A local language model can turn source files into useful project documentation, but one successful experiment is not proof that it can reliably reconstruct any codebase. Rich Edmonds reported strong results with small PHP projects using Ollama and Qwen 3.6:27b-q4_K_M, then noted omissions when he tried a larger game project. His description is a promising case study, not a measured accuracy result.
What the local LLM documented
In a September 25, 2026 report, Rich Edmonds described feeding project files to Ollama running Qwen 3.6:27b-q4_K_M and asking it to document the projects. The examples ranged from a single plugin file to a 16-file PHP game. His account is available in the original report.
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A single-file MyBB plugin
For an automated affiliate-link management extension for MyBB, Edmonds says the model described the admin area, retailer and override configuration, URL rewriting on new replies, and caching. This suggests that a focused source file can expose enough structure for a model to produce a useful functional overview.
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The smallest multi-file example was a household waste collection microsite. Edmonds reports that its four PHP files totaled less than 15 KB:
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index.php: 4.3 KBsync.php: 360 Bapi.php: 2.1 KBadmin.php: 7.5 KB
He says the generated documentation covered the dashboard, administration functions, and a JSON endpoint used by Home Assistant. The report quotes this sample model output: “Based on the attached PHP files, this project is a lightweight, self-hosted web application designed to track and manage household waste collection schedules.” That sentence is the model’s generated summary as quoted by Edmonds, not an independent assessment.
A larger PHP game
The game example involved 16 files, including controllers, services, and frontend files. Edmonds says the model captured much of the backend functionality but missed some details. That qualification matters: documenting a larger project is not simply the same task at a bigger file count. Relevant behavior may be distributed across components, and a summary can sound coherent while leaving gaps.
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How strong is the “nearly perfect” result?
“Nearly perfect” is Edmonds’s judgment of selected examples. His report does not give a measured accuracy rate, a systematic comparison against verified documentation, or an independent evaluation. It also does not include the full prompts, context-window settings, generation parameters, or the complete generated documents. The examples show what happened in those cases; they do not establish how well the model would perform on arbitrary repositories.
Project scale is another important distinction. The waste site was reported as less than 15 KB of PHP across four files, while the game example had 16 files and already produced omissions. File count alone does not determine difficulty: dependencies, implicit behavior, configuration, external services, and undocumented conventions all affect what can be inferred from source.
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What the experiment says about hardware
For the small waste collection project, Edmonds says he used an AMD Radeon RX 7900 XT with 20 GB of VRAM. That is a description of one test setup, not evidence that this exact GPU is required, or that it is a recommended minimum. The report does not provide a hardware comparison or enough configuration detail to establish what other systems could run the same model and workload.
How to use generated documentation safely
A model-generated document is best treated as a draft that can accelerate code reading, not as a replacement for verification. The reported omissions in the game example are a practical reason to check claims against implementation before relying on them.
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- Start with bounded scope. Provide a small, coherent part of the project and ask for a description of its purpose, components, inputs, outputs, and external dependencies.
- Request traceable explanations. Ask the model to identify the files or functions supporting each important claim. Then verify those references in the code rather than trusting a polished summary.
- Separate observed behavior from inference. Require explicit labels for behavior visible in the source versus assumptions about runtime configuration, deployment, or intended use.
- Check the consequential details. Review routes, permissions, data changes, caching, API responses, and integration behavior against the relevant code and configuration.
- Record unresolved questions. If a behavior depends on files or services the model did not receive, mark it as unverified instead of letting a plausible explanation become project documentation.
- Keep the result synchronized. Once reviewed, store documentation with the project and revisit it when the documented code changes.
What can reasonably be concluded
This experiment supports a limited but useful conclusion: a local LLM can infer and describe meaningful structure from source files, particularly in small, focused projects. It also shows why fluency is not the same as completeness. The reported PHP examples were encouraging, but one author’s account—without a ground-truth score or independent validation—cannot establish general reliability.
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