SCC Comets

Tess Rempel Oct, 19 2026 Artificial Intelligence

Vibe Coding for Marketers: Building Lead Capture Apps with No-Code

Discover how marketing teams use vibe coding and no-code platforms to build custom lead capture apps. Learn realistic timelines, top tools like Lovable, and security best practices.

Tess Rempel Oct, 18 2026 Artificial Intelligence

What Makes a Language Model 'Large': Beyond Parameter Counts

Discover why parameter count alone fails to define 'large' language models. Learn about emergent abilities, Virtual Logical Depth, and how architecture impacts reasoning capabilities in modern LLMs.

Tess Rempel Oct, 17 2026 Software Architecture

Design Reviews for Vibe-Coded Features: Using ADRs and Architecture Boards

Vibe coding speeds up development but risks architectural rot. Learn how Architecture Decision Records (ADRs) and Architecture Boards provide the governance needed to keep AI-generated code maintainable and aligned with system goals.

Tess Rempel Oct, 16 2026 Artificial Intelligence

Layer Normalization and Residual Paths in Transformers: Stabilizing LLM Training

Discover how Layer Normalization and residual paths stabilize LLM training. Learn why Pre-LN and RMSNorm outperform Post-LN, and get practical tips for avoiding common pitfalls.

Tess Rempel Oct, 15 2026 Artificial Intelligence

Generative AI Careers: Essential Roles, Skills, and Certifications for 2026

Discover the essential roles, skills, and certifications for Generative AI careers in 2026. Learn which credentials matter, from entry-level foundations to advanced AWS and Databricks certifications.

Tess Rempel Oct, 14 2026 Artificial Intelligence

Vibe Coding Ethics: Who Owns the Bugs in AI-Generated Code?

Explore the ethical dilemmas of vibe coding, where AI generates code and humans take the blame. Learn who is responsible for security flaws, the risks of technical debt, and how to maintain accountability in AI-assisted development.

Tess Rempel Oct, 13 2026 Artificial Intelligence

Evaluation Datasets for Domain-Specific LLM Fine-Tuning

Learn why generic benchmarks fail for specialized LLMs and how to build effective evaluation datasets. Discover sourcing strategies, key metrics, and pitfalls.

Tess Rempel Oct, 12 2026 Artificial Intelligence

Governance Policies for Bias Management in Large Language Model Programs

Discover how to implement robust governance policies for managing bias in Large Language Model programs. Learn about lifecycle approaches, technical controls like tokenization, and key fairness metrics.

Tess Rempel Oct, 11 2026 Artificial Intelligence

Accessibility Risks in AI-Generated Interfaces: WCAG and Real-World Failures

Discover why AI-generated interfaces often fail WCAG standards and how to fix common accessibility risks. Learn from real-world failures and best practices.

Tess Rempel Oct, 10 2026 Artificial Intelligence

Capacity Planning for Seasonal Peaks in Large Language Model Usage

Learn how to forecast and manage LLM seasonal peaks using predictive scaling, token-aware metrics, and workload segmentation to maintain SLAs and cut costs.

Tess Rempel Oct, 9 2026 Artificial Intelligence

Self-Consistency Decoding: Boosting LLM Reliability with Multi-Sample Reasoning

Learn how self-consistency decoding boosts LLM reliability by generating multiple reasoning paths and using majority voting. Discover implementation tips, limitations, and when to use this technique for complex tasks.

Tess Rempel Oct, 8 2026 Artificial Intelligence

Prompt Robustness: Handling Noisy Inputs in LLM Systems

Discover why clean test data fails in production and how to build resilient LLM systems. Learn about prompt robustness techniques like MoF and RoP.