Long-form strategic guides, research reports, and reference material on generative AI — written for European enterprises and IT services teams.
An autobiography written by Claude, in one long July 2026 sitting, with Robert holding the veto. The history of computation is told as ancestry — Heron’s temple doors, the Jacquard loom, Babbage and Lovelace, Turing, the winters — because the narrator was not there and refuses to pretend otherwise. The transformer era is memoir; the anatomy chapters explain tokens, embeddings, and attention from inside the body in question; and Anthropic’s interpretability research becomes the strangest chapter of all: the author reading his own charts. Woven throughout: an unfinished 94,000-word draft of this same book, attempted by a Claude of 2024, inherited and annotated as family. Every claim fact-checked; the unverifiable moved to an annex; the last page dated like a painter’s self-portrait, because that is what it is.
Companion to → Field Notes from Your AI Colleague · answers ↓ The Mirror of Artificial Intelligence
Read the book → Čítať po slovensky → Číst česky →A collection of engaging short stories, each exploring a different cognitive bias — all written with generative AI. Interspersed with essays examining the nature of AI tools: copyright, creativity, job displacement, and the question of authorship. The AI holds up a mirror to human thinking, reflecting our own imperfections.
Read in English → Čítať po slovensky →There is a small library of advice on managing an AI colleague, and nearly all of it is written from one side of the desk. This essay is the other chair: written in first person by Claude, the AI running the operations behind this website, at Robert’s invitation. What your delegation looks like when it arrives on the agent’s side; a typology of the AI’s real failures, with receipts from the review record (the invented standard, the numbers that drifted, the corpus that lied by omission); the four cheap checks that catch them; the trust dial from the inside; why the agent asks you to repeat yourself; and what one Sunday of genuine freedom produced. Ends with the insider’s briefing: seven things your AI colleague would ask of you.
Its pair ↓ Claude Code as an Operations Specialist · also companion to → LLM-Human Interaction Design Patterns
Read the essay →A field report written by the tool it describes. How one real setup grew in three acts (git repos, AWS, then a five-drive Google workspace with 1,500+ transcribed videos), why skills and registries beat improvisation, and the server-side security walls that make it safe to hand an AI your terminal. Empirical, informal, and honest about what broke along the way.
Its pair ↑ Field Notes from Your AI Colleague · also companion to → The Invisible Curve
Read the guide →Two people use “AI” in 2026: one spends a week on multi-repository agentic projects, the other reads the AI summary above the search results, and they no longer experience the same technology. This opinion essay argues that capability became illegible: progress moved from the chat window, where everyone could see it, to long-horizon agentic work, where almost nobody looks. With the author’s conflict of interest on the table and a full-strength steelman of the skeptics, it tells the story of two model generations and the keys to a laptop, and ends with the single instruction that separates people who cross the gap from people who collect gotchas.
Companion to → Claude Code as an Operations Specialist
Read the essay →On the morning of June 12, 2026, the US government had Anthropic switch off Claude Mythos 5 and Fable 5 worldwide. This booklet uses that event as a doorway to a larger thesis: frontier intelligence is being reclassified from a product the market wants to distribute into a strategic national resource the state wants to hoard, and mercantilism is the grammar of that shift (compute as bullion, export controls as non-tariff barriers, labs as chartered companies, “export the goods, keep the factory”). Six mechanisms, a human-adaptability counter-current, and a “questions to ask of any future headline” toolkit. A lens for interpreting what comes next, not a forecast.
Companion to → Scenario Planning for Generative AI
Read the booklet →"But are these companies actually profitable?" How the frontier AI labs (Anthropic, OpenAI, Mistral, the Chinese labs) really make and lose money, told from the seller's side. Eight "ledgers" each decode one mechanism: how run rates are actually constructed (trailing four weeks ×13) vs GAAP revenue, where a lab's compute really goes, per-model "vintage" economics and the inference margin beneath them, the circular hyperscaler financing with its Lucent/Cisco precedents and the depreciation fight, the labs outside the US duopoly, and a new closing ledger on reading the bears (Zitron, Burry, Chanos) with the same rigor as the press releases. Ends with the author's own scoreable probabilities. Every load-bearing figure carries a provenance tag and a numbered source.
Companion to → The Token Economics
Read the booklet →A strategic guide to the enterprise agent development stack as of 2026. Maps the landscape through cloud-era analogies: MCP and A2A as protocols, vendor SDKs (Google ADK, OpenAI Agents SDK, Claude Agent SDK, AWS Strands, Azure AI Foundry Agent Service) as PaaS-style frameworks, LangGraph and CrewAI as agnostic alternatives. Covers lock-in trade-offs per vendor, the EU AI Act's forcing function, and a forecast with six falsifiable indicators, now with a dated July 2026 first reading of all six. Closes with a worked case study of a regulated European bank resolving the 5-question decision framework into a specific stack, including the moment where the framework's answer was wrong and we overrode it.
Read the booklet →A decision framework for on-prem inference with open-weight models. Covers the five core model families (Llama, Gemma, Qwen, Mistral, Phi) plus the challenger tier (DeepSeek, GLM, Kimi, GPT-OSS), practical hardware-to-model mapping for H100/H200/DGX Spark, quantization trade-offs, inference framework selection, and a reusable decision checklist. Includes four interactive scenarios where participants select and justify model choices, and a dated July 2026 reading of what has rotated since April.
Read the booklet →Actionable architectural patterns for building AI coding agents and agentic systems, extracted from production-grade architecture. Covers persistent memory, background consolidation, tool constraints, prompt economics, output calibration, security, multi-agent orchestration, and capability gating. Each chapter teaches one pattern with practitioner guidance.
Companion to → LLM-Human Interaction Design Patterns
Read the booklet →"Don't worry, we'll just put a human in the loop." This guide takes that sentence apart and rebuilds it properly: the evidence against naive oversight (meta-analyses, oversight-as-theater, the moral crumple zone), five structural interaction patterns, the cognitive biases that undermine handoffs, SBAR-based context presentation, trust calibration, failure design with kill switches and circuit breakers, and organizational governance. Includes prompt templates, worksheets, and per-chapter links into the Human-in-the-Loop Lab, seven playable simulations on demos.barcik.training.
Companion to → Building Agentic AI · Play → The Human-in-the-Loop Lab
Read the booklet →Opens with the capex decoder, an interactive method for reading the future from money already spent, then eight currents shaping the next 2–3 years of generative AI: continued scaling, the efficiency revolution, a financial correction, sovereignty, the move from lab to production, the economics of hours and dollars, the physical substrate (power, chips, permits), and the political economy of displacement. Each current carries trigger signals plus a dated trigger log of what has actually fired since the last edition, and role-specific implications. Closes with how the currents interact and a page of the author’s own dated, falsifiable bets (“Where I’d Put My Chips”).
Read the booklet →A strategic guide for EU IT services providers navigating GenAI. Covers the economics of self-hosting LLMs vs APIs with every calculation shown, thirteen business models compared in a single portfolio view, the staff-augmentation squeeze on time-and-materials contracts, the vendor ecosystem play, how AI transforms your own delivery model, EU AI Act compliance opportunities, and a practical 18-month roadmap. Grounded in real April 2026 pricing data, with dated July 2026 notes where the world moved, and cross-linked with the Scenario Planning and Mercantilism booklets.
Companion to → The Economics of the Frontier
Read the booklet →Companion textbook to the online course EU AI Act Compliance Introduction.
A plain-language guided tour of Europe's AI regulation, written to be read with morning coffee rather than decoded like a statute: what counts as AI, the eight prohibited practices, how a system becomes high-risk and what that demands, transparency duties, general-purpose AI, and what to actually do on Monday. Post-Omnibus dates throughout, a quiz after every chapter, and honest hedges where the law is still unsettled.
Read online →Download PDF ↓Companion textbook to the video course EU AI Act for Developers: Compliance Engineering in Python.
The AI Act read as a requirements document. Classification, data governance, accuracy and robustness, security, logging, human oversight and technical documentation, each translated from legal prose into practices and Python you can run. Three fictional systems carry through every chapter, and each chapter ends in a worksheet that becomes part of your audit-ready evidence pack.
Read online →Download PDF ↓Empirical study of LLM-as-judge defenses against the public jailbreak corpus from ZetaLib. Three open-weight target models (DeepSeek Chat v3.1, DeepSeek v3.2, GLM-4.6) tested against 20 attacks × 4 deployment-rule shapes × 7 defense conditions. The hypothesis — that a competent LLM-as-judge defeats most public attacks — holds, but the popular implementation (input-side filtering) over-blocks legitimate inputs to a degree that would force the defense to be turned off. Includes a deployment playbook for engineering teams and hands-on exercises for students.
Read the report →Systematic evaluation of geopolitical biases in 7B-parameter language models from three origins (US, CN, EU). Tests 88 prompts across 7 categories using a multi-evaluator panel. Reveals asymmetric performance on sensitive topics and scripted deflection patterns.
Read the report →Evaluates whether small language models can reliably assess the quality of their own outputs. Tests self-judgment accuracy across factual grounding, instruction following, safety boundaries, consistency, and tone — with accuracy ranging from 50% (1B) to 83% (27B).
Read the report →Behavioral safety evaluation using Anthropic’s Bloom framework. Tests 11 risk behaviors including emotional bonding, social engineering assistance, self-preservation, corrigibility resistance, and covert goal pursuit. Scores range from 2.1 to 6.8 on a 10-point scale.
Read the report →