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SkillDuel is a learning app its developer describes as a local, AI-powered tutor: it asks a question, evaluates the learner’s answer, explains the concept, and adjusts the next question’s difficulty. Its published design runs the language model through Ollama on the user’s computer, rather than sending prompts to a cloud model. The project post describes the app and its setup; it does not establish learning gains, performance on particular hardware, or privacy through an independent audit.
Why the developer built SkillDuel
In a project post dated October 5, 2026, developer dineshkottaKota describes building SkillDuel for a friend who had been learning Python for three months. She could follow tutorials but was unsure whether she understood the concepts or had mainly learned to recognize familiar patterns. The app is intended to make the learner explain and apply ideas rather than simply move through a lesson.
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The developer says the same question-and-feedback approach could be used for subjects such as chess, cooking, math, and language learning. Those are proposed uses, not reported evaluations across those subjects.
How a SkillDuel learning round works
According to the developer, a round follows a short cycle: SkillDuel poses one question, assesses the response, gives warm feedback and an explanation, and offers a deeper follow-up insight. It then adapts the difficulty of the next question. The post also describes XP and streak tracking as engagement features.
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The developer reports that the friend said, “This is the first time I’ve actually had to think instead of just type.” That is one learner’s anecdotal reaction, not evidence that SkillDuel improves learning outcomes generally. The project post reports no controlled study or measured learning results.
What runs locally—and what that means
The architecture described in the post connects a browser interface to a REST API on localhost:8000. A FastAPI backend, using LangChain Core for prompt orchestration and output parsing, communicates with Ollama at localhost:11434. The developer says the backend uses two prompts per round: one to generate an adaptive Socratic question and another to evaluate the answer and prepare a follow-up.
In this design, model inference is handled by a local Ollama service on the same computer. That describes the intended data flow; it is not an independent privacy audit, and it does not establish that every possible use or configuration keeps all data private. The developer’s “free” framing refers to avoiding an API bill in the described setup. It does not mean there are no hardware, electricity, download, or setup costs.
Setup and model size
The project post’s setup sequence is to install and start Ollama, download the model, install the backend dependencies, run the API, and open the frontend file. The post names Llama 3.2 as the default and mentions Mistral, Gemma, Phi, and CodeLlama as alternatives, without comparing their quality or suitability.
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Install Ollama and start its local service.
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Download the default model with
ollama pull llama3.2. Ollama’s model library listsllama3.2as a 2.0 GB download andllama3.2:1bas a 1.3 GB download. These are model download sizes, not total memory requirements or guarantees of speed. Ollama describes the text-only Llama 3.2 family as 1B and 3B models and documentsollama run llama3.2as a run command. -
From the project’s backend directory, install dependencies listed in
backend/requirements.txt.Rank #4
Teacher Record Book- Keep track of everything from attendance to test scores
- Spiral bound
- Measures 8-1/2" x 11"
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Start the backend with
python main.py. -
Open
frontend/index.htmlin a browser to use the interface.Special offer. See more information about Outbyte and uninstall instructions. Please review EULA and Privacy policy.
The project post does not specify minimum RAM, CPU, or GPU requirements, nor does it provide a tested computer configuration. A listed download size alone cannot show whether a particular machine will run the model comfortably.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the project does—and does not—show
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It describes a question-led learning flow with answer evaluation, feedback, follow-up insights, and adaptive difficulty.
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It gives a local-runtime architecture and setup sequence that avoids a model API bill in the described configuration.
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It offers a single reported reaction from the developer’s friend, but no measured results or controlled evidence of learning effectiveness.
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It does not provide independently verified performance, a hardware requirements list, or a privacy audit.
For anyone considering a similar local tutor, the practical questions are whether the chosen model handles the subject well, whether the computer runs it at a usable speed, how much setup is acceptable, and whether local processing fits the learner’s privacy needs. The available project description does not establish how SkillDuel compares empirically with cloud tutors or other local learning apps.
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