Tri-City AI Links
COPPA and Generative AI: Navigating Children's Data Privacy Rules
Learn how the 2025-2026 COPPA updates change data collection for Generative AI. Discover new rules on parental consent, biometrics, and data retention to avoid FTC penalties.
MoE Architectures: Balancing Cost and Quality in Large Language Models
Explore the trade-offs of Mixture-of-Experts (MoE) in LLMs. Learn how sparse activation reduces compute costs while increasing memory demands for better AI scale.
Building PII Detection and Redaction Pipelines for LLMs
Learn how to build PII detection and redaction pipelines for LLMs using hybrid Regex/NER methods and tools like Microsoft Presidio to ensure data privacy.
Multimodal Evolution in Generative AI: 3D, Haptics, and Sensor Fusion
Discover how AI is evolving from late fusion to unified architectures. We explore the rise of 3D, haptics, and sensor fusion in 2026.
Bias in Generative AI: How Training Data, Selection, and Algorithmic Design Shape Outcomes
Explore how training data selection and algorithm design drive bias in generative AI. Learn about real-world impacts, mitigation techniques like the MIT method, and practical steps to reduce discrimination.
Red Teaming Prompts for Generative AI: Finding Safety and Security Gaps
Learn how to identify and fix safety gaps in generative AI using red teaming strategies. Covers prompt injection, automation tools, and regulatory compliance.
Risk and Controls for Generative AI: Policies, Approvals, and Monitoring Strategy
A comprehensive guide to managing risk and controls for generative AI in 2026. Covers NIST frameworks, ISO certifications, policy enforcement, and continuous monitoring strategies.
Beyond CRUD: Vibe Coding Complex Distributed Systems
Explore how vibe coding transforms distributed systems development in 2026. Learn about AI tools, governance strategies, and real-world risks beyond simple CRUD apps.
Mastering Dependency Management in Vibe-Coded Apps: Upgrade Safely
Learn how to manage software dependencies in AI-generated apps safely. Avoid breakage during upgrades with practical workflows, version pinning strategies, and audit techniques.
Supervised Fine-Tuning for Large Language Models: A Practitioner’s Playbook
A practical guide to Supervised Fine-Tuning for LLMs. Learn data prep, tools like Hugging Face TRL, and avoid common pitfalls like catastrophic forgetting.
Scaling Open-Source LLMs: Hardware, Serving Stacks, and Playbooks for 2026
Learn how to scale open-source LLMs in 2026 with the right hardware, serving stacks like vLLM, and a strategic playbook for enterprise deployment.
Ensembling Generative AI Models: How Cross-Checking Outputs Cuts Hallucinations by Up to 70%
Ensembling generative AI models by cross-checking outputs reduces hallucinations by up to 70%. Learn how combining multiple LLMs cuts errors in healthcare, finance, and legal applications - and when it’s worth the cost.
