Rutvik Acharya

Rutvik Acharya

Senior Manager - AI Engineering

About

ML/AI professional with over 14 years of experience in developing and scaling solutions in advanced machine learning, with a focus on NLP and LLMs. Skilled in managing end-to-end systems, including pipelines, model development, evaluation, and deployment. Demonstrated success in implementing advanced AI/ML techniques in production environments, while collaborating effectively with engineering, product, and business teams to drive impactful outcomes. Experience in hiring and mentoring teams for over 4 years.

Can today's AI coding agents build software that's actually ready for production? 

In this live challenge, three developers each represent a different coding agent: Claude Code, Codex, and Google Antigravity 2.0. Starting from the same React + Vite application, already connected to a live streaming LLM API, they have just 15 minutes to complete the exact same task. 

The task: Build a production-ready AI chat assistant. That means far more than getting the model to generate a response. The application must stream responses in real time, render Markdown and code blocks correctly, handle loading and error states gracefully, support file uploads, and deliver an interface that feels polished enough to ship. 

Every prompt, code change, and engineering decision happens live on stage. The audience sees both the code and the running application side by side, making it easy to follow how each coding agent approaches the same problem. Once the builds are complete, the developers come together to discuss what worked, where the agents struggled, and when human judgment still made the difference. 

The question is simple: Can today's coding agents independently build production-quality software, or are they still best used as copilots? 

WHAT YOU'LL SEE 

  • Three developers solving the exact same front-end challenge with Claude Code, Codex, and Google Antigravity 2.0.  
  • A chat application evolve from a basic text box into a polished, streaming AI interface.  
  • Live engineering decisions around component architecture, state management, streaming, Markdown rendering, code formatting, error handling, and file uploads.  
  • Every prompt, code diff, and application update shown in real time.  
  • A side-by-side comparison that makes each agent's strengths, weaknesses, and working style easy to spot. 
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