Case Study 01Personal project · Open sourceBYOK or local inference

A growing share of code is written by AI. Decoder helps you read it.

Decoder is a personal study project, open source (MIT) and free. It works with your own API key (BYOK) or in local-inference mode.

Works with the providers you prefer

01. OPENAI02. ANTHROPIC03. GOOGLE04. OPENROUTER05. OLLAMA
RAW_INPUT
PARSED_SCHEMA.V1
Analysis · Logic Visualization

Open Source · MIT

Public, inspectable code

Decoder is released under the MIT license. Code, analysis prompts and rules live in the repository, readable and editable.

MIT LicenseNon-profitOpen to contributions

What Decoder does

An open tool to read AI-generated code

Decoder helps you re-read code produced by an AI using a second model and public prompts. It is not a correctness guarantee: it is one more reading layer, to be combined with your own judgement.

AI source
Decoder
You

Decoder helps you re-read code produced by an AI using a second model and public prompts.

  1. 01

    Output to verify

    Every AI answer is a hypothesis and should always be verified. Decoder shows explanations, quality and security observations line by line.

  2. 02

    Public code and prompts

    Source code, prompts and analysis rules live in the repository. You can read, edit or fork them.

  3. 03

    Extendable by the community

    Analysis rules can be extended via pull requests to cover new languages or new smells.

Context

Reading AI-generated code takes time.

AI assistants ship code faster than a team can review it line by line. Decoder offers a second, assisted reading layer.

A growing share of code is AI-generated

Copilot, Cursor, Claude Code — an increasing portion of every new project wasn't written by the people who have to maintain it.

More diffs to read

More AI output means more code to review. A comprehension layer helps you not skip the important parts.

Public prompts and rules

Analysis prompts live in the repository and can be extended by the community. No hidden logic behind the answers.

How it works

Three steps from source to knowledge.

  1. 1

    Import code

    Upload a ZIP or import a public GitHub repository. Files stay in your account.

  2. 2

    Choose a provider

    OpenAI, Anthropic, Gemini, OpenRouter — or local with Ollama / LM Studio.

  3. 3

    Explain and analyze

    Human or technical summaries, quality and security analyses, Markdown export.

Integrations

Works with the AI providers you already use and with your public GitHub repositories.

OpenAI
Anthropic
Google Gemini
OpenRouter
Ollama
LM Studio
GitHub
100%
Open Source
Privacy
Your data stays yours
BYOK
Use the models you prefer
Multiple depth levels
From concise to technical
DecoderYour code and generated documentation belong to you. Decoder never trains on your repositories.

Decoder is an open-source educational code-understanding case study. It is not a certified security audit tool, legal/compliance tool, or production decision system. AI-generated outputs may be inaccurate and must be reviewed by a qualified person.