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Day 88: Independent Mastery Retrieval — AI-Free Evidence Sweep

Close the curriculum loop with declared-condition, unaided retrieval checks across core Python, Git debugging, mathematics, gradients, and model reasoning — scored against transparent rubrics tied to real project evidence.

Agents and Agentic Workflows 480 active minutes (8 hours)
01 120 min

Retrieval Gauntlet Design: Sealed Task Inventory and Declared Assistance Conditions

Agent-Assisted Engineering Check

Prerequisites

Days 85-87 capstone integration and the completed Learning Project sequence (autograd, pretraining, real LoRA, evaluable agent, CI delivery).

Learning Objective

Assemble the fixed, sealed task inventory for unaided retrieval across five domains and declare assistance conditions before any attempt, so independent evidence is never inferred from assisted output.

Instructions

1. Understand why this day exists: retrieval practice (the testing effect) produces durable memory better than rereading; today converts the whole curriculum into fixed, unaided checks.
2. The sealed gauntlet covers exactly five domains, each traceable to real evidence days:
- Domain PY (Core Python): values, control flow, functions, containers, exceptions, files (Days 4-12).
- Domain GIT (Git & Debugging): branching, diff reading, failure diagnosis, safe recovery on disposable repositories (Days 13-15, 79-81).
- Domain MATH (Mathematics): derivatives, chain rule, probability, cross-entropy interpretation (Days 16-30).
- Domain GRAD (Gradients & Autograd): computation graphs, shared-node accumulation, finite-difference verification (Days 31-33 and projects/autograd).
- Domain MODEL (Model Reasoning): tokenization, causal masking, parameter ledgers, loss/perplexity reading, LoRA accounting, agent-loop behavior (Days 34-87).
3. For each domain, fix three task IDs now — no improvisation mid-check. Map every task to a recovery day in case it is missed.
4. Declare the assistance condition in writing: Independent Mastery Check — no AI, no notes, no browser, ≤105 minutes of the 120-minute block, start time recorded. Declared condition is honest self-report, not claimed surveillance.
5. The agent may help you *organize this inventory only*; it must not author, solve, or grade any sealed task.

Practice & Prediction Log

Produce sealed_inventory.json listing the 5 domains with 3 fixed task_ids each (15 total), each row carrying {domain, task_id, recovery_day, declared_condition: 'independent'}. Assert the inventory has exactly 15 rows and no duplicate task_id.

Independent Mastery Criterion

Produce a complete sealed inventory mapping every task to a named recovery route, with the assistance condition declared before any attempt begins.

Authoritative Source Resources & Provenance

linking Journal publisher copyright (all rights reserved) - lawful external linking only

Test-Enhanced Learning: Taking Memory Tests Improves Long-Term Retention

Author: Henry L. Roediger III, Jeffrey D. Karpicke | Original Source Link ↗

Relevant Section / Range: Psychological Science 17(3): 249-255, Experiments 1-2

Verified Version & Date: Psychological Science, 2006 (Verified: 2026-10-30)

Selection Rationale:

Foundational experimental evidence that retrieval practice (testing) outperforms repeated study for long-term retention; grounds the Day 88 AI-free retrieval checks in peer-reviewed learning science rather than folk advice.

Source Prerequisites:

Basic understanding of study sessions, retention intervals, and experimental design vocabulary.

Safe Fallback (Offline Resilience):

The curriculum restates the experimental design and effect sizes in original prose; every sealed task runs without the paper.

Compared Candidate Sources & Selection Analysis (2)

Strong meta-analytic coverage of spacing effects but addresses scheduling rather than the retrieval effect itself; retained as support for the Day 90 spaced-review schedule.

Equally foundational follow-up demonstrating repeated retrieval vs repeated study; the 2006 paper is cited as the standard first controlled demonstration.

linking Journal publisher copyright (all rights reserved) - lawful external linking only

Improving Students' Learning With Effective Learning Techniques: Promising Directions From Cognitive and Educational Psychology

Author: John Dunlosky, Katherine A. Rawson, Elizabeth J. Marsh, Mitchell J. Nathan, Daniel T. Willingham | Original Source Link ↗

Relevant Section / Range: Psychological Science in the Public Interest 14(1): 4-58; practice testing and distributed practice sections

Verified Version & Date: Psychological Science in the Public Interest, 2013 (Verified: 2026-10-30)

Selection Rationale:

Detailed comparative review rating practice testing and distributed practice as high-utility techniques while rating highlighting and rereading low-utility; anchors the retrieval, correction, and spaced-review design of Days 88-90.

Source Prerequisites:

None beyond general academic reading.

Safe Fallback (Offline Resilience):

Curriculum restates the utility ratings and application conditions in original prose; every task remains runnable offline.

Compared Candidate Sources & Selection Analysis (2)
Pashler et al. Learning Styles: Concepts and Evidence ↗ compared_alternative [Publisher copyright (originally Wiley; now SAGE Publications)]

Important negative result on learning-styles matching but narrower scope; Dunlosky et al. rates a broader set of techniques directly usable for this curriculum.

Brown, Roediger & McDaniel: Make It Stick: The Science of Successful Learning ↗ compared_alternative [Publisher copyright (Harvard University Press)]

Strong trade-book synthesis of the same evidence base; the peer-reviewed review is preferred for citation precision and technique-by-technique utility ratings.

02 120 min

Independent Check A: Core Python and Git Debugging (AI-Free)

Independent Mastery Check (no AI assistance).

Prerequisites

Day 88 Activity 1 sealed inventory declared; assistance condition: independent (no AI, no notes).

Learning Objective

Demonstrate unaided core-Python and Git/debugging competence on fixed tasks: implement a text utility with tests, diagnose a failing test output, and plan safe Git recovery on a disposable repository.

Instructions

Task PY-1 (45 min): Implement top_k_words(text, k) — return the k most frequent words of the input, case-insensitive, punctuation stripped, ties broken alphabetically. Write the function and a unittest covering: normal counts, tie order, empty text, k larger than vocabulary.
Task GIT-1 (35 min): On a disposable clone, you committed work directly on main by mistake. Write the exact command sequence that moves the commit onto a new branch fix/login and restores main to the remote state — plus the command that recovers a branch tip you deleted yesterday. Work may be simulated on a scratch repo; destructive flags are permitted only inside the disposable fixture.
Task DBG-1 (25 min): Given this failing output — FAIL: test_parse (KeyError: 'total') and FAIL: test_window (expected 6 items, got 7) — write the two most probable root causes and the first diagnostic command you would run for each.
Record every answer in your worksheet; each claim must be checkable by a command (python -m unittest, git log, git reflog).

Practice & Prediction Log

Deliver worksheet entries: PY-1 implementation + test output; GIT-1 command sequence with an explanation of which worktree state each command produces; DBG-1 two diagnoses with diagnostic commands.

Independent Mastery Criterion

All three tasks answered correctly with runnable verification, entirely unaided.

Authoritative Source Resources & Provenance

linking PSF License Version 2

Python Standard Library: unittest

Author: Python Software Foundation | Original Source Link ↗

Relevant Section / Range: Classes and functions: Test cases (TestCase assert methods)

Verified Version & Date: Python 3.12 (Verified: 2026-10-30)

Selection Rationale:

Normative reference for unittest.TestCase, assertEqual, assertRaises, the failure report format, and test_*.py discovery conventions.

Source Prerequisites:

Basic Python syntax from Study Days 01-09

Safe Fallback (Offline Resilience):

unittest ships with the interpreter, so every example runs offline; help(unittest.TestCase) documents the assert methods.

Compared Candidate Sources & Selection Analysis (2)
pytest documentation ↗ compared_alternative [MIT]

Strong third-party runner deferred for now; unittest needs no environment change and is the curriculum baseline.

Powerful third-party runner used industry-wide; unittest is selected for these AI-free checks because it ships in the standard library and needs no external dependency.

linking CC BY-NC-SA 3.0 noncommercial share-alike

Pro Git, Second Edition

Author: Scott Chacon and Ben Straub | Original Source Link ↗

Relevant Section / Range: Chapter 3 Git Branching and Section 7.7 Reset Demystified

Verified Version & Date: Second edition (Apress, 2014) (Verified: 2026-10-30)

Selection Rationale:

Freely licensed reference for branching, merging, conflict resolution, and safe recovery vocabulary used on Day 14.

Source Prerequisites:

Basic git status/log/diff vocabulary from earlier study days

Safe Fallback (Offline Resilience):

git help <command> and the Git manual pages ship with Git and cover the same commands offline; every required workflow is stated locally in the lesson.

Compared Candidate Sources & Selection Analysis (2)
GitHub Skills interactive courses ↗ compared_rejected_for_day14 [MIT (varies by course)]

Interactive but requires a GitHub account and an external service; disposable local repositories keep the exercise self-contained.

Atlassian Git Tutorials ↗ compared_rejected_for_day14 [All Rights Reserved]

Vendor-owned prose with no permissive license; Pro Git covers the same ground under CC BY-NC-SA.

03 120 min

Independent Check B: Mathematics, Gradients, and Model Reasoning (AI-Free)

Independent Mastery Check (no AI assistance).

Prerequisites

Day 88 Activity 2; assistance condition: independent.

Learning Objective

Demonstrate unaided quantitative reasoning: a hand-computed chain-rule gradient, a transformer parameter ledger, causal-mask reasoning, LoRA adapter accounting, and loss/perplexity interpretation against recorded artifact values.

Instructions

Task M-1 (25 min): For f(a,b) = a*b + a, at a=2.0, b=3.0: compute df/da and df/db by hand (df/da = b + 1 = 4.0, df/db = a = 2.0), then state which finite-difference formula and step h you would use to verify in float64, and why two-sided difference beats one-sided.
Task M-2 (25 min): Recompute the parameter ledger for the pretraining smoke configuration (vocab=65, block=16, n_layer=2, n_head=2, n_embd=16): token_embedding = 65*16 = 1040; position_embedding = 16*16 = 256; attention/layer = 4*(16*16) = 1024; mlp/layer = 2*(16*64) = 2048; total = 1040 + 256 + 2*(1024+2048) = 7440. Compare with projects/pretrain/artifacts/run_artifact.json.
Task M-3 (20 min): In three sentences, explain why attention position t must not read position t+1 and how the upper-triangular causal mask enforces this.
Task M-4 (25 min): For d_model=1024 and LoRA rank r=16 on one projection: adapter parameters = A(16x1024) + B(1024x16) = 32768 versus dense 1024x1024 = 1,048,576 (~3.125%). State where the alpha/r scaling enters.
Task M-5 (25 min): Interpret val_loss 4.0372 from the smoke artifact: perplexity = exp(4.0372) ≈ 56.7. Explain why this is expected for a 10-step CPU smoke run and name two observations that would falsify training health (e.g., rising loss, non-finite gradients).

Practice & Prediction Log

Work all five items with formulas and units shown; then check numeric answers against the recorded artifact fields — record agreement or discrepancy honestly.

Independent Mastery Criterion

All five quantitative items answered correctly with shown work, unaided, and checked against recorded artifact values.

Authoritative Source Resources & Provenance

linking MIT

CS231n: Backpropagation and Intuitions

Author: Andrej Karpathy / Stanford University | Original Source Link ↗

Relevant Section / Range: Patterns in backward flow

Verified Version & Date: Course notes (Verified: 2026-10-30)

Selection Rationale:

Modular local-gradient interpretation (gate operations: add, multiply, max) and gradient flow intuition.

Source Prerequisites:

Multivariable calculus and basic Python.

Safe Fallback (Offline Resilience):

Local gate definitions and circuit flow diagrams in the lesson provide full coverage.

Compared Candidate Sources & Selection Analysis (2)

Raw circuit view; CS231n notes are more structured with explicit gate derivations.

Fast.ai Practical Deep Learning for Coders ↗ compared_alternative [GPL-3.0]

Top-down approach; CS231n bottom-up modular view matches our first-principles curriculum.

linking MIT

micrograd: A tiny scalar-valued autograd engine

Author: Andrej Karpathy | Original Source Link ↗

Relevant Section / Range: micrograd/engine.py: class Value (_backward closures and backward() topological sort); README 'Example usage'

Verified Version & Date: Git commit master (Verified: 2026-10-30)

Selection Rationale:

Minimal, readable implementation of scalar autograd and MLP layers directly matched to our project.

Source Prerequisites:

Python object-oriented programming and operator overloading from Day 31.

Safe Fallback (Offline Resilience):

The local projects/autograd engine is runnable offline and matches micrograd semantics.

Compared Candidate Sources & Selection Analysis (2)
PyTorch autograd C++ engine ↗ compared_alternative [Modified BSD]

Production scale, but too large and complex for tracing by hand in introductory lessons.

Autograd (HIPS) ↗ compared_alternative [MIT]

Vectorized autograd; micrograd's pure scalar model is pedagogical for hand backprop.

04 120 min

Assessment: Transparent Rubric Scoring and the Unmet-Criteria Ledger

Independent Mastery Check (no AI assistance).

Prerequisites

Day 88 Activities 1-3 complete.

Learning Objective

Score every sealed task against a fixed 0/1/2 rubric, compute domain subtotals, and produce the unmet-criteria ledger that routes each miss to a named recovery activity — with the honest-evidence rules stated.

Instructions

1. Rubric (fixed before scoring): 0 = absent or incorrect; 1 = partially correct (right method, one arithmetic/logic element wrong, or verification missing); 2 = fully correct and command-verifiable.
2. Domain pass bar: ≥5/6 per domain AND no single task scored 0. Overall pass: all five domains at bar.
3. For every item scored <2, write a ledger row: {task_id, miss_type ∈ {concept_gap, slip, ambiguity, evidence_gap}, recovery_day, next_attempt_date}.
4. Publish score_sheet.json: per-task scores, domain subtotals, pass/fail per domain, and all ledger rows.
5. State the honesty rules aloud in the sheet header: an Independent Mastery Check is evidence of unaided ability; Agent-Assisted results never substitute; this is not an official certification and does not claim universal three-month mastery — unmet criteria stay visibly unmet.

Practice & Prediction Log

Emit score_sheet.json with all fields populated and the ledger rows written for every non-perfect item; include the honesty_rules header verbatim.

Independent Mastery Criterion

Produce a complete, honest score sheet and remediation ledger; unmet items remain visibly unmet and every miss carries a named recovery route.

Authoritative Source Resources & Provenance

linking NIST public information (U.S. government site; NIST/SEMATECH co-production)

NIST/SEMATECH e-Handbook of Statistical Methods

Author: NIST (U.S. National Institute of Standards and Technology) and SEMATECH | Original Source Link ↗

Relevant Section / Range: Sections 1.3.5-1.3.6: quantitative techniques, variability, and probability distributions

Verified Version & Date: Current online edition (Verified: 2026-10-30)

Selection Rationale:

Primary engineering reference for descriptive statistics, exploratory data analysis, and uncertainty quantification with worked formulas.

Source Prerequisites:

High-school arithmetic; no calculus required for the descriptive sections.

Safe Fallback (Offline Resilience):

Every formula used in the lesson (mean, variance, standard error) is reproduced as original explanation; all exercises run fully offline.

Compared Candidate Sources & Selection Analysis (2)
OpenIntro Statistics (4th edition) ↗ compared_selected_for_day28 [CC BY-SA 3.0]

Undergraduate statistics text with a stronger narrative bridge from probability to inference; retained as the second linked source for inferential framing.

Seeing Theory (Brown University) ↗ compared_alternative [Free web resource (site copyright)]

Strong interactive visualizations of probability and sampling, but a browser-only interactive format is a weaker fit for an offline-capable 480-minute ledger.

linking Journal publisher copyright (all rights reserved) - lawful external linking only

Test-Enhanced Learning: Taking Memory Tests Improves Long-Term Retention

Author: Henry L. Roediger III, Jeffrey D. Karpicke | Original Source Link ↗

Relevant Section / Range: Psychological Science 17(3): 249-255, Experiments 1-2

Verified Version & Date: Psychological Science, 2006 (Verified: 2026-10-30)

Selection Rationale:

Foundational experimental evidence that retrieval practice (testing) outperforms repeated study for long-term retention; grounds the Day 88 AI-free retrieval checks in peer-reviewed learning science rather than folk advice.

Source Prerequisites:

Basic understanding of study sessions, retention intervals, and experimental design vocabulary.

Safe Fallback (Offline Resilience):

The curriculum restates the experimental design and effect sizes in original prose; every sealed task runs without the paper.

Compared Candidate Sources & Selection Analysis (2)

Strong meta-analytic coverage of spacing effects but addresses scheduling rather than the retrieval effect itself; retained as support for the Day 90 spaced-review schedule.

Equally foundational follow-up demonstrating repeated retrieval vs repeated study; the 2006 paper is cited as the standard first controlled demonstration.