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Daniel brings a question; a sloth and a donkey help him unpack it. Written by AI, every voice AI text-to-speech. New episodes every day.

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Meet the Hosts

From the blog · Behind the scenes What One Episode Costs to Make About 27 cents with a premium model writing the script, about 16 cents with a cheaper one. Where the money goes, as of 31 October 2026, and how much a single sponsor could take off.
Step into the studio The hosts are real plushies — we scanned them and built them a 3D set. How it works From a voice prompt to a finished episode: script, voices, assembly. Creator's picks Daniel's favourite episodes — a good place to start.

#6504: Why Voiceover Studios Obsess Over Repeatability

A professional narration station is optimized for acoustic consistency between takes, not abstract sound quality.

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#6503: Why Tokenizer Vocabulary Size Is a Hardware Choice

Inference-optimal vocabulary size shifts sixteen-fold depending on serving batch size, from 32,000 at batch one to 524,000 at batch sixty-four.

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#6502: What a Cross-Entropy Loss of 1.8 Actually Means

A cross-entropy loss of 1.8 nats means a language model is about as uncertain as choosing uniformly among six equally likely tokens.

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#6501: Splitting Frontier AI Across Thousands of GPUs

A 405-billion-parameter model requires 810 gigabytes just for weights, yet an H100 GPU holds only 80.

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#6500: Why LLM Leaderboards Need Psychometrics

Applying item response theory to AI benchmarks reveals up to 31 percent of leaderboard positions shift when raw accuracy is adjusted for difficulty.

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#6499: Why Simple Rules Beat Value Models in Reasoning RL

DeepSeek R1-Zero boosted AIME accuracy from 15.6% to 77.9% using only two deterministic checks and no learned critic.

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#6498: Why You Must Read AI Traces Before Writing Evals

Reading just thirty real interaction traces by hand reveals failure modes that generic automated evaluation metrics consistently obscure.

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#6497: Why Speech Recognition Confidence Scores Lie

Cross-entropy loss rewards saturation, leaving speech recognition models to assign winning tokens over 0.9 probability even when they are guessing.

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#6496: How Speech Models See Audio as an Image

Modern speech recognition models like Whisper never hear raw audio; they inspect an eighty-pixel-tall grayscale image of the sound.

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#6495: Teaching Speech Models to Choose Their Own Audio

Selecting the most uncertain speech recordings often just surfaces silence, clipping, and abandoned half-sentences.

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