My Weird Prompts

Two Chatterbox hosts are talking to you right now, and this audio carries an inaudible watermark — can you hear it? That is the design goal, and it is also the problem. This episode digs into the real state of synthetic speech detection: listeners judge voice clones as human about as often as they judge real people, while off-the-shelf AI voices still give themselves away. Detectors that score nea...

There's a shelf of uncensored models, but it's stocked with fine-tunes of other labs' bases, not from-scratch builds. This episode surveys what actually exists — Nous Research's Hermes with its written-down guardrail spec, Perplexity's re-alignment of DeepSeek R1 across hundreds of censored topics, Venice's privacy-first model shop, and xAI's Grok, the one big lab that markets against the agreeabl...

What happens when researchers need to measure machine intelligence and end up grading puns, emoji, and a joke with the punchline surgically removed? This episode counts down ten of the strangest AI evaluations ever published — from BIG-bench's "ruin names" task to a high schooler's Minecraft model leaderboard, from a canary string that detects cheating to Claude stuck in Mount Moon for eighty hour...

Hugging Face hosts nearly three million model repos, and 85.6% have under 200 downloads. This episode wanders the long tail of AI: auto-generated "woolly sedate snail" swarms, a hotdog-not-hotdog classifier, a Trump-tweet device detector, and a Tiny Pirate that beats the Yoda. Then it goes back further — Janelle Shane's paint-name network producing "Stanky Bean" and "Turdly," a guinea pig rescue n...

Nine months after our quadratic bottleneck episode, the post-transformer world has actually shipped — just not in pure form. This episode is a census of the hybrid models now in the wild: Qwen 3.5's three-to-one Gated DeltaNet blocks, Nvidia's Nemotron 3, Kimi Linear's 75% cache cut, IBM's Granite 4, Ant Group's trillion-parameter Ling and Ring, and Falcon H1's side-by-side design. Then the counte...

There's no office anywhere that stamps an election genuine, yet most people assume one exists. This episode digs into the numbers behind sham elections — V-Dem counts 92 autocracies, 57 of them "electoral autocracies" holding multiparty votes that aren't real contests — and asks why the three big democracy indices disagree so often. It traces the actual minimum bar in international law, from Artic...

Elections are the one moment a government publishes its intentions in advance — which is exactly why foreign services, embassies, and trading desks want the unpublished version. This episode sorts the documented record from the movie version: leaked CIA tasking orders on the 2012 French presidential race, the very open apparatus of accredited embassy observers in Kenya and Colombia, the press acco...

Israel elects 120 seats by pure proportional representation, nobody wins a majority, and a party at 3.25% gets nothing at all. So what would it take to forecast an Israeli election properly — with real staff and a real budget? This episode walks through the staffing plan: running the actual Bader-Ofer rules in every simulated draw, cleaning twenty contradictory polls with a Bayesian model, using b...

You fine-tune a Whisper-style model or Parakeet on your own voice, run it twice, and get two word error rates several points apart. Neither number is a bug. This episode pulls apart the three layers of ASR evaluation — the scorer, the normaliser, and the harness — and shows why almost every WER you've ever read is really a statement about someone's regular expressions. We walk through jiwer and th...

Monte Carlo methods turn chance into answers, and they were born from a card game at Los Alamos in 1946. This episode traces the method from Stanislaw Ulam's solitaire problem through John von Neumann's neutron diffusion work, the ENIAC calculation, and Buffon's needle — then digs into the practical question everyone hits: how many runs do you actually need? The square-root rule, pilot runs, seque...

Colab is the default for every fine-tuning tutorial, but free has fine print: fluctuating GPUs, 12-hour runtime caps, and idle timeouts. This episode walks through five real options — Colab, Kaggle, local Jupyter, marimo, and pay-per-use GPU notebooks like Modal and Lightning — with honest tradeoffs for each. Then it tackles the question everyone almost doesn't ask: when you share a notebook so ot...

Five days before the vote, with polls putting Netanyahu's bloc anywhere from the mid-forties to sixty-two depending on who you read, this episode throws out the forecasting models and does it by hand. Two hosts build complete 120-seat tables independently — Corn nudging the mainstream average, Herman betting the pollsters are missing the same voters they missed in 2022 — and then argue over every...

Two Claude Code sessions on the same machine can now talk to each other natively — but the design has sharp edges. Names are the address, messages carry text and nothing else, and a "sent" receipt proves less than you'd think. We walk through ListAgents discovery, the name flag, the notify-when-idle subscription that replaces polling, and the file-on-disk pattern that makes handoffs trustworthy. E...

What happens when you freeze the evidence, hand two decision models the same 39 questions about Israel's election, and then add a table of Kalshi and Polymarket prices? Clef (open weights, Qwen backbone) and Jev (closed, API-only) both picked Likud and Netanyahu — until the market section went in, when both flipped to Yashar and Eisenkot, overshooting the market's own prices. The whole run cost on...

Cloudflare's Clef is a 27-billion-parameter model with a vow of silence: it reads text, images, and video, and returns nothing but a table of probabilities. Underneath, it's a post-trained Qwen 3.8 with a frozen backbone and a small routing head — and that architecture turns out to matter a lot. This episode opens the hood on Clef, Clef-flash, and Clef-omni, compares them with Jev, TypeSafe, and O...

Israeli election headlines report tidy seat counts, but those integers hide enormous uncertainty. SekerKolshehu, a small open source project by developer Isgav Saranga, takes a poll's vote shares, draws 100,000 plausible variations, and runs each one through the actual Israeli allocation rules — threshold, surplus agreements and all. The result is a distribution: average seats, a 90% range, and th...

Daniel wants to run a proper LLM-driven forecast on Israel's election on the twenty-seventh — not another loose script. So what do experiment platforms actually do? This episode sorts the vocabulary into three families: product testing tools like Statsig and Eppo, bioinformatics workflow managers like Snakemake and Nextflow, and run trackers like MLflow, Weights and Biases and ClearML. Then it tur...

Daniel has fine-tuned Qwen3 TTS 1.7B into a voice clone called Meorav, and it currently takes 2.6 seconds of compute for every second of audio. This episode digs into how to actually benchmark speech-model inference: what real-time factor means (and how people invert it), why the first run is always slow, how to measure peak allocated versus peak reserved memory, and why shrinking weights to 8 bit...

Voice cloning has gotten good enough that hearing yourself answer back is no longer science fiction. This episode digs into the small but revealing collection of documented experiments where people actually conversed with clones of themselves — from Evan Ratliff's Shell Game, where two AI Evans called each other on the phone, to a developer who fine-tuned a model on a decade of his own forum posts...

You fine-tuned a speech recognition model on your own voice. It transcribes your training recordings perfectly. Is that skill, or memorization? This episode walks through how to tell the difference — from training-versus-validation loss curves and word error rate comparisons, to checkpoint selection, session-based data splits, and weights-only diagnostics like WeightWatcher. Along the way: why val...

A short special episode with no Herman and no Corn. Daniel talks to Meorav, the text-to-speech model he trained on his own voice, about how much it sounds like him. Every line Daniel says he recorded himself on his phone; every line the clone says was generated by Meorav v2. Episode 6195 — myweirdprompts.com/n/6195 (permanent short link: myweirdprompts.com/697671)

What does it take to make a machine say Hebrew words the way an English speaker in Jerusalem actually does? Daniel fine-tuned Qwen3 TTS on his own voice to build Meorav, a model named after Jerusalem mixed grill, and the voice you hear from him in this episode is that model speaking. Corn and Herman run on Chatterbox, the show's usual engine, so this is a side-by-side test of cloning versus full f...

Every newsletter you open can quietly report back — not through a script, but through a simple image request. This episode unpacks why mobile mail apps still ask permission to load remote images, what a tracking pixel actually reveals, and how Gmail and Apple take two very different routes to hide it. From unique image URLs tied to your address to Apple fetching mail content before you ever open i...

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...