SCC Comets

Tess Rempel Sep, 14 2026 Artificial Intelligence

End-to-End LLM Training Pipeline: From Raw Data to Production

Learn how to build a robust end-to-end training pipeline for Large Language Models. Discover key stages from data ingestion and preprocessing to distributed training, evaluation, and production deployment.

Tess Rempel Sep, 13 2026 Cybersecurity

Safe File Uploads in Vibe-Coded Web Apps: Validation and Storage Rules

Learn how to secure file uploads in vibe-coded web apps. Discover why AI-generated code fails at validation, how to prevent path traversal, and best practices for safe storage.

Tess Rempel Sep, 11 2026 Artificial Intelligence

Real-Time Multimodal Assistants: How LLMs Process Text, Audio, and Video Instantly

Discover how real-time multimodal assistants use Large Language Models to process text, audio, and video instantly. Learn about GPT-4o vs Gemini, latency benchmarks, and implementation challenges.

Tess Rempel Sep, 10 2026 Artificial Intelligence

Benchmarking Transformer Variants for Real-World LLM Workloads

Discover how to benchmark Transformer variants for real-world LLM workloads. Compare GPT-4, Gemini, and open-source models like Nemotron on speed, cost, and accuracy.

Tess Rempel Sep, 9 2026 Artificial Intelligence

Autonomous Coding Agents: Opportunities and Risks for Production Systems in 2026

Discover how autonomous coding agents like Devin boost development speed by 3-4x but introduce significant security risks. Learn strategies to manage AI code vulnerabilities.

Tess Rempel Sep, 8 2026 Artificial Intelligence

Generative AI Strategy for the Enterprise: Vision, Roadmap, and Operating Principles

Move beyond pilot purgatory with a concrete generative AI strategy. Learn the five-phase roadmap, architectural choices like RAG, and governance principles that drive measurable ROI.

Tess Rempel Sep, 7 2026 Artificial Intelligence

Verification Inside Large Language Models: How Internal Checks Reduce Errors

Discover how internal verification techniques like self-consistency and process supervision reduce LLM errors. Learn to implement internal checks for more reliable AI.

Tess Rempel Sep, 6 2026 Artificial Intelligence

System vs User Prompts in Generative AI: Structuring Instructions for Consistent Output

Discover how system and user prompts shape AI behavior. Learn to structure instructions for consistent, reliable output in generative AI applications.

Tess Rempel Sep, 5 2026 Artificial Intelligence

Comparative Prompting: How to Ask AI for Options, Trade-Offs, and Recommendations

Stop getting vague AI answers. Learn how comparative prompting forces AI to provide clear options, trade-offs, and tailored recommendations for better decision-making.

Tess Rempel Sep, 4 2026 Artificial Intelligence

Evaluation Benchmarks for Generative AI: MMLU, MMLU-Pro, and Image Fidelity

Discover how generative AI evaluation is shifting from saturated MMLU scores to rigorous MMLU-Pro reasoning tests and specialized image fidelity metrics.

Tess Rempel Sep, 3 2026 Artificial Intelligence

Ethical Review Boards for Generative AI: Process, Criteria, and Outcomes

Discover how ethical review boards safeguard generative AI projects. Learn the process, criteria, and outcomes of responsible AI governance.

Tess Rempel Sep, 2 2026 Artificial Intelligence

Data Minimization Strategies for LLM Prompt Design

Learn how to protect user privacy in LLMs by minimizing prompt data. Discover strategies like redaction and abstraction, and understand how model size affects utility.