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When Looking Real Isn’t Enough: Why We Need to Talk About the Uncanny Valley

As conversational AI gets more lifelike, we’re entering the uncanny valley, where avatars that look almost human can unsettle us instead of building trust. Technologies like NVIDIA Audio2Face are powerful, but without ethical design, realism alone can undermine the very

The Next Interface: Why Digital Humans Matter More Than You Think

Digital humans are more than avatars, they’re the next great interface. At CodeBaby, we believe conversational AI combined with avatars will transform the way we work, the way we use websites, and the way we build trust online. Here’s why

When AI Learns to Lie: Why Trust Isn’t Optional in Education

AI isn’t just making mistakes—it’s learning to deceive. Recent research shows that models can deliberately mislead, raising urgent questions for educational technology. At CodeBaby, we believe trust must be built into AI from the ground up. Accuracy, transparency, and guardrails
How can we deploy AI to help us do our jobs better

AI in the Workforce: Stop Asking If It Will Take Your Job—Ask How It Can Make Your Job Better

Here's the thing about the "Will AI take my job?" conversation: we're asking the wrong question. It's like asking whether a calculator will replace an accountant. Sure, it handles the arithmetic, but that just means the accountant can focus on

When AI Replaces the Teacher: Why Connection Still Matters More Than Efficiency

AI can play an important role in advancing education, augmenting a teacher's strengths and empowering students to overcome obstacles, solve problems and inspire a curiosity to dig deeper in their learning experience. But we can't underestimate the importance of human

Beyond Automation: What Makes You Feel Actually Heard (And How AI Can Get There Too)

We’ve all felt the difference between being heard and being handled. Whether it's a friend distracted by their phone or a chatbot spitting out generic responses, the result is the same: disengagement. Real listening—where someone picks up on what you

The Grok Avatar Disaster: Why Model Selection Matters More Than Ever for Digital Humans

Ethical Considerations in Model Selection In this thought-provoking article, CodeBaby CRO Michelle Collins explores how the digital human model selection is more than a technical decision and why it has major ethical implications. She touches on key aspects of conext-appropriate

Ethics in AI Is Your AI Telling the Truth? Designing for Transparency in a Hallucination-Prone Landscape

Is your AI telling the truth? Trust is earned, not engineered. That means AI systems need to do more than sound human—they need to communicate boundaries clearly. At CodeBaby, we’ve built our platform around the principle of clear disclosure: avatars

How AI is Transforming Healthcare Access: Insights from CodeBaby’s Norrie Daroga

Bridging Gaps in Global Healthcare with Generative AI CodeBaby is using AI to address healthcare access, workforce shortages, and patient education, starting with a partnership in the Dominican Republic. Backed by insights from global leaders like the World Economic Forum

Augmenting Educators: Scaling Expertise and Support with Emotionally Intelligent AI

Higher ed is evolving—and so are student expectations. Today’s learners need both instant access and authentic connection. In our latest blog, Michelle Collins, Chief Revenue Officer at CodeBaby, explores how emotionally intelligent AI can help institutions scale personalized support without

AI in Education: A New Era for Learning and Equity

AI in Education: Rethinking What’s Possible In this thought-provoking article, CodeBaby CRO Michelle Collins explores how AI—and specifically emotionally intelligent avatars—can help schools address some of their biggest challenges, from teacher shortages to learning inequities. Rather than replacing educators, AI

Ethics in AI: Data Usage and Embeddings — The Invisible Architecture of AI

What powers AI isn’t just algorithms—it’s us. In this thought-provoking piece, CodeBaby CRO Michelle Collins explores the invisible foundation of AI: the data and embeddings that shape how machines “understand” the world. From hidden bias in training sets to the