Hire a private AI tutor for anything you want to learn. Your AI reads how you actually learn — and teaches the next lesson just for you. Built on Bloom's 2-Sigma research: 1-on-1 tutoring = top 2%. 🌐 Website · Chinese · English In 1984, educational psychologist Benjamin Bloom discovered that students receiving one-on-one tutoring scored 2 standard deviations (+2σ) above the classroom average — jumping to the top…
Hire a private AI tutor for anything — it reads how you actually learn and teaches the next lesson just for you. Bloom's 2-Sigma research as a Claude Code skill + self-hostable web app · 中文优先 苏格拉底式 AI 家教
In 1984, educational psychologist Benjamin Bloom discovered that students receiving one-on-one tutoring scored 2 standard deviations (+2σ) above the classroom average — jumping to the top 2%. Bloom called this the “2 Sigma Problem”: the effect is proven, but personal tutors don’t scale.
Bloom solves this with AI. It generates a structured syllabus, delivers lessons one at a time, reads your annotations and feedback, then tailors the next lesson to your exact understanding level — just like a real tutor would.
Two Ways to Use
Mode
Setup
Best for
CLI
Claude Code + terminal
Power users who like Markdown editors
Web
Browser (React + FastAPI)
Visual learners, shareable setup
Both follow the same flow: syllabus → lesson → annotate → feedback → next lesson → evaluation → summary.
git clone https://github.com/Li-Evan/Bloom.git
cd Bloom
# Install the tutor skill locally for this clone
mkdir -p .claude/skills
cp -R skills/bloom-tutor .claude/skills/
claude
Then say: Create a new folder and help me learn [any topic]
Or install it as a plugin (bundles bloom-tutor plus the learn-* skills) — in Claude Code:
An OpenAI-compatible LLM API key (e.g. DashScope, OpenAI, etc.)
Setup
git clone https://github.com/Li-Evan/Bloom.git
cd Bloom
# Configure
cp .env.example .env
# Edit .env — fill in LLM_API_KEY
# Backend
cd backend && uv sync && uv run uvicorn app.main:app --reload --port 8000
# Frontend (new terminal)
cd frontend && npm install && npm run dev
Open http://localhost:5173. Click New Course, choose Topic, Source Upload, or Project Files, and start learning.
Docker
cp .env.example .env # fill in API key
docker compose up -d # visit http://localhost:3000
How It Works
Topic Mode
Create course → AI generates syllabus + lesson 01
↓
Read lesson → highlight text → add annotations
↓
Write feedback → answer thought questions
↓
Click "Done Reading" → AI generates next lesson
(answer review + annotation responses + new content)
↓
Repeat until all mastery items checked ✅
↓
Auto-generate evaluation → then summary
Source Mode
Upload PDF / TXT / MD → AI generates syllabus + source-reading chapter
↓
Read source → highlight text → ask and get an immediate answer
↓
Click "Done Reading" → AI reads the full source + Q&A, then generates the next lesson
↓
Continue with the normal adaptive lesson flow
Project Files
Upload a file / multiple files / a whole folder → each file renders directly as one page
↓
Read each file → highlight text → ask and get an immediate answer
↓
No syllabus, no next-lesson generation; the files and highlight Q&A feed next-step recommendations
Features
Three course modes — generate from a topic, upload a PDF/TXT/MD source, or Project Files (upload files/a folder, render each directly, highlight-ask anytime, no syllabus or next-lesson generation)
Learning depth — choose simple, standard, or deep syllabus expansion when creating a course; shown as a badge on each course card
Reference material — paste textbook chapters, papers, or notes when creating a topic course
Next-topic recommendations — generate 3 course-ready topics from your full learning history, refresh the set, save ideas to a learning queue, or start one directly through the normal syllabus → lesson flow
Highlight Q&A sessions — select any text in any lesson (or source) and a small icon pops up; click it to ask. The highlighted text stays marked in yellow, the AI answers instantly, and you can keep asking follow-ups in the same thread. The window is draggable and collapses into a margin dot you can reopen anytime. Each session sees the full lesson + the highlighted span + its own conversation, and your questions still feed the next lesson.
Adaptive lessons — each lesson addresses your specific gaps from the previous one
Chapter sidebar — quick-jump between lessons while reading
Collapsible syllabus — track mastery progress without clutter
Streaming generation — watch AI write the next lesson in real-time
Personal center & learning calendar — open the profile from the header to see a month calendar shaded by daily activity; click any day to see which courses, lessons, and highlights you studied, alongside overview stat cards and a six-month contribution heatmap (streaks and grouping use your local date)
Skills
Bloom ships a set of portable Claude Code skills in skills/ — self-contained capability packs you can copy into ~/.claude/skills/ (global) or any project’s .claude/skills/ and use anywhere.
Skill
What it does
bloom-tutor
The full interactive tutoring system as one skill — syllabus → adaptive lessons → ??? annotations → evaluation → summary. CLI mode, packaged and portable.
learn-deep
Default deep-dive entry — runs all five lenses below in one pass, then helps you pick a direction
learn-crossover
Learn a new concept by leveraging what you already know (structural analogies)
learn-occam
Decide whether / how deeply something is worth learning (ROI, just-enough)
learn-graph
Build a knowledge-graph map of a field plus a learning path
learn-prototype
Learn by building the crappiest working prototype, then iterating
learn-feynman
Verify true understanding by explaining it back
Each folder is dependency-free: copy it into a skills directory, then just talk to Claude Code (e.g. “help me learn X”, “I’m done reading”).
Tech Stack
Layer
Technology
Backend
Python, FastAPI, SQLAlchemy, SQLite
Frontend
React, Vite, Tailwind CSS
AI
Any OpenAI-compatible LLM API
Container
Docker, docker-compose
Font
Outfit, JetBrains Mono
Commands
make dev-backend # backend with hot reload
make dev-frontend # frontend dev server
make test # run pytest
make up / make down # docker start / stop
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