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.
#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?
#6028: ASR vs Multimodal LLMs: What Actually Won
Dedicated ASR plus a cleanup model is quietly beating end-to-end dictation on the hardware you already own.
#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.
#6016: What 70 Billion Parameters Actually Means
A single artificial neuron, built from scratch — then the hard part: does that neuron analogy still describe a real transformer?
#6014: Why Every AI Model Is Secretly a Decoder
Encoder, decoder, or both? The three transformer shapes, why one won, and why BERT still refuses to die.
#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.
#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.
#6010: Ten Ridiculous Wearables, Ranked by Sincerity
Brainwave cat ears, a tomato-dispensing shoulder robot, and a $3,000 sock tracker — all built with total sincerity.
#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.
#6007: What a $1M AI Inference Contract Actually Contains
The sticker price isn't the deal. Inside the commit floors, discount curves, and overage traps of enterprise AI contracts.
#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.
#6005: What's Actually Behind a Government AI Chatbot
Singapore runs open-weight models on commercial cloud. The UK built its own chatbot, then shut it down for Copilot. What's really behind civil-serv...
#6004: Nobody Can Buy Unlimited AI Inference
Every published tier, every reserved deal, every prepaid reservation — all bounded. Even Azure says it out loud: quota isn't capacity.