ReVisit

Most flashcard tools make you rate yourself, which is unreliable and easy to game. goflash feeds your answers to an AI that scores them objectively (0-5), then uses SM-2 spaced repetition to resurface cards at the right time. Everything runs in the terminal and stores locally in SQLite.

Features

AI evaluation - answers scored by DeepSeek against the stored reference answer
SM-2 scheduling - cards resurface based on your performance; easy cards disappear longer, hard cards come back sooner
Topic hierarchy - cards are organized as a browsable tree; bring your own topic or start with the included system design deck (114 topics, 1996 cards)
Local-first - SQLite database, no account, no cloud sync
Terminal UI - keyboard-driven, Catppuccin Mocha theme

Prerequisites

Go 1.21 or higher
A DeepSeek API key (for evaluating answers and seeding)
direnv for environment variable management (recommended)

Quick Start

git clone https://github.com/codehia/goflash.git
cd goflash
cp .envrc.example .envrc
# edit .envrc and set DEEPSEEK_API_KEY
direnv allow
go run main.go

The repo ships with a pre-seeded system design database (1996 cards) so you can start immediately. To study your own topics, see Seeding Your Own Topics.

If you use Nix, a devenv shell is included: devenv shell sets up the full environment.

Keybindings

Key	Action
`up` / `down`	Navigate topic list
`enter`	Select topic / advance
`ctrl+s`	Submit answer
`n`	Next card
`q` / `ctrl+c`	Quit

How It Works

Pick a topic from the list
Read the question, type your answer freely, submit with ctrl+s
DeepSeek scores your answer (0-5) and shows feedback alongside the reference answer
SM-2 calculates the next due date - nail it and the card will not appear for days; struggle and it comes back tomorrow

Tech Stack

Layer	Technology
Language	Go
TUI	bubbletea v2 + bubbles + lipgloss
Database	SQLite via modernc.org/sqlite (pure Go, no CGo)
Query builder	go-jet/jet - type-safe SQL, schema-generated models
Migrations	goose
AI eval	DeepSeek
Card seeding	DeepSeek via OpenRouter
Scheduling	SM-2 algorithm

Architecture

goflash/
├── cmd/
│   ├── seed/       reads topic JSON -> calls DeepSeek -> writes output.json
│   └── import/     reads output.json -> upserts cards + tags into SQLite
├── internal/
│   ├── ai/         DeepSeek eval client
│   ├── scheduler/  pure SM-2 math, no DB deps
│   ├── store/      DB layer: cards, topics, attempts (jet queries + goose migrations)
│   └── tui/        Elm-style screens: topic list -> question -> attempt -> eval -> done
└── main.go         opens DB -> launches bubbletea TUI

Data flow per review:

user answer -> DeepSeek eval -> score -> SM-2 -> new due_date written to DB

Seeding Your Own Topics

Prepare a JSON file describing your topic hierarchy:

{
  "name": "Your Topic",
  "children": [
    {
      "name": "Subtopic",
      "children": [
        { "name": "Leaf Topic", "notes": "Your notes here." }
      ]
    }
  ]
}

go run cmd/seed/main.go seedfile.json   # generates output.json (resumable)
go run cmd/import/main.go               # imports into SQLite
go run main.go                          # start studying

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