Tri-City AI Links
Prompting LLMs for Code: Patterns for Unit Tests and Refactors
Master LLM prompting for code with proven patterns for unit tests and refactors. Learn to write precise, context-rich prompts that generate reliable, secure, and testable code without endless iterations.
Multi-Head Attention in LLMs: How Parallel Processing Powers AI Language
Discover how multi-head attention powers large language models by processing language from multiple perspectives simultaneously. Learn its mechanics, benefits over RNNs, and real-world impact.
Rapid Prototyping with APIs vs Production Hardening with Open-Source LLMs
Explore the trade-offs between rapid API prototyping and production hardening with open-source LLMs. Learn cost strategies, hybrid architectures, and operational best practices.
AdamW vs Adafactor vs Lion: Choosing the Right LLM Optimizer in 2026
Compare AdamW, Adafactor, and Lion optimizers for LLM training in 2026. Analyze memory usage, convergence speed, and accuracy to choose the right tool for your pipeline.
Source Selection Policies for RAG: Balancing Relevance and Diversity
Explore how balancing relevance and diversity in RAG source selection improves AI accuracy. Learn about MMR, lambda tuning, and enterprise implementation strategies.
Roles for Vibe Coding at Scale: AI Champions, Architects, and Verification Engineers
Explore the critical roles of AI Champions, Architects, and Verification Engineers needed to govern vibe coding at scale. Learn how to balance AI speed with security and structure.
Understanding Per-Token Pricing for Large Language Model APIs: A Cost Guide
Learn how per-token pricing works for LLM APIs, why output costs more, and strategies to optimize your AI budget in 2026.
Per-Token Pricing Explained: How LLM APIs Charge You in 2026
A clear guide to per-token pricing for LLM APIs. Learn how input vs output costs work, compare provider rates, and find tips to reduce your AI billing expenses.
Accessibility in Generative AI: A Guide to Inclusive Design for All Users
Learn how to build inclusive generative AI products by integrating accessibility from the start. Discover practical strategies, ethical considerations, and tools to ensure your AI serves all users effectively.
Refactoring Sprints for Vibe-Coded Apps: Scheduling and Scope
Learn how to schedule and scope refactoring sprints for vibe-coded apps. Improve security, reduce technical debt, and maintain AI-generated code with practical strategies.
Vibe Coding Ethics: Who Is Responsible When AI Code Fails?
Explore the ethical risks of vibe coding. Who is responsible when AI-generated code fails? Learn about security vulnerabilities, legal liabilities, and best practices for safe adoption.
Scientific Workflows with Large Language Models: Hypotheses and Method Summaries
Explore how Scientific Large Language Models (Sci-LLMs) transform research workflows in 2026. Learn to generate hypotheses and summarize methods safely, avoiding common pitfalls like hallucinations and protocol errors.
