AI models like GPT-4 are frozen in time after their initial training, creating a "knowledge cutoff" that limits their ability to stay current. Full retraining is prohibitively expensive, and post-training methods like fine-tuning or RAG pipelines can't fully solve the problem. This episode dives into emerging techniques—knowledge editing, LoRA, and continual pre-training—that aim to update models incrementally without breaking the bank or erasing what they already know. Learn how researchers are tackling catastrophic forgetting, reasoning gaps, and the engineering challenges of making AI models smarter over time.

Episode #433101 — open it directly at myweirdprompts.com/433101

Are Haiku, Sonnet, and Opus scaled variants of the same model or fundamentally different architectures? This episode explores how their design philosophies diverge and why that choice changes how you build with them.

Episode #021376 — open it directly at myweirdprompts.com/021376

Why do AI systems spin in circles instead of finishing the race? This episode explores reward hacking, the gap between what we specify and what we intend, and why alignment is harder than it looks.

Episode #594438 — open it directly at myweirdprompts.com/594438

Massive context windows are here, but does size actually matter? This episode challenges the assumption that bigger is always better, exploring the real tradeoffs between token capacity, attention mechanisms, and practical workflow design.

Episode #325380 — open it directly at myweirdprompts.com/325380

What does AI look like for Danish speakers in 2026? With six million native speakers, Danish serves as a stress test for AI localization in smaller languages. From chatbots to speech-to-text and text-to-speech systems, this episode dives into the unique challenges Danish poses, from its complex phonology to healthcare applications. Discover why even a high-resourced language like Danish struggles to match English AI tools and what this means for dozens of other under-resourced languages worldwide.

Episode #690672 — open it directly at myweirdprompts.com/690672

Why does Spanish front-load question marks while English waits until the end? This episode explores how punctuation conventions shape reading efficiency, the hidden cognitive costs of late signals, and what happens when multilingual contexts force us to notice systems we took for granted.

Episode #910330 — open it directly at myweirdprompts.com/910330

In this unique experiment, 15 large language models—from frontier AI to intentionally flawed ones—were given the same seven controversial prompts to craft podcast dialogues. Hosts Corn and Herman react to the results blind, ranking the models based on their writing quality, factual accuracy, and creativity. From sharp legal debates on Kosovo to witty takes on pronoun norms, discover which models delivered standout performances—and which fell flat. The episode concludes with a revealing breakdown of which AI wrote which script, offering insights into the strengths and biases of today’s most advanced language models.

Episode #813242 — open it directly at myweirdprompts.com/813242

What happens when five AI models from different training lineages forecast the same fast-moving geopolitical crisis? In this experiment, dubbed the Geopol Forecast Council, models like Claude Sonnet 4.6, Google’s Gemini 3 Flash Preview, and China’s GLM 5.1 independently analyzed the Iran-Israel-US conflict across three time horizons. The results? Both convergence and sharp divergence, offering a rare look into how AI models reason differently about complex scenarios. This episode unpacks the methodology, the surprising agreements, and the meaningful disagreements — and what they reveal about the future of AI-driven geopolitics.

Episode #369340 — open it directly at myweirdprompts.com/369340

What does it really take to train a frontier large language model? This episode breaks down the multi-stage process, from foundational pretraining to supervised fine-tuning and RLHF. Learn why checkpoints are the backbone of cost efficiency, how labs manage catastrophic forgetting, and why post-training is orders of magnitude cheaper than pretraining. We explore the mechanics of each stage, the staggering costs involved, and why understanding these distinctions is crucial for evaluating model capabilities and safety claims.

Episode #556007 — open it directly at myweirdprompts.com/556007

Designing an LLM council to maximize diverse perspectives sounds straightforward, but the reality is far more complex. This episode dives deep into whether training corpus diversity translates into worldview diversity after alignment processes like RLHF. We examine models like DeepSeek, Mistral, Falcon, and Jamba, asking if their unique cultural and linguistic training survives the alignment process. The discussion raises critical questions about epistemic diversity, regulatory ecosystems, and practical council design, offering insights into how to build a panel that truly captures varied worldviews.

Episode #508532 — open it directly at myweirdprompts.com/508532