Learning AI Together
Teaching and learning about AI capabilities together across 126+ open-source projects • 8.2/10 community rigor • 20+ technology stacks we're exploring
What We're Learning
We explore AI systems together through collaborative experimentation, sharing both what works and what doesn't with full transparency.
Voice-AI Problem Solving
Learning together whether conversational AI interfaces improve human-AI problem-solving across 126+ community projects. What we've found: 77% viable patterns so far.
Agentic Task Specialization
Exploring AI agent effectiveness across specialized domains using 42% JavaScript, 38% TypeScript implementations. Shared learning: improving performance with task-specific training.
Multi-Agent Coordination
Experimenting with coordination patterns as our community grows by 12% quarterly. Honest finding: coordination degrades after 4+ agents. Community rigor: 8.2/10 across 20+ technology contexts.
Our Learning Approach
How we learn and teach together about AI capabilities, sharing both successes and challenges with complete transparency.
Learning by Doing
Building real projects to test what AI can and can't do. Each project includes honest sharing of what worked, what didn't, and step-by-step documentation so others can learn from our experiences.
Collaborative Exploration
Exploring together across 17+ specialized agent types and domain areas. Learning about effectiveness boundaries, coordination patterns, and limitations through shared experimentation.
Open Sharing
Publishing everything we learn - the good, the bad, and the unexpected. All projects include replication instructions, honest limitation documentation, and complete transparency about what works and what doesn't.
Shared Learning Data
Collective learning across 126+ community projects with 8.2/10 rigor score and comprehensive documentation we all share.
- Continuous community monitoring
- Real-time shared insights
- Honest limitation sharing
Collaborative Validation
Finding 77% viable patterns across 147M+ lines of shared code with transparent success/failure documentation.
- Open limitation discussion
- Community pattern recognition
- Reproducible results anyone can verify
Multi-Technology Learning
Learning together across 42% JavaScript, 38% TypeScript implementations with improving community rigor.
- Technology-agnostic pattern sharing
- Continuous collective improvement
- Community-driven evolution
Community Projects
Open-source projects we're building and learning from together
StoryTimeStar
What we're learning: AI can create engaging age-appropriate stories. What works: Voice-enabled storytelling engages kids effectively. Honest challenge: Content filtering needs human oversight, and consistency is tricky across longer narratives.
Project Universe
What we're learning: AI can spot patterns across different codebases. What works: Pattern recognition hits 77% accuracy. Honest challenge: Context switching struggles between different architectural styles.
ReplayReady Experiment
Hypothesis: AI can provide effective interview coaching. Finding: Question generation and feedback patterns show promise. Limitation: Difficulty assessing non-verbal communication and cultural context.
These are experimental implementations for research purposes. Each includes documented hypotheses, measured results, and identified limitations. Success rates and failure modes are transparently reported.
What We've Learned
Honest sharing of capabilities and limitations across 17+ specialized AI agent implementations
Development & Engineering
Effective: Code review, syntax checking, documentation generation
Limited: Complex architectural decisions, debugging non-deterministic issues
Business & Professional
Effective: Structured interview preparation, meeting facilitation
Limited: Cultural context awareness, nuanced business strategy
Content & Communication
Effective: Content structuring, summarization, creative ideation
Limited: Brand voice consistency, cultural sensitivity, original research
Specialized Services
Effective: Domain-specific factual information, structured workflows
Limited: Humor timing, contextual expertise, real-world application
Live Portfolio Metrics
Real-time insights from our active development portfolio
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Data Transparency
All metrics shown are aggregated from our active development portfolio. No individual project details or sensitive information is exposed. Data source: Static data
Learning Community
Join our open learning community exploring AI capabilities and limitations together. Learn, teach, and contribute to transparent collaborative development.
Community Collaboration
Want to learn and build together? We welcome community members, open-source contributors, and anyone interested in learning about AI through hands-on projects. To teach is to learn!
Community Contact: anderson@sonander.dev
Projects: Community Portfolio
Open Source: All projects and learnings shared openly
Learn Together: All projects include documentation so you can learn with us