Dataset: 2026-09-18-v3.0
Ruleset: b2e8889c66f7
Scanned: 2026-09-16
Window: February–September 15, 2026

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01Result

practical-tutorials/project-based-learning ↗

practical-tutorials/project-based-learning AI coding statistics: 36.4%

of 22 commits in February–September 15, 2026 were committed by an AI agent — a floor, not an estimate

Too little recent activity to publish. Only 22 commits landed in this window, so the percentage is noisy. Repositories below 100 are excluded from the index for this reason.

36.4% agent · top contributor 100%1 contributor

individual

One person is driving this. The headline number reflects a single contributor's workflow, not a project-wide shift.

agent commits

other considered commits

agent contributors

100%

largest contributor

02Drivers

What shapes this result

Mode, timing, and detected signatures show whether the result reflects broad adoption or one concentrated workflow.

Assisted or autonomous

8assisted

0autonomous

Assisted — a developer’s identity authored the commit and the agent added itself as co-author. Autonomous — a bot identity authored the commit without a human identity attached. Across the whole index, 96% of agent commits are assisted.

Which agents

Agent	Vendor	Commits	Share
Claude Code	Anthropic	8	36.4%

03Evidence

Inspect the matches

Public commit samples let you inspect the signatures behind the aggregate result.

Matched commit samples

f55fe25b on GitHubAdd Networking from Scratch to C/C++ Network programming (#864)Claude Code
25df2892 on GitHubRemove dead 'Write a NoSQL Database in Python' entry (#850)Claude Code
25546408 on GitHubRemove two dead Medium links from JavaScript section (#849)Claude Code

04Method

What entered the denominator

The result begins with commit history, removes merges and routine automation, and reports literal agent signatures as a lower bound.

Exclusion ledger

Commits in window	34
Merge commits removed	−0
CI / dependency bots removed	−12
Considered	22

Bots are excluded from the denominator entirely. Agent attribution requires a literal supported signature; no code-style inference is used.

05Provenance

Pinned to a source state

This observation names the repository state, release, and detector that produced it. Use these identifiers when reproducing or citing the result.

Dataset release: 2026-09-18-v3.0
Scan date: 2026-09-16
Ruleset: sha256:b2e8889c66f72c18a61839f0bf1a9f77b5481ba2def044dba33c797b2f2bdcae
Canonical repository: practical-tutorials/project-based-learning
Default branch: master
Resolved HEAD: fcd321e0cf091914417a10948f6a9284276adf1c ↗
Resolved: 2026-09-16
Measurement window: February–September 15, 2026

Download observation JSONDeterministic export · same values as this page

06Peer context

Position among comparable repositories

The cohort uses the same release and measurement window, then narrows to the repository’s ecosystem and removes low-activity observations.

CohortPython · 247 eligible repositories · at least 100 considered commits2026-09-18-v3.0 · February–September 15, 2026 · 145 of 392 ecosystem repositories excluded

Comparison withheld for low activity

This observation does not meet the 100-commit publication threshold. The cohort definition and sample size remain visible so the omission is auditable.

Continue exploring

Claude Code commitsBrowse repositories with detected Claude Code signatures →GitHub AI commit analyzerInspect another repository with the same evidence model →

Reproduce this observation

Run the same detector locally.

Deterministic signature matching; no code-style inference. Squash merges can remove trailers, so this result remains a measured floor.

npx qmmit-cli practical-tutorials/project-based-learning

Scan another repository

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