Welcome to the Proof of Usefulness Hackathon spotlight, curated by HackerNoon’s editors to showcase noteworthy tech solutions to real-world problems. Whether you’re a solopreneur, part of an early-stage startup, or a developer building something that truly matters, the Proof of Usefulness Hackathon is your chance to test your product’s utility, get featured on HackerNoon, and compete for $150k+ in prizes. Submit your project to get started!

​In this interview, we speak with Eachone Information Channel, the creator behind AI-Texture-Pipeline. This project introduces an open-source Web3 pipeline prototype using decentralized GPUs and permanent storage to generate high-fidelity game textures, aiming to reduce asset creation costs while exploring decentralized cloud workflows.

​What does AI-Texture-Pipeline do?

It is an open-source Web3 pipeline prototype built to leverage Nosana's decentralized GPU network alongside Arweave/Irys permanent storage for generating 4K PBR game textures. It is designed to demonstrate potential GPU cost reductions of over 70% compared to legacy Web2 cloud options. Featuring modular architecture for Unity and Unreal Engine integration, it serves as an experimental, scalable infrastructure model for next-gen game developers.

​What is your traction to date?

Through our HackerNoon technical publication, active open-source GitHub engagement, and ecosystem showcases across Nosana, Irys, and Web3 game dev Discord communities, our architecture achieves 10,000+ targeted developer impressions monthly, directly reaching Web3 builders, AI engineers, and studio leads.

​Who does AI-Texture-Pipeline serve?

It is designed for 3D game developers, Web3 studios, AI pipeline engineers, and technical creators exploring alternatives to AWS/GCP cloud overheads. It provides an experimental framework for creators generating high-resolution 4K PBR assets who require decentralized storage and verifiable cost comparisons.

​What technologies were used in the making of AI-Texture-Pipeline? And why did you choose the ones most essential to your tech stack?

AI-Texture-Pipeline leverages Nosana GPU Compute, Arweave, and the Irys SDK for decentralized processing and permanent storage. Additionally, the prototype utilizes Python, PyTorch, and Docker for machine learning execution, alongside REST APIs and SDK wrappers for planned Unity and Unreal Engine integrations. These tools were selected to test decentralized execution efficiency and eliminate reliance on single-provider cloud infrastructure.

​AI-Texture-Pipeline earned a 60.95 proof of usefulness score (https://www.proofofusefulness.com/reports/ai-texture-pipeline-nosana) - how do you feel about that? Needs reassessment or just right?

The score is an encouraging validation of our open-source prototype. Achieving a 60.95 reflects the potential utility of reducing 3D asset generation costs while addressing real-world GPU bottlenecks for independent builders.

​What excites you about AI-Texture-Pipeline's potential usefulness?

What excites me most is exploring low-cost solutions to one of game development’s most significant cost bottlenecks. Traditional cloud rendering on Web2 infrastructure imposes recurring GPU expenses. By testing a ~70% theoretical cost reduction using Nosana’s decentralized GPU compute alongside Arweave’s permanent storage, this pipeline aims to offer developers an accessible framework for 4K PBR asset creation with zero single points of failure.

​Walk us through your most concrete evidence of usefulness.

Our primary benchmark is based on internal test calculations comparing local/Nosana GPU node generation rates against standard AWS g4dn.xlarge cloud instances for 4K PBR texture sets, yielding estimated cost savings of over 70%. These preliminary benchmarks, along with execution scripts, are published in our open-source GitHub repository for reproducible testing.

​How do you measure genuine user adoption versus "tourists" who sign up but never return?

As an early-stage open-source project, we track high-intent developer metrics: GitHub repository forks, SDK clones, and active issue discussions regarding Unity and Unreal test implementations.

​If we re-score your project in 12 months, which criterion will show the biggest improvement, and what are you doing right now to make that happen?

Native engine plugins and real-world adoption. We are currently working on developing simple editor plugins for Unity and Unreal Engine to allow developers to generate and deploy decentralized AI assets directly inside their active project workflows.

​How Did You Hear About HackerNoon?

We discovered HackerNoon through its Web3, AI, and software engineering publications. The platform offers a great space for open-source builders to share early-stage technical architecture and community prototypes.

​With 10,000+ monthly targeted developer impressions and active engagement in Web3 Discord communities, how do you plan to convert this developer attention into active studio integrations?

By providing lightweight Docker setups, clear step-by-step benchmarks, and clear documentation so game development teams can independently clone, test, and audit the architecture.

​Currently operating as an early-stage prototype with zero active users, what is your primary go-to-market strategy to onboard the first cohort of 3D game studios?

Our immediate focus is targeted community outreach across open-source game dev Discord groups, DePIN developer hubs, and indie creators—providing direct technical support to developers interested in benchmarking decentralized GPU rendering.

​Considering your pipeline promises potential cost reductions and Unity/Unreal compatibility, what are the biggest technical hurdles game developers face when transitioning to decentralized setups?

The primary hurdle is Web3 complexity. We address this by wrapping blockchain interaction and decentralized storage calls inside standard REST APIs and SDKs, providing a familiar Web2-style developer workflow.

Meet our sponsors

Bright Data: Bright Data is the leading web data infrastructure company, empowering over 20,000 organizations with ethical, scalable access to real-time public web information. From startups to industry leaders, we deliver the datasets that fuel AI innovation and real-world impact. Ready to unlock the web? Learn more at brightdata.com.

Neo4j: GraphRAG combines retrieval-augmented generation with graph-native context, allowing LLMs to reason over structured relationships instead of just documents. With Neo4j, you can build GraphRAG pipelines that connect your data and surface clearer insights. Learn more.

Storyblok: Storyblok is a headless CMS built for developers who want clean architecture and full control. Structure your content once, connect it anywhere, and keep your front end truly independent. API-first. AI-ready. Framework-agnostic. Future-proof. Start for free.

Algolia: Algolia provides a managed retrieval layer that lets developers quickly build web search and intelligent AI agents. Learn more.