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WorkThoseKeys is a typing trainer built by Claes Järudd with Lovable, a prompt-based AI app builder. Its design has three defining choices: a course divided into 31 sub-levels, practice targeted to individual character and letter-pair errors, and a net WPM score that accounts for mistakes rather than rewarding speed alone. The result is a build story about more than making a typing test: it shows how practice structure, error data, and feedback can shape what a trainer teaches.
Why build a typing trainer with an AI app builder?
Järudd says he chose Lovable because he wanted to make a specific tool without first learning conventional coding from scratch. That is the practical starting point for WorkThoseKeys: use a prompt-based AI app builder to create an application around a defined learning problem, rather than begin with a broad programming curriculum.
The distinction matters. The tool’s design is not simply a typing test with an AI label attached. It combines a staged curriculum, data-driven drills, score interpretation, and longer-form text practice. Those are separate parts of the learning experience, and each addresses a different question: what should a learner practice next, how should progress be measured, and how can the practice remain useful beyond short test passages?
Järudd’s account describes the product and the reasoning behind it, not a controlled evaluation. It does not establish that building with an AI app builder is easier for every project, or that WorkThoseKeys makes users faster by a particular amount. Its value here is in the concrete design choices and the way they fit together.
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How is the course organized into 31 sub-levels?
WorkThoseKeys has eight top-level stages divided into 31 sub-levels, labeled 1a through 8c. Järudd’s rationale is that moving through fundamentals too quickly can leave weak habits that learners must later unlearn.
Four repeated steps within each level
Each sub-level follows the same four-part rhythm:
- Warmup: begin with a short preparation activity.
- Weak points: work on the errors identified as needing attention.
- Integration: bring the current skill into broader typing practice.
- Progress check: assess how the learner is doing before moving on.
The structure makes the eight-stage outline more granular: rather than treating each stage as one large block, it creates smaller checkpoints. A learner can spend more time reinforcing a skill before progressing, while the repeating sequence gives each level a recognizable shape. The design reflects Järudd’s view that fundamentals should be consolidated rather than rushed.
Those details describe the curriculum’s organization; they do not show that every learner must complete all 31 sub-levels in the same amount of time. The account gives no completion-time estimate or comparative learning data.
How do the weak-spot drills use per-bigram errors?
The weak-spot engine builds practice from per-character and per-bigram error rates, rather than relying only on a fixed word list. A bigram is a pair of adjacent characters, so the trainer can focus on letter combinations a learner tends to mistype as well as individual keys.
That changes the question behind a drill. A fixed list asks every learner to practice the same words; error-targeted practice asks which characters or pairs are causing trouble for this learner. As Järudd puts it, “Two people with the same overall accuracy can have completely different weak points.” A single accuracy percentage may look identical while masking different patterns of mistakes.
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For example, one learner may have trouble with a particular letter, while another may mistype a specific two-letter sequence. Their overall accuracy could be similar, but repeating the same generic word list would not necessarily address both weaknesses equally. A drill system informed by each person’s error pattern can direct attention toward the errors recorded for that typist.
The important qualification is that personalization here means practice is generated from recorded errors; it does not establish that the resulting drills are guaranteed to fix those errors or improve speed. The account provides no measured improvement rate or comparison with fixed-list practice.
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WorkThoseKeys treats keyboard layout and the language of the practice content as independent controls. Järudd cites Swedish and US layouts as examples, with support for Swedish characters such as å, ä, and ö, and an on-screen keyboard that highlights the next key.
Separating the two settings reflects a real distinction: the language of a passage and the physical or logical arrangement of keys are not the same choice. Someone typing text in one language may use a different layout, and a trainer that conflates the settings can make practice less representative of the learner’s setup. The on-screen next-key highlight provides a visual cue alongside the text being typed.
The described examples establish support for Swedish characters and a US layout option, but they do not constitute a complete list of all supported languages or keyboard layouts. Nor does the account specify operating-system requirements or explain whether the on-screen keyboard can be hidden.
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What does net WPM mean, and why not use raw WPM?
Raw words per minute reports typing speed without necessarily making errors visible in the headline number. Net WPM, as Järudd describes it, subtracts errors, making the speed-accuracy tradeoff clearer. His summary is: “Raw words-per-minute rewards fast sloppy typing. Net WPM subtracts errors, which is a more honest number but a less flattering one.”
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The practical issue is that fast output is not automatically useful output. If a score emphasizes speed while leaving mistakes out of view, a learner can appear to be progressing even when the text contains errors that would need correction. A score that accounts for mistakes makes that cost harder to overlook.
Net WPM is therefore useful as a training signal when accuracy matters alongside pace. It is not, on the evidence available, a universally standardized formula or proof that a particular learner is improving. Järudd’s account does not specify the exact arithmetic used in WorkThoseKeys, and it does not provide an independent validation of the metric. Treat it as an error-conscious way to discuss typing speed, not as a scientifically established measure that settles every comparison between typists or trainers.
What does the AI coach do that drills do not?
The coach interprets typing data and turns it into plain-language tips. The weak-spot engine and the coach are separate parts of the product: one creates practice sessions from recorded errors, while the other explains those errors to the learner.
That separation matters because identifying a weak pair and explaining what to work on are related but distinct jobs. A drill can give someone more practice with a troublesome combination; coaching can help make the data understandable as advice rather than a raw percentage. Järudd describes the coach as translating results into tips instead of leaving the user with a statistic such as “you’re at 65% on this bigram.”
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The account does not provide examples of the coach’s full recommendations, explain which AI model it uses, or report whether its advice is more effective than other forms of feedback. The supported claim is narrower: it is an interpretation layer over typing data, distinct from the mechanism that generates drills.
How does book mode work?
Book mode lets users type public-domain literature in segments and saves their position between sessions. That offers a different kind of practice from short, isolated drills: the user can return to a longer text and continue where they left off.
Järudd gives two examples of scale: a three-stanza Emily Dickinson poem and the complete Moby-Dick, which he describes as 1.19 million characters. The figure is the author’s reported character count, not an independently verified measurement. The examples show how book mode can accommodate both a brief selection and a very long work without requiring the learner to finish it in one sitting.
Because the described material is public-domain literature, book mode also gives practice a clear content boundary. The account does not say that users can upload arbitrary texts, nor does it establish support for copyrighted books, custom passages, or every public-domain title.
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Can you use WorkThoseKeys without signing up?
According to Järudd, lessons, drills, and books are available in guest mode without signup or stored account data. Google sign-in is offered for users who want statistics and streaks to persist.
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This divides access from persistence. A person can try the core practice without first creating an account, while a user who wants ongoing stats and streak tracking can sign in. The account does not explain how long guest activity remains available in a browser, whether guest progress can later be attached to an account, or whether data is stored locally; those details should not be assumed from the stated no-signup access model.
What does this design say about typing trainers?
WorkThoseKeys brings together design ideas that address different weaknesses in ordinary practice: a more granular course structure, drills shaped by specific errors, score reporting that makes mistakes count, and coaching that translates data into advice. Its layout and language controls recognize that typing text and typing on a particular keyboard are related but separate conditions, while book mode makes practice extend beyond short exercises.
These features are not proof that one trainer is better than another. Adaptive error analytics and net-versus-gross reporting are recognizable concepts in the typing-trainer category, but feature overlap does not establish comparative performance. The useful takeaway is architectural: a trainer can distinguish the curriculum, the practice generator, the metric, and the explanation layer instead of treating all feedback as one feature.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesJärudd’s account is a first-person product description, not an independent usability study. It supplies no controlled test, sample size, or measured typing-speed gains. What it does establish is a specific design: eight stages made into 31 sub-levels, four recurring activities within each, drills based on character and bigram errors, an AI coach that interprets those errors, and practice options extending from lessons to public-domain books.
Frequently Asked Questions
Can I import my own book into WorkThoseKeys?
The description covers public-domain literature in book mode, but does not establish that users can import their own files or passages. It is safest not to assume custom-text support from the book feature alone.
Does WorkThoseKeys show the exact net WPM formula it uses?
Järudd explains the score as subtracting errors, but the account does not give a formula or define how different error types are weighted. That means its net WPM should not be treated as directly comparable to every other trainer’s score without checking each product’s definition.
Has WorkThoseKeys been shown to increase typing speed?
The available product account reports no controlled test, participant sample, or measured speed gain. It explains the intended learning design, but does not establish an outcome percentage or prove that the app improves speed.
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