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In one September 2026 project report, Kanfu Panda says three software features reached review completion in 88 minutes using pdlc-skills, separate worktrees and processes, and a coordinating session. The account also shows why that machine-run time is not the same as the roughly two and a half hours of personal effort the author estimates—and why passing tests alone did not establish release readiness.
What the project set out to build
Panda was working on a console he ran personally. The three features were:
- Cross-node review.
- Notification filtering by task origin.
- Two review flags for a benchmark runner.
The benchmark flags depended on the remote dispatch and verdict-return path built for cross-node review. Notification filtering was independent, so it could proceed alongside that dependency chain. This mattered to how the work was scheduled: not every feature had to wait for every other feature.
How the work was divided and run
The author says pdlc-prd generated three PRDs from raw requirements in 17 minutes. Before implementation, the coordinating session resolved ten open questions. Design work then ran in parallel, followed by test-driven development (TDD), implementation, and review for each feature through the loop engine.
An outer Bash script handled scheduling: it started feature processes in separate worktrees and observed their dependencies. The loop engine handled the per-feature TDD, implementation, and review steps. Put simply, the outer layer coordinated which feature could run and where; the feature loops carried out the planned stages. The account describes three loop steps per feature, not three features being run as one undifferentiated task.
After the feature processes converged, the coordinating session reviewed diffs and checks, opened pull requests, and handled release and deployment commands. The reported 88 minutes ends at review completion; it should not be read as the full elapsed time for requirements, human review, merging, and release.
How long it took—and what the figures mean
All project metrics below are figures reported by Panda for this run, not independently verified benchmarks. They describe different kinds of time and output, so they should not be treated as a single productivity score.
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| Measure | Reported result |
|---|---|
| PRD generation | 17 minutes for three PRDs |
| Time until all three features reached review completion | 88 minutes |
| Estimated personal effort | About two and a half hours across requirements, review, merges, and release-gate completion |
| Added code | 3,351 lines, including 1,770 test lines |
| Unit tests | 421 before; 516 after |
| Coverage | 89.6% before; 90.3% after |
| End-to-end cases | 52 before; 65 after |
The timings are not interchangeable. The 88 minutes is the reported machine-run interval to review completion, while the two-and-a-half-hour estimate includes the author’s own work around the automated stages. The account also reports that a retrospective tool calculated a median TDD duration of 2.1 hours, while outer-script polling recorded 10, 21, and 13 minutes for the three features. That discrepancy is a warning against treating a single generated state file or summary as a reliable clock.
Rank #3
What held quality up—and what the first release check caught
The described safeguards worked at more than one level. The loop steps were checked using objective command exit codes. Once the features converged, the coordinating session reviewed diffs and checks. A pre-release quality report then checked acceptance coverage, and real-machine acceptance followed deployment.
The first release quality report found that 19 acceptance items across the three features were missing from the core-flow registry. Existing flows could have a green test suite while those new acceptance items remained unregistered. Panda says the team added the missing registry entries and nine end-to-end tests; the report then passed. The practical lesson is that release readiness depends on checking whether new requirements are represented in the release’s acceptance machinery, not only whether existing tests pass.
What deployment revealed
The account says the release was deployed to two machines and then exercised through acceptance. Cross-node review reached the other machine, but the review command failed there because that machine was not logged in; the result-return path still ran. Acceptance also exposed a missing lock around result collection.
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Why progress records needed cross-checking
Panda reports that model-written timestamps and inconsistent stage labels made state files unreliable as the sole record of progress or duration. For this run, the author recommends checking event logs, Git evidence, and process exits instead. Each helps answer a different question: event logs show recorded activity, Git shows what changed, and process exits indicate whether a command completed successfully.
This distinction is useful when reviewing automated work. A state label can say a step is complete without providing dependable evidence of when it happened or what changed. A stronger account of progress combines the declared state with observable execution and repository evidence.
What this run does—and does not—show
This is one author’s account of one project, published in September 2026; the available records list September 22 on DEV Community and September 18 on the author’s homepage, so the precise original-versus-cross-post date is not established here. The reported timings, code volume, test counts, and coverage changes are project-specific observations, not expected outcomes for another team, repository, or feature set.
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