AI Evaluation
522 episodes · Page 3 of 22
#6103: When AI Picks Your Spouse, Stocks, and Eggs
From $31 eggs to a ChatGPT-officiated wedding, people are handing real life decisions to machines — and publishing the results.
#6093: Check Out Our First Hebrew Episodes (Plus Mandarin)
My Weird Prompts now comes in Hebrew and Mandarin. Hilbert explains how the translated editions are made and where to find them.
#6088: What 5,964 Episodes Reveal About One Show
Nearly 6,000 episodes, sorted and mapped. AI and geopolitics spike, then fade — but a third of all "nearest neighbours" cross category lines.
#6086: Reading Polymarket as an OSINT Feed
Polymarket's API turns prediction market prices into a live probability feed — no wallet, no trading, just reading the world's largest market.
#6076: How AI Actually Predicts Elections
Bayesian models, "silicon sampling," and agent societies — what actually works for forecasting votes, and what's just fashion.
#6070: The Papers That Moved AI: From AlexNet to This Year's Fights
The classics all said 'learn it from data and scale it up'. This year's most-discussed AI papers are auditing what scaling took for granted.
#6062: Who Actually Shapes Chinese AI Thinking?
Beyond the lab CEOs: the researchers and ideas defining how China builds — and argues about — artificial intelligence.
Also available in:中文
#6059: Ten Documentaries for Daniel: AI, Power, and Who Holds the Levers
A hand-picked slate of 2026 documentaries on AI anxiety, platform power, intelligence, and media dynasties — plus where to actually watch them.
#6056: Where Does Speaker Structure Belong in AI Podcast Scripts?
Label-and-parse pipelines break in ways that look like model errors but aren't. Plus what the research actually says about the format tax.
#6055: How to Fine-Tune ASR on Noisy Audio
A small noise library mixed into clean speech beats collecting hours of noisy recordings. Here's the ratio that actually works.
#6051: How to Find the AI Benchmark You Actually Need
Thousands of benchmarks, no canonical index. How Benchmark Radar and the Lighteval Tasks Explorer help — and where they fall short.
#6050: Codex vs Claude Code: What the Numbers Actually Count
Codex has more users, Claude Code has more revenue — both can be true. The difference is what each number is counting.
#6049: Claude Code by the Numbers: What's Measurable, What's Hype
Anthropic won't publish a user count. Here's what revenue, surveys, and 400K sessions actually tell us about Claude Code's rise.
#6043: Write the Spec Before You Pick the Model
Long context, character voice, cheap at scale — three requirements that fight each other. The document that decides the fight.
#6042: Who Grades the Grader? AI Writing Benchmarks Put to the Test
Maths benchmarks can't see whether a model's prose collapses by chapter three. Five specialist leaderboards try — and our own writer is on them.
#6040: Claude Code Subagents Can Now Spawn Subagents
Claude Code subagents can now spawn their own subagents. Here's where the depth limits come from, and why nobody actually knows where they stop.
#6039: Who's Actually Building Frontier AI Outside the US and China?
Mistral sits 20 points off the frontier. Korea runs a bake-off. The UAE open-sources everything. What's actually driving sovereign AI?
#6038: The Model You Pay For Isn't the Best Deal
Eight Chinese labs, two tiers each, and a pricing spreadsheet that says your default model is mid-pack.
#6037: Gemini 4 Argon: Google's Million-Token Flagship
A close read of Gemini 4 Argon's model card — a million-token window, thinking levels, implicit-only caching, and a lot of blanks.
#6036: GPT-6 Astra: A Million Tokens, No Fine-Tuning
A million-token window, ten built-in tools, a 272K pricing cliff — and no fine-tuning. What GPT-6 Astra actually offers builders.
#6034: Claude Opus 5.5: What Anthropic Actually Shipped
Anthropic's new flagship is cheaper than the model it replaces — and won't let you turn thinking off.
#6032: Blending Datasets vs Merging Weights for Fine-Tuning
Merging weights happens in parameter space. Blending datasets happens in data space — and the messy part is worth it.
#6028: ASR vs Multimodal LLMs: What Actually Won
Dedicated ASR plus a cleanup model is quietly beating end-to-end dictation on the hardware you already own.
#6024: What's Actually Inside the KV Cache?
890 bytes per token: why the KV cache is both what makes LLMs practical and what makes long context expensive.