Project Info

BenchPilot

Devpost

This project did not submit a demo video on Devpost.

Inspiration

All of us have had some microbiological research experience, and all of us have had to deal with experiments involving bacteria, fume hoods, and biosafety cabinets where, to prevent contamination, we have been limited in our ability to take notes and reference protocols.

What it does

We made a webapp that serves as an AI based research journal and protocol tool specializing in voice command based utility, allowing the user to take notes, set timers, and navigate protocols without the need to touch their device.

How we built it

We used a combination of Google Stitch and Claude Code to design much of the UI, while using Devin and Claude Code to create the backend. The frontend and backend were stitched together, again with a combination of Devin and Claude Code, with Codex as a reviewer for Claude's plans.

Challenges we ran into

The main challenges we ran into were in adding functionality to Google Stitch, it produced a static page with non-interactable elements. As such many elements that were meant to be interactable ended up as placeholders that would complicate the process of merging the frontend and backend. We also struggled with parsing intent from the (sometimes unclear) Deepgram output, so we had to include bias for keywords, router guards, and step-aware answers to the LLM call.

Accomplishments we're proud of

This was the first hackathon any of us had participated, and we are quite proud of what was produced within the 24 hours. It has more functionality than any of us expected it to have, and in addition, we felt the user interface turned out quite well.

What we learned

We learned how to prompt and integrate LLMs into various applications. We further learned how API keys functioned, and how to separate them from the git repo to keep them private.

What's next

Additional functionality needs to be added. Method exportation, reagent QR code scanning and logging, improve accuracy of time estimates, and improved PDF reading.

Analysis

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Metric

Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.

Technology

Found in codeClaimed only
  • AnthropicIn code
  • CSSIn code
  • FastAPIIn code
  • HTMLIn code
  • JavaScriptIn code
  • PythonIn code

6 of 6 appear in the indexed code.

AI coding agents

  • Claude CodeConfig · Commits

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

776 KB

Source files

83

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

0 stars