FIELD_NOTES.md

Field Notes: PitchFight AI

Build log for the Hugging Face Build Small Hackathon (Backyard AI track).
PitchFight AI is a practice arena for student founders — not a replacement for real mentors, investors, or hackathon judges.

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What we built

PitchFight AI is an AI founder pressure arena where student builders:

1. Enter a startup idea (quick pitch, advanced form, or voice pitch)
2. Get an AI-structured founder briefing
3. Choose a judge persona and difficulty mode
4. Survive multi-round pitch Q&A in a battle-style UI
5. Enter an optional deal-style pressure round
6. Receive a scorecard with coaching and a retry path for the weakest answer

Stack: Hugging Face Spaces · Gradio Server (app.py) · custom HTML/CSS/JS frontend · NVIDIA Nemotron API (backend-only) · ffmpeg for audio · optional MongoDB persistence.

The browser talks only to /api/*. Model calls never run in the frontend.

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Why this exists

Most student founders rehearse slides alone or with friends who are too nice. The first hard questions usually come from a real panel — and that is when answers fall apart.

We wanted a low-friction place to practice under pressure before that moment. The goal is coaching and repetition, not pretending an AI judge is a substitute for human feedback.

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Product decisions

• Arena, not chatbot. Flow is staged: landing → briefing → battle → deal → scorecard. It should feel like stepping into a fight, not opening a generic assistant.
• Structured briefing first. Raw ideas get structured before the judge attacks, so users practice defending a clear narrative.
• Personas + difficulty. Skeptical VC, technical judge, and hackathon judge modes change tone and pressure without swapping the whole product.
• Scorecard over vibes. Output is actionable: what landed, what was weak, what to rewrite — not just “good job.”
• Voice as an entry path, not the whole app. Voice pitch and voice answers are supported where enabled, but text mode stays fully usable.

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AI judge design

Judges are prompt-driven personas routed through a single backend model layer. Design choices:

• Follow-up questions, not monologues. Each round pushes on claims, gaps, and vague traction.
• Persona-specific pressure. VC mode leans market and defensibility; technical mode probes AI necessity and feasibility; hackathon mode focuses on demo clarity and MVP.
• Attack tags / rubric hooks (in config) keep scoring and feedback aligned with what was actually said, not generic startup advice.
• Retry weakest answer. After the scorecard, users can rework one weak response — closer to coaching than a one-shot grade.

This is simulated judgment for practice. Real panels bring context, chemistry, and standards no API can fully replicate.

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NVIDIA Nemotron usage

• API: https://integrate.api.nvidia.com/v1
• Primary model: nvidia/nemotron-3-nano-omni-30b-a3b-reasoning
• Key handling: NVIDIA_API_KEY lives in .env locally and in HF Space Secrets in deployment — never in the frontend or git.
• Routing: core/model_router.py → core/nvidia_client.py for judge questions, structuring, scoring, rewrites, and voice-related paths where enabled.

Practical note: This is a reasoning model. It can spend tokens on internal reasoning before the visible reply. We tuned max_tokens per task so the user-facing answer stays in character and concise.

We use Nemotron as the main inference backend for the demo build. We are not claiming fine-tuning or custom model training.

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Custom UI / Off Brand decisions

We deliberately avoided default Gradio widgets for the product surface.

• Custom frontend (frontend/) — cinematic landing, briefing layout, battle stage, deal screen, scorecard.
• Game-like presentation — spotlights, arena framing, persona cards, pressure-meter styling. Pitch practice should feel memorable in a demo, not like a form filler.
• Gradio as host, not as UI. app.py serves the static frontend and REST API; Gradio is the deployment shell on Spaces.

This aligns with an Off-Brand spirit (custom experience on HF infrastructure) without claiming we built a different framework.

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Hugging Face deployment notes

• Space type: Gradio Space, app_file: app.py, custom / homepage serving frontend/index.html.
• Secrets: NVIDIA_API_KEY required; optional flags for voice, deal battle, deck critique, MongoDB.
• System deps: packages.txt includes ffmpeg for audio pipelines.
• Layout tuning: Landing and briefing screens were adjusted for the Spaces desktop viewport — including the white HF navbar above the app iframe. We use 100svh / 100dvh with safe padding and a small JS viewport sync so the hero fills the visible app area without a dead band at the bottom or content hidden under chrome.

Local dev at 127.0.0.1:7860 does not show the HF navbar; we tested both locally and in the live Space while iterating on layout.

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What worked well

• Backend-only inference kept secrets out of the browser and simplified deployment.
• Custom UI + /api/* contract made it easy to iterate on flow without fighting Gradio components.
• Phased product shape (brief → battle → deal → scorecard) reads clearly in a short demo.
• Persona and difficulty toggles give variety without multiplying model integrations.
• Honest coaching tone in prompts and scorecard copy — useful feedback without overclaiming “investor approval.”

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Tradeoffs and limitations

• Coaching tool, not truth. AI judges can be sharp but wrong, repetitive, or too harsh. Users still need humans for real validation.
• Reasoning model latency and token budget. Longer waits and token tuning are part of the tradeoff for quality follow-ups.
• Voice depends on environment. ffmpeg, browser permissions, and API availability affect voice mode reliability on Spaces.
• MongoDB persistence is optional — not every deployment enables it.
• No production-scale eval suite. We did not run formal benchmarks or user studies for this hackathon build; quality is judged by manual demo runs and iterative prompt tuning.
• Layout is viewport-sensitive. Short or embedded viewports may still need scroll on dense screens (e.g. advanced briefing); landing is tuned for HF desktop first.

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Submission evidence

Item	Link
Live Space	[Add your HF Space URL]
Demo video	[Add demo video URL]
Hugging Face blog / write-up	[Add blog or post URL]

Repo highlights for judges: app.py (API + frontend host) · core/api_handlers.py · core/model_router.py · frontend/ (custom UI)

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Not claiming

• Not claiming OpenBMB / MiniCPM
• Not claiming Modal
• Not claiming Tiny Titan
• Not claiming Off the Grid
• Not claiming fine-tuning