My Weird Prompts

Everyone's first mental picture of training a model is two folders dragged onto a script: data goes in, model comes out. The reality is fussier and more interesting. Starting from a Whisper fine-tune on Common Voice, this episode works through what a dataset row actually is, why transcript conventions and sample rates matter more than they sound, and why validation and test sets are consumables ra...

You copy the notebook, change the dataset path, hit run, and hours later a folder appears with new weights that understand your accent. What happened in between? This episode walks the full training step — safetensors files, batches, forward passes, the loss, the backward pass, and Adam's quiet bookkeeping — and explains why a 7B full fine-tune is a memory problem before it's a compute problem. Al...

You ran the evals, you pinned the version, and your invoice parser worked perfectly in October. Then December arrives and the same API name returns something subtly different. This episode digs into what actually changes behind an unchanged endpoint name — weights, system instructions, routing, classifiers, quantization, even the GPUs — and why an alias like "latest" is effectively a subscription...

A leaderboard shows a bolded number: one model beat another. But is that a real improvement or ordinary statistical wobble? This episode digs into what "chance" actually means in AI evaluations — the hypothetical worlds behind a null hypothesis, why repeating the same questions with fresh answers is a different experiment than fresh questions, and why the American Statistical Association had to pu...

Daniel's 16GB laptop runs six Claude Code sessions and stays alive, while his 64GB desktop barely notices. What's the difference? This episode walks the entire ladder of memory pressure relief in Linux — from dropping file cache for free, through zram and zswap compression, swap files, the swappiness dial, page-cluster, and multi-generational page tracking, down to the out of memory killers at the...

A laptop down to two gigabytes of free memory, a desktop one room over with sixty-four gigabytes doing nothing. What if the laptop could swap pages out over the home network and borrow the desktop's idle RAM? The pieces exist — nbdkit's memory plugin, nbd-client, a swap device that looks exactly like a local disk — and the setup is only a handful of steps. The catch is in the physics: a swap-in is...

Daniel wants a pocket of storage inside his ext4 home server — a virtual disk that lives as a single file, mounts from fstab, and behaves like a real partition. No VM required. The kernel already knows how to pretend a file is a disk, and that trick is called a loop device. This episode walks through building image files with truncate versus fallocate, why sparse files can fail at 3am, how LUKS en...

Every major American AI lab now sells a premium tier above its everyday flagship: Anthropic's Claude Fable, OpenAI's GPT-6 Astra (Pro), Google's Deep Think, and xAI's SuperGrok Heavy. The pitch is PhD-level science and novel proofs, but the developer docs tell a different story — "for most workloads, start with Opus." This episode digs into the pricing (Fable runs 2.5x the flagship), the usage dat...

In August, Anthropic described an internal model it calls Model Two — a step up on its released flagship, used heavily inside the company, and never given a release date. This episode digs into what's really behind the gap between what labs build and what the public gets: documented internal-first timelines, forecasts of roughly 65 days from internal use to outside access, and government access vi...

Making a private podcast feed is easy; keeping it private is where things fall apart. This episode walks the whole ramp — from paid hosts like Transistor and Castos that hand each subscriber a tokenized feed address, down to a storage bucket and a Worker that signs every enclosure on the fly. Along the way: why "private, not secure" is the honest label, why pre-signed URLs go stale in seven days,...

A zip file in a shared Drive folder is a software release process with no mechanism for the second release. So how do you run your own APT repository — and keep it private — without turning into your family's unpaid update service? This episode walks through the modern client-side setup: the new deb822 .sources format, why Signed-By matters more than ever, and the Sequoia-based signature strictnes...

Building an app with AI is now an afternoon's work — getting it onto your aunt's phone is the hard part. This episode walks through running a private F-Droid repository from GitHub Actions: signed APKs, a signed index, HTTP Basic Auth, and a QR code treated like a house key. Along the way: why GitHub Pages can't host it, why Cloudflare Access would probably break the client, what Google's new deve...

Code-switching research has a quiet problem: the field counts language pairs, not people. A dataset paper claims 113 code-switched pairs, up from 19 six years earlier — yet none of those numbers describe an actual speech community. This episode digs into why the NLP world, the sociolinguists, and the benchmarks all draw different maps that never meet. From Gumperz in a Norwegian village to the Ban...

Voice cloning now needs just five to ten seconds of reference audio, and the consent check on most platforms is a single checkbox. This episode traces how the industry's guardrails were built — not by protecting voice actors, but in response to 4chan trolls in 2023 — and follows the cases that reveal the real gaps: Greg Marston discovering his voice in an IBM text-to-speech product called Conor, B...

You clone your voice, and out comes a stranger with a vague American accent. It's called accent displacement, and it's not a bug in your recordings — it's baked into the training data. This episode digs into why TTS models flatten accents, whether a "neutral" English accent even exists, and what actually helps: fine-tuning on many speakers of your accent rather than hours of just one. We look at S...

Voice cloning from a short clip isn't the model learning your voice — it's the model finding you inside a voice space it already built from hundreds of thousands of hours of speech. This episode digs into how Chatterbox's two-stage pipeline actually uses your reference audio, why feeding it more doesn't help, and what changes when you move from instant cloning to a trained model. Along the way: VA...

Google's Gemini audio docs promise emotion labels with timestamps. A benchmark called LISTEN tested whether the models actually use the sound of a voice or just transcribe and guess — and the numbers aren't flattering. This episode digs into the gap between what speech models claim to detect and what they really hear, what specialist open models like emotion2vec+ and SenseVoice offer instead, and...

Video-capable AI models can describe a room in detail but stumble when asked whether someone is moving slowly, which way they turned, or what happened in what order. Benchmarks like TOMATO, TimeBlind, FAVOR-Bench, and MotionBench all point the same direction: models are good at what's in the frame and shaky about what happens between frames. This episode digs into why — default sampling of one fra...

Can a single microphone tell where you're standing, where you're from, or which way you're walking? This episode sorts the real capabilities of audio AI from the claims that don't hold up. From language ID models that nail 107 languages but stumble on regional accents, to bioacoustic models that learned birds and turned out to work on whales, to industrial systems that detect failing machinery wit...

Your model is flattering users and you can't say why. This episode walks through the process a well-funded lab would actually follow: build an eval for the exact failure, bisect between the last good and first bad checkpoint, ablate the suspects, then reach for the research-grade tools — influence functions, persona vectors, sparse autoencoders. Along the way we look at what OpenAI's GPT-4o sycoph...

Ask an AI to warm up your photo and one of two things happens: it hands you a recipe of exact slider values you can read and adjust, or it repaints every pixel and hopes you don't notice the logo grew a letter. This episode digs into the recipe camp — JarvisArt's agent over 200+ Lightroom tools, PhotoArtAgent's render-look-revise loop, and VeraRetouch's fully differentiable renderer — and the rege...

Restoration tools have quietly become generative — they don't subtract noise, they rebuild the speech they think was underneath it. That works beautifully until it invents a phoneme you never said. This episode maps what actually ships in 2026: DeepFilterNet 3, ClearerVoice-Studio, Adobe Podcast's stem-splitting Enhance Speech update, ElevenLabs' voice isolator, and NVIDIA Maxine on the mature sid...

Daniel wanted an inference manager the way he already has a secrets manager, and what he described is basically a LiteLLM-style proxy. The failover machinery is genuinely built out: cooldowns, health probes, latency-based and least-busy routing. The gap is memory. Ollama will tell you a model's VRAM footprint, not the headroom left, so "is this box full?" isn't a question you can put to it — you w...

Most explanations of how a language model works show you a diagram. This one hands you a coat and walks you through the building. Starting from the token "is" in the sentence "The capital of the state containing Dallas is," the episode follows a single vector through eighty layers of a transformer — the Look Back stops where attention pulls in what earlier words are offering, the memory rooms wher...

If you dictate in English but live in Jerusalem, stock speech recognizers mangle words like arnona and makolet. Hebrish is a not-for-profit project that fixes this with two new models, Jachnun and Sabich, both built on NVIDIA's Parakeet. We walk through every evaluation, from term recall to never-seen Hebrew words, and explain why Sabich is the one the apps recommend. Plus: the Shakshuka streaming...