Bonkers

Podcast 2 — In conversation

Speaker A: I have noticed that bonkers has a kind of affectionate quality that mad or crazy does not always have. Like when someone says the whole plan was bonkers but I loved it. Is that tone difference real or am I reading into it?

Speaker B: That tone difference is absolutely real and it is one of the most useful things to understand about the word. Bonkers has a playful quality that mad and crazy do not consistently carry. Mad in British English can mean angry, can mean intensely passionate about something, mad about football, or can mean genuinely mentally unstable; it covers a wide emotional range. Bonkers almost never is serious. When you call something bonkers, there is almost always a sense of can you believe this, is it not marvellous in a demented sort of way.

Speaker A: Is bonkers uniquely British, or has it spread into American or international English?

Speaker B: It is strongly associated with British English and remains much more common in British speech and media than elsewhere. You will find it in American English, it has permeated through British culture exports, but it tends to be used there with a slight sense of performing Britishness. In British English it is completely unselfconscious. An equivalent American informal term might be nuts or wacky. Barmy is a close British synonym with a similar register, slightly more dated, and crackers is another British option that overlaps almost entirely with bonkers in both meaning and tone.

Speaker A: Does bonkers work as a noun or a verb at all, or is it purely an adjective?

Speaker B: It is almost exclusively an adjective: that is bonkers, she has gone bonkers, the whole thing is absolutely bonkers. You can use it predicatively after be, go, or drive: this is driving me bonkers is a perfectly natural construction meaning this is making me mad with frustration. She has gone bonkers means she has lost her mind or embarked on some characteristically eccentric course of action. You do not use bonkers as a noun or verb. It has nice flexibility as an adjective but stays firmly in its lane as one.

Podcast 3 — Prompt engineering

Speaker B: Bonkers in development prompts belongs in the domain of chaos engineering, edge case testing, unexpected system behaviour, and debugging bizarre production issues. It is the word that signals you are dealing with something that defies normal expectations, behaviour that seems logically impossible but is happening anyway. Let us work through the scenarios.

Speaker B: First, a chaos engineering test suite with bonkers failure targets:

Speaker B: PROMPT: Build a chaos engineering test suite for a distributed microservices platform. The suite should simulate bonkers failure scenarios that production systems encounter but unit tests never cover: network partitions mid-transaction, database connections dropping during a write, a service receiving a correct request in the wrong byte order, a cache that returns stale data for exactly one request in every thousand, and a message queue delivering the same message twice with a five-second gap. Log each scenario's effect on system state. Stack: Node.js and Docker Compose.

Speaker A: Using bonkers failure scenarios shifts the AI from standard error handling tests to genuinely bizarre, production-realistic edge cases. Standard chaos engineering prompts tend to produce simple service-down scenarios; asking specifically for bonkers scenarios, like cache returning stale data one in a thousand times or duplicate messages with a delay, produces the kind of subtle, hard-to-reproduce failures that actually cause production incidents.

Speaker B: Good. Now a bug report analysis tool for bonkers production behaviour:

Speaker B: PROMPT: Build a production incident analysis dashboard that categorises bugs by their bizarreness with a bonkers_score integer from one to five. Factors increasing the bonkers_score include: the bug only reproduces on the third Tuesday of the month, the fix involves a seemingly unrelated system, the root cause contradicts documented behaviour, or no one believed the report initially. Each incident stores incident_id, description, bonkers_score, root_cause, resolution, and time_to_root_cause_hours. Stack: Node.js, PostgreSQL, and React.

Speaker A: The bonkers_score field, capturing how implausible the root cause was, is the retrospective quality metric that makes the incident database useful for team learning beyond individual post-mortems. Sorting by bonkers_score finds the incidents that taught the team the most about unexpected system interactions.

Speaker B: Here is a UI component set for bonkers application edge cases:

Speaker B: PROMPT: Design UI error state components for a web application that handles truly bonkers edge cases gracefully. Components needed: a screen for when the user session exists but their account was deleted mid-session, a component for when an API returns HTTP 200 with an error message in the body, a state for when the user has two conflicting active sessions, and a fallback for when the browser clock is more than five minutes out of sync with the server. Each component explains what happened in plain language without technical jargon. Stack: vanilla JavaScript and CSS.

Speaker A: Naming these bonkers edge cases generates error states for scenarios that most UI code completely ignores. A 200 response with an error body, conflicting active sessions, clock skew: these are real production edge cases that cause confusing blank screens or silent failures in applications that only handle standard error states.

Speaker B: Now a test data generator for bonkers but valid inputs:

Speaker B: PROMPT: Build a test data generator that produces bonkers but technically valid inputs for a user registration form. The generator should create: names with Unicode characters from multiple scripts mixed together, email addresses with unusual but valid characters like plus signs before the at-symbol, phone numbers in every valid international format, passwords exactly one character over or under the limit, and birthdates that make the user zero days old or exactly one hundred and fifty years old. All inputs must pass field-level validation but stress-test downstream logic. Stack: Node.js.

Speaker A: The bonkers but technically valid framing generates a test data set useful for exposing the gap between field validation and business logic validation. A name mixing Arabic and Latin Unicode characters may pass the non-empty string check but break display logic; a birthdate of one hundred and fifty years ago may pass the date format check but break the age calculation. That gap is where real bugs live.

Speaker B: One final prompt, a full application with a bonkers behaviour monitoring module:

Speaker B: PROMPT: Build a full-stack application health monitoring platform with a bonkers behaviour detector. The detector analyses application logs and flags sequences of events that are statistically improbable: a user completing checkout before adding items to cart, a payment succeeding after a session timeout, or a foreign key constraint passing for a record that does not exist. Flagged sequences are stored in a bonkers_events table with event_sequence JSONB, probability_score float, and auto_assigned_severity. A dashboard shows bonkers event frequency over time and clusters similar patterns. Stack: Next.js, Node.js, and PostgreSQL.

Speaker A: The probability-score approach, flagging statistically improbable event sequences rather than individually invalid events, is the monitoring architecture that catches subtle system failures that rule-based monitors miss entirely. A payment succeeding after a session timeout is individually valid at each step; it is only the sequence that is bonkers. The JSONB event_sequence storage lets the dashboard cluster similar patterns and identify shared root causes.

Speaker B: The lesson: bonkers in prompts signals statistically improbable, edge-case-defying, expectation-breaking behaviour. It generates chaos test scenarios targeting subtle production failures, incident databases that track architectural surprises, UI error states for scenarios developers never anticipate, and monitoring systems that detect impossible-seeming event sequences. Use it whenever you want the AI to go beyond the happy path and sad path and think about the genuinely weird paths that real systems travel.