Reasoning Models
227 episodes · Page 2 of 10
#6139: Which AI Writes Our Scripts Best? We Tested Five
Five models, three scripts each, one rubric. Sol won on length, Grok on voice — and the runner-up title hides a catch.
#6136: How We Chose Our New Script Writer
Ten models audition to write the show. Seven fail the censorship gate — including the workhorse that wrote three episodes in four.
#6135: How Many Runs Does Your AI Eval Actually Need?
Reruns shrink one kind of noise and leave another untouched — so where should your eval budget really go?
#6134: Cheap Tokens, Expensive Scripts
A model with worse list prices can cost less per script — if the cache hits. The bill hides in tokens, not rates.
#6133: One Rater Is Enough: Designing Daniel's Taste Test
When the ground truth is one person's taste, agreement with strangers is the wrong bar. Stability with yourself is the right one.
#6131: Judging the Judges: How AI Evaluates Creative Writing
LLM judges match human raters — but the human ceiling is low. What that means for evaluating creative writing.
#6126: Can You Train a Machine to Be Funny?
Comedy fights everything next-token prediction is built for. Turns out you can train a model to land a joke — and then measure it.
#6115: Claude Cracked the NSA's 1997 Curve Seeds
A 25-year crypto mystery solved in under 30 minutes — by finding a bit that wasn't there.
#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.
#6069: The Whole Machine: How an Episode Gets Made, Send to Publish
The complete tour of the pipeline that makes the show, stage by stage, from the send form to publishing, including the new eight-turn batched voice decode.
#6068: Chinese AI Models: Refusing vs. Reframing
New studies show Chinese AI models have shifted from refusing sensitive questions to answering them — in the Party's voice.
#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.
#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.
#6048: Under the Hood, October 2026: Seventeen Prompt Versions and Three-Times-Faster Voices
Four weeks of pipeline changes, grounded in the code: grace periods, duplicate detection, Hilbert's rules, echo deletion, and voices nearly three times faster.
#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.
#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.
#6029: One Model, Two Modes: Does AI Consolidation Hold Up?
OpenAI hides a router, DeepSeek merged two modes, Anthropic removed the knob. Is the model landscape really consolidating?
#6025: When Should Claude Code Start a New Session?
Long context windows don't fix stale context — they hide it. Why task boundaries, not token counts, are the real problem.
#6013: What Happens Inside One LLM Forward Pass
One trip through the network, layer by layer — and why a classifier needs one pass while an LLM needs hundreds.
#6006: Data Residency vs. Inference Residency: What the Law Actually Says
GDPR doesn't require local GPUs. Here's what actually drives geographically restricted AI inference — and the three layers everyone conflates.