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What Graphite compares
Harish Kotra’s Graphite project takes a question and a CSV, builds a shared charting task, and sends it to two model slots concurrently. Each model receives the instruction: “Write Python code using pandas and matplotlib that produces the single most insightful chart of this data. Output only code.”
The generated snippets are executed separately. The interface displays the resulting PNGs side by side, along with each model’s code, measured chart properties, and any traceback from a failed run. A third model then scores the outputs and recommends which chart to ship. Its scorecard is visible, so the recommendation can be checked against the charts rather than accepted on faith.
How the run is assembled
Kotra describes three main parts: a React and TypeScript browser frontend, a Node gateway for model calls, and a separate FastAPI service that executes generated Python. The gateway gives both charting slots the same task, adds a fresh nonce to the prompt, and calls the judge after both executions finish. The executor returns the rendered image and measured metadata.
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The walkthrough lists Particle.ai, Ollama, LM Studio, OpenRouter and compatible /v1 endpoints as provider options. That is the provider support reported in the project article, not a guarantee that every option remains available or compatible today.
Failure handling shown in the walkthrough
- If a model returns empty output, the gateway retries with a larger token budget.
- If generated code crashes, the app tries a one-pass repair prompt.
- A mock provider supports development without an API key.
- Execution tracebacks remain visible instead of being hidden behind a failed chart.
These are implementation details reported by Kotra; they have not been independently reproduced here. Keeping failures visible is especially useful for interpreting a comparison: a blank or broken chart is evidence about that run, not a valid visualization to rank as if it had rendered.
Rank #2
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What the example build-log data is meant to show
The project’s bundled CSV is described as containing 215 working days of build-log data, including build time, stack, test results, changed lines and a cache flag. Kotra says a remote build cache was enabled on 2025-01-15 and that build times fell across stacks afterward, while Swift builds remained slower than the others. The article suggests plotting a rolling mean and marking the cache cutover to make the change visible.
Those patterns are the project author’s description of the sample dataset, not independently audited findings or a general benchmark of build systems. A chart comparison can show whether the models expose the intended signal clearly, but it cannot establish that the underlying data or interpretation is correct on its own.
Rank #3
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How to judge the charts yourself
The scorecard is one input. Inspect each rendered chart and the code that produced it, then ask whether the visual answers the stated question and whether its construction supports that answer.
- Did execution succeed? Check for a rendered image and read any traceback. A failure should not be mistaken for a weak-but-valid chart.
- Does the chart address the question? For the build-log example, look for whether it makes the apparent change around the cache cutover understandable.
- Are labels and axes clear? Units, time periods, series names and scales should let a reader interpret the values without guessing.
- Does aggregation help or conceal? A rolling mean may clarify a broad shift, but inspect whether it obscures variation or distinctions between stacks.
- Is the visual encoding readable? Check whether colors, marks and layout make comparisons legible rather than merely attractive.
- Does the judge explain what is visible? Compare its stated reasons with the PNG and the measured properties. Treat unsupported claims in the scorecard as claims to verify.
Because both charting models receive the same data context and task, Graphite offers a controlled side-by-side comparison of their outputs for that prompt. It does not show that either model is generally better at visualization, nor does a third model’s score prove that its preferred chart is the right one.
Rank #4
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What the execution sandbox does—and does not—establish
Kotra describes the executor as applying static Python syntax-tree checks, using a fresh temporary directory with limited files, removing network access, limiting memory to 512 MB and stopping execution after 20 seconds. The project summary also mentions a macOS Seatbelt layer. These are controls intended to limit generated code during a run; the author describes the sandbox as “best-effort.”
As the walkthrough puts it, “Model-written code is untrusted code.” The listed controls do not prove that arbitrary generated code is safe, and the project description does not establish that this setup is suitable for hostile multi-tenant execution. Anyone adapting the design should treat isolation as a security boundary requiring careful review, not infer safety from a timeout or a restricted working directory.
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Which project this is
Here, Graphite means Kotra’s CSV chart-comparison app. It is distinct from Graphlink, whose README describes a graph-based AI workspace and says that project was renamed from a different Graphite to avoid a naming collision.
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