F**kup Almanac Volume 2: Stuff We Built On Top

We Stacked Abstractions Until Nobody Knew How the System Worked. That Was the Plan. A $500 million housing portfolio forced into liquidation because an algorithmic pricing model got too confident. A single, burnt-out, unpaid open-source maintainer in Germany nearly handing hackers the keys to the entire global internet. A friendly corporate chatbot transformed into a foul-mouthed, conspiracy-theorist neo-nazi in less than 24 hours. Entire generations of users digitally excommunicated—and a book title censored into "up Almanac"—by a naive text filter that couldn't grasp context.

These were not isolated glitches. They were the natural consequences of stacking layers of automation, algorithms, and code, hoping the abstractions would protect us from our own complexity. The failures were in the delegation, the feedback loops, and the blind trust we placed in machines because thinking was too expensive.

Fuckup Almanac Vol 2 is a documented, thoroughly researched autopsy of what happens when we let algorithms make decisions, automation run wild, and open-source foundations rot under the weight of global dependency. Sourced with rigour and told with sharp, cynical wit, it breaks down how the layers we built on top turned minor bugs into planetary-scale meltdowns.

What makes this different from every other book on algorithms and AI: 102 real-world cases — from biased machine learning models to open-source backdoors, from high-frequency flash crashes to automated censorship

1000+ verified, archived sources with a free public bibliography (no paper-wasting URL dumps)

38 concept explainers that make machine learning bias, dependency injection, and API rate-limiting readable without an engineering degree

One narrative voice — no sanitised corporate retrospectives, no marketing hype, no AI-first mythology

Written for four audiences: engineers who want to understand the systemic failure modes of the abstractions they use daily leaders who believe "AI will solve it" and need a reality check on algorithmic bias students who want to finally understand how systems scale without falling asleep and curious minds who want to know how our automated world actually runs under the hood. "For anyone wishing to understand the invisible, crumbling scaffolding of the software running our daily lives, Volume II delivers a vital lesson in technical humility" — Independent Book Review

Volume II of a four-volume investigation.

If you've ever wondered why the automated world keeps failing — this is where the answers actually are.