Reading paths
Ordered sequences through the reference: the field, the mathematics, the research, and how to evaluate a system.
| Path | What it covers | Outcome | Steps |
|---|---|---|---|
| AI foundationsStart here | Build a precise vocabulary before comparing products, models, or research claims. | You will be able to separate the field, the training method, the model layer, and the evidence used to evaluate a system. | 8 |
| The mathematics of modern AIBuild the intuition | Move from vectors and probability to optimization, backpropagation, and the attention equation. | You will have a connected mathematical map for reading architecture diagrams and introductory research papers. | 9 |
| Landmark AI researchRead the lineage | Follow selected primary-source milestones from machine intelligence to modern generative and scientific systems. | You will be able to place current techniques in a research lineage instead of reading them as isolated breakthroughs. | 8 |
| Evaluate an AI systemRead claims carefully | Learn how to distinguish system layers, inspect a claim, read a benchmark, and compare candidates with context intact. | You will have a repeatable checklist for product research, model selection, and benchmark interpretation. | 7 |
| Enterprise AI landscapeMap the stack | Move from system-selection principles through major managed platforms and operational deployment concerns. | You will be able to compare platform layers without confusing them with the underlying model or end-user product. | 7 |
Reading and method entries
| Entry | Summary | Claims | As of |
|---|---|---|---|
| Building source-backed AI researchfield guide | Source-backed research separates atomic claims, evidence links, as-of dates, uncertainty, and editorial interpretation. Versioned claims make corrections observable and let a reader inspect what changed instead of silently replacing an article. | 0 | 2026-07-13 |
| Choosing an AI systemfield guide | Choosing an AI system requires workload evidence across quality, latency, cost, privacy, safety, and operational fit. A leaderboard can narrow candidates but cannot replace tests on representative prompts, tools, failure modes, and traffic patterns. | 0 | 2026-07-13 |
| How to read a model claimfield guide | A model claim is useful only when it names the model version, task, conditions, metric, and source. System cards are stronger evidence than marketing adjectives, but independent replication and workload-specific tests answer different questions. | 0 | 2026-07-13 |
| Model, product, and platform are different layersfield guide | A model generates predictions, a product organizes an experience, and a platform supplies infrastructure and controls for building systems. Comparing layers as peers creates category errors—for example, contrasting a chatbot subscription with an API model benchmark. | 0 | 2026-07-13 |
| Open source, open weight, and open accessfield guide | Open source refers to license-granted freedoms for source artifacts, while open weights describes parameter availability and open access describes reachability. A public paper, repository, or model page does not by itself grant permission to redistribute every artifact it references. | 0 | 2026-07-13 |