KY

    Gemini Hackathon Post-Mortem: Lessons for Next Time

    Built a working voice AI demo in 7 hours. Here's what I learned about hackathon strategy.

    TECH

    Quick Context

    Night before: Reviewed past winners with teammates. Planned prediction markets project. 12am discovered track themes exist. Night prep

    Morning chaos: Car ride

    • Left JB at 7am, reached Google office 8am
    • Teammates rejected at entry (didn't register individually)
    • Niels: "FOUNDERS MODE!" - used rejection as motivation Founders mode
    • Got in eventually. Drained before we started.

    Setup failures:

    • No breakfast, no table, no seat
    • WiFi wouldn't connect → couldn't claim API credits
    • Lost first hour to logistics instead of building

    End of day failure:

    • Left only 20 mins for video recording AND deployment
    • Didn't know how to use my video tool
    • Tried to host to Cloud Run in the same 20 mins
    • Submitted with no demo video

    What I Built

    Meeting Prep Agent - Voice-triggered briefings before meetings

    "Prepare me for my next meeting"
  ↓
Research attendee (Google Search)
  ↓
Generate bio card
  ↓
Voice briefing
  ↓
"Send confirmation email?"

    Deployed to Cloud Run. Working demo. Only one who shipped.

    Vision vs Execution (40% Shipped)

    The idea was S-tier. The execution was B-tier. Here's what I planned vs what I actually built:

    What shipped:

    • ✅ Voice Output (TTS) - Google Cloud TTS works
    • ✅ Function Calling - Gemini orchestration works
    • ✅ Web Research - Serper API integration works

    What didn't:

    • ❌ Calendar Monitoring - Mock data, no real Google Calendar OAuth
    • ❌ Gmail History - Mock JSON, no OAuth
    • ❌ Bio Card (Nano Banana) - Didn't implement image generation
    • ❌ Proactive Triggers - User-initiated only, not "before you ask"
    • ❌ Send Email Action - Demo shows it, doesn't actually send
    • ⚠️ Voice Input - Web Speech API fallback, not Gemini Live

    Execution rate: ~40%

    The Bigger Vision I Had

    Not just meeting prep - a mentor/coach that:

    • Tracks your goals across relationships over time
    • Remembers what you committed to in past meetings
    • Suggests strategic asks ("You've met Sarah 4x. Ask for the intro this time.")
    • Proactively briefs you before you even open your calendar

    This is fundamentally different from Gong/Fireflies. It's a relationship operating system, not a meeting recorder.

    The problem wasn't the idea. It was 7 hours.

    Product Instinct vs Hackathon Instinct

    I kept envisioning a real user the entire time. How would someone use this? What would make them say "this is useful"? I optimized for immediate user benefit, not judge impression.

    Different game, different rules. Good to know for next time.

    Gap to S-Tier

    Here's why I ranked Tier B-:

    • Tools Used: 5 vs 25+ (Neuroflix)
    • Architecture: Single agent, vanilla JS vs Multi-agent orchestration
    • Demo: No video vs Full production video
    • Novel Tech: API wrapper vs EmergentDB's custom algorithm in Rust
    • Engineering: No tests, flat files vs TypeScript, CI/CD, Vitest

    What S-Tier Had That I Didn't

    1. Breadth over depth - Winners showcased MORE Google tools, not deeper integration
    2. Multi-agent systems - Neuroflix had 8 specialized agents (Director, Scriptwriter, Editor...)
    3. Demo video - Judges watch videos, not live demos
    4. Novel algorithm - EmergentDB invented MAP-Elites for vector search. I assembled APIs.
    5. Enterprise architecture - EarthLinks had TypeScript + tests + CI/CD

    The Takeaway

    Hackathons and products optimize for different things.

    Hackathon success factors:

    • Tool breadth over depth
    • Video polish matters as much as code
    • Demo storytelling is a core skill

    Next Time Checklist

    Before the Event (Race Day Rules)

    Like a marathon - don't wear new shoes on race day. All setup done BEFORE:

    • [ ] API credits claimed and tested
    • [ ] All tools familiar (no learning during competition)
    • [ ] Video recording tool practiced (I didn't know how to use mine)
    • [ ] Hosting/deployment pipeline ready (don't deploy on deadline)
    • [ ] Fallback data prepped in case APIs fail
    • [ ] Demo script outline drafted

    Day Of

    • [ ] Write the 60-second demo script FIRST
    • [ ] Work backwards from demo to code
    • [ ] Use ONLY familiar tools (Claude Code > AI Studio)
    • [ ] Scope down until embarrassingly simple
    • [ ] Allocate last 2 hours for VIDEO, not features

    What Wins

    • [ ] Maximize tool count (breadth > depth)
    • [ ] Multi-agent > single agent
    • [ ] Polished video > working code
    • [ ] Novel algorithm > API wrapper
    • [ ] Memorable demo moment > practical utility
    Group photo

    Resources