Because model.fit() isn't an explanation.

no-magic-ai is an umbrella for studying how modern AI actually works. Not a framework. Not a course platform. Not a chatbot. Every repo under this org enforces a single-sentence constraint. Every algorithm points back to a paper. Everything else is off-charter.

Our audience is two people: the ML engineer who uses frameworks daily and wants the internals, and the career switcher who read the papers and needs a working implementation they can run on a laptop. Everything we build serves one of these two.

Read the full manifesto at no-magic-ai.github.io/about.html.

One constraint per repo. No bleed. Repos do not share build systems, do not import each other, do not cross-reference code. The website is the only surface that joins them.

The value of this org is in what it won't do. Every feature we reject keeps the scope tight enough that the code stays readable and the constraints stay honest. These are off-charter, not deferred.

• Not an LMS. No user accounts. No progress tracking. No auth. No server-side state.
• Not a chatbot. No RAG over the catalog. No AI tutor. A good single-file implementation is already the explanation.
• Not a framework. No shared base classes. No abstract interfaces. No no_magic.core package.
• Not a tutorial mirror. No translations or ports of fast.ai, Karpathy, HuggingFace, or dive-into-llms. We implement from primary sources only.
• Not a production library. The code is pedagogical. Not performant, not distributed, not hardened.
• Not a benchmark. We show how algorithms work, not which beats which.

• Paper-first sourcing. Every implementation cites the original paper. No content is ported from tutorials, courses, or other educational repos.
• Single-sentence charter per repo. A new repo requires a one-sentence constraint that governs it. Violating the constraint is grounds for PR rejection.
• Slug as the common key. Each algorithm has a canonical slug (e.g. microrope). Every repo references that slug. No other coupling exists.
• Reproducible under commodity hardware. Everything runs on a laptop CPU. No GPU. No cloud. No dataset larger than a few MB.
• Comments are the curriculum. Code is the primary teaching artifact. Prose explanations live in no-magic-papers/lessons/ and remain optional, never authoritative.
• Provenance over abstraction. Three similar implementations beat one abstracted parent class. Duplication is honest; abstraction hides.
• Deletion is a feature. Content that stops serving the two audiences gets removed. The catalog is curated, not accreted.
• Static or nothing. No databases. No services. No background jobs.

Binding on every contributor and every agent that operates on these repos.

1. Paper-first. Every algorithm implementation cites the original paper in its thesis docstring. If no paper exists, the algorithm does not belong in the catalog.
2. No tutorial contact. Do not read other educational repos (fast.ai, Karpathy's nn-zero-to-hero, HuggingFace courses, dive-into-llms) while writing a no-magic script. Topic inspiration from their tables of contents is permitted.
3. Attribution is a line, not a link. Cite sources in docstrings with full bibliographic detail.
4. License vigilance. Before adopting any external asset, verify and record the license in ASSETS.md.
5. Contributor pledge. Every contributor affirms they implemented from papers, not from tutorials. Enforcement is by maintainer review, not by CI.

• One file per algorithm. No local imports, no utils.py, no companion files.
• Zero external dependencies. Python standard library only.
• python script.py runs everything. Train + inference in one command.
• Comments are the curriculum. 30–40% comment density, math-to-code mappings, intuition over jargon.

Every script is written for the person reading it for the first time. Contributions, including paper summaries and lessons, are manually reviewed by the maintainer. See CONTRIBUTING.md in each repo for the review checklist.

Decisions live in versioned documents, not processes:

• no-magic-ai-expansion-strategy.md — top-level strategy doc.
• paper-ingestion-process.md — how papers enter the ecosystem.
• Per-repo CONTRIBUTING.md — encodes each repo's single-sentence constraint.
• GitHub Discussions — open proposals and curriculum debates.

Created and maintained by Tom Mathews (LinkedIn).