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Reviewed curriculum

Learning Paths

Follow a deliberate sequence through existing FugitiveAI challenges and references. Paths organize the material; they do not redefine the underlying techniques or canonical Concepts.

Choose a path Explore Concept Graph
Reviewed paths
3
Ordered steps
29
New content copies
0

Curriculum, not taxonomy

One sequence. Existing authorities.

Each step points to an existing public resource. Concept chips use the canonical mt.* identity and jump into the Concept Graph for definitions and relationships. Challenge, article, technique, and glossary content stays in its existing source.

Three reviewed sequences

Choose the question you want to answer.

Work top to bottom, or use the curriculum as a reference map for material you already know.

Path 1 · 9 steps

Representation Forensics

Learn to separate what a human sees from what software can recover across Unicode, DOM structure, metadata, and controlled transformations.

Outcome

Trace a suspicious artifact across representation layers and describe the exact differential without confusing appearance with machine-readable state.

Path 2 · 12 steps

Linguistic Channels

Progress from an acrostic into reviewed lexical, punctuation, voice, ordering, spelling, and parity carriers that encode structure through language choices.

Outcome

Recognize a declared linguistic carrier rule, keep segmentation and codebook assumptions explicit, and distinguish surface fluency from encoded structure.

Path 3 · 8 steps

Defensive Detection

Build from anomaly detection to matched controls, reproducible extraction, causal intervention, transformation profiling, and held-out replication.

Outcome

Make the strongest claim the evidence supports—and no stronger—while using controls and interventions to reduce false positives.

  4. Challenge Step 4 of 8

    CASE 019 — Controlled Differential

    Apply an exact representation rule to matched repeats, an ordinary control, and a visually suspicious decoy that must remain a false positive.

    Representation differential mt.representation.differential Homologous evaluation mt.detection.homologous_evaluation Reproducible decoder evidence mt.detection.reproducible_decoder_evidence Causal channel evidence mt.detection.causal_channel_evidence False-positive control mt.detection.false_positive_control Normalization as diagnostic transformation mt.detection.normalization_diagnostic
    Open

After the curriculum

Turn the sequence into evidence.

When you want to move from practice into structured analysis, use the Investigation Workspace to record observations, controls, decoder evidence, interventions, and replication without raising confidence past the evidence you actually collected.

Open Investigation Workspace Review canonical Concepts →