Development
Building software: programming, developer tools, APIs, deployment and DevOps, and building with AI (agents, MCP servers, RAG, AI coding tools). An extra channel alongside an episode's main one, so AI engineering episodes sit in both.
#6041: How CENO Moves a Web Page Into a Censored Country
A walk through CENO's actual plumbing: signed caches, volunteer bridges, and why the chain is deliberately short.
#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.
#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.
#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.
#6027: Can You Snap Two Fine-Tunes Together Like LEGO?
Two fine-tunes, one base model, and a question from Jerusalem: can you merge Hebrew code-switching with technical vocab — without retraining?
#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.
#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.
#6023: How Much Voice Data Is Enough?
Two hours of phone recordings took Hebrew code-switching from 20% to 60%. But when does more data stop mattering?
#6022: Fine-Tuning for Code-Switched Hebrew on 2 Hours of Audio
Two hours of one man's voice beat models trained on 120,000 hours — but only for code-switched Hebrew.
#6021: Why Chat Templates Break Models Silently
Every chat API flattens your messages into special tokens. Get one delimiter wrong and the model just gets quietly dumber.
#6012: Inside the Decode Loop: How LLMs Generate One Token at a Time
The model doesn't choose tokens or decide when to stop — the engine does. A step-by-step tour of one decode iteration.
#6011: Inside LLM Inference: Prefill, Decode, and the KV Cache
One 8K-token sequence on a 13B model eats 6.7GB of KV cache—while the weights are only 26GB. Here's what actually happens per request.
#6009: Vertex AI Is Now the Gemini Agent Platform
Vertex AI just became the Gemini Enterprise Agent Platform. Here's the family tree, the five products, and when to actually move.
#6008: Why Codex Writes Code for a Dead API
Models don't hallucinate your API — they quote real docs from the wrong year. Here's why, and what actually fixes it.
#6000: 6,000 Episodes and a GPU Bill: Inside My Weird Prompts
Six thousand episodes, one human in the loop, and a Modal bill that keeps growing. A behind-the-scenes update on the podcast.
#5994: Hebrew in Latin Letters: A TTS Pipeline Fix
A token classifier that finds Hebrew words written in Latin letters so TTS stops mangling them — built for under $3.
#5995: NVIDIA ASR: Parakeet, Canary, and Nemotron Explained
Whisper gives you one dial. NVIDIA gives you five. A practical map of the Parakeet, Canary, and Nemotron speech models.