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Gradient Coach

The AI co-founder that turns your hackathon idea into a shipped repo with code, Cline tasks, and a pitch—all in 60 seconds.

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Inspiration

Every hackathon has the same tragic pattern:

  • Someone pitches a brilliant idea
  • The team spends 4–6 hours wrestling with setup, boilerplate, and docs
  • By the time real coding starts, the deadline is already looming

We kept hearing variations of the same sentence:

“If we had an extra day, this idea would have been amazing.”

Gradient Coach was born from that pain.

We asked a simple question:

What if your first teammate at a hackathon was an AI co-founder that knew the rules, the docs, the best stacks, and how much you can really ship in 24 hours?

With DigitalOcean GradientAI giving us agents, knowledge bases, and serverless inference, we realized we could build a meta-project: an AI that helps hackers win hackathons built on the same stack judges want to see.

What it does

Gradient Coach is your AI hackathon co-founder that takes you from vague idea → concrete plan → repo scaffold → pitch.

Given a rough idea and your experience level, it:

  1. Idea Shaper (Planner Agent)
  • Checks 24-hour feasibility
  • Recommends a minimal, fast-to-implement tech stack
  • Produces a time-boxed build plan and highlights high-risk areas
  1. Repo Scaffolder (Architect Agent)
  • Generates a complete repo structure (files, starter code, README, dependencies)
  • Includes TODOs so you know exactly what to implement next
  • Optionally emits a Cline task list so you can paste it into VS Code and let your AI coder run
  1. Docs Oracle (Knowledge + RAG)
  • Answers questions about MLH rules, DigitalOcean, and GradientAI usage
  • Returns concrete code snippets instead of vague explanations
  1. Pitch Generator (Pitcher Agent)
  • Takes your repo + a few bullets
  • Drafts a Devpost description and a 60-second pitch script

In short, Gradient Coach is designed to compress the usual hackathon boot-up time from hours to minutes:

[ \text{time_to_first_commit} \approx \frac{\text{hours}}{10} ]

How we built it

Architecture

We aimed for a clean, hackathon-friendly architecture:

  • Backend: FastAPI (Python)
  • Frontend: Lightweight vanilla JS + HTML/CSS
  • AI layer: DigitalOcean GradientAI (agents + serverless inference)
  • Deployment: DigitalOcean App Platform (ready for 1-click deployment)

Core Components

  1. Gradient Client Wrapper

  • A small gradient_client.py module that encapsulates calls to:

    • Chat completion endpoint (serverless inference)
    • Knowledge base / RAG queries

  • Handles auth, timeouts, and basic error handling

  1. Agents

  • HackathonPlannerAgent

    • System prompt tuned as a seasoned hackathon mentor
    • Takes idea + experience level
    • Returns JSON: feasibility, confidence, recommended stack, timeline, risks, and tactical advice

  • TechnicalArchitectAgent

    • System prompt as a senior rapid-prototyping architect
    • Uses function calling to populate a structured repo spec:
      • File tree
      • File contents with TODOs
      • Setup commands
      • Optional Cline task JSON

  • PitcherAgent (planned / partially implemented)

    • Generates Devpost copy and pitch scripts from your repo summary and outcomes
  1. Knowledge Base + RAG

  • A small curated set of:

    • MLH guidelines
    • DigitalOcean + Gradient snippets/examples
    • Common hackathon stack templates

  • Queried via Gradient’s knowledge base API so the agents can:

    • Cite realistic examples
    • Suggest practical stacks (FastAPI, React, Node, etc.)
  1. Frontend UX

  • Single-page app:

    • Textarea: “What do you want to build?”
    • Experience selector: beginner / intermediate / advanced
    • Button: “Validate Idea ✨” → calls /api/validate
    • Shows feasibility, stack, timeline, risks, and advice
    • Button: “Generate Repo 🏗️” → calls /api/scaffold
    • Collapsible view of generated files and contents

Challenges we ran into

  1. RAG signal vs noise We wanted the Docs Oracle to answer like:

“Here’s a minimal FastAPI + Gradient example” not “GradientAI is a powerful platform…”

Tuning prompts + knowledge base content to bias toward code snippets and away from generic marketing language took several iterations.

  1. Function Calling & JSON Robustness Getting the Architect agent to reliably output structured JSON that:
  • Matches our generate_repo_structure schema
  • Doesn’t hallucinate weird paths or invalid Python involved:
  • Lowering temperature
  • Adding explicit type hints in the prompt
  • Implementing simple validation and fallback behavior

  1. Cline Task Format Cline has an internal task representation that prefers certain fields (title, description, commands, files). We had to reverse-engineer and design a stable JSON format the model could hit consistently without producing invalid or incomplete tasks.

  2. Time-boxing Scope It’s tempting to build:

  • GitHub integration
  • Multi-user collaboration
  • Sponsor prize optimizers But in a hackathon setting, the hardest part was saying no and keeping the MVP laser-focused on a powerful end-to-end flow.

Accomplishments that we’re proud of

  • End-to-end flow in under a day We shipped the full journey: idea → plan → scaffold → (proto) pitch in a single hackathon window.

  • Real, runnable scaffolds The repos Gradient Coach generates are not just pretty trees; they come with:

    • Working FastAPI skeletons
    • Clear installation and run instructions
    • Minimal but real code you can run and extend

  • Meta-product resonance The most validating moment: people immediately wanted to use Gradient Coach on their own hackathon ideas. That’s the best sign you’re solving a real problem.

  • Clean integration with GradientAI We’re using Gradient the way it’s meant to be used:

    • Specialized agents
    • Knowledge bases for RAG
    • Serverless inference in the loop

What we learned

  • Hackathons are about decisions, not just code The biggest value is not writing lines of code; it’s:

    • Choosing the right tiny slice of the idea
    • Picking a stack that won’t fight you
    • Planning your time with realistic trade-offs

Agents that clarify decisions are just as important as agents that generate code.

  • RAG > giant prompts Stuffing every doc into a prompt leads to noise and token bloat. A small, curated knowledge base with targeted queries gives better, more actionable answers.

  • Structured outputs make agents feel like tools, not toys Once we moved to function calling and strict JSON schemas, Gradient Coach stopped feeling like “chat with an LLM” and started feeling like a real dev tool.

  • The best AI UX often looks deceptively simple A textarea, a button, and a results panel can hide a surprising amount of intelligence.

What’s next for Gradient Coach

We see Gradient Coach as the starting point of a full hackathon operating system:

  1. GitHub Integration
  • One-click: “Create this repo in my GitHub account”
  • Auto-commit initial scaffold and open issues from the Cline tasks
  1. Team Mode
  • Each teammate gets tasks assigned based on their experience and interests
  • The Planner agent rebalances work as tasks are completed
  1. Sponsor Prize Optimizer
  • Parse sponsor prize categories from the hackathon page
  • Suggest specific features (e.g., “add LiveKit voice demo”, “use DigitalOcean DBaaS here”) to maximize prize coverage
  1. Progress & Reality Check Loop
  • Periodically ask: “What have you actually completed?”
  • Adjust the plan to match reality and keep the demo shippable
  1. Post-Hackathon Mode
  • Help you refactor the “hack” into a maintainable project
  • Suggest next milestones, testing, docs, and deployment hardening

Gradient Coach started as a way to win one hackathon. We’d love to see it evolve into a standard tool that powers thousands of hacks—and makes “we ran out of time” a much rarer sentence.

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