---
type: source
title: "I Will Win the Nvidia AI Agents Developer Contest"
description: "Cole announces he will win the Nvidia/LangChain AI agents developer contest by building an agentic, human-in-the-loop application that helps personal trainers generate personalized weekly workouts for their clients."
youtube_id: zjtDBaIPnTQ
url: https://www.youtube.com/watch?v=zjtDBaIPnTQ
slug: i-will-win-the-nvidia-ai-agents-developer-contest
published: 2024-06-09
duration: "0:06:53"
recency_rank: 191
raw: "../raw/i-will-win-the-nvidia-ai-agents-developer-contest.md"
tags: [ai-agents, human-in-the-loop, contest, langchain]
created: 2026-07-21
updated: 2026-07-21
---

# I Will Win the Nvidia AI Agents Developer Contest

In this early announcement video, Cole introduces the Nvidia and LangChain Generative AI Agents Developer Contest and his plan to win it by building an AI agent application for the personal training industry. His wife is a personal trainer who spends 30-60+ minutes per client each week building workouts, so Cole frames AI as an opportunity to save that time while keeping workouts highly personalized. He explains the contest premise, building an AI agent project using Nvidia and LangChain technologies, and its judging criteria: real-world usefulness, quality of technology integration, and quality of the submission (a 45-90 second demo plus documentation).

Cole defines an AI agent as a large language model augmented with tools to interact with the outside world (booking calendars, querying databases), and notes that when multiple agents work together toward one goal you can do far more than a bare LLM. His application takes client criteria (weight, goals, equipment access, number and length of workouts) and generates the week one day at a time: it produces a day's workout, returns it to the trainer for feedback and alternatives, revises, then moves to the next day in a loop until the full week is planned. This human-in-the-loop design is deliberate. Cole argues AI is not yet good enough to replace personal trainers, especially on accountability, so the goal is to augment trainers rather than replace them (contrasting with individual-focused apps like Dr. Muscle).

He reports the backend agent application already works, with prompting tuned from his wife's feedback and currently running on Groq with Llama. The remaining work is integrating Nvidia technologies and building a front end. He commits to livestreaming his build every day until the June 17 submission deadline to push himself and bring viewers along.

## Concepts covered

- [What Is an AI Agent](../concepts/what-is-an-ai-agent.md)
- [Agent Teams](../concepts/agent-teams.md)
- [Human in the Loop](../concepts/human-in-the-loop.md)

## Entities

- [Cole Medin](../entities/people/cole-medin.md)
- [LangChain](../entities/tools/langchain.md)
- [Nvidia](../entities/organizations/nvidia.md)
- [Groq](../entities/organizations/groq.md)
- [Llama](../entities/tools/llama.md)

## Key moments

- **[0:00:15]** Introduces the Nvidia + LangChain Generative AI Agents Developer Contest
- **[0:01:19]** Explains why he's building for personal trainers (his wife) and the time cost of manual workout planning
- **[0:02:06]** Contest premise and judging criteria (usefulness, tech integration, submission quality)
- **[0:03:06]** Defines an AI agent as an LLM augmented with tools; agents working together
- **[0:03:43]** Walks through the day-by-day workout generation loop with trainer feedback
- **[0:05:18]** Argues AI should augment, not replace, personal trainers (accountability)
- **[0:05:52]** Current stack: Groq + Llama; next steps are Nvidia integration and a front end
- **[0:06:07]** Commits to livestreaming the build daily until the June 17 deadline

## Transcript

[Raw transcript](../raw/i-will-win-the-nvidia-ai-agents-developer-contest.md)
