Tools on Pfisterer Consulting

My LinkedIn strategy as code: building a multi-agent content pipeline

Tue, 19 May 2026 15:00:00 +0200

Last Sunday, 22:00. On my desk are eleven Markdown files, next to them an open spreadsheet with 90 days of LinkedIn analytics. Cold mate in the glass, an honest question in the back of my head about why I am building a pipeline rather than writing the next post.

The spreadsheet gives a clear answer. My profile has 511 followers and delivered 44,483 impressions over the last 90 days at an engagement rate of 0.18 percent, a factor of 11 to 16 below the LinkedIn benchmark of two to three percent. On 52 of those 90 days the feed stayed entirely silent, without a single reaction to any of my posts.

Context Rot in AI Agents: Why Quality Degrades

Tue, 31 Mar 2026 14:30:00 +0200

Anyone who works with Claude Code, Cursor, or Copilot knows the pattern. The problem has a name: Context Rot. The first results are sharp and the architecture holds up. Then quality drops off. After 20, 30 minutes, the answers start repeating themselves. The AI agent loses context. It produces code that contradicts what it wrote five prompts ago.

Not a user error – an architecture problem. The solution lies in context engineering: the deliberate management of context windows through multi-agent orchestration. These are exactly the kinds of real-world problems I address in my AI & Automation consulting.

Inside OpenClaw #5: Production-Ready AI on WSL2

Thu, 05 Mar 2026 09:00:00 +0100

Your inference server runs fine on Friday afternoon. You set the right vLLM flags, tool calling works, the agent responds correctly. Monday morning, the server is dead. No crash log. No error message. WSL2 simply stopped running.

This is the gap between “works on my machine” and “runs in production.” And it has nothing to do with code. Production stability of AI systems is an aspect of my AI and automation consulting.

Inside OpenClaw #4: Why AI Agents Need a Personality

Tue, 03 Mar 2026 10:00:00 +0100

Ask a bare language model the same question twice, and you’ll get two different personalities. One response is formal and thorough, the next is casual and terse. There’s no consistency, no sense of role, no memory of what it’s supposed to be.

For experimentation, that’s fine. For production use in a business, it’s a dealbreaker — which is exactly why configuration is a central aspect of my AI and automation consulting. When your AI agent behaves differently every session, users lose trust — and they lose it fast.

Inside OpenClaw #3: Why We Replaced Our AI Model — and Need 6x Less Memory

Fri, 27 Feb 2026 07:00:00 +0100

A Model That Changes the Rules

Model changes like this and their implications feed directly into my AI and automation consulting. Our previous setup was solid: Mistral Small 24B on vLLM, AWQ-quantized, the right flags configured, tool calling working reliably. But two limits remained: 32,000 tokens of context and 14 GB of VRAM consumed by model weights alone — on a GPU with 24 GB total.

Then Qwen3.5-35B-A3B appeared. A model that sounds impossible on paper: 35 billion parameters, but only 3 billion active at any given time. A 262,000-token context window. And faster than our previous, smaller model.

Inside OpenClaw #2: The Hidden vLLM Flags for Mistral

Thu, 26 Feb 2026 10:00:00 +0100

Running a Mistral model on vLLM should be simple. Load the model, start the server, send requests. And for basic text generation, it is. But the moment you need tool calling — the backbone of any AI agent — things start breaking in ways that are surprisingly hard to diagnose. Technical details like these are part of my AI and automation consulting.

The Silent Failures

When vLLM’s tool-calling configuration is wrong, it doesn’t throw an error. Instead, you get one of these failure modes:

Inside OpenClaw #1: Web Search Without Hallucination

Wed, 25 Feb 2026 10:00:00 +0100

Ask a local LLM to search the web, and something interesting happens: it doesn’t tell you it can’t. Instead, it confidently generates search results — complete with URLs, snippets, and source attributions. The problem? They’re entirely made up.

This isn’t a minor nuisance. For an AI agent that’s supposed to retrieve real information, fabricated sources are a critical failure mode. I address challenges like these in my AI and automation consulting. And it’s one of the hardest problems to solve when building on local models.

Zero API Costs: How We Run an AI Agent on a Single GPU

Tue, 24 Feb 2026 16:00:00 +0100

Most AI agents depend on cloud APIs — and every request costs money. With agent workloads, those costs add up fast: a single complex task can consume 50,000 to 100,000 tokens. Run that a few dozen times a day, and you’re looking at a serious monthly bill.

We took a different approach with OpenClaw. Our AI agent runs entirely on local hardware — one GPU, no cloud dependency, no recurring API fees. Local AI solutions are a focus of my AI and automation consulting.

OpenClaw: How We Got Three AI Agents to Debate Each Other

Wed, 18 Feb 2026 14:00:00 +0100

In the first article about OpenClaw, we covered the basic idea: an AI assistant that runs on your own hardware and is controllable via WhatsApp. Since then, we’ve been experimenting further — and built something that shows where AI automation is actually heading. Experiments like these feed directly into my AI and automation consulting.

We got three AI agents to debate each other in separate terminal sessions — until they agreed on a solution.

Website Relaunch with AI Instead of an Agency: What Used to Cost €15,000 Now Takes 2 Days

Fri, 13 Feb 2026 10:00:00 +0100

In early February 2026, I faced a decision: my website needed a relaunch. New design, better structure, more professional presentation. The classic route would have been: brief an agency, collect quotes, wait 4–8 weeks, invest €10,000–20,000.

I chose a different path. And this article describes honestly how it went. It is a practical example of my AI and automation consulting – only this time for myself.

The Tech Stack: Hugo, Claude, and OpenCode

Hugo – the Static Site Generator

Hugo is an open-source framework that generates websites from Markdown files. No database, no WordPress, no PHP. Just HTML, CSS, and JavaScript – lightning fast, secure, and easy to host.

OpenClaw: The Personal AI Assistant That Siri Should Have Been

Thu, 12 Feb 2026 14:00:00 +0100

For years, the tech industry has been promising us the perfect personal assistant. Siri, Alexa, Google Assistant — all disappointing the moment requirements go beyond “set a timer.” Now there’s an open-source project that actually delivers on that promise: OpenClaw. Evaluating solutions like these is part of my AI and automation consulting.

What Is OpenClaw?

OpenClaw is an AI assistant that runs on your own computer (or server) — not in a tech giant’s cloud. You communicate with it via WhatsApp, Telegram, Discord, or iMessage — exactly where you’re already writing all day.