SCC Comets

Autonomous Agents Built on Large Language Models: Capabilities and Limits

Autonomous Agents Built on Large Language Models: Capabilities and Limits

Discover the real capabilities and limits of autonomous agents built on LLMs in 2026. Learn what they can do today, where they fail, and how to deploy them effectively.
Structured vs. Unstructured Pruning: Making LLMs Efficient

Structured vs. Unstructured Pruning: Making LLMs Efficient

Learn how structured and unstructured pruning differ in compressing Large Language Models. Discover when to use methods like Wanda or FASP for optimal efficiency.
Model Customization: Why Open-Source LLMs Beat APIs for Fine Control

Model Customization: Why Open-Source LLMs Beat APIs for Fine Control

Discover why open-source LLMs offer superior control over commercial APIs. Learn how fine-tuning, LoRA, and RAG enable precise customization for niche business needs.
Pilot-to-Scale in Generative AI: KPI Baselines and Post-Launch Reviews

Pilot-to-Scale in Generative AI: KPI Baselines and Post-Launch Reviews

Stop guessing if your AI pilot is ready for scale. Learn how to set strict KPI baselines, conduct rigorous post-launch reviews, and avoid the 78% failure rate of enterprise AI projects.
Token-Level Logging Minimization: Securing LLM Privacy

Token-Level Logging Minimization: Securing LLM Privacy

Discover how token-level logging minimization protects LLM privacy by scrubbing PII at the source. Learn implementation strategies, performance impacts, and regulatory benefits.
Scaling Laws for LLMs: A Practitioner's Guide to Compute-Optimal Training

Scaling Laws for LLMs: A Practitioner's Guide to Compute-Optimal Training

Discover how LLM scaling laws guide compute-optimal training. Learn why Chinchilla scaling favors balanced data and parameters, and how data quality impacts performance.
Cross-Functional Committees for Ethical Large Language Model Use

Cross-Functional Committees for Ethical Large Language Model Use

Discover how cross-functional committees mitigate risks in Large Language Model deployment. Learn composition strategies, operational frameworks, and real-world benefits for ethical AI governance.
Runtime Protections for Vibe-Coded Services: WAFs, RASP, and Rate Limits
Tess Rempel

Runtime Protections for Vibe-Coded Services: WAFs, RASP, and Rate Limits

Secure your AI-generated code with runtime protections. Learn how WAFs, RASP, and rate limiting defend vibe-coded services against common vulnerabilities.
End-to-End LLM Training Pipeline: From Raw Data to Production

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

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

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.
Benchmarking Transformer Variants for Real-World LLM Workloads

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.