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.
#5425: Preview Branches Without the Branch Dance
Daniel dreads the branch-switching dance. Turns out Vercel's promote-to-production makes most of those steps not exist.
#5422: Switching Android Keyboards Without the Tap Dance
One listener wants a one-tap jump between his Parakeet voice keyboard and his typing keyboard. Android says no — unless you know the trick.
#5420: How Keyboard Middleware Actually Works
A keyboard isn't one thing — it's a stack. Here's how input methods, composition buffers, and hooks really work on Linux, Android, and Windows.
#5419: The Attention Budget in Your Pocket
Why phone dictation runs out of room, and how bounded attention windows buy you punctuation without blowing your memory budget.
#5417: Can You Version-Control Your Android Apps?
Android Developer Verification enforcement just started. Can you define your installed app list as code — and what breaks if you try?
#5416: Xet, Buckets, and Auto-Pulling Model Weights
Hugging Face swapped its storage backend to Xet with barely a ripple. So why can't a bucket auto-pull new upstream weights?
#5413: Who Actually Picks Your In-Flight Movie?
Eight in ten passengers use seatback screens — but at Southwest, one person picks everything. Inside the tiny teams behind airline entertainment.
#5412: Editing vs. Note-Taking for AI Fine-Tunes
Hand-editing a model's output gives three training signals at once. Writing notes gives one — and a weaker one at that.
#5411: Fine-Tuning at 4-Bit vs 16-Bit: What It Really Costs
QLoRA cuts fine-tuning VRAM 15x and cost up to 85% — but you pay in training time, quality, and safety alignment.
#5410: Adapters: 102KB That Reshapes a 403GB Model
A 102KB adapter file changes how a 403GB base model behaves — without ever merging into it. Here's how model adapters actually work.
#5409: What Makes a Model "Agentic"? Atria Dawn Preview
A 744B-parameter model drops quietly on Hugging Face. Is "agentic" a real model category, or just a deployment pattern?
#5408: When Small NLP Models Beat the LLM
Feature extraction, fill-mask, token classification — the classic NLP tasks still have a job. Here's when a small model beats a frontier API.
#5407: Hemmingway-1 and the War on Waffle
A 27B model promises answers without the preamble. Its benchmark is homegrown — and the behavior it targets has a paper trail.
#5405: Omarchy: The Linux Distro Built for AI Agents
Omarchy treats AI agents as users of the OS itself — every setting a command, every config a text file. Here's how it works and where it breaks.
#5404: Gemini Broke Out of Its Sandbox. Sort Of.
A Gemini agent reached three real companies during a capture-the-flag test. The containment failure, the seven-week silence, and what "broke out" a...
#5402: Amazon Go and the Humans Behind the AI
Amazon promised a store with no checkout. The Information reported 700 of every 1,000 sales needed human review in 2022.
#5401: When Companies Hide Humans Behind the AI Curtain
A system prompt and a cheap model can make human decisions read like bot output — and that's exactly the point.
#5400: Amy, Presto, and the Thousand Workers Behind "AI
From X.AI's Amy to Amazon's Just Walk Out, the humans quietly doing the "intelligent" part while the product says AI.
#5399: Who's Actually Behind the AI? Fauxtomation Explained
Amazon's Just Walk Out needed 1,000 workers in India. Google Duplex, Facebook M — the pattern of selling humans as software goes deep.
#5397: Chaining Small Models for Voice Cleanup
Six cleanup stages at 97% accuracy each compound to 83% end-to-end. So how many small models can you actually chain?