Artificial Intelligence
Episodes whose main subject is AI itself: building with, training, evaluating, or debating AI systems. AI used only as a tool for another topic goes to that topic.
#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.
#6052: What's Actually in the Hugging Face ASR Catalog?
Four real architectural bets sit underneath thousands of Hugging Face ASR repos. Here's how to tell the bets from the repackagings.
#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.
#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.
#6047: A Human Guest on His Own AI Podcast
Daniel built an AI podcast with cloned voices — then sat down as a guest on it. What six thousand episodes taught him about prompts.
#6044: One Door for Every AI: Cloudflare MCP Portals
Cloudflare's MCP Portals put every on-prem MCP server behind one authenticated endpoint — and why service tokens can't work for ChatGPT.
#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.
#6035: Who Else Is Making AI Podcasts? A Survey
A survey of AI-hosted podcasts: one full match, a 730-episode run, a home NAS rig, and a graveyard of good tooling.
#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.
#6033: Progressive Skills Discovery: When Bigger Context Windows Aren't Enough
Bigger context windows were supposed to make skill discovery moot. Here's why the listing budget still bites at scale.
#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.
#6030: Who Do You Actually Hire to Train a Model?
Research engineers and data engineers are diverging. Here's who you actually hire to train a model — and why the org chart keeps changing.