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When an endpoint changes, the important question is not only what changed in its repository, but which wrappers, handlers, services, databases, external calls, documentation, and tests may depend on it. Where am I is an early-MVP tool designed to make those possible connections easier to inspect by combining Git change information with static code evidence in a clickable graph.
How Where am I builds an impact graph
The described scanner gathers several kinds of change information: edits in the working tree and staging area, commits on the current branch relative to its base reference, and changes available from a selected remote or base reference. It combines those changes with configured priority files and API catalog files to connect implementation changes with related contracts, wrappers, documentation, and tests.
Rather than presenting a code change in isolation, the intended workflow is to ask: “If I change this endpoint, where does the change flow across the whole system?” The graph can cross repository boundaries when the configured repositories contain recognizable evidence of those relationships.
Evidence the scanner is intended to recognize
- Functions and relationships between calls
- API paths, frontend wrappers, and backend handlers
- Database access and external HTTP requests
- OpenAPI documents, documentation, and tests
- Application error codes
The project article’s example fixture follows evidence from a frontend request to a Go route, then to a database call, an external training request, and a Python error code. Selecting graph nodes or edges is described as a way to inspect supporting source evidence, including file lines, representative diffs, and the rules used to infer a relationship. Those details are the project author’s description, not an independently verified feature assessment.
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How to use the graph during review
The interface is described as having views for current work, team changes, API contracts, and the analysis engine. Search and filters are intended to narrow attention to changed impact or items that need review. A change briefing and suggested next checks can help turn a broad graph into a review queue: inspect the connected evidence, then decide which code paths or contracts deserve direct validation.
The graph is most useful as a navigation aid across dispersed clues. An edge means that the scanner found a recognizable relationship worth examining; it does not establish that the two components are connected at runtime or that every downstream effect has been found. Treat each connection as a lead to verify in code, tests, and the relevant system context.
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What is known about local setup
The project 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. Since the repository and its current documentation could not be independently verified, check the project’s current instructions before relying on those setup steps or version requirements.
Configuration is described as supporting either repository discovery beneath a workspace or an explicit list of repositories. Scan limits can be adjusted for files, API catalog files, changed files, facts, edges, and nodes. The article also describes disabling autoFetch or running the scanner with --no-fetch when network access is not wanted. Confirm the current option names and behavior in the project documentation before applying them.
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Limits to account for before trusting a connection
Where am I is presented as an early MVP. Static extraction can miss dynamic calls, reflection, generated code, and complex metaprogramming, while language and framework coverage varies. A connection in the graph is therefore evidence to review, not a complete dependency map. The project author explicitly frames its output as supporting evidence rather than a replacement for tests, security checks, or engineering judgment.
That distinction matters most when a change crosses boundaries that static patterns may not recognize, or when the cost of a missed dependency is high. Use the graph to identify plausible review targets, then validate behavior using the checks appropriate to the affected system. The article provides no benchmark or comparative evaluation, so it does not establish that this workflow is faster or more complete than other impact-analysis approaches.
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Local-first analysis still needs careful sharing
Keeping analysis local can reduce the need to send repository contents to a hosted service, but it does not make generated artifacts safe to distribute. Local configuration or snapshots may include absolute paths, repository names, branches, commit messages, pull request titles, function names, API paths, or source evidence lines. Review exported output and screenshots for sensitive project details before sharing them.
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- Does the scanner recognize the languages and frameworks used in the repositories that matter to your team?
- Can reviewers inspect the source evidence behind an inferred connection?
- Do the current-work and team-change views align with how your team receives and reviews changes?
- Can scan limits keep the graph focused enough to use on your repositories?
- Will your team treat graph links as review prompts rather than proof of runtime behavior?
The project’s own adoption questions point to the same practical trade-off: which extractors a codebase needs, what evidence would make an inferred relationship trustworthy, and when a graph becomes too large to help rather than adding noise. Those answers depend on the team’s stack and review practices; the article does not provide measured thresholds or language-coverage guarantees.
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