SkillPilot: An AI That Learns How You Learn

#devchallenge #weekendchallenge #hf26challenge

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

What I Built

I built SkillPilot — Your AI-powered personal learning intelligence, a personalized learning system designed for a friend who is already serious about becoming a better developer.

My friend solves coding problems regularly, studies technical concepts, explores new technologies, and prepares for technical interviews. The problem was not a lack of learning resources. The problem was that his learning journey was scattered across coding platforms, notes, PDFs, screenshots, documentation, AI conversations, and different practice sessions.

SkillPilot brings these activities into one personal learning intelligence system.

Instead of behaving like a generic AI tutor, SkillPilot builds an evolving understanding of the user's technical journey.

It connects:

Coding practice and AI code analysis
Personal Technical Memory
Knowledge Vault for notes, documents, screenshots, formulas and code
Context-aware AI conversations
Adaptive weekly planning
Technical assessments
Timed aptitude practice
Adaptive interview practice
Career skill-gap analysis

The core idea is simple:

Learn → Practice → Analyze → Remember → Adapt → Improve

As the user practices, makes mistakes, completes assessments, and improves, SkillPilot uses that evidence to adapt future questions, revision priorities, assignments, and weekly plans.

Demo

Deployed Link: https://skillpilot-alpha.vercel.app/

Code

GitHub Repository: https://github.com/Gopika252006/skillpilot

How I Built It

SkillPilot is built around open-weight AI and local inference.

Tech Stack

Frontend

React
Vite
Tailwind CSS
Framer Motion
Recharts

Backend

Python
FastAPI
Pydantic
SQLAlchemy
SQLite

Ollama
Open-weight local models
AI orchestration layer
Structured AI outputs with validation
Deterministic fallback when the local model is unavailable

Architecture

The application follows a layered architecture:

React UI → FastAPI API → Service Layer → Database / AI Orchestrator

For AI-powered features:

User Context → Knowledge Memory → Context Builder → AI Orchestrator → Local Model → Structured Output → Validation → Database / UI

SkillPilot maintains a distinction between:

Knowledge Vault — information the user has saved.

and

Personal Technical Memory — evidence about what the user has actually practiced, understood, struggled with, or needs to revise.

This distinction is important because saving a PDF should not automatically mean that the user understands its contents.

Why Does Open Innovation Matter?

Open innovation matters because a personal learning system should not require sending someone's entire learning history to a closed AI service.

SkillPilot explores a different approach:

local AI + open-weight models + user-controlled data

Using local inference makes it possible to build a system where:

Personal learning context can remain local
Users have more control over their data
The AI model can be changed without rebuilding the entire application
The system can work without depending entirely on a proprietary API
Developers can inspect and modify the AI pipeline
The application can evolve with different open-weight models
AI costs can be reduced during development and experimentation

The open approach also gives developers more control over the complete AI pipeline — from context construction and prompting to validation, fallback behavior, and persistence.

For SkillPilot, this is especially important because the system is designed to understand a user's long-term technical journey.

The goal is not simply: