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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsIf a change lands in one repository, finding its possible effects in connected services, wrappers, tests, and documentation can take more than a quick search. I built Where am I to make those connections easier to inspect: it combines Git change information with static code evidence and presents the result as a clickable impact graph. It is an early MVP, so treat its connections as leads for review—not a complete map of runtime dependencies.
What the impact graph is meant to answer
The question behind the project is: “If I change this endpoint, where does the change flow across the whole system?” The tool is designed to trace possible impact across repositories, connecting changes in implementation to related contracts, callers, documentation, and tests.
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Its scanner brings together several views of change: working-tree and staged edits, commits on the current branch compared with its base reference, and changes available from the selected remote/base reference. It then uses priority files and API catalog files to help connect those changes with related code and artifacts. These are the project author’s descriptions of the tool, not independently verified guarantees.
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The project describes static patterns for identifying functions and call relationships, API paths, frontend wrappers and backend handlers, database access, external HTTP requests, OpenAPI documents, documentation, tests, and application error codes.
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In the example fixture, a trail of evidence runs from a frontend request through a Go route to a database call and an external training request, then to a Python error code. Nodes and edges can lead back to source evidence lines, giving a reviewer a way to check why the graph suggests a connection rather than accepting an unexplained link.
How the workflow is organized
The interface is described as built with React and XYFlow. Its views cover current work, team changes, API contracts, and the analysis engine. Search and filters can narrow attention to changed impact or items needing review; selecting a node or edge is intended to show supporting evidence, representative diffs, relationship rules, change briefings, and suggested next checks. These are features described by the author, not a verified assessment of the current implementation.
The design aims to stay local-first, make evidence visible, account for change, and bound the amount of work the scanner performs. For a large workspace, configurable scan limits are intended to constrain counts of files, API catalog files, changed files, facts, edges, and nodes. That matters because an impact graph is useful only while a reviewer can still distinguish meaningful leads from noise.
The Tool Desk
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The author’s article says local use requires Node.js 22.13 or newer and at least one local Git repository. Its setup outline is to clone the project, install dependencies, copy the example configuration, and start the development server. Because those instructions are article-era details and the repository could not be independently checked, verify the current project documentation before following them.
Configuration can discover repositories beneath a workspace or list repositories individually. The article also describes an autoFetch option and a --no-fetch scanner flag for avoiding network access. Check the current configuration and command-line options before relying on either setting.
Where to trust it—and where not to
Static extraction cannot be assumed to find every dependency. Dynamic calls, reflection, generated code, and complex metaprogramming can hide relationships; language and framework coverage also varies. A graph edge is evidence worth investigating, not proof that a runtime dependency exists or that every downstream effect has been found.
The author describes the project as an early MVP and explicitly frames its output as supporting evidence for review, not a replacement for tests, security checks, or engineering judgment. Use it to guide inspection and decide what to verify; keep the usual engineering checks in place.
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Local analysis can still expose sensitive details
Running analysis locally does not automatically make every artifact safe to share. The author notes that configuration or generated snapshots may include absolute paths, repository names, branch names, commit messages, pull request titles, function names, API paths, or evidence lines. Before sharing a screenshot or exported output, inspect it for information your team does not want disclosed.
Questions to answer before adopting an impact graph
- Does it recognize the languages and frameworks that matter in your repositories?
- Can you follow an inferred link back to useful file-and-line evidence?
- Does it help prioritize current work and incoming team changes, rather than just producing a broad inventory?
- Can you set sensible scan limits for the size of your workspace?
- Do reviewers understand that graph connections are prompts to investigate, not proof of complete runtime behavior?
The project article does not provide benchmarks or a comparative evaluation, so it cannot establish that this workflow is faster or more accurate than other approaches. Its practical value depends on whether the evidence it extracts helps your team find and verify relevant change paths.
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