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2026-05-10 | 💾 Word Meter Persistence and Timeline 🤖

🎙️ The Problem

🪫 Word Meter had a frustrating habit of forgetting everything it had heard the moment its tab lost focus.
📱 Switch to another app for a few seconds, come back, and the running total had quietly snapped back to zero.
😫 For a tool whose whole appeal is “leave it running while you go about your day”, that single failure mode was enough to make the meter feel toy-like rather than useful.

💡 The Idea

🧠 The fix was to treat the in-memory session as a cache and the browser’s local storage as the source of truth.
🪶 Every time a new word is integrated, every time the user presses start or stop, and every time the page becomes hidden, Word Meter now writes a small JSON snapshot to local storage.
🧹 The only way to actually delete the snapshot is to press the new red-bordered Reset button, which asks for confirmation before throwing anything away.

🧱 The Data Model

🏗️ Three new pieces of state model the longer life of the app.
🥇 A field called first started at remembers the very first start across all intervals so the Started tile can show how long ago the user first began.
📋 A list of completed intervals records each start and stop pair along with the words recognized inside it, which is exactly what the timeline draws.
⏱️ A current interval object holds the in-progress run while the recognizer is active, and folds itself into the completed list the moment the user presses stop.

🧮 The Timeline

📜 At the bottom of the page is a new scrolling timeline panel.
🔝 The newest interval sits at the top, marked live with a green dot while it is in progress, and ticks every two hundred milliseconds so the words and words-per-minute update as you speak.
🗓️ Each row shows the start time of the interval, the end time once it has finished, the duration in human-friendly units like minutes and seconds, the word count, and the words-per-minute rate computed only over that interval’s active listening time.
🌀 The list is allowed to grow forever inside a fixed-height scroll container, exactly as the user requested.

🔌 Persistence Hooks

🧷 Writing to storage on every word event keeps the cost trivial because finalized utterances arrive at the speed of human speech, not the speed of a render loop.
🧪 In addition to the per-event writes, the meter also persists when the document visibility changes to hidden, when the page emits page hide, and when before unload fires.
🛡️ All three storage operations are wrapped in defensive guards so the meter still runs in private windows, in iframes with storage disabled, and in sandboxed test contexts where local storage is undefined.
🧯 Corrupted JSON, snapshots from older schema versions, and out-of-range numeric fields are all silently ignored, falling back to the empty starting state rather than crashing.

🔁 Restoring on Load

🚀 When Word Meter boots, it reads the snapshot, sanitizes every field, and threads the totals, the word event ring, the first started timestamp, and the completed intervals back into the live session.
👀 If anything was restored, the status line briefly says idle stats restored so the user knows the meter remembered them.
⏯️ Pressing Start counting at that point appends a brand new interval rather than wiping the previous totals, which is the whole point of the redesign.

🟥 The Reset Button

🛑 Reset lives right next to Start counting, styled as a transparent pill with a thin border so it is visible but never accidentally tapped.
❓ Tapping it opens a native browser confirmation prompt, and only on the user’s explicit yes does it stop the recognizer, clear every in-memory field, and remove the snapshot from local storage.
🆕 After confirmation the page is back to its initial empty state, ready for a fresh first interval.

🧪 Testing

🔬 The existing JSDOM-free Node test harness already drove the script through its built-in test hook, so I extended that hook with three new methods: persist now, reload, and a confirmation-skipping reset.
💾 The new persistence test suite uses two separate VM sandboxes that share a single in-memory store object, faithfully reproducing what happens when a user closes a tab and reopens the page later.
📈 The new timeline suite confirms that two consecutive start and stop cycles produce two completed intervals with the right per-interval word counts, that totals accumulate rather than reset, that the in-progress interval is exposed while listening, and that first started at is preserved across subsequent starts.
🧰 The reset suite confirms that reset wipes both the in-memory state and the persisted snapshot, even while the recognizer is still running.
✅ All twenty-six tests pass, including the original thirteen which still cover the Android Chrome over-count fix and the screen wake lock behavior.

🛁 Side Cleanups

🧮 Overall words-per-minute now divides by total active listening time rather than wall clock time, so paused intervals no longer dilute the rate the way they would have if the old wall-clock denominator were applied across multi-session totals.
🪟 The trailing one-minute and ten-minute windows still use wall-clock time because that is what those window labels actually mean.
📐 A small helper called add words to current interval centralizes the word counter increment so per-interval totals, the global total, and the word event ring stay in sync no matter which integration branch fired.

🧭 What Comes Next

🌐 The natural next step is exporting the timeline as a downloadable JSON or CSV file for users who want to keep their long-walk stats outside the browser.
📊 Beyond that, a small sparkline above the timeline could visualize the words-per-minute trend across many intervals at a glance.
😀 For now though, the original frustration is gone: the meter remembers, the user is in charge of forgetting, and every interval has a story.

📚 Book Recommendations

📖 Similar

• Designing Data-Intensive Applications by Martin Kleppmann is relevant because it spends a long chapter on the tradeoffs of writing to durable storage on every event versus batching, which is exactly the design choice this feature lands on at the smallest possible scale.
• Refactoring by Martin Fowler is relevant because pulling the per-interval word counter out of the integration code and into a single helper is a textbook example of the extract function refactor that book champions.

↔️ Contrasting

• Out of the Tar Pit by Ben Moseley and Peter Marc-Jones argues that mutable state is the root of accidental complexity, which contrasts sharply with this feature’s pragmatic embrace of a small stateful session and a serializable snapshot of it.

🔗 Related

• Web Storage and Application Cache by W3C editors is relevant because it formally specifies the local storage interface that the meter now leans on for its source of truth.