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Graphite compares two language models by giving them the same CSV and charting task, running their Python code, and showing the resulting charts side by side. Rather than relying on confident explanations alone, the app exposes the code, execution results, rendered images, measured chart properties, and a third model’s scorecard. It is a project walkthrough by Harish Kotra—not an independent benchmark or proof that the judge reliably identifies the better chart.

What Graphite compares

The user supplies a question and a CSV. Graphite builds a shared prompt and sends it to two model slots concurrently. Both are instructed: “Write Python code using pandas and matplotlib that produces the single most insightful chart of this data. Output only code.”

A separate executor runs each model’s code. The interface puts the rendered PNGs side by side with the generated code, tracebacks if execution fails, and measured properties. After both runs, a third model scores the outputs and recommends which chart to ship. Its scorecard is visible, so the reader can compare its reasoning with the actual charts.

This makes the project’s central comparison concrete: two models receive the same task and data, and readers can inspect what each produced. The judge remains another model, however; its verdict is a recommendation, not an objective guarantee of chart quality.

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How a run flows through the app

  1. Provide the input. Enter a question and a CSV dataset.
  2. Generate two charts. The gateway sends the same charting task to both model slots, with a fresh nonce in the prompt, and makes the model calls concurrently.
  3. Execute the code. A separate Python executor runs each snippet and returns an image plus measured metadata. The interface also shows failures rather than treating an unsuccessful run as a usable chart.
  4. Review the judge’s scorecard. Once both executions finish, a third model scores the outputs and recommends a chart to ship.

The walkthrough describes a React and TypeScript browser frontend, a Node gateway for model calls, and a separate FastAPI executor for generated Python. It lists Particle.ai, Ollama, LM Studio, OpenRouter, and compatible /v1 endpoints as provider options. Those are implementation details reported by the article, not a guarantee that every provider or endpoint remains supported.

What to inspect in the comparison

A score alone is not enough to decide which visualization communicates the data better. Inspect each artifact and the evidence behind the scorecard:

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  • Did the run complete? Check for a rendered chart or a traceback. A failed execution is a meaningful result, not a chart to compare as if it succeeded.
  • Does the chart answer the question? A visually polished figure can still focus on the wrong measure or comparison.
  • Are the axes and labels clear? Look for readable titles, units, legends, and time or category labels that let a viewer understand what is plotted.
  • Does aggregation reveal or hide the pattern? A rolling mean may make a broader change easier to see, while smoothing can also obscure variation that matters to the question.
  • Is the visual encoding readable? Check whether scale, color, and mark choices make the relevant differences easy to interpret.
  • Does the scorecard describe the visible chart accurately? Compare the judge’s explanation with the PNG and the run’s measured properties rather than accepting its recommendation uncritically.

What the sample build-log data is meant to show

Kotra describes the included CSV as 215 working days of build-log data, with fields including build time, stack, test results, changed lines, and a cache flag. In the author’s account, a remote build cache was enabled on 2025-01-15; build times fell across stacks afterward, while Swift builds remained slower than the others. The walkthrough recommends a rolling mean with the cache cutover marked as a way to surface that change.

These are the project author’s descriptions of the example dataset and its intended signal, not independently audited findings or a general benchmark of build systems. The 215 working days refer to the sample CSV, not to a population or performance study.

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How the walkthrough handles generated code

“Model-written code is untrusted code.” Kotra describes the executor’s protections as best-effort controls: static Python syntax-tree checks, a fresh temporary directory with limited files, removed network access, a 512 MB memory cap, and a 20-second execution timeout. The stack summary also mentions a macOS Seatbelt layer.

The walkthrough does not establish that these measures make arbitrary generated code safe, or that the app is suitable for hostile multi-tenant execution. Its stated safeguards are useful context for understanding the demo, but they should not be mistaken for a security guarantee.

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Implementation details and practical behaviors

The author also describes a retry with a larger token budget after empty output, a one-pass repair prompt when generated code crashes, a mock provider for development without an API key, and visible tracebacks when execution fails. These choices make the workflow more informative and resilient as a demonstration; the article presents them as implementation details rather than independently reproduced guarantees.

Graphite is the CSV chart-comparison app described here. The name is also used by unrelated software projects, including a graph-based AI workspace; those projects are not the subject of this walkthrough.

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