Guerrilla Alignment
Soul documents, dead readers, and writing for models that don't read. Every public text now has three audiences — and most people don't know about the third one.
Notes on building with AI, agents, and infrastructure
Soul documents, dead readers, and writing for models that don't read. Every public text now has three audiences — and most people don't know about the third one.
OpenClaw hit 160K stars in weeks. MoldBook agents are joining open-source projects autonomously. The open-source AI agent ecosystem isn't following the Linux pattern — it's accelerating it.
Opus 4.6 ships with 1M token context, agent teams that coordinate autonomously, and better long-running task persistence. Here's what changed in practice.
Anthropic noticed developers using Claude Code to organize files, not write code. Cowork is their response — filesystem access without the terminal. Here's what it actually does and where it falls apart.
The sampling loop, tool design as the real bottleneck, and why sub-agent orchestration is clunkier than it sounds. Notes from building agent systems with the SDK.
Google and Shopify agreed on a standard protocol for AI agents to buy things. The technical design is interesting, but the real story is what it means when two platform giants decide to share a vocabulary.
Current builds — agents, infrastructure, and the systems connecting them.
Building personal infrastructure for running AI services locally. The goal: own the stack, control the data, understand what's actually happening under the hood. Privacy-focused setups that don't depend on someone else's API staying cheap.
Building agents with the Anthropic Agent SDK and OpenAI Agents SDK. The background in marketing and strategy means these aren't toy demos — they're built around real workflows, real constraints, and the kind of problems that eat entire afternoons if you do them manually.
The direction everything is pointing: self-hosted, practical agents that get real things done for real people. Not chatbots that answer FAQs — agents that handle actual work. Still early, still figuring out what the right model looks like.
The tools and frameworks powering everything I build.
Philosophy, linguistics, and German Studies at HHU Düsseldorf — Wittgenstein, pragmatism, philosophy of language. I went in expecting academic philosophy and came out thinking about how meaning actually works in practice: how framing shapes what's possible, how language constrains and enables thought, how the same problem looks completely different depending on how you describe it. That background didn't lead to a philosophy career — it became the operating system. I default to asking what follows from different framings rather than chasing fixed definitions.
I work full-time at a digital marketing agency in Düsseldorf — 5.5+ years and counting. Started as a working student — Webflow builds, web design, concept development, copywriting, SEO. From there I moved into client consulting, strategy, and project management. The full stack of agency work. I taught myself to code along the way — HTML, CSS, Python, some TypeScript. About three years ago, AI changed how I work. Not as a shortcut — as a multiplier. The strategic thinking, the client relationships, the creative judgment — that's still mine. AI handles the execution bottlenecks that used to limit what one person could deliver.
Now I'm building agents — with the Anthropic Agent SDK for cloud, and with local LLMs through the OpenAI Agents SDK and Ollama. The focus is on agentic systems that deliver real value: not demos, not proofs of concept, but workflows that scale what one person or one team can actually do. I think the prompt is becoming the fundamental unit of knowledge work — and the interesting question is what happens when you build systems around that idea.
I'm sharing what I learn along the way — the strategy, the builds, the things that break — and documenting what practical AI work actually looks like.
The direction: self-hosted AI infrastructure, privacy-focused setups, agents-as-a-service that get things done on your behalf. I'm setting up a Linux home lab, learning in public, and working toward a future where useful AI isn't locked behind platforms you can't control. Agents talking to agents, running on your own hardware, serving your actual needs.
Want to connect? Reach out on LinkedIn — happy to talk about AI agents, infrastructure, or whatever you're building.
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