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.
#5964: Containers vs VMs for GPU Inference: What You Actually Pay
Containers cost 0.03%. Passthrough VMs cost 1%... or 36%. Same hardware, different systems — here's how to tell which you'll get.
#5963: Running One Model Across Mismatched GPUs
Tensor, pipeline, or offload? What actually happens when you run one model across several GPUs — including mismatched ones.
#5962: How Word Error Rate Lies to Us
WER is the number everyone reports for speech recognition — and the one that agrees with human judgment least. Here's what it actually measures.
#5961: What Your GPU Is Actually Waiting On
Your GPU says 100% utilized. It's multiplying zeros. Here's what the counters actually measure during inference.
#5960: There's No Such Thing as Tokens Per Second
Why one tokens-per-second number can't describe an LLM server — and which metrics actually tell you if it's working.
#5959: What RTF Actually Tells You About Speech Models
Accuracy gets the headlines, but speed decides what ships. How RTF, latency percentiles, and memory shape real ASR deployments.
#5957: The Scientific Plugins Hiding in Plain Sight
ChatGPT's plugin directory has 5,723 entries — but the real scientific AI work is happening in arXiv papers, not plugins.
#5956: Will ChatGPT Eat Your Grocery App?
MCP servers, ChatGPT apps, and a prediction that AI vendors will absorb your grocery list one micro-app at a time.
#5950: Self-Hosting Your Search Stack: SearXNG, Crawl4AI, and the Real Bill
Can SearXNG, Crawl4AI, and an open-source aggregator really replace a commercial search API? Yes — with a very large asterisk.
#5949: Inside Deep Research Agents: Claims, Citations, and the 84.7% Orchestrator Problem
Why 84.7% of citation errors originate at the orchestrator — and how a claim ledger fixes it.
#5948: LangGraph, LangChain, and Deep Agents in 2026
Runtime, framework, harness — how three LangChain layers actually fit together, and what changed in 2026.
#5946: Inside Humanity's Last Exam: 2,500 Questions AI Can't Ace
2,500 questions, ~1,000 experts, and a top score that jumped from 2.7% to 54.8% in 18 months. What is HLE actually measuring?
#5945: How a Reward Model Actually Trains an LLM
One number, no gradients, four models in the room. What a reward model actually does inside the RLHF loop.
#5944: Keeping the Rollout Engine Fed: Inside RLHF Pipelines
Most of an RLHF run is spent waiting on generation, not gradients. Here's how TRL, OpenRLHF, and verl actually differ.
#5941: Five Minutes Reading Your Dictation Prompt
A listener's speech-to-text cleanup prompt, read out in full — then stress-tested against what ASR actually gets wrong.
#5940: The Missing Middle: Content APIs and ISR for a 6,000-Episode Catalog
When your catalog is too big to rebuild on every change, what has to change? Turns out the missing middle might already exist.
#5939: Android Notifications: PWA vs Native, What You Actually Get
Push, channels, haptics, and the scheduling gap — what a PWA can and can't do that a native Android build can.
#5938: One Regex Rule Worked, One Was Impossible
A dictation app, two text-replacement rules, and the mathematical line between what regex can match and what it provably cannot.
#5937: The Fine-Tuning Career: Titles, Skills, and What It Pays
The job above the labeling layer exists — it just doesn't have one name. What it takes, what it pays, and how to sell it.
#5936: Training a Model When You Don't Know the Labels Yet
Daniel wants to extract adapter specs from his shelf — but he can't name the labels until he's already annotating.