ConfiDict: A Private AI Vocabulary Coach
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle . What I Built I built ConfiDict , a local-first vocabulary trainer that helps learners concentrate on the words they understand least. Most flashcard applications show an answer and ask the learner to decide wh
Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle Submission 💸
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle.
I built ConfiDict, a local-first vocabulary trainer that helps learners concentrate on the words they understand least.
Most flashcard applications show an answer and ask the learner to decide whether they were correct. That self-assessment is subjective: an answer may be partially correct, use a valid synonym, or miss one important part of the meaning.
ConfiDict changes that workflow. The learner sees a word and explains its definition in their own words. A local open-weight Gemma model evaluates the response, identifies whether it is strong, correct, partial, or incorrect, and gives concise feedback.
Every word has a visible confidence score between 0 and 100. Each day, ConfiDict creates a five-card session from the learner’s 15 lowest-confidence words. Weaker words have a greater chance of appearing, but weighted selection prevents every session from repeating the exact same cards.
A learner is not paying for another digital deck of cards. They are paying for focused practice, objective feedback, and less time wasted reviewing words they already know.
The project is open source under the MIT License:
ConfiDict is a local-first vocabulary confidence trainer. Learners import the words they want to remember, define five words in their own language each day and receive semantic grading from an open-weight model that runs in the browser.
This project is being built for the Hacktoberfest Open-Source AI Challenge: Week 2 — Side Hustle.
The copy-ready DEV submission draft is in docs/dev-submission.md.
Traditional flashcards usually ask learners to reveal an answer and grade themselves. That makes progress subjective and gives learners little help when their answer is close but incomplete.
ConfiDict uses open-source AI to compare the learner's free-text definition with the reference meaning. It then adjusts a transparent confidence score and prioritizes the words that need the most attention.
The first version deliberately supports one learning task:
See a word and explain its definition in your own words.