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Whiteboard is an open-source desktop workspace where people and coding agents can review software together using diagrams, code-linked navigation, and agent traces on a canvas. Its value is not that a canvas automatically gives an agent better context; it is that spatial structure offers another way to select and explain context. That distinction matters: Whiteboard documents an in-app canvas and an agent SDK, while specific methods for turning canvas geometry into model context remain conceptual examples, not verified product features.
What Whiteboard does
Whiteboard presents itself as a shared architecture and review workspace. Its official README says users can connect Claude Code, Codex, or another coding agent; the agent receives an SDK to draw on the in-app canvas. The documented starting workflow is to download and open the app, connect an agent, then ask it to review a branch against the current main branch.
The product combines visual explanation with code review rather than replacing source files or version control. Diagrams can link to the underlying code, and the workspace includes VS Code keybindings and LSP support for navigation. A Rust-based semantic diff viewer is designed to account for abstract syntax trees, while a decision log connects requirements with agent traces and autonomous choices. Large added functions can be summarized as pseudocode; tests and documentation changes can be collapsed or hidden. The README says diff behavior can be customized through a WASM-based plugin system.
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →These documented features explain why a canvas could be useful: the reviewer can inspect a system-level picture and follow a diagram element into code. They do not establish that Whiteboard uses any particular spatial-context algorithm or representation internally.
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How spatial context could change what an agent sees
A code agent ordinarily works with text, file paths, and relationships it can infer from source. A canvas adds a possible layer of explicit structure: visual grouping, proximity, and connections drawn between components. In an architecture discussion, a service box connected to its dependencies can make the intended relationship easier to see than a list of filenames alone.
The September 30, 2026 mech.app article on Whiteboard and agent context management explores how such information might be made usable by an LLM. It discusses possible approaches such as representing canvas items as nodes and edges, selecting a region, following connections, and treating proximity as an initial relevance signal. The practical question is not merely “which files are relevant?” but also “which nodes are within a spatial region?” and which connected items should be considered alongside them.
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These are explanatory models, not confirmed Whiteboard implementation details. The official README confirms a canvas and an agent SDK for drawing, but does not say Whiteboard serializes the canvas to JSON, stores positions separately from code, uses a 200-pixel relevance radius, or exposes sample tools such as get_node or query_spatial_region. A visual arrangement can suggest context; how a particular system encodes or selects that context must be established separately.
Where a canvas can help—and where it can mislead
| Review need | What a spatial view contributes | What still needs checking |
|---|---|---|
| Architecture and system design | Groups components and makes diagrammed relationships visible. | Confirm that the diagram reflects the current code and includes relevant dependencies. |
| Refactoring or data-flow discussion | Can orient a reviewer to components and the paths between them. | Follow links into source; a diagram may omit a dependency or implementation detail. |
| Line-level code inspection | Can provide context for why a file or function matters. | Use the source and semantic diff; the canvas is not a substitute for inspecting changes. |
| Deep debugging, performance work, or security audits | May help frame the system under discussion. | Use systematic source review and appropriate checks; local visual context may hide global constraints. |
Proximity is especially easy to over-trust. A relevant dependency may be positioned elsewhere, while a security rule or other global constraint may apply beyond the visible neighborhood. A context-selection method that follows only nearby objects could therefore create a neat but incomplete picture. Spatial selection is better treated as a starting point for relevance than as proof that everything important has been included.
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Risks in spatial context management
The conceptual article raises several plausible failure modes; these are risks to consider, not incidents measured in Whiteboard.
- Stale references: after a layout or code change, a spatial item may no longer point to the intended implementation unless references are maintained.
- Ambiguous nodes: similar-looking components can be confused if names, links, or other identifiers are unclear.
- Growing connections: as a system gains relationships, a canvas can become crowded and harder to interpret.
- Context overflow: including every node and edge can overwhelm a model’s available context rather than clarify the task.
- Concurrent layout conflicts: people and agents changing a shared arrangement may produce competing or confusing layouts.
- Agent myopia: a narrowly selected region may hide a distant dependency or a rule that applies across the system.
These trade-offs describe the broader challenge of converting a visual map into a trustworthy working set: omit too much and the agent misses constraints; include too much and useful signals get buried. The best use is to pair a focused canvas view with direct access to code and checks appropriate to the task.
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Whiteboard’s practical boundaries
Whiteboard is currently a review and architecture workspace, not an in-app file editor: its README says files cannot be edited in Whiteboard. It also says that multi-repository work within a single review is not well supported. A shared review does not automatically reflect later updates for its recipients; it must be shared again.
The project says Whiteboard runs against local checkouts, is MIT-licensed, and will remain self-hostable; a hosted team product is planned. Its privacy statement says anonymous telemetry excludes code, diffs, Whiteboard text, prompts, and model output, and that telemetry can be disabled. Those are the project’s own statements, not an independent privacy audit.
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For a reader deciding whether the approach fits, the distinction is straightforward: a canvas can make relationships explicit for people and potentially inform an agent’s task context, while code remains the authority for implementation details. Whiteboard’s documented code links, navigation, and semantic diff support that combined review workflow; the broader mechanics of spatial context selection remain a design question rather than a confirmed feature description.
Quick Recap
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