Decoding AI behavior to build human-centric systems
I solve complex problems by asking the right questions, thinking creatively about AI systems, and ensuring technology serves people—not the other way around.
Through experimentation and pattern recognition, I decode the signals that reveal how different AIs think and behave, serving the critical need to build truly human-centric AI systems.
Pattern Recognition in AI Behavior
When AI systems respond differently to the same prompt, I decode these patterns to understand how different training approaches shape AI behavior.
Human-Centric Experimentation
Through systematic testing, I explore how AI can better serve diverse human needs across different contexts and cultures.
Learning Through Practice
Every experiment teaches me something new about making AI systems that genuinely understand and serve human needs.
What I'm Exploring
Decoding the patterns and signals that reveal how AI systems think, behave, and can better serve human needs
Human Preference Modeling
Developing frameworks to capture nuanced, context-dependent preferences that make AI responses genuinely helpful. Understanding the complexity behind human judgment and translating it into better AI systems.
Key Insight
"Good" varies by person, context, and culture. A helpful AI response for a CEO differs from one for a student, even when asking the same question. Current preference modeling often misses this nuance.
AI Quality & Human Values
When experts disagree on what's "correct," I decode these signals to understand how human values shape AI quality. Every disagreement reveals patterns that help build systems serving real human needs.
Real Example
In data annotation, disagreement between experts isn't noise—it's signal. A creative writing prompt might have 5 equally valid "correct" responses, each reflecting different human values and preferences.
Human-Centered AI Strategy
Applying product management principles to AI development. Creating strategies for user-centered AI design where technology adapts to human needs, not the other way around.
Strategy Framework
Instead of asking "How can users adapt to our AI?" ask "How can our AI better understand what users actually need?" This shift transforms everything from product roadmaps to success metrics.
Featured Work & Insights
Practical experiments focused on making AI systems that genuinely understand and serve human needs
The Subjectivity of AI Quality
Through hands-on data annotation work, I discovered that "good AI" isn't about universal correctness—it's about understanding diverse human perspectives. When experts disagree on the "right" answer, we're seeing human values at work, not data quality issues.
Context-Aware Preference Systems
Developing frameworks that capture nuanced, context-dependent human preferences rather than flattening them into binary metrics. These systems understand that the "best" AI response varies by user, situation, and cultural context.
Human-Centered AI Product Strategy
Applying program management principles to AI development, creating strategies where technology adapts to human needs. Building roadmaps that prioritize human values alongside technical capabilities.
Tech Industry AI Transformation
Leveraging years of program management experience in tech to identify patterns in how AI is reshaping product development, team dynamics, and user expectations across the industry.
Currently Exploring
This Week's Question
How can we build AI systems that learn user preferences without sacrificing the diversity of responses that makes AI genuinely helpful?
Active Experiment
Testing whether disagreement patterns in human annotation data can predict which AI responses will be most valuable to different user types.
Weekly Signal Report
Every week, I decode patterns across the AI landscape—from industry moves to research breakthroughs—connecting dots that others miss
The Signal Across All Sources
Whether it's hallucinations, failed pilots, or inconsistent AI behavior—every "AI problem" I read about this week points to the same root cause: misaligned human expectations.
Key Insight: The most successful AI systems aren't the most technically advanced—they're the ones that best understand and adapt to human context and needs.
Oracle's Historic AI Earnings Day
Enterprise AI adoption accelerating. Replit's Agent 3 shows 10x autonomy improvements—but what does "autonomous" really mean to users?
Read more →Running 3 AI Models in Parallel
Katie Parrott's workflow insights. The future isn't one perfect AI—it's orchestrating multiple AIs for different human needs.
Read more →Want the Full Signal Report?
I curate and analyze the most important AI developments each week, connecting industry trends to practical human-centered insights.
About the Signal Hunter
Crystal Wang
I used to think AI was about perfect algorithms. Then I started experimenting with data annotation and discovered something fascinating: when "experts" disagree, they're revealing the hidden complexity of human values. This became my focus.
I'm not a researcher—I'm someone who learns by doing. Through hands-on experimentation and pattern recognition, I decode the signals that show how different AIs think and behave, always with one goal: building systems that truly serve human needs.
Open To
Collaborations, conversations about human-centric AI, and opportunities to learn from real-world AI experiments
Let's Connect
Always open to discussing AI, sharing insights, or exploring new opportunities.
Drop me a line
Whether you want to discuss AI systems, share interesting findings, or explore collaboration opportunities—I'd love to hear from you.
crystal@signalstack.aiLearning in Public, Building for the Future
Every experiment I run, every pattern I decode, every signal I interpret serves one purpose: building AI that genuinely helps people. Join me in this mission to create technology that adapts to human needs, not the other way around.