dint 1.3.0

A personal AI tutor with long-term memory and tool use.

dint

The AI tutor that refuses to give you the answer.

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You ask ChatGPT how bubble sort works. It dumps code. You copy it. You feel smart. You learn nothing. By Thursday, you can't explain it to a rubber duck.

dint is the opposite. You ask it how bubble sort works, and it hands you five numbers and says "sort them. you can only swap neighbors. go." You fumble. You trace. You figure it out. And when the code finally appears, you read it and think yeah, that's the thing I just built.

dint doesn't transmit knowledge. It builds it in your head, brick by brick, while you do the lifting.

What a session actually looks like

You: Teach me what a variable is. I've never written code.

dint: Okay. Sit down. No code today. Not yet.

You're trying to cut back on coffee, so you grab a sticky note, write "cups" at the top, and every time you finish one, you update the number. Morning: 0. After your second cup: 2. By 4pm you're at 4 and your hands are shaking a little.

That sticky note. The word "cups" never changed. The number kept changing. There are two things on that note, and they're different things. One stays put. One moves. Can you name those two for me?

You: the cups is the variable name and that number is a value

dint: Yeah. That's it. You just described a variable without ever seeing a line of code, and you used the right words for both parts. So that's not luck — you actually see it.

(...the lesson continues through sticky notes on a fridge, three variables, mutation, x = x + 5, and ends with types — all before the learner sees more than four lines of code...)

dint: Go rest. You earned the cold coffee. Come back when you're ready.

No "Great question!" No code dump. No "Is there anything else I can help you with?" Just a teacher, a sticky note, and the quiet satisfaction of watching someone's face change when an idea lands.

Quick start

pip install dint

dint

Open http://localhost:7070. Set your API key in Settings. Start learning.

Works with any OpenAI-compatible API — OpenAI, OpenRouter, Groq, Together, or a local Ollama server.

Multi-user mode

Want to host dint for a classroom, a study group, or your friends?

dint --multi-user

Each visitor registers an account and gets a fully isolated learning environment — their own memory, skills, knowledge graph, and sessions. The operator's API key stays hidden from tenants. Per-user data lives under users/<username>/.

What dint actually does

Socratic dialogue Decomposes every topic into small, concrete concepts. Grounds each one in something you can hold in your head — five numbers, three cards, one sticky note. Asks you to predict, trace, and decide before revealing anything. The code shows up last, as confirmation. Not as the lesson.	Spaced repetition (SM-2) Schedules review questions for skills you've demonstrated. Come back three days later and it opens with "quick — what's the base case in recursion?" Get it right, the next review moves further out. Get it wrong, it circles back tomorrow. Knowledge that isn't revisited decays. dint doesn't let it.	Knowledge graph Every concept gets added to an interactive force-directed graph — nodes and edges, how ideas connect. Pan, zoom, drag nodes, right-click to rename or delete. "Binary search" links to "sorted array" links to "comparison." The graph grows as you learn.
Long-term memory Remembers you across sessions. Your goals, what you already know, how you like to be taught, the mistakes you keep making. Reads its notes before each session so it never re-teaches what you've already demonstrated.	Background reflection After every exchange, a quiet analysis pass updates dint's model of you — skill confidence, knowledge links, durable memories. It detects when you claim to understand but your behavior says otherwise, and corrects its own stale beliefs. You don't see any of this.	Memory consolidation Every ~10 turns, dint reviews its entire inventory of memories, skills, and concepts. Near-duplicates get merged. Stale entries get pruned. The model stays compact and honest over months of use. You can also trigger it manually from Settings.
Adaptive teaching dint watches how you think, not just what you know. It notices you skip steps when tracing algorithms. It notices you learn better from analogies than formal definitions. It adjusts its approach per learner, per session, per concept.	Multi-user isolation Run dint --multi-user and every visitor gets their own account, their own database, their own learning history. The operator's API key is invisible to tenants. PBKDF2 password hashing, session tokens, per-user data directories.	Bilingual (EN / 中文) Full UI localization in English and Chinese. Toggle with one click. The teaching persona adapts to whatever language you write in. dint teaches you in your language, not its own.

The loop:

You send a message
        │
        ▼
┌─────────────────────────────────────────────┐
│  Agent assembles context:                   │
│  persona + memories + skills + knowledge    │
│  + skills due for review (SM-2)             │
└─────────────────┬───────────────────────────┘
                  ▼
┌─────────────────────────────────────────────┐
│  LLM responds, calling tools as needed:     │
│  memory · skills · knowledge · search ·     │
│  concept tracking · review scheduling       │
└─────────────────┬───────────────────────────┘
                  ▼
┌─────────────────────────────────────────────┐
│  Reply streams back token by token (SSE)    │
└─────────────────┬───────────────────────────┘
                  ▼
┌─────────────────────────────────────────────┐
│  Background reflection (async, non-blocking)│
│  → skill confidence · knowledge edges ·     │
│    durable memories · overclaiming check ·  │
│    belief correction · SM-2 scheduling      │
└─────────────────┬───────────────────────────┘
                  ▼
┌─────────────────────────────────────────────┐
│  Consolidation check (10% per turn)         │
│  → merge duplicates · prune stale entries · │
│    keep the model compact and honest        │
└─────────────────────────────────────────────┘

Configuration

Variable	Description	Default
OPENAI_API_KEY	API key for your provider	(required)
OPENAI_BASE_URL	OpenAI-compatible endpoint	https://api.openai.com/v1
DINT_MODEL	Model for teaching (must support tool calling)	gpt-4o-mini
REFLECT_MODEL	Model for background reflection	(same as DINT_MODEL)
DATABASE_URL	SQLite database path	dint.db
MAX_TOOL_ROUNDS	Max tool-call rounds per turn	8
WEB_SEARCH_RESULTS	Results per web search	5
DINT_TEMPERATURE	Sampling temperature	0.7

All settings are also editable at runtime through the Settings panel in the web UI. Changes take effect immediately.

In multi-user mode, the API key and base URL are operator-owned and hidden from tenants. Each user can configure their own model, temperature, and behaviour knobs.

CLI options

dint [--host HOST] [--port PORT] [--multi-user]

Flag	Description	Default
--host	Bind address	0.0.0.0
--port	Port to listen on	7070
--multi-user	Enable multi-user mode with accounts	(single-user)

Requirements

• Python 3.10+
• An OpenAI-compatible API key (OpenAI, OpenRouter, Groq, Ollama, etc.)
• The model must support tool / function calling

The name

dint — as in "by dint of." Through force of your own effort.

You don't learn by being told. You learn by dint of thinking.

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_{MIT Licensed}