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Claes Järudd built WorkThoseKeys with Lovable, a prompt-based AI app builder, to avoid learning a conventional programming stack for a single typing tool. The result is an intentionally granular trainer: eight broad stages are divided into 31 sub-levels, weak-spot exercises are generated from each typist’s character and bigram errors, and progress is reported with net WPM so accuracy affects the score.
That combination addresses three common weaknesses in typing practice. A short course can hide missing fundamentals, fixed word lists can ignore the mistakes you actually make, and raw speed can make sloppy output look impressive. WorkThoseKeys is a product account rather than an independent usability study, so it demonstrates a thoughtful design, not a proven percentage improvement in typing speed.
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What is WorkThoseKeys, and why build it with a no-code AI tool?
WorkThoseKeys is a typing trainer designed around adaptive practice rather than a fixed sequence of word lists. Järudd says he chose Lovable because he did not want to learn conventional coding from scratch for one tool. In a prompt-based builder, he could describe the behavior he wanted, refine the interface, and concentrate on the training model instead of constructing every software layer manually.
That choice affects what the project is—and what it is not. It is a working product built around a clear learning design, not evidence that no-code development is always faster or cheaper than programming. The available account does not report development time, cost, user numbers, or controlled tests. It does show how a narrowly focused app can be organized when the builder starts with a specific learning problem: make practice slower, more targeted, and easier to interpret.
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“Raw words-per-minute rewards fast sloppy typing. Net WPM subtracts errors, which is a more honest number but a less flattering one,” Järudd writes.
How does an eight-stage course become 31 sub-levels?
The course has eight top-level stages, but they are split into 31 smaller levels labeled from 1a through 8c. The extra granularity is deliberate. Järudd’s rationale is that learners who skip fundamentals often have to relearn them later as bad habits.
| Course element | How WorkThoseKeys uses it | Why it matters |
|---|---|---|
| Top-level structure | Eight stages | Provides a broad progression from early skills to later mastery without pretending that each stage is a single lesson. |
| Sub-levels | 31 levels, labeled 1a through 8c | Creates shorter checkpoints so a learner can reinforce one skill before moving on. |
| Warmup | First step in every level | Gets the hands and attention ready for the specific material that follows. |
| Weak points | Second step in every level | Directs practice toward errors recorded from the typist’s sessions. |
| Integration | Third step in every level | Places the isolated skill back into more complete typing. |
| Progress check | Fourth step in every level | Tests whether the skill holds up after practice instead of assuming completion equals mastery. |
The four-part loop repeats at every level: warmup, weak points, integration, and progress check. The source description does not provide names or lesson content for all 31 levels, so it would be misleading to invent a detailed syllabus. The important design decision is the repeated loop itself: prepare, target, recombine, and verify.
How do per-character and per-bigram drills adapt to a typist?
The trainer records errors at the level of individual characters and two-character sequences, known as bigrams. It then builds drills around the pairs that cause trouble instead of handing every user the same fixed list.
Consider a hypothetical session in which one learner repeatedly mistypes th while another struggles with er. Their overall accuracy could be identical, but their useful next exercise would be different. An adaptive engine can assign more practice to each person’s troublesome pair. Järudd summarizes the point this way: “Two people with the same overall accuracy can have completely different weak points.”
Per-character data catches problems such as consistently missing one key. Bigram data catches transitions between keys, where hand movement, rhythm, or finger assignment can break down even when each key is reliable in isolation. The trainer’s model can therefore distinguish a single-key weakness from a sequence weakness.
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This is also why the feature should not be described as a promise of automatic improvement. The system can identify and rehearse errors; it cannot guarantee that a learner will transfer the correction to ordinary writing. The integration and progress-check steps are intended to test that transfer.
What does net WPM mean, and is it better than raw WPM?
Net words per minute is a speed score that accounts for mistakes. Raw, or gross, WPM measures how quickly characters are entered and converted into words without fully penalizing incorrect output. Net WPM applies an error deduction, so a fast session with many corrections produces a lower score.
| Metric | What it emphasizes | What it can hide |
|---|---|---|
| Raw/gross WPM | Keystroke speed before an error penalty | A high number may include text that would need substantial correction. |
| Net WPM | Usable speed after accounting for errors | The score is lower and cannot be compared fairly with another test unless both use the same formula and time conditions. |
| Accuracy | Percentage of entered characters that are correct | A high percentage alone does not show how quickly the text was produced. |
| Consistency | How evenly speed is maintained during a run | A single consistency value does not identify which letters or transitions caused a slowdown. |
There is no universal formula supplied for WorkThoseKeys, and the account does not claim that net WPM is scientifically superior in every context. The practical point is transparency: a speed number without an error penalty can reward output that is unusable until edited. Anyone comparing scores across trainers should check the error deduction, test duration, word definition, and whether backspaces count before treating the numbers as equivalent.
What does the AI coach add beyond automated drills?
The AI coach is an interpretation layer, not the component that creates the exercises. Järudd describes it as turning typing data into plain-language advice rather than merely displaying a percentage for a weak bigram.
The distinction is important:
- Weak-spot drill engine: reads character and bigram error rates and creates practice material from them.
- AI coach: explains what those patterns mean and suggests what to fix in understandable language.
For example, a drill engine might increase practice containing a troublesome pair. The coach might explain that the pair breaks down during a particular hand transition or that accuracy falls when the typist accelerates. The source does not document a fixed library of coaching messages, so the feature should be understood as contextual interpretation rather than a prescribed method for every learner.
“This is separate from the weak-spot drill engine (which builds practice sessions from your errors); the coach is the layer that explains them back to you,” Järudd says.
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Why are keyboard layout and language separate settings?
Keyboard layout determines where keys are physically arranged; content language determines which characters and words appear in practice. Treating them as independent controls is useful for people who type in more than one language or use one physical layout for text written in another language.
Järudd gives Swedish and US layouts as examples. Swedish practice can include å, ä, and ö, while an on-screen keyboard shows the active arrangement and highlights the next key. Separating the settings avoids forcing a learner to choose between an accurate physical layout and the language they actually write.
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How does Book mode make long-form practice different?
Book mode lets users type public-domain literature in segments and saves their position between sessions. That changes the practice task from short, artificial prompts to sustained text with punctuation, capitalization, repeated vocabulary, and changing sentence patterns.
The examples include a three-stanza Emily Dickinson poem and the complete Moby-Dick, listed at 1.19 million characters by Järudd in 2026. A saved position means a learner can stop without losing the place in a long work, then return to the same text later. The description does not state that every public-domain title is available, so the examples should not be treated as a complete catalog.
Long-form mode also gives the metrics a different context. A short drill can isolate one bigram; a book session shows whether that correction survives when attention is divided across a real passage. It is therefore complementary to targeted weak-spot practice rather than a replacement for it.
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Can you use the trainer without creating an account?
Yes. Lessons, drills, and books are available in guest mode without signup or stored account data, according to Järudd. Google sign-in is optional for people who want statistics and streaks to persist.
This access model lowers the commitment required to try the trainer. A guest can begin a lesson immediately, while an account becomes relevant when longitudinal records—such as streak history or accumulated statistics—matter. The account description does not specify retention periods, export tools, or what happens when browser data is cleared, so those details should not be assumed.
How should a learner use the four-step practice loop?
The level structure suggests a practical routine that keeps speed from overwhelming accuracy.
- Start with the warmup. Use the opening exercise to settle into the layout and notice the target pattern before trying to go faster.
- Read the weak-point feedback. Look for repeated character or bigram errors rather than judging the session only by its headline WPM.
- Complete the targeted drills. Let the adaptive exercises concentrate on the transitions that your own sessions exposed.
- Use the coach for interpretation. Read the plain-language explanation so the numbers point to a behavior you can consciously change.
- Move to integration. Type broader material to check whether the isolated correction survives normal word and sentence flow.
- Take the progress check. Treat the result as evidence about retention, not as permission to skip the next fundamentals.
- Use Book mode periodically. Longer public-domain passages reveal whether accuracy and rhythm hold up beyond short drills.
If the goal is a reliable typing habit, net WPM should be read alongside accuracy and consistency. Chasing the largest raw number can encourage rushing; watching net speed while errors fall gives a more useful picture of usable output.
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Adaptive analytics and net-versus-gross reporting are now recognizable features in the typing-trainer category. For example, ClickClack’s current product site advertises net and gross WPM, accuracy, consistency, correct/incorrect/missed-character tracking, and bigram/trigram mistake analysis. That overlap establishes a category pattern, not proof that one product is better.
| Comparison axis | Question to ask | What WorkThoseKeys documents |
|---|---|---|
| Curriculum granularity | Are broad stages split into manageable mastery steps? | Eight stages expanded into 31 sub-levels labeled 1a through 8c. |
| Error adaptation | Does practice use your own mistakes? | Per-character and per-bigram error rates generate weak-spot drills. |
| Layout and language | Can physical layout and typed language be selected separately? | Swedish and US layouts are examples, with Swedish characters supported. |
| Scoring | Are errors reflected in the speed number? | Net WPM is used alongside accuracy and other typing data; the exact formula is not stated. |
| Explanation | Does the product interpret data or only display it? | An AI coach turns error patterns into plain-language advice. |
| Access and persistence | Can someone start without an account, and what persists? | Guest lessons, drills, and books work without signup; Google sign-in preserves stats and streaks. |
| Long-form practice | Is there text beyond short generated exercises? | Book mode uses segmented public-domain works and saves the reading position. |
These are useful evaluation axes when choosing any trainer. They also prevent a misleading single-number comparison: a product with excellent analytics may have a shallow curriculum, while a carefully sequenced course may offer little explanation of individual errors.
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What are the limitations of the WorkThoseKeys account?
The available description is a first-person product account by Claes Järudd, dated 2026. It does not report a controlled comparison, sample size, independent usability testing, or a measured percentage increase in typing speed. The figures of 31 sub-levels and 1.19 million characters describe the product’s own structure and book count, not a market-wide benchmark.
The account also leaves some implementation details open: the exact net-WPM formula, the full list of course lessons, the number of available books, and the persistence rules for guest data are not specified. Those omissions do not invalidate the design, but they matter when comparing scores or making claims about learning outcomes.
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It is most relevant to learners who want deliberate fundamentals, feedback tied to their own errors, or practice that extends from isolated transitions into complete prose. Multilingual typists may also benefit from the independent layout and language controls, while readers who dislike repetitive word lists can use public-domain books for longer sessions.
Someone seeking a verified speed-improvement program should wait for independent measurements rather than infer them from the feature list. The strongest defensible claim is narrower: WorkThoseKeys combines granular progression, adaptive bigram practice, explanatory coaching, and an error-aware speed metric in one free-to-start trainer.
Is this approach worth trying?
It is worth trying if you care about accurate, repeatable typing rather than the highest raw WPM displayed after a short test. The design’s practical advantage is coherence: 31 small checkpoints slow down progression, per-bigram data chooses practice, the AI coach explains the pattern, and net WPM keeps speed tied to correctness. Treat the scores as guidance within this trainer until the formula and test conditions are known, and judge progress by whether errors decline while usable speed and consistency hold up in ordinary text.
Frequently Asked Questions
What is a bigram in typing practice?
A bigram is a pair of consecutive characters, such as “th” or “er.” Tracking pairs can reveal a hand transition that fails even when each individual key works correctly.
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Can net WPM scores be compared between different typing websites?
Only cautiously. Different services may use different error penalties, test lengths, word definitions, and backspace rules, so compare scores only after confirming that those conditions match.
Can I use a US keyboard layout while typing Swedish text?
The trainer treats layout and content language as separate controls, so the documented design supports that kind of combination. Swedish examples include å, ä, and ö; availability of additional language packs is not specified.
Should a beginner chase a higher WPM immediately?
Not necessarily. Early practice is usually more informative when accuracy and repeatable finger movement are stable; net WPM helps expose the cost of rushing instead of rewarding speed alone.
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