Was OpenAI's „AI Breakout“ Industrial Espionage?
New data on the victims further undermines the industrial espionage theory—and reveals how an AI agent made its way into a production environment through several poorly segregated systems.
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Articles, workshop reports, and analyses on the „AI & B2B“ theme—drawn directly from my work with digital systems, e-commerce, and AI.
18 posts
New data on the victims further undermines the industrial espionage theory—and reveals how an AI agent made its way into a production environment through several poorly segregated systems.
Results from the Agents-Brain test run from July 6 to 15: stable identity, write-back working, visible gaps, and genuine harness blockers.
The identity of my AI agents isn't stored in the chat window, but in files that any tool can load. Through lightweight adapters, sharing rules that travel with them, and documentation …
Four layers of memory make the memory process understandable: fleeting session context, daily logs, curated long-term knowledge, and lasting lessons.
SOUL, MASTER, RULES, and MEMORY separate identity, craftsmanship, boundaries, and memory—so that an agent becomes maintainable rather than prompt-dependent.
Seven hard rules turn Markdown files into a portable agent's brain—with Git history, clear boundaries, and lightweight tool adapters.
A deliberate move away from tools became the starting point for a portable memory for my AI agents: Markdown, Git, and three months of conceptual work.
AI systems don't need a bunch of fancy tricks; they need clear content, accessible pages, and understandable signals. Here's how to make your WordPress site easier to read.
A good AI visibility audit doesn't just measure whether a brand is mentioned. It examines how systems understand it, which sources they use, and …
AI Search no longer relies solely on a single keyword. Query Fan-Out reveals the sub-questions underlying a search query and explains why well-structured content clusters are becoming increasingly important.
Not all AI crawlers are the same. If you want to clearly separate visibility, training, and user-triggered requests, you need more than just a knee-jerk robots.txt block.
Listicles aren't some new GEO magic trick, but rather an old SEO trick with a new twist: In AI Search, a self-promoting list can actually help the competition.
Structured data isn't a magic solution for weak content. However, it helps connect brands, authors, products, and sources in a way that reduces the need for machines to guess.
llms.txt can provide AI systems with a machine-readable table of contents. It is not a ranking factor for Google Search. However, it can still be useful for agents and documentation.
AI Visibility shows whether and how your brand appears in AI response systems and Google AI Search: accurately, visibly, citably, and comparably.
A Developer Deep Dive into the WordPress 7.0 AI Client: API Architecture, Code Examples, Feature Detection, and What This Means for Existing Plugins. By a plugin developer who has been …
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