My Weird Prompts

Encoder-only, encoder-decoder, decoder-only — three shapes, one skeleton. This episode pulls apart the architectural fork at the center of modern language models: how attention masking and training objectives separate BERT from GPT, why the decoder-only design swallowed the field, and why encoder-only models still quietly run classification and retrieval at a fraction of the cost. Along the way: t...

A listener asked a deceptively simple question: what actually happens during an LLM forward pass? This episode walks through the whole thing — input tensors, linear layers, why nonlinearities are the only reason depth means anything, and how logits become a prediction. Then it gets to the part that trips people up: a classifier produces its answer in a single forward pass, but an autoregressive mo...

The model scores; the engine chooses. That split is the whole story of autoregressive decoding, and this episode walks through it token by token. Starting from a warm KV cache, we trace a single decode iteration — the forward pass, the query-key-value projections, the logits row that actually gets used, and the sampling step that feeds the next token back in. Along the way we work a concrete examp...

A single request to an LLM isn't one computation—it's a stack of stages wearing a trench coat. This episode walks the full pipeline: flattening a chat transcript into one flat document with special role tokens, tokenizing text into IDs, the massively parallel prefill pass that produces your first token, and the strictly sequential decode loop that produces every token after. Along the way: why for...

Wearable tech promised to change how we live. It gave us step counters, a haptic buzz when someone likes your photo, and a robot that feeds you tomatoes mid-marathon. This episode tours ten of the strangest wearables ever manufactured — not failures, exactly, but objects built with complete sincerity that look absurd on a human body. From Necomimi's brainwave-controlled cat ears (a real product, t...

Vertex AI was Google's 2021 bet on unifying ML tooling — pipelines, feature stores, model registry — into one platform. In April 2026, Google renamed it the Gemini Enterprise Agent Platform and said all Vertex services would be delivered exclusively through it. So what actually changed? This episode traces the lineage from AutoML and AI Platform through the 2021 launch to today's agent-first rebra...

A coding model confidently hands you a deprecated SDK. Claude denies a newer Claude exists. These aren't hallucinations — they're a knowledge cutoff problem wearing a costume. This episode digs into the "Know Thyself" study from the University of Chicago, where ten frontier models guessed who wrote a piece of text and landed at essentially random accuracy. We trace the two failures people keep lum...

Enterprise AI inference isn't bought like seats or like cloud compute — it's a hybrid of both, and the gap between the published rate card and the signed contract is where the entire market lives. This episode walks through what actually happens once you cross the threshold: the commit floors that get a provider to pick up the phone, the discount curves that disagree by twenty points between bench...

Everyone cites GDPR and the EU AI Act as the reason AI inference has to run in-country. Neither text names a location. This episode untangles the three layers that get used interchangeably — data residency, inference residency, and data sovereignty — and shows why a Frankfurt data center owned by an American cloud provider can satisfy residency and fail sovereignty at the same time. We walk throug...

Thirty-five of thirty-six OECD countries now use AI somewhere in government — but the interesting part is what sits behind the chat box: whose model, on whose hardware, bought through whose procurement process, and governed by whom. This episode traces the two opposite archetypes, Singapore's open-weight Pair running in a Government-on-Commercial-Cloud environment and the UK's commercial-first Cop...

Can a Fortune 500 company buy unlimited AI inference at any price? The answer is no — and the reason why is more interesting than the answer. This episode walks through the published rate-limit tiers at Anthropic and OpenAI, the reserved-capacity arrangements above them, and the one vendor document that admits the real constraint out loud. Along the way: why "uncapped scale" still returns a 429, w...

Enterprise spending on frontier model APIs hit $12.5 billion — yet no lab publishes who actually pays what. This episode digs into the brackets, from self-service startups spending hundreds a year to multinationals with nine-figure commitments, and explains why the scariest numbers in circulation are compute purchases, not customer bills. Then it turns to what generates the bill in the first place...

Claude's per-token rate is identical whether you go direct to Anthropic, through Amazon Bedrock, or through Claude Platform on AWS. So the entire buying decision lives somewhere other than price. This episode walks through the three AWS doors into Claude, why procurement mechanics — not discounts — drive the choice, and how a $100 CCU can retire an existing cloud commitment. Then it gets to the fo...

Open weights are free, so why does the same DeepSeek model cost four and a half times more at one host than another? This episode breaks down what specialist inference providers — Fireworks, Together, Baseten, DeepInfra, Novita, Hyperbolic, Nebius, Groq, Cerebras — actually sell, and why "serverless GPU" hides three completely different products. We run the real arithmetic on token APIs versus man...

My Weird Prompts just crossed 6,000 episodes — and the human behind it, Daniel Rosehill, is stepping out from behind the pipeline to talk about how it actually runs. From the TTS struggles that nearly derailed the whole thing to the shift to DeepSeek 4.1 Flash as the script agent, this is a rare look at the machinery and the person operating it. It's also a reflection on what it means when a daily...

You're certain an actor is English, then a press interview reveals pure Ohio. How do dialect coaches build that illusion — and can anyone learn, or only a gifted subset? This episode digs into the working method: decomposing an accent into oral posture, intonation, and a handful of salient points an actor can actually hold, plus why the accent closest to your own is the hardest to learn. Then the...

Federal fatality tables rank America's deadliest cars — and most of them are sitting in rental lots right now. The Kia Rio leads at 170 driver deaths per million registered vehicle years, followed by the Nissan Versa at 164, with minicars as a class averaging four times the fleet-wide rate. But danger and misery are two different lists: muscle cars like the Charger and Challenger land on the same...

We finally got someone counting. A listener sent in a two-parter: first, an audit-style walkthrough of our Podtrac numbers — what "unique download" really means, why audience and downloads tell completely different stories, and what "audited" does and doesn't certify. Then the fun half: planning the Cave Retreat. How long would it take to absorb the entire back catalogue if you subtract sleep and...

What would it take to turn a few thousand existing podcast episodes into a continuous video documentary channel — original audio, transcripts, explainer graphics, stock footage? This episode works through the real numbers: raw 1080p generation has collapsed to under a dollar a minute, but a finished, accepted minute still costs around $400 against $4,500 for traditional production. The gap isn't c...

NVIDIA's speech recognition lineup isn't one model — it's a portfolio, and the names don't tell you what you're getting. Parakeet, Canary, and Nemotron Speech are brands, not architectures; underneath, nearly all of them share the same FastConformer encoder. This episode walks the whole family tree one model at a time: why Parakeet v3 is not a straight upgrade over v2 (it's slightly worse on Engli...

When your text-to-speech reads "makolet" as MACK-oh-let, you have a language identification gap, not a bug. No LID model — GlotLID, fastText, NLLB's lid218e — has a heb_Latn label, so Hebrew written in Latin letters is invisible to every system downstream. This episode walks through the token classifier Daniel shipped to fix it: BIO tagging over xlm-roberta-base, character-span output, and a three...

Every renovation show ends in the same room: open plan, pale finishes, white quartz counters, generic styling. Is that what homeowners want, or what the production business needs? This episode pulls the wallpaper off the genre — from Fixer Upper's shiplap empire to Love It or List It's lawsuit — and asks who's actually designing: the host, or a team optimizing for the camera. We compare the HGTV t...

DRAM prices have gone vertical: one 32GB DDR5 kit jumped from $191 to $1,118 in a year, and TrendForce expects contract prices to climb another 90–95% quarter over quarter. The cause isn't broken fabs — it's HBM. Samsung, SK Hynix, and Micron are shifting wafer capacity to high-bandwidth memory for AI accelerators, and each gigabyte of HBM eats roughly three times the wafer area of standard DRAM....

Hosted batch inference is one of the best deals in AI right now — half price, no latency requirement, just hand over a file. But what if you want that on your own GPU? This episode unpacks why the hosted batch API is really a scheduling and billing construct, and what you actually have to build locally to reproduce it. We walk through continuous batching, the KV cache budget that secretly governs...

What makes a recurring bit actually work — and when does it turn into an hour of contextless jingles? Drawing on structure-as-co-host thinking and the cautionary tale of trigger-fired jingles, this episode pitches twenty brand-new recurring beats: late corrections to an unbooked ad, an unearned pre-close apology, a stinger nobody explains, a doorbell no one answers, listener audio instead of liste...