Seattle Skeptics on AI

Tamara Weed, Mar, 29 2026

Explore why AI hallucinations happen and learn practical strategies like RAG and RLHF to reduce factual errors in generative systems.

Tamara Weed, Mar, 28 2026

Traditional metrics like BLEU fail to capture LLM meaning. Learn why semantic metrics like BERTScore and LLM-as-a-Judge provide accurate quality assessment for modern AI deployments.

Tamara Weed, Mar, 27 2026

Discover how vibe coding transforms global team productivity by turning natural language into executable code. Learn about real-world use cases, velocity gains, and infrastructure needs.

Tamara Weed, Mar, 26 2026

Learn how positional encoding solves the word order problem in Transformers. We explore absolute, relative, and rotary methods, recent research findings, and future trends.

Tamara Weed, Mar, 25 2026

Explore how Large Language Models transform enterprise knowledge management by turning static documents into dynamic Q&A systems. Learn about RAG architecture, security challenges, and implementation costs.

Tamara Weed, Mar, 23 2026

Learn how memory planning techniques like CAMELoT and Dynamic Memory Sparsification reduce OOM errors in LLM inference by 40-60% without sacrificing accuracy - and why quantization alone isn't enough for long-context tasks.

Tamara Weed, Mar, 23 2026

Memory planning techniques like CAMELoT and Dynamic Memory Sparsification let LLMs handle long contexts without OOM crashes-cutting memory use by 50% while improving accuracy. No more brute-force GPU scaling needed.

Tamara Weed, Mar, 22 2026

Moving from an LLM pilot to production requires more than technology-it demands strategy, governance, and phased rollout. Learn how top enterprises avoid costly mistakes and scale AI effectively.

Tamara Weed, Mar, 21 2026

Scientific Large Language Models are transforming research by accelerating literature review, automating experimental design, and connecting cross-disciplinary insights-but they come with serious risks. Learn how they work, where they succeed, and why human oversight is still essential.

Tamara Weed, Mar, 20 2026

Secure generative AI development requires rethinking secrets, logging, and testing. Learn how prompt injection, AI-BOMs, red-teaming, and short-lived credentials protect your models from emerging threats in 2026.

Tamara Weed, Mar, 18 2026

Databricks AI red team uncovered critical vulnerabilities in AI-generated game and parser code, revealing how prompt injection and data leakage can bypass traditional security tools. Learn how to protect your systems.

Tamara Weed, Mar, 17 2026

Ensembling generative AI models by cross-checking outputs reduces hallucinations by 15-35%, making AI safer for healthcare, finance, and legal use. Learn how majority voting, cross-validation, and model diversity cut errors-and when it’s worth the cost.