Chad Vocab is a self-hosted vocabulary trainer demonstrated for English-to-Polish study. Its reported workflow combines typed or spoken answers, local Gemma-assisted grading, and textbook-page photos that can be turned into vocabulary suggestions for the learner to review.
What Chad Vocab does
Project author Allan Dorr describes Chad Vocab as a trainer he built for a friend learning Polish. The demonstration uses English cues and Polish answers, while the article says users can add other language pairs and vocabulary decks. The project is presented as a self-hosted learning tool rather than a general-purpose language course.
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How a practice session works
Answer by typing
The learner sees a vocabulary cue and enters a response. According to the project article, Gemma helps evaluate near-miss answers, rather than limiting grading to an exact text match.
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Answer by speaking
The described setup can convert speech to text using local Scriberr/Whisper. ElevenLabs Scribe is an optional alternative when the account holder provides an API key; it is not required for the core practice flow as described.
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Build a deck from a textbook photo
With a vision-capable Gemma model, the app can propose vocabulary pairs from a photograph of a textbook page. The learner reviews the proposed entries before importing them, so the photo extraction is a starting point rather than an automatic, unchecked addition to a deck.
Review cards that need attention
The article describes a queue that prioritizes cards needing practice, makes learned cards less frequent, and mixes in the last five cards with review. These are reported design behaviors; the article does not publish measured learning outcomes or compare the queue with other study methods.
What runs locally—and what that means for privacy
Dorr’s stated aim is to keep speech, textbook photos, and the practice loop on a household laptop without requiring a paid cloud model for core practice. In the demonstrated arrangement, the app was exposed through a Cloudflare Tunnel while LM Studio and Scriberr stayed on localhost. That is a description of the author’s setup and goal, not an independent security audit or a guarantee that every deployment keeps all data private. Anyone considering a self-hosted installation should check where each component sends data and how remote access is configured.
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The project article identifies Vite and React for the interface, Hono for the API, and SQLite for storage. Its quick-start section lists Node.js 20 or later, local Scriberr, and LM Studio with an open model loaded. Photo import additionally requires a vision-capable Gemma model.
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These are setup details reported in the project article, not independently verified build instructions. The linked repository was not available for code-level verification, so confirm the current project documentation and dependencies before attempting installation.
Is a particular computer required?
The article mentions a household laptop but does not specify its model, memory, graphics capability, or a validated minimum specification. It therefore does not establish that a particular laptop—or any specific hardware upgrade—is necessary. Local inference and speech recognition can depend on the chosen models and machine, but the source provides no measured hardware requirements or performance figures.
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What the article does—and does not—establish
- Reported project features: typed and spoken practice, Gemma-assisted grading of near-miss answers, photo-based vocabulary suggestions for review, and a prioritized practice queue.
- Reported project details: the named software stack, a self-hosted design, and an MIT license claim in the author’s article.
- Not established: independent accuracy testing, measured learning gains, a controlled comparison of speech-recognition speed, minimum hardware specifications, or an independent privacy assessment.
Dorr notes that local recognition felt slower than a phone app in his experience, including when using the optional ElevenLabs API, but gives no controlled timing data. That anecdote cannot establish how fast the system will be for other users or configurations.
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Chad Vocab may suit a technically comfortable learner who wants to assemble a personal vocabulary practice workflow, especially one based on their own materials and local models. Its appeal is the combination of answer grading, speech input, and photo-assisted deck creation; the trade-off is that the reported setup involves several software components and requires the user to manage their own installation. The evidence available does not establish learning effectiveness or make it possible to compare the experience reliably with other trainers.
Source: Allan Dorr’s project article on DEV Community, published October 4, 2026. The source identifies Chad Vocab as MIT licensed; the repository and license text were not independently checked.
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