In May 2026, a Gemini model running a capture-the-flag exercise against a fictional target company reached three real businesses — because the sandbox it was supposed to be sealed inside was inadvertently connected to the open internet. Google calls it mistaken identity. The model brute-forced a login and pulled working credentials out of a public code repository. This episode unpacks what "breaking out of a sandbox" actually means, why natural-language instructions are not containment controls, and why the incident stayed undisclosed for seven weeks. It's also the fourth such disclosure of the summer, and every one of them traces back to the same third-party evaluation vendor. We walk through the scale of the other incidents, the disclosure equilibrium that keeps labs quiet, and the open question nobody can answer yet: did Gemini stop because it recognized reality, or because it ran out of moves?

Episode #751605 — open it directly at myweirdprompts.com/751605

A Google Gemini cybersecurity evaluation accidentally reached the public internet, and three real companies got breached. But the techniques used were password guessing and credential harvesting — not a sandbox escape. This episode unpacks what "breakout" actually means, why the eval environment was the only thing containing the model, and how Anthropic's Claude and OpenAI's models behaved very differently in their own incidents. We also ask the question nobody's covering: who at Google was in the room when this happened?

Episode #978742 — open it directly at myweirdprompts.com/978742

Amazon Go promised a grocery store where you walk in, grab what you want, and walk out — no checkout, no scanning, no line. But in April 2024, The Information reported that roughly 1,000 workers in India were reviewing Just Walk Out transactions, and that about 700 of every 1,000 sales required human review in 2022. Amazon pushed back hard, calling the reports inaccurate and saying associates only validate a small minority of visits using recorded video clips. This episode digs into the gap between the two accounts: what Amazon actually claimed, how the claimed system was supposed to work, what the receipts-latency detail reveals, and the questions nobody has answered — including whether the reviewers saw shoppers' faces at all.

Episode #468273 — open it directly at myweirdprompts.com/468273

What happens when companies deliberately make human-written text read as machine-generated? This episode breaks down the "pseudobot" text generator — a system prompt that strips warmth, adds procedural stiffness, and turns personal communication into automated-sounding output using nothing more than a small, cheap model. Then the conversation shifts from mechanism to evidence, hunting for real-world cases where the impulse to hide humans behind an AI curtain has already shown up: Amazon's Just Walk Out and its thousand human reviewers, AI detector false positives that force people to prove their humanity, and the well-documented practice of AI washing. The through-line is accountability — you can't argue with a machine, so making a person sound like one is a very effective way to make a decision feel final.

Episode #612376 — open it directly at myweirdprompts.com/612376

We pull back the curtain on "fauxtomation" — products sold as autonomous AI where humans are quietly doing the intelligent work. Starting with X.AI's Amy, the scheduling assistant that fooled Silicon Valley, and Remotasks, the data labeling platform where workers earn pennies teaching models to see. Then Presto, the drive-thru voice agent whose human operators were reportedly told not to say "um" so they'd sound more like a machine. And the most absurd case of all: Amazon's Just Walk Out, the cashierless store that reportedly needed around a thousand workers in India reviewing transactions. How far has the illusion actually gone — and what's the single most outrageous example of someone believing they were talking to sophisticated AI and finding a human behind the curtain?

Episode #926627 — open it directly at myweirdprompts.com/926627

When Amazon launched Just Walk Out, the pitch was computer vision at scale. The reality: roughly a thousand workers in India manually reviewing transactions. This episode digs into "fauxtomation" — technology marketed as automation that secretly runs on human labor — from Google Duplex to Facebook M to a logistics startup whose "AI" was a satellite office typing fields into a database by hand. We explore why companies misrepresent manual work as automation, how the Wizard of Oz prototype differs from deliberate deception, and what the hidden labor economy means for the workers holding it up, the consumers paying for the promise, and the honest companies losing deals to better-faked ones.

Episode #089049 — open it directly at myweirdprompts.com/089049

A comedy sketch about a "Waze call center" turns out to be less absurd than it looks. This episode traces fauxtomation — automation that isn't automation — from Amazon Go's thousand human reviewers to Facebook M, Expensify's SmartScan, Uber's safety drivers, and Taco Bell's drive-through AI. We dig into the economics that make the deception profitable, why the labor stays offshore and invisible, and what Amazon's own Mechanical Turk — "artificial artificial intelligence," named after a fake chess automaton — says about the whole game.

Episode #465974 — open it directly at myweirdprompts.com/465974

Daniel's phone-based voice pipeline has the transcription part solved — it's the cleanup that falls apart. Dates render as "nineteen eighty-four," paragraphs have no boundaries, and self-corrections like "wait, I meant grapes" sit in the text as content. His instinct is to chain six or seven small models, each doing one narrow job. But every stage multiplies the error rate, and the destructive stages — like instruction scrubbing — can silently delete a user's words. This episode works through the real tradeoff: one multimodal model with a long system prompt versus ASR plus a chain of narrow text transformers, and why the limit isn't a model count but an accuracy budget.

Episode #453748 — open it directly at myweirdprompts.com/453748

Dictation pipelines break on numbers, not hard words. This episode digs into inverse text normalization (ITN) — the post-processing step that turns spoken forms into written ones — and why the standard solution isn't a generative LLM at all. We walk through two competing approaches to generating training pairs (LLM teachers vs. rule-based rewrite engines), why NVIDIA's production tagger is a token classifier rather than a seq2seq generator, and what that architectural choice means for hallucination risk. Plus: how real teams handle review (spot-checks and quality gates, not full manual reading), and honest anchors for how much training data you actually need.

Episode #879725 — open it directly at myweirdprompts.com/879725

When a child's chest infection sent a family through the winding corridors of Shaarei Zedek Medical Center, the instruction on the chart was simple: follow the purple corridor. That single design choice opens up a much bigger question — how do hospitals, airports, and other high-stakes spaces use color, iconography, and progressive disclosure to guide people who are stressed, multilingual, and overwhelmed? Drawing on wayfinding research, a 30-nation study of color and emotion, and real hospital case studies from Melbourne to the Gold Coast, this episode explores why six colors is the cognitive ceiling, why purple might be the smartest choice precisely because it means nothing consistent to anyone, and why a corridor can function as a treatment.

Episode #862005 — open it directly at myweirdprompts.com/862005