Founder & CEO here at EncypherAI, focused on building trust and transparency in the age of AI-generated content.
Building EncypherAI!
- What it is: An open-source Python library designed to embed verifiable metadata directly into AI-generated text using Unicode variation selectors and cryptographic signatures.
- The Goal: To establish an open standard for AI content provenance, moving beyond unreliable detection methods and ensuring authenticity without impacting readability. We aim to provide developers and platforms with the tools needed for responsible AI deployment.
- Key Tech: Python, FastAPI, Next.js, Google Cloud Run, Railway, Cryptography (Digital Signatures), Unicode Standards.
I'm actively developing the core library, building out cloud infrastructure for verification services, engaging with potential partners, and navigating the pre-seed fundraising process.
- Advanced applications of cryptography for data integrity and provenance.
- Best practices for building and scaling Commercial Open-Source Software (COSS) businesses (inspired by models like MongoDB).
- Strategies for fostering vibrant and sustainable open-source communities.
- Integrating seamlessly with diverse LLM APIs and platforms (OpenAI, Gemini, Claude, etc.).
- The evolving landscape of AI ethics, policy (like the EU AI Act), and content authenticity standards (like C2PA).
- EncypherAI: Contributions, feedback, and ideas are always welcome! Check out our contributing guidelines.
- Integrations: Building integrations or plugins for EncypherAI within other developer tools, LLM frameworks (like LangChain, LlamaIndex), or content platforms.
- Research & Standards: Projects related to AI content provenance, digital trust, AI ethics, and the development of open standards in the AI space.
- Exploring novel applications for verifiable metadata.
- Feedback: Insights and constructive criticism on EncypherAI's approach, usability, and documentation from developers and potential users.
- Community Building: Tips and best practices for growing an active, engaged open-source community around developer tools.
- Partnerships: Connections to LLM providers, content platforms, educational institutions, or other organizations interested in implementing verifiable AI content provenance.
- EncypherAI and verifiable AI content provenance.
- Using Unicode for embedding metadata in text.
- Python development (FastAPI, backend systems, library design).
- Building full-stack applications (especially with Next.js).
- The challenges and limitations of current AI detection tools.
- Open-source strategy and dual-licensing models (AGPL + Commercial).
- Bootstrapping tech projects and micro-SaaS.
- AI ethics and responsible technology development.
- LinkedIn: linkedin.com/in/eriksvilich
- EncypherAI Website: encypherai.com
- EncypherAI Issues: github.com/encypherai/encypher-ai/issues (for project-specific questions/feedback)
- He/Him
- I built the full stack for EncypherAI's core platform and website frontend/backend myself and for a few other SaaS platforms! Also a former FIRST robotics enthusiast & Eagle Scout.