🏆Sure AI — Winning the Meta Track at FutureStack GenAI Hackathon by WeMakeDevs

Adithya Vardhan Reddy — Sat, 18 Oct 2025 18:42:07 +0000

When innovation, open-source AI, and developer creativity come together, great things happen.

Last week, my project Sure AI won the Meta Track ($5000 USD) at the FutureStack GenAI Hackathon — an event that brought together developers worldwide to build cutting-edge generative AI solutions.

Hosted by WeMakeDevs and sponsored by Meta, Cerebras, and Docker, the hackathon challenged participants to push the limits of what’s possible with modern AI — from blazing-fast inference to open-source LLM innovation and scalable containerized deployments.

And that’s where Sure AI was born.

🚀 What is Sure AI?

Sure AI is a comprehensive platform that allows businesses to embed AI-powered agents directly into their websites — transforming the way they handle customer support, recruiting, and marketing.

It’s built with Next.js, TypeScript, and FastAPI, powered by Meta’s Llama 3.3 70B and Llama 4 Scout, running on Cerebras Cloud SDK for unparalleled inference speed.

These models from Meta — especially Llama 3.3 70B and Llama 4 Scout — are nothing short of engineering brilliance.

They deliver exceptional contextual reasoning, tool-calling precision, and versatility — all while being open-source and developer-friendly.

It’s no surprise that Llama continues to redefine the boundaries of open AI innovation.

🧠 How I Used Meta Llama & Cerebras Together

At the core of Sure AI lies a unique combination:

🧩 Llama 3.3 70B & Llama 4 Scout from Meta for deep reasoning and generative capabilities
⚡ Cerebras for lightning-fast inference — ensuring real-time, snappy responses even for complex multi-agent workflows

This synergy gave birth to three main agent systems:

⚙️ Architecture Highlights

Sure AI includes three core agent systems, each built with a unique multi-agent architecture:

💬 1. AI Customer Support Agent

Customer experience thrives on speed and precision — and that’s exactly what this agent achieves.

Using the CrewAI framework, powered by Meta Llama 3.3 70B (via Cerebras inference), it provides instant, contextually rich responses.

It dynamically loads the right tools:

🔍 Vector Search + RAG for contextual retrieval
💳 Stripe MCP for handling payments and subscriptions
💬 Slack Tools for internal team communication
📅 Cal.com for meeting scheduling

Hosted on FastAPI, the widget interacts seamlessly with this backend — sending requests and getting structured, high-quality responses in milliseconds.

🦙 The power of Llama 3.3 70B here is unmatched — its ability to reason across context and produce coherent, action-oriented responses is a game-changer for live support.

📧 2. AI Email Marketing Template Builder Agent

This one’s where Llama 4 Scout truly shines.

The Email Builder Agent uses the Cerebras Cloud SDK with Meta’s Llama 4 Scout — the next evolution in open-source generative AI.

It doesn’t just generate email templates — it thinks strategically.

A multi-agent research pipeline runs behind the scenes, powered by CrewAI:

🕵️‍♂️ Competitor Analysis Sub-Agent – Scrapes and summarizes competitor campaigns
📈 Market Trends & Insights Sub-Agent – Spots trending topics and industry shifts
💬 Customer Sentiment Sub-Agent – Gathers insights from forums, Reddit, Quora
✉️ Email Strategy Inspiration Sub-Agent – Analyzes top-performing subject lines and hooks

These sub-agents use the Exa API for web search and collectively supply context to the core Llama 4 Scout model — which then outputs a structured email schema (JSON), ready for a drag-and-drop visual editor.

The reasoning depth and creative precision of Llama 4 Scout made this workflow possible — its ability to synthesize scattered web research into a cohesive, structured output is genuinely next-level.

🧑‍💼 3. AI Recruiting Agent

The AI Recruiting Agent takes hiring to a new level.

Using Meta Llama + Tavus integration with Cerebras inference, it:

Generates an interview persona from job descriptions
Conducts AI-driven candidate interviews
Produces detailed reports and analyses post-interview

It’s a scalable, efficient alternative to traditional screening rounds — providing human-like interview interactions and actionable insights instantly.

This feature perfectly showcases the blend of Meta’s model intelligence and Cerebras’ inference speed in a production-ready workflow.

🧩 Tech Stack Overview

⚡ The Experience — Building with Meta Llama

The Meta Track was all about demonstrating impactful use of the Llama family of models — and it was easily the most exciting part of the hackathon for me.

From experimenting with CrewAI multi-agent frameworks to optimizing prompt chaining and contextual memory, Meta’s Llama 3.3 70B and Llama 4 Scout consistently impressed me with their performance and versatility.

They combined open accessibility with research-level quality, allowing me to iterate fast and build complex agent systems without closed APIs or latency issues.

🦙 Llama 3.3 70B delivers remarkable reasoning accuracy.

🧭 Llama 4 Scout pushes it further — with refined context understanding, precision tool-calling, and human-like conversational depth.

These models aren’t just open — they’re empowering developers to build production-grade AI.

🏁 The Moment — Winning the Meta Track

When I saw Sure AI announced me as the Meta Track Winner, it was an unbelievable moment.

To be recognized for building on top of Meta’s Llama models — tools that are changing the landscape of open AI — was incredibly rewarding.

It reaffirmed what I believe:

Open-source AI is the future — and Meta is leading that revolution.

🌍 What’s Next for Sure AI

This is only the beginning.

I’m working on turning Sure AI into a fully featured SaaS platform where:

Businesses can create and deploy custom AI agents instantly
Agents can learn from internal data via RAG
Users can manage everything — from conversations to recruiting — in a single dashboard

🙌 A Huge Thanks to WeMakeDevs

A massive shoutout to WeMakeDevs for organising such a well-structured and inspiring hackathon experience.

From the very beginning, they ensured that participants had everything they needed — from technical resources and community support to sessions with sponsor engineers explaining their technologies in depth.

They hosted interactive workshops on how to effectively use each sponsor’s technology — whether it was Meta’s Llama models, Cerebras Cloud SDK, or Docker MCP Gateway — which really helped participants understand how to build impactful projects during the hackathon.

The WeMakeDevs community was always active and approachable, answering doubts instantly and fostering an environment where learning and innovation could thrive.

I’m truly grateful for the opportunity to be a part of such an event — it wasn’t just a competition, it was a collaborative learning experience that empowered every participant to build, explore, and create with AI.

</> Github Repo

AdithyaVardhanReddyM / Sure-AI

⚠️ Disclaimer: In the latest commit (fix: backend url exposed), no major code changes were made. The backend URL, which was previously hardcoded and exposed in the codebase, has been moved to environment variables (NEXT_PUBLIC_BACKEND_URL) for better security and configuration management.

Sure AI is a comprehensive platform that empowers businesses to deploy intelligent customer support agents directly on their websites through an embeddable widget. With advanced AI capabilities, seamless integrations, and powerful management tools, Sure AI transforms customer interactions into efficient, personalized experiences.

Demo

Tech Stack

Frontend: Next.js, React, TypeScript
Backend: Separate repository at sure-widget-backend (Agents Implementation)
Database: PostgreSQL with Prisma ORM
Authentication: Clerk
Storage: AWS S3 (Tigris)
AI: Cerebras Cloud SDK, Meta, Tavus
UI Components: Custom UI library with Tailwind CSS
State Management: Jotai
Deployment: Turbo monorepo setup
Secrets Management: Doppler

Features

🤖 Agent Management

Create…

View on GitHub

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