The Political Compass Test is the go-to tool for measuring political bias in large language models — but a growing body of research suggests it's fundamentally broken. This episode unpacks why the PCT can mask bias rather than reveal it, then walks through four better alternatives: IssueBench's open-ended prompt approach, Stanford's perception-based user study, OpenAI's granular five-axis internal evaluation, and UT Austin's moral foundations framework. Along the way, we explore why measuring bias requires a reference point — and why picking what counts as "neutral" is itself a political act.

Episode #977719 — open it directly at myweirdprompts.com/977719

Everyone argues about whether Chinese LLMs are censored, but almost no one asks how we actually know. This episode unpacks the validated benchmarks—CHiSafetyBench, SafetyBench, ChineseSafe, FLAMES, JailBench, and the PNAS Nexus longitudinal study—that researchers use to measure political refusal. We explore the three different things "censorship" can mean, why multiple-choice tests inflate safety scores, how the CAC's Clear and Bright campaign drove refusal rates above 98%, and the growing arms race between models that produce evasive responses and the detectors trying to catch them. If you want to understand the measurement itself—not just the headlines—this is the episode.

Episode #072990 — open it directly at myweirdprompts.com/072990

Frontier models score poorly on cultural benchmarks, but worse: they cheat by pattern-matching culture names. This episode unpacks the methodologies that expose the shortcuts and what they reveal about AI's understanding of human culture.

Episode #283845 — open it directly at myweirdprompts.com/283845

What actually happens inside a neural network when it learns from its mistakes? This episode breaks down backpropagation — the algorithm that computes gradients for every weight in a network by propagating error signals backward through the same connections that carried data forward. We walk through a concrete example using the chain rule, explain the credit-assignment problem that kept neural networks shallow for decades, and trace the history from Rumelhart, Hinton, and Williams's landmark 1986 paper to the vanishing gradient crisis that nearly killed deep learning. Along the way, we cover reverse-mode automatic differentiation, why caching forward-pass values is essential for efficiency, and how solutions like ReLU activations and Xavier initialization finally made deep networks trainable.

Episode #955898 — open it directly at myweirdprompts.com/955898

The FAA's own data shows pilots aren't hand-flying enough to stay sharp—and the current regulatory floor only requires three landings every 90 days, none of which need to be manual. This episode examines the automation dependency problem exposed by Air France 447 and Asiana 214, the specific regulatory gaps under FAA and EASA rules, and what airlines like Lufthansa, Delta, and Cathay Pacific are doing beyond the minimum. We also cover the FAA's unusually blunt January draft advisory circular on manual flying proficiency, the tension between fuel efficiency and manual skills, and whether pilots trained entirely on glass cockpits have the same baseline stick-and-rudder instincts as the previous generation.

Episode #435530 — open it directly at myweirdprompts.com/435530

The needle-in-a-haystack benchmark is saturated — every frontier model hits 99% on it. But that doesn't mean they can actually reason across long documents. This episode explores four benchmarks that replaced it: RULER, BABILong, NoCha, and Michelangelo. We break down why models that ace million-token retrieval tests collapse at 11,000 tokens on multi-hop reasoning, and what this gap between claimed and effective context windows means for anyone relying on AI for long-document analysis.

Episode #243292 — open it directly at myweirdprompts.com/243292

Almost every model release blog post you've read has a statistical problem. When OpenAI, Anthropic, or Google claims their new model beats the previous one by two points on MMLU, that difference is often well within the noise floor. This episode gets into the weeds on power analysis, McNemar's test for paired evaluations, bootstrapped confidence intervals, and why the decimal-place precision in benchmark tables is a tell that something's wrong. We also break down the math behind Chatbot Arena's Elo ratings and explain why the rankings people obsess over may be essentially meaningless. If you want to understand what's actually happening under the hood of LLM evaluations — and why most public benchmarking is statistically indefensible — this is the episode for you.

Episode #743307 — open it directly at myweirdprompts.com/743307

Most tool-calling evaluations hide more than they reveal. This episode breaks down three fundamentally different benchmarks — the Berkeley Function Calling Leaderboard, tau-bench from Sierra Research, and Nexus from Nexusflow — and explains what each one actually measures versus what it misses. BFCL's AST evaluation catches structural errors but is blind to semantic wrongness. Tau-bench grades on final database state instead of tool-call sequences, revealing reliability gaps that single-shot scores hide. Nexus exposes how models collapse on long-tail, specialized APIs they've never seen. Then we go deeper into the production failure modes no benchmark tests: hallucinated tool names, parallel call ordering errors, schema drift across model versions, and sycophantic confirmation of wrong arguments. If you're building agents that call tools in production, this episode explains why leaderboard numbers are dangerously incomplete.

Episode #739240 — open it directly at myweirdprompts.com/739240

An opinionated architectural shootout of four major LLM evaluation harnesses — Inspect, Promptfoo, DeepEval, and Braintrust — stress-tested on multi-turn conversations, tool-using agents, async execution, and CI integration. No hedging: we pick winners for research labs, startups, and enterprise teams.

Episode #108034 — open it directly at myweirdprompts.com/108034

Satellite imagery isn't just for spies anymore. Industries from agriculture to insurance are scrambling to hire analysts who can turn pixels into profits—using tools like QGIS, Planet Labs data, and Python’s geospatial stack. This episode maps out the hottest job markets (spoiler: John Deere paid $300M for this tech), the surprising crossover between war zones and soybean futures, and the exact skills that command six-figure salaries. Learn how Ukraine rewrote the rules and why free Sentinel data unlocked a $134B industry.

Episode #288719 — open it directly at myweirdprompts.com/288719