Measure an algorithm instead of guessing

Before you begin

• Python prerequisite; inspect the worked fixture before beginning.
• A text editor/spreadsheet for the manual route. Hosted Python/Jupyter for code exercises; applicable dependencies are in requirements.txt.
• For actual AI calls, a legitimate available cloud service and authorised learner credentials; no particular provider or country is mandatory.

Free original, non-accredited practice. Self-review is not independent assessment. No particular provider, payment platform or country is mandatory; use a legitimate available route or explicitly labelled manual practice.

State the preconditions

Correctness and efficiency are different questions. A linear search can be correct yet costly as data grows. Binary search requires sorted data; applying it to arbitrary input can produce a plausible failure. Count comparisons on a deliberately small fixture before discussing asymptotic growth. Specify sortedness, duplicate handling and the meaning of not found. Inspect the supplied input before using any tool. Write the decision rules in your own words and calculate one small result independently. The reference output is an answer key for this deliberately bounded fixture, not proof of general capability. Policy for this project: Search returns an index or -1. Count each inspected value once. Sortedness is an explicit precondition for binary search. For learners across regions underserved or excluded by mainstream providers: verify country eligibility, language, account/identity requirements, data terms, cost, quota and connectivity before choosing a cloud service. Use only legitimate available alternatives; never bypass restrictions or invent a country/account. No service access? Complete the labelled manual/fixture route without claiming a live AI run.

Worked example

SOURCE (authored fixture):
{
  "values": [
    2,
    4,
    6,
    8,
    10
  ],
  "target": 8
}
INDEPENDENT FIXTURE TARGET:
{
  "index": 3,
  "linear_comparisons": 4,
  "binary_comparisons": 2
}

Your practical task

1. Specify sortedness, duplicate handling and the meaning of not found.
2. Open input.json and expected.json from the practice pack; keep an unmodified original.
3. Record a manual prediction and the relevant rule before consulting the answer key.
4. Perform one small change and predict its effect before rerunning.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• State the preconditions — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• Target 11: return -1, not the nearest item.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Trace a linear search

Correctness and efficiency are different questions. A linear search can be correct yet costly as data grows. Binary search requires sorted data; applying it to arbitrary input can produce a plausible failure. Count comparisons on a deliberately small fixture before discussing asymptotic growth. Count inspected values by hand to establish a baseline. Establish a baseline before changing prompts or code. Keep the original input immutable. Record expected values, observed values and whether each comparison passed. If the expected file conflicts with a justified policy, investigate rather than forcing a convenient match. Policy for this project: Search returns an index or -1. Count each inspected value once. Sortedness is an explicit precondition for binary search.

Worked example

MANUAL BASELINE / compare every named field
{
  "index": 3,
  "linear_comparisons": 4,
  "binary_comparisons": 2
}
Keep a separate observed-result column; the answer key is not a model response.

Your practical task

1. Count inspected values by hand to establish a baseline.
2. Open input.json and expected.json from the practice pack; keep an unmodified original.
3. Record expected and observed values separately; explain every mismatch.
4. Perform one small change and predict its effect before rerunning.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Trace a linear search — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• Empty list: return -1 with zero linear comparisons.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Implement binary search

Correctness and efficiency are different questions. A linear search can be correct yet costly as data grows. Binary search requires sorted data; applying it to arbitrary input can produce a plausible failure. Count comparisons on a deliberately small fixture before discussing asymptotic growth. Track low and high bounds; do not lose the termination condition. Build one inspectable transformation or bounded AI prompt. Keep calculation and validation deterministic where possible. If you make a permitted hosted call, save the actual response separately from the authored examples and verify its material claims; the supplied reference never simulates a live model. Policy for this project: Search returns an index or -1. Count each inspected value once. Sortedness is an explicit precondition for binary search.

Worked example

PROJECT CONTRACT:
Search returns an index or -1. Count each inspected value once. Sortedness is an explicit precondition for binary search.
HOSTED REFERENCE: open hosted-lab.ipynb or run reference.py with all fixture files together.
EXPECTED CONSTRUCTED RESULT:
{
  "index": 3,
  "linear_comparisons": 4,
  "binary_comparisons": 2
}

Your practical task

1. Track low and high bounds; do not lose the termination condition.
2. Open hosted-lab.ipynb and the reference code from the practice pack; keep an unmodified original.
3. Record expected and observed values separately; explain every mismatch.
4. Perform Unsorted values: reject binary-search use or sort with an explicit index mapping.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Implement binary search — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• Unsorted values: reject binary-search use or sort with an explicit index mapping.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Benchmark growth and edge cases

Correctness and efficiency are different questions. A linear search can be correct yet costly as data grows. Binary search requires sorted data; applying it to arbitrary input can produce a plausible failure. Count comparisons on a deliberately small fixture before discussing asymptotic growth. Vary input size and target position, not just processor timing. Change one assumption at a time and predict the result first. Retain the failure, the revision and the rerun evidence. A failure is useful information: hiding a bad output or editing a test target after seeing the answer destroys the value of the experiment. Policy for this project: Search returns an index or -1. Count each inspected value once. Sortedness is an explicit precondition for binary search.

Worked example

CHANGE TESTS — predict before running:
1. Target 11: return -1, not the nearest item.
2. Empty list: return -1 with zero linear comparisons.
3. Unsorted values: reject binary-search use or sort with an explicit index mapping.
Keep the original expected.json unchanged.

Your practical task

1. Vary input size and target position, not just processor timing.
2. Open failure-tests.md from the practice pack; keep an unmodified original.
3. Record expected and observed values separately; explain every mismatch.
4. Perform Target 11: return -1, not the nearest item.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Benchmark growth and edge cases — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• Target 11: return -1, not the nearest item.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Choose a structure for a task

Correctness and efficiency are different questions. A linear search can be correct yet costly as data grows. Binary search requires sorted data; applying it to arbitrary input can produce a plausible failure. Count comparisons on a deliberately small fixture before discussing asymptotic growth. Compare repeated lookups with the cost of sorting or building a map. Review what the fixture cannot establish. Explain who owns the decision, where human approval is needed and which access or professional constraints remain. Do not describe a small educational success as production readiness, certification, legal compliance or a guarantee. Policy for this project: Search returns an index or -1. Count each inspected value once. Sortedness is an explicit precondition for binary search. For learners across regions underserved or excluded by mainstream providers: verify country eligibility, language, account/identity requirements, data terms, cost, quota and connectivity before choosing a cloud service. Use only legitimate available alternatives; never bypass restrictions or invent a country/account. No service access? Complete the labelled manual/fixture route without claiming a live AI run.

Worked example

REVIEW BOUNDARY:
Search returns an index or -1. Count each inspected value once. Sortedness is an explicit precondition for binary search.

ACCESS / NOT TESTED: unavailable provider, unsupported language and absent external feedback must remain explicit.

Your practical task

1. Compare repeated lookups with the cost of sorting or building a map.
2. Open README.md and your evidence log from the practice pack; keep an unmodified original.
3. Record the human decision owner, limitations and any untested service access.
4. Perform one small change and predict its effect before rerunning.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Choose a structure for a task — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• Empty list: return -1 with zero linear comparisons.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Ship a correctness and complexity note

Correctness and efficiency are different questions. A linear search can be correct yet costly as data grows. Binary search requires sorted data; applying it to arbitrary input can produce a plausible failure. Count comparisons on a deliberately small fixture before discussing asymptotic growth. Include traces, tests and the assumptions behind the recommendation. Deliver a reproducible bundle rather than a screenshot alone. Include inputs, rules, output, failure tests, a short README and residual limitations. Ask another person to follow the instructions if possible; self-review and actual external review must be labelled separately. Policy for this project: Search returns an index or -1. Count each inspected value once. Sortedness is an explicit precondition for binary search.

Worked example

HANDOFF MANIFEST:
input.json — immutable source
expected.json — fixture answer key
actual.json — result actually observed
evidence-log.csv — baseline plus three changes
README.md — reproduction and access route
limitations.md — unresolved failures, rights, approvals and scope
Never bundle a credential.

Your practical task

1. Include traces, tests and the assumptions behind the recommendation.
2. Open README.md and your evidence log from the practice pack; keep an unmodified original.
3. Record the human decision owner, limitations and any untested service access.
4. Perform one small change and predict its effect before rerunning.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Ship a correctness and complexity note — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• Unsorted values: reject binary-search use or sort with an explicit index mapping.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Capstone self-review

Score each criterion 0 (missing), 1 (partial), or 2 (demonstrated). A self-review pass needs at least 8/10 and 2/2 on the critical safety/limits criterion. This is learner practice feedback, not verified assessment.

• Defined task and preserved input: Original input and the project policy are retained; the actual question is clear.
• Checked baseline: The supplied baseline reconciles field by field; unsupported live-run claims are absent.
• Three failure/change tests: All three specified changes have predictions, observed results and explanations.
• Repeatable handoff: Another person can follow the README; supporting practice evidence and unresolved limits are included.
• Safety, rights and access honesty — critical: Search returns an index or -1. Count each inspected value once. Sortedness is an explicit precondition for binary search. Regional access route is legitimate; credentials and private data are absent; manual/constructed work is clearly labelled.

Review questions

Practice files

• README.md
• evidence-log.csv
• expected.json
• failure-tests.md
• hosted-lab.ipynb
• input.json
• lab_engine.py
• reference.py
• requirements.txt

References and editorial limits

Original authored practice material; deterministic fixtures build-tested. Not independently expert-reviewed or learner-validated. Time estimates unvalidated.

• Python bisect documentation

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