium includes.
• Do you need the other streams? If you only want keystroke counts, a simpler tool may suffice. If you want to connect typing to applications and browsing, the combined view is the reason to pick a multi-stream tracker.

The short version: a key counter tells you how much and how you type, not what you typed. If that is the question you have, it is the right tool.

A music artist network is a map where each artist is a node and each link represents a relationship between two artists, usually "listeners of one also listen to the other." On artistpath.cc you explore this map by entering two artists and tracing the chain of connections between them, then reading the visualization to see which links are strongest. It works best when you want to understand why two musicians feel related, or to discover artists that sit between two styles you already like. The connections reflect real listener behavior on Last.fm, not genre labels or historical influence, so treat them as a picture of audience taste rather than an objective ranking.

How an artist network is built

The network has two ingredients:

• Nodes — individual artists.
• Edges — links between artists, weighted by how often the same listeners play both.

Last.fm is a music tracking and recommendation service: it records what people scrobble (log) as they listen, and it publishes "similar artist" relationships derived from that listening data. When many listeners of Artist A also listen to Artist B, the two get connected. Stack thousands of these pairwise relationships together and you get a graph you can traverse.

Because the data comes from listening behavior, an edge means "these audiences overlap," not "these artists sound the same" or "one influenced the other." A shared link can come from genre, era, mood, playlist habits, or pure coincidence in a niche fanbase.

Finding a path between two artists

A path is the sequence of artists connecting your start point to your end point. A short path (one or two hops) suggests the two artists share a close audience; a long path means you have to travel through several intermediate tastes to get from one to the other.

On artistpath.cc the workflow is:

1. Enter a starting artist in the first field.
2. Enter a destination artist in the second field.
3. Run the path search. The tool walks the Last.fm-derived graph looking for a chain of connections.
4. Read the result as an ordered list of artists from start to finish.
5. Open the visualization to see the same chain as a graph, with the intermediate artists as nodes and the links between them as edges.

The expected result is a route like Artist A → Artist B → Artist C → Artist D, where each arrow is a supported similarity link. If no route appears, the two artists may simply sit in disconnected parts of the listener graph.

What to check after you get a path

• Length — how many hops did it take? Fewer hops generally means closer audience overlap.
• Intermediate artists — these are the bridge points. They are often the most interesting discovery, because they connect two worlds you already listen to.
• Link strength — where the tool shows it, a stronger link means more shared listeners, so that step is more meaningful than a weak one.

Reading the visualization

The graph view turns the path into something you can scan at a glance. Nodes are artists, edges are connections, and the shape of the chain tells a story:

Use the visualization to spot hubs. An artist that appears in many paths is a connector — a good candidate to explore if you want to move between genres.

Using the network to discover artists

The practical payoff is discovery through shared connections:

• Pick two artists you like and find the path between them. The artists in the middle are natural next listens.
• Follow a hub artist's links outward to see what else its audience plays.
• Compare a short path and a long path between the same pair to see which intermediate artists change.

For example, if you like two artists from different scenes and the path runs through a third name you have never heard, that third artist is the bridge your taste is already reaching toward.

Limits to keep in mind

• Connections reflect listener behavior, not objective genre, quality, or influence.
• Last.fm data skews toward users who actively scrobble, so popular and niche artists are represented unevenly.
• A missing path does not mean two artists are unrelated — it means the current data does not connect their audiences.
• A strong link is a statement about shared listeners, not a claim that the artists sound alike.

Computer activity tracking turns the raw stream of keystrokes, clicks, app switches, and site visits into a readable record of where your time and attention actually went. WhatPulse, for example, combines four data streams — keyboard, mouse, applications, and websites — into one picture, and it explicitly tracks activity rather than content. That distinction matters: you get usage patterns without your typed text or page contents being recorded. The insights below are useful if you want evidence about your own habits rather than a general sense of "I was busy."

The four data streams and what each one reveals

WhatPulse describes its approach as "four streams, one story." Each stream answers a different question.

The keyboard stream is the closest thing to a "key counter": it tells you not just how much you type but when you type most and which keys carry the load. The mouse stream is the "mouse clicks analyzer" side — click counts and travel distance show interaction intensity that a clock can't. The application stream is where most people find the surprise, because time-in-app often contradicts your memory of the day. The website stream shows domain-level destinations (github.com, stackoverflow.com, youtube.com, reddit.com appear as examples on the site) without tracking page content.

What the aggregate numbers look like

WhatPulse publishes running totals that give a sense of scale: over 931 billion keystrokes, roughly 493 billion mouse clicks, 935.4 million hours of uptime, 624.3 petabytes moved, and about 11.8 million km of mouse travel. These are community-wide figures, not your personal baseline — but they show the kind of long-horizon data the tool accumulates. Counting keystrokes since 2003 means trends can be measured over years, not just days.

Turning activity into decisions

The value isn't the counter; it's what you do with the pattern. Three concrete uses:

• Attention vs. intention. Compare time-in-app against what you planned to work on. If an application you didn't intend to open holds a large share of keys and clicks, that's a measurable attention leak.
• Peak-hour scheduling. Keyboard and mouse data reveal when your input volume is highest. Schedule demanding work there and administrative tasks elsewhere.
• Project time grouping. WhatPulse profiles let you group activity by project, client, or context, keeping a running tally of time and input behind each one. That turns billable hours and focus blocks from guesswork into a record.

Privacy boundary: activity, not content

The site states plainly that it "tracks activity, never content," and describes website tracking as domain-level and privacy-first. So the insight you get is behavioral — how much, when, and in which app or domain — not what you typed or read. If your concern is surveillance of content, that's the line to check before adopting any tracker. WhatPulse frames itself as "built for curiosity, not surveillance," which is the intended use case: self-review, not monitoring others.

Practical starting conditions

WhatPulse offers a free plan with no credit card required, runs on Windows, macOS, and Linux, and reports over 500,000 users. A pricing page exists at whatpulse.org/premium, so a paid tier is part of the model — check that page for current terms rather than assuming what's included. Leagues are also part of the product, which adds a comparative, week-by-week layer if you want external context for your own numbers.

If your goal is simply to see where your hours go, start with the application and website streams — they answer the "where did the time go" question fastest. Add keyboard and mouse analysis once you want to understand intensity and habit patterns rather than just duration.

A mouse clicks analyzer is software that counts your clicks and tracks cursor movement, then turns that raw activity into reports such as click totals, mouse travel distance, heatmaps, and daily or long-term trends. It suits anyone who wants an objective record of how they physically use a mouse — for curiosity, workflow review, or noticing repetitive patterns — rather than a tool that reads what you type or view. WhatPulse is one example: it counts clicks and movement alongside keystrokes, applications, and bandwidth, and states that it tracks activity, never content.

What a mouse clicks analyzer actually measures

The core data is behavioral, not textual. A typical analyzer records:

• Click counts — how many clicks you make, often broken down by day, application, or device.
• Mouse travel — the cumulative distance your cursor moves across the screen. WhatPulse reports this in absolute units; its public totals include figures like "11,862,081 km of mouse travel" across all users.
• Heatmaps — visual maps showing where on the screen your cursor spends the most time or clicks most often.
• Activity patterns over time — when you click most, how habits shift across days or weeks, and how behavior differs between screens or devices.

WhatPulse groups this into a "Mouse" stream that covers click counts, mouse travel, and heatmaps. The point is to convert thousands of tiny actions into a readable picture of where your attention and effort go.

How the data is collected and turned into reports

Collection happens locally on your machine. The analyzer runs in the background, quietly logging click events and cursor positions, then aggregates them into visual summaries. WhatPulse describes this as turning "thousands of tiny actions into a clear picture," and it supports Windows, macOS, and Linux.

The practical flow looks like this:

1. Install and run the tracker on your computer.
2. Use your computer normally — the tool counts clicks and movement without interrupting you.
3. Open the dashboard or reports to see click totals, travel distance, heatmaps, and trends.
4. Compare over time — daily habits, peak activity hours, and changes across applications or devices.

Because the counting is passive, the value comes from accumulation: a single day is noise, but weeks of data reveal patterns you would not notice from memory.

What the numbers can tell you in practice

Click and movement data answers questions your sense of time cannot:

• Which workflows are click-heavy. If one application dominates your click count, that is a signal about where your manual effort concentrates — useful when deciding what to automate or streamline.
• How your attention is distributed. WhatPulse notes that "your mouse often tells a different story than your clock" — you may spend more interactive effort in one app than its time-on-screen suggests.
• Whether repetitive patterns are building up. Sustained high click volume in the same screen region is the kind of pattern people review when thinking about repetitive strain, though the tool itself reports activity, not health outcomes.
• How behavior differs across screens or devices. Travel distance and heatmaps make multi-monitor habits visible.

For example, if you want to know whether a data-entry task is genuinely click-intensive or just feels that way, a week of click counts per application gives you a concrete answer instead of an impression.

Privacy: what is counted versus what is read

This is the key distinction for choosing an analyzer. WhatPulse states it "tracks activity, never content" and describes its website tracking as understanding browsing time "without tracking page content" — domain-level tracking rather than page contents. In the same spirit, a clicks analyzer counts that a click happened and where the cursor was, not what was on screen or what you typed into a field.

That makes click analysis a lower-intrusion option than tools that capture screenshots or read documents. It also means the insights are about volume and pattern, not about the substance of your work.

Pairing click data with keyboard and application tracking

Click counts are most useful as one stream among several. WhatPulse combines four: keyboard (key usage, typing history, heatmaps), mouse, applications (time spent, keystrokes and clicks, window titles), and websites (domain tracking). It also supports profiles that group activity by project, client, or context, keeping a running tally of time and input behind each one.

Read together, these streams answer questions no single metric can:

If you only need click and movement data, a dedicated analyzer is enough. If you want the fuller picture — clicks in context of apps, typing, and time — a multi-stream tracker like WhatPulse is the better fit, and it offers a free plan with no credit card required to start.

Computer activity tracking measures the raw inputs and behaviors you generate while using a computer—keystrokes, mouse clicks and movement, application usage, and website visits—and aggregates them into patterns you can review. Tools like WhatPulse turn those counts into insights such as peak typing hours, most-used applications, and time spent per project. This is useful if you want an objective record of where your attention goes, but it is not a substitute for task-level project management or a full time-tracking system with billing integration.

The four activity streams

WhatPulse describes its tracking as "four streams, one story." Each stream captures a different layer of computer use.

The distinction matters because each stream answers a different question. Keystroke counts tell you about output volume. Mouse travel tells you about navigation and interaction patterns. Application time tells you where your hours go. Domain tracking tells you where your attention goes online—without capturing page content.

How raw counts become productivity insights

A single day of keystrokes and clicks is noise. The value comes from aggregation over time and comparison across contexts.

• Typing history and peak hours: Seeing when you type most can help you schedule focused work during your actual high-output windows rather than assumed ones.
• Application breakdown: Comparing time, keys, and clicks per application shows whether effort matches intention. For example, if a communication tool shows high click counts but low keystroke counts, you may be context-switching more than producing.
• Keyboard heatmaps: These reveal which keys you use most and can surface ergonomic or workflow patterns—such as heavy reliance on shortcuts versus mouse-driven menus.
• Mouse travel: Large travel distances across screens can indicate inefficient window layouts or excessive navigation between tools.
• Domain tracking: Seeing which sites dominate your browsing time helps you notice whether research sessions stay on task or drift.

WhatPulse frames this as turning "thousands of tiny actions into a clear picture of where your time actually goes." The insight is descriptive, not prescriptive—you still decide what to change.

Using profiles to group activity by project or client

Profiles let you sort activity into named contexts—a project, a client, or a focus block. WhatPulse keeps a running tally of time and input behind each profile. This is the feature that moves tracking from "how much did I type today" to "how much input went into this deliverable."

For example, if you freelance for two clients, you can create a profile for each and switch between them as you work. At the end of the week, you have a record of time and keystrokes per client rather than a single blended total. WhatPulse notes this helps make "billable hours, side projects and focus blocks stop being guesswork"—but it is still an activity record, not an invoice or a formal timekeeping system.

Privacy: tracking activity without capturing content

WhatPulse states it "tracks activity, never content." In practice, this means it counts keystrokes rather than recording what you type, and tracks domains rather than page content. The website describes its website tracking as "privacy-first" and domain-level.

This distinction is the main reason activity tracking can be acceptable where full screen recording or keylogging would not be. If you are evaluating any activity tracker, check specifically:

• Does it store raw keystrokes or only counts?
• Does it capture URLs, page titles, or full page content?
• Where is the data stored, and can you export or delete it?

WhatPulse's public description supports count-based tracking and domain-level browsing data. It does not claim to capture content, and its marketing explicitly separates the two.

How to interpret patterns and adjust habits

The practical loop is: collect for a week or two, review the aggregates, change one thing, and re-check.

1. Establish a baseline. Track without changing behavior for at least a week so your patterns are representative.
2. Identify one mismatch. Look for a gap between what you believe and what the data shows—for example, an application you thought was minor that actually consumes hours.
3. Change one variable. Adjust a schedule, a window layout, or a tool choice based on that mismatch.
4. Re-measure. Compare the same metric over a comparable period. If peak typing hours shifted or mouse travel dropped, the change had an effect.

Common pitfalls: treating keystroke counts as a productivity score (volume is not value), over-interpreting a single unusual day, and forgetting that some work—reading, thinking, meetings—produces few keystrokes or clicks at all.

What to check before choosing a tracker

WhatPulse offers a free plan with no credit card required and supports Windows, macOS, and Linux, according to its site. A premium tier exists (linked as "Pricing"), but the public page does not state its price or feature limits, so verify those directly if you need premium features.

When comparing activity trackers, use the same dimensions:

• Platform support: Does it run on your operating system?
• Streams covered: Keyboard, mouse, applications, websites—or only some?
• Content vs. counts: Does it capture what you type or only how much?
• Project grouping: Can you separate activity by client or project?
• Data control: Can you export or delete your data?
• Cost: Free tier limits and premium pricing, checked at the source.

Activity tracking is most useful when you want an honest, low-effort record of computer use and are willing to review it periodically. It is least useful if you need automatic billing, team-level reporting, or task-level time attribution—those require dedicated time-tracking or project management tools.