From Hackathon to Production: Building the Future of Automated Video with AI Orchestration

How spec-driven development and conversational AI transformed a weekend hackathon project into a production-ready text-to-video pipeline

The Challenge: Beyond Single-Shot Generation

The landscape of AI-powered video generation has been dominated by what I call the "vending machine" model: you input a prompt, wait, and hope the output matches your vision. If it doesn't, you start over. This transactional approach works for demos, but it breaks down when building real production systems that need consistency, control, and reliability.

During a recent hackathon, I set out to solve this fundamental limitation by building ttv-pipeline (Text-to-Video Pipeline) - an open-source orchestration framework that treats video generation not as a single AI call, but as a coordinated workflow of specialized tools working together.

The Orchestration Revolution: Building with Kiro's Spec-Driven Approach

The breakthrough came from using Kiro's spec-driven development framework to build ttv-pipeline. Rather than diving straight into code, I leveraged Kiro's three-phase approach: requirements specification, design documentation, and structured implementation. This methodology proved perfect for orchestrating multiple AI models:

• Gemini 2.5 Flash Image for consistent character generation and keyframe creation
• Google Veo 3 for video segment generation from reference images
• Image-to-image keyframe generation for maintaining character consistency across scene transitions
• Automated scene planning for narrative coherence

The key insight was treating each model as a specialized worker in a larger assembly line, with Kiro's specification framework serving as the shared interface for alignment and handoff between tools.

Technical Architecture: State Machines Over Transactions

Our pipeline implements what I call a "Generative-Conversational State Machine." Unlike traditional text-to-video tools that operate in isolation, our system maintains state across the entire generation process:

This stateful approach solves the consistency problem that plagues most AI video generation: characters maintain their identity, scenes flow logically, and the overall narrative remains coherent.

The Gemini 2.5 Flash Image Breakthrough

A critical component of our success was leveraging Google's Gemini 2.5 Flash Image (the model that dominated LMArena under the codename "nano banana"). This model's conversational editing capabilities and robust identity preservation made it perfect for our keyframe generation pipeline.

The model's ability to maintain character likeness across dramatic scene changes - what we could call "implicit runtime embedding" - eliminated the need for complex fine-tuning processes. We could generate dozens of keyframes featuring the same character in different poses, environments, and lighting conditions, all from a single reference image.

From Spec to Production: The Kiro Development Experience

What made this hackathon project successful was how Kiro's spec-driven development enabled us to add two critical new capabilities to the existing ttv-pipeline framework: a production-ready API server and advanced image-to-image keyframe generation.

The existing pipeline had the core video generation functionality, but lacked the infrastructure for real-world deployment and the consistency mechanisms needed for professional use. Using Kiro, I started with comprehensive specifications for both new components:

API Server Specification:

• RESTful endpoints for video generation requests
• Asynchronous job processing and status tracking
• Authentication and rate limiting
• Error handling and graceful degradation

Image-to-Image Keyframe Specification:

• Character consistency across scene transitions
• Pose and environment variation while maintaining identity
• Integration with Gemini 2.5 Flash Image's conversational editing
• Seamless handoff to Veo 3 for video segment generation

Kiro's autonomous implementation capabilities handled the heavy lifting of both the backend API development and the complex image-to-image pipeline integration. Its steering files maintained consistency across the multi-service architecture, ensuring the new components integrated seamlessly with existing functionality.

The Conversational Parallel: Nano Banana Meets Spec-Driven Development

There's a fascinating parallel between Gemini 2.5 Flash Image's conversational editing approach and Kiro's spec-driven development workflow. Just as "nano banana" maintains state across iterative image edits ("make it a convertible," then "change the color to yellow"), Kiro maintains context across the three phases of development: requirements → design → implementation.

Both systems solve the same fundamental problem: moving beyond transactional, single-shot interactions toward stateful, iterative collaboration. In ttv-pipeline, I've essentially implemented this conversational state machine pattern at the video generation level, allowing users to refine and iterate on their video content through natural language instructions.

Real-World Impact: The Hackathon Breakthroughs

The hackathon additions transformed ttv-pipeline from a proof-of-concept into a production-ready system. The two key innovations delivered have had immediate impact:

API Server Impact:

• Enables integration with web applications and mobile apps
• Supports concurrent video generation requests
• Provides real-time progress tracking for long-running jobs
• Handles authentication and usage analytics for commercial deployment

Image-to-Image Keyframe Impact:

• Solves the character consistency problem that plagued earlier versions
• Enables complex scene transitions while maintaining visual continuity
• Reduces generation time by eliminating the need for character re-prompting
• Unlocks professional storytelling capabilities with reliable character arcs

These advances mean the pipeline can now generate coherent 2-3 minute videos with consistent characters across dramatic scene changes - something that was impossible before the hackathon work. More importantly, it's now packaged as a scalable service ready for production deployment.

The Future of AI Orchestration

This project reinforced my belief that the future of AI development isn't about building better individual models - it's about orchestrating existing models more intelligently. The most impactful AI applications will be those that combine multiple specialized tools through thoughtful workflow design.

Key lessons from our hackathon experience:

1. Specs enable velocity: Clear specifications accelerate development by reducing ambiguity and enabling parallel work
2. Orchestration beats optimization: Combining good models intelligently often outperforms trying to build one perfect model
3. State management is crucial: Maintaining context across AI operations unlocks entirely new capabilities
4. Production thinking from day one: Designing for scale and reliability from the start prevents technical debt

Open Source and Community

I've open-sourced the entire ttv-pipeline project because I believe the future of AI video generation lies in collaborative development, not proprietary black boxes. The codebase includes:

• Complete pipeline implementation with Docker deployment
• Integration examples for multiple AI providers
• Performance benchmarking and quality assessment tools
• Comprehensive documentation and contribution guidelines

The response from the community has been enthusiastic, with contributors already extending the pipeline to support new models and use cases we hadn't considered.

Conclusion: The Orchestration Era

The hackathon taught us that the most exciting AI applications emerge not from individual model capabilities, but from thoughtful orchestration of multiple tools working in concert. By embracing spec-driven development and treating AI models as collaborative partners rather than magic boxes, we can build systems that are both powerful and reliable.

The future belongs to teams that can architect these complex AI workflows, and frameworks like ttv-pipeline are just the beginning. As AI models continue to improve, the real competitive advantage will lie in how effectively we can orchestrate them to solve real-world problems.

The ttv-pipeline project is available on GitHub, and we welcome contributions from developers interested in pushing the boundaries of automated video generation.