Building a Bespoke GUI for DSP Research with LLMs — Lessons from 1 Year of Daily Use

After a year of daily LLM use for DSP and algorithms research, u/diydsp from r/ClaudeAI shares a practical workflow centered around an ongoing GUI — a bespoke app built incrementally with LLM assistance. The key insight: rather than wasting time on GUI programming from scratch, let the LLM do the heavy lifting and add features as you need them.

Core Workflow Components

• Maintain a problem_description.md file to keep the LLM focused on the research context.
• Write 2-5 reports per day in both .md and .pdf formats — include executive summaries and plot interpretation descriptions.
• Human → LLM Coding App → Human → LLM Chat App Loop — alternate between coding tasks and chat for iterative refinement.
• Don't let the LLM be dramatic — keep prompts concise and grounded to maintain sanity during long sessions.
• Share reports with coworkers regularly to avoid silos.

Developing an Ongoing GUI

Start by asking your LLM to create a simple GUI that browses data folders and makes generic plots. Then, each time you need a specific visualization (spectrogram, FFT, theta-domain conversion), add a tab with the prompt: "Please add a tab to my GUI that does it."

Plotting Best Practices

• Sync all X and Y axes across plots.
• Start all plots zoomed to fill 85% of vertical space.
• Make plots with similar units share the same range.
• When you need a variant of an existing analysis, prompt: "Remember that plot we added to the 'MCAP Analyzer' tab that performs the full analysis? Please make a second button below it named 'Extract' that only extracts the load cell values."

Adding Export Capabilities

Ask the LLM to write key values from plots into a .csv or .json file, or generate a textual description of each analysis step. This makes it easy to paste results into other software.

Tech Stack Recommendation

The author recommends Python with VisPy and Tkinter for cross-platform GPU-accelerated plotting. Matplotlib is an alternative (slower but with better zoom tools). If you're unfamiliar, just paste the recommendation into your LLM — it will handle the implementation.

Open Questions for the Community

• How to share LLM-coded programs with team members without endless code reviews?
• How to use databases on large shared drives (specifically CIFS NAS, which is terrible for DBs)?
• How to get LLMs to think out of the box — the author has spent days reinventing the wheel when the LLM could have suggested known approaches.
• What other tools to connect to the main LLM coding app to multiply its power?

The author also recorded a 27-minute talk covering 7 more sections. Full video at the source link.