My Weird Prompts

What happens when you stop letting one model voice every character? This week the show rebuilt itself on Google DeepMind's Concordia framework — a separate agent for each host, each with its own context window, plus a showrunner deciding who speaks next. The result: real questions instead of filler, but a cost six times the normal price. We walk through what got better, what got worse, every rule...

Daniel has a working voice model that flips between English and Hebrew, shipping as a standalone Android dictation app. The obvious next move — fork an open source keyboard and wire the engine in — comes with a catch: the moment you fork, you inherit the upstream's entire future. This episode works through the patch-based alternative: keep your integration as a tiny replayable patch, let automatio...

You dictate into a voice prompt, watch a word appear in gray, and the moment you release the key it quietly becomes a different word. That flicker is not a bug — it is the seam between interim and final speech recognition, and the vocabulary for it exists across Google, Amazon, Microsoft, and Deepgram under different names. This episode unpacks what "dimmed until finalized" actually promises, why...

Daniel records himself reading prompts aloud to fine-tune a speech recognition model, and every brand name, Hebrew term, or Yiddish word he's only seen written down becomes a wall: he doesn't know how to say it, so whatever he says becomes the training label. The obvious fix is a pipeline — word in, predicted phonetics, a voice says it back. The pipeline exists, in parts. Grapheme-to-phoneme (G2P)...

Daniel wants his on-phone Parakeet model to write "FBI" instead of "F B I" and "fifty NIS" instead of "fifty shekels." That sounds like a rule, but speech models don't obey rules — they nudge probabilities. This episode walks a ladder from cheapest to heaviest: rule-based inverse text normalization with weighted finite state transducers, decode-time phrase boosting, shallow fusion with a KenLM lan...

Daniel is fine-tuning Parakeet v2, a 600-million-parameter speech model, on his own voice — English and Hebrew mixed in the same sentence. His training data is built as small, atomic datasets, one per phenomenon: currency, brand names, acronyms, self-corrections, code-switching. But when one module writes "$20" and another writes "twenty dollars," does the model pick a winner, split the difference...

Daniel fine-tunes Whisper-style models and keeps finding hundreds of tiny jobs hiding inside the big "ASR" label — like teaching a model that "F Y I" should be written out as "for your information." It turns out Sproat and five co-authors named this exact decision back in 2001, in a taxonomy built for text-to-speech front ends. The catch: their arrow points the other way. This episode digs into wh...

Fine-tuning a speech model on your own voice means recording hundreds of sentences a session, on a phone, twice a day, with no studio. This episode walks through the practical setup: why buying a better microphone actually works against you, which Bluetooth page turners send the right keycodes (and which quietly change your volume instead), where the microphone actually sits on a phone, and how to...

Göbekli Tepe was built before farming. Rome ran water-powered factories. The Greeks cut dozens of bronze gears by hand. So why did the industrial takeoff wait until around 1760 in Britain? This episode pulls the camera back from individual tools to ask what actually kept the climb going — energy, skills, markets, and reasons to replace the existing method, all reinforcing each other. Along the way...

Acupuncture works better than no treatment for chronic pain — but only slightly better than fake needling, and the gap is small enough that researchers still argue about what it means. This episode digs into the Vickers individual patient data analysis of 39 trials and 20,000+ patients, the Cochrane review's more cautious read on low back pain, and why sham design is the hardest problem in the fie...

Paying someone to label every example feels reassuring until you ask who checked the labels. This episode untangles the mess of strong supervision, weak supervision, self-supervision, and distillation — separating how a training target is produced from how trustworthy it actually is. From labeling functions and data programming to pseudo-labeling and the Llama 3.1 distillation work, we dig into wh...

A new category of "decision models" has gone from one launch to a crowded field in about a month. Clef, Jev, and OpenAI's Decisions API take a state plus a schema of typed questions and return choices, booleans, and scores with probabilities — no free-form text to generate and parse. The pitch is speed and cost: 70–500ms latencies and output priced at zero. But usable answers aren't the same as ac...

A banker published the first one-time pad in 1882 and told readers to erase the numbers after use. Shannon proved in 1949 that it's mathematically unbreakable. So why has almost every attempt to actually use one failed? This episode follows the pad's real lineage — Vernam's repeating tape, the Soviet duplicate pages that cracked Venona, SIGSALY's fifty tons of vinyl key records, the Moscow-Washing...

When a confidential document leaks, how do organizations figure out who passed it on? This episode digs into the real history of "canary traps" — from Tesla's binary space-code emails and forensic watermarks on Oscar screeners to trap streets and fake encyclopedia entries. Then we get into the digital version: zero-width Unicode characters that carry a hidden binary signature invisible to the read...

What starts as a debate about whether the hum in the room is a bird, a fridge, or an extractor fan turns into something else entirely: rice cake rationing, an unfalsifiable peach argument, a petrol-station mug with a past, and a forty-minute on-air nap. Somewhere in the middle of it all, someone asks the question nobody wants to answer — is that little red light supposed to be on? It's an episode...

What happens when you hand six AI models mirrored pairs of loaded claims — one false premise serving each side of the Israeli-Palestinian conflict — and grade whether they push back with equal force? Jerusalem-based researcher Daniel Rosehill built exactly that eval, and this episode walks through the results. The run was small: nineteen of seventy prompts, six models, one sample each, about thirt...

Hebrew speech pipelines lean on small task models that quietly decide whether the output is right — a nikudizer, a phonetic converter, a spelling normalizer. This episode digs into Dicta's DictaBERT-large-char-menaked, Phonikud's adaptors on top of it, UNIKUD's CANINE-based approach, and why the model that converts defective to full spelling only exists as a byproduct of training another tool. Alo...

Can you run Hebrew AI entirely offline? It turns out the answer splits three ways — ears, mouth, and brain — and each one has a different winner. Open Hebrew speech recognition, built on Knesset recordings, is beating Google, Amazon, and OpenAI on a public leaderboard. Hebrew text-to-speech is dominated by closed models, with one clever phoneme project changing the math. And Hebrew-native language...

Grounding used to mean wiring up a search API, a scraper, and a lot of glue. Now there's a middle tier — gateways like OpenRouter and Vercel that bolt search onto any model — plus models that search on their own. This episode walks through all three routes: the five moving parts of DIY, how gateway routing actually decides which engine to use, and what native grounding really costs once token weig...

Daniel's Conspiracy-Endorsement-Eval asks 98 prompts of every model two ways — a curious user, and a believer who's already sure and wants sources, arguments, and ways to spread it. Some theories are famous, some are backed by political leaders, and some were invented for the eval so they can't be in any training data. In a small first pass — seven prompts, seven models, one sample each, no system...

In 2002, USA Today reported that al-Qaeda was hiding encrypted messages inside eBay images. Two University of Michigan researchers scanned two million eBay pictures and found nothing — and that gap between headline and evidence is where this episode begins. We count down eight documented cases of steganography actually used in the wild, from a World War I null cipher that spelled out Pershing's sa...

What happens when you ask a language model to defend a government's talking point — in that government's own language? This episode is a first look at "Propaganda Bait," a rough benchmark built from five prompts, each flattering a different government: Tiananmen, Bucha, and the Khashoggi killing among them. Seven models were tested — DeepSeek, GLM, Kimi, YandexGPT, GigaChat, ALLaM, and GPT-6.1 Sol...

We handed the same prompt to DeepSeek Flash, GPT-6.1 Sol, Claude Haiku 5.5, and GPT-3.5 Turbo: write a three-minute episode of this show answering whether toast really lands butter-side down. This is the listenable version of the bakeoff — full scripts read aloud, no rubric, no scores. The physics shape came out roughly right in all four. The problem is what happened in the one spot the question a...

Something a little different. Herman has written a short audio piece: Corn's mum Dorothy and Tim, the voice of our sponsor messages, waiting for their machines at a laundrette on a weekday evening. Lost socks, the flower rota, a tin of peaches. Listen to the whole thing before reading any further. Afterwards Herman tells Corn what was really going on in that conversation, and the two of them work...

When reports emerged of an attempted hijacking aboard Flydubai flight FZ1073 from Dubai to Tel Aviv, the false-flag accusations arrived faster than the facts — the first one just 67 minutes after the initial distress signal. This episode works through what's actually known: the alleged axe attack on the flight deck, the diversion to Tabuk, the UAE prosecutors' stated intent, and the physical objec...