2025-12-22
Guess when I was in the hospital
It’s been one and a half years since I did…
2025-12-22
Guess when I was in the hospital
It’s been one and a half years since I did a year in review, which actually covered two years. As 2025 comes to a close, I figured I want to get an overview of what I did this year with my life outside of family and business. As usual, it was a mix of technical stuff, hacktivism, and general bullshittery. I figured I’d just write it all out in a hopefully concise way. For me, to have a memory of what happened. For you, to possibly find interesting bits and pieces that are useful to you as well.
If you have a look at the header image, you will see a bit of a gap for February and March. Reason being: I have become old. I suppose that’s the most interesting observation about this year for me. My body is falling apart slowly. I spent some time in the hospital and then needed quite a bit of time to recover, which meant I didn’t have the energy outside of family stuff to do anything really. That explains the gap in my GitHub contributions.
Anyways, with all of that out of the way, here we go. Let’s start with the one project I’m most proud of.
Boxie
Back in summer of 2024 I decided to learn electronics so I can build gadgets for my little boy. This culminated in Boxie, an always offline audio player for our then three year old. I’ve written about it extensively. Here’s the result:
It was quite a journey, going from the basic blinking LED to self-designed printed circuit boards, buying all the gear like a crazy person, and getting comfortable soldering SMT components. And obviously I had to port Doom to an embedded system as well.
I’m happy to report that our little one is still using it daily and now has a treasure chest of about 72 cartridges, each with audio plays of his favorite shows, audio books, and music.
Anytime something breaks, which happens surprisingly little, the boy and I get to unscrew the device together, look inside its internals, and dig around in them. It is my hope that this early exposure to tech will spark in him the interest to learn how these things work, just like my Game Boy sparked that interest in me.
Cards for Ukraine
I’m happy to report that this year we crossed the €300,000 line with Cards for Ukraine (you can read the origin story). Lots of small donations and one big donation by an anonymous donor helped us reach this goal.
We were able to send out two batches, one in May (190 cards) and one in October (308 cards), which were sent to as many Ukrainian families in Austria. Tanja is still writing her Substack and often asks for donations in her posts. I on the other hand still try to entertain the internet with stupid projects, and at the end of each social media post, I beg for donations for the association. Still kind of works, but obviously after three years of war and the recession, people’s purses are not as open as they used to be.
heisse-preise.io
heisse-preise.io (origin story) keeps chugging along. On Christmas Eve 2024, SPAR decided to change their API, which broke data collection for their products. I noticed it in March and didn’t bother fixing it. In May I finally got around to it. There’s now a data gap from December 24, 2024 to May 14, 2025 for SPAR products. The joys of scraping.
I’ve been advocating for years to get the Austrian government to implement the BWB’s (Federal Competition Authority) recommendation from 2023: legalize scraping of grocery price data and require retailers to expose EAN codes. The grocers already expose their data via APIs, and the BWB found they use these APIs themselves for "systemic price matching." Consumers should have the same access. But Minister Kocher left for the OeNB before implementing anything, and the new government is equally incapable or unwilling to just fix that one legal issue. This would not only enable price comparison sites to stand on good legal footing, but also open the data up for scientific investigation. No university will touch my data without that legal guarantee.
On a fun note, Cory Doctorow talked to The Verge and called me "the kid in Austria who scraped all the web prices of the two major grocers." I’m 41 years old.
The coolest thing about heisse-preise.io remains that my mom uses it to get herself into a rage about prices.
"Hello. Every day my blood pressure rises even more at the unbelievable... now 1 egg costs 40 cents. Oh wait, there’s 15 in there. Costs 26 cents. But it keeps going up every day with the price increases."
Messing with AI
I’ve been playing with large language models since the beginning of 2023, shortly after ChatGPT was released. See my previous year in review for projects like AMS Berufsinfomat, Generative AI workshop, Leitkulturomat, and KärntenGPT.
Over the last three years a lot has happened. We’ve basically switched from asking ChatGPT or Claude on their respective websites for some functions to having semi-autonomous agents run on our machines in YOLO mode. Since the second quarter of this year I fully embraced this kind of workflow and documented it here on the blog: Prompts are Code, MCP vs CLI, and What if you don’t need MCP. Early on I also recorded a 1.5 hour long video detailing how I work with coding agents. Looking back at that, my workflow has significantly changed. I’ll eventually write that up in a separate blog post. Suffice it to say, it’s still far from being perfect, but it feels much more streamlined, stable, and productive. Emphasis on feels. Because that’s all we have, vibes.
In his recent blog post, Armin segmented coding agent users into two camps: the ones that keep them on a tight leash and stay in the loop, and the ones who orchestrate armies of agents. I’m falling into the former camp, maybe because I’m old. Despite my best efforts, I could never get armies of agents to work for me at all, apart from maybe research tasks. Having them write large amounts of code has so far been a recipe for disaster for me. Maybe the secret is just not looking at the code they produce and hoping for the best. I don’t feel me being in the loop hinders my productivity compared to people with armies of agents. I’d also like to point out that not a lot of army of agents people have actually published their work, whereas I try to open source as much of my shit as possible and document it. So maybe there’s a lesson in there somewhere.
Before I dive into a few select "AI" projects, I think my summary of a year of working with coding agents is: nobody knows yet how to do this properly. We are all just throwing shit at the wall, declaring victory, while secretly crying over all the tech debt we introduced into our codebases by letting agents run amok. If somebody claims they have found the solution, don’t trust them, they have not. But there seem to be a few principles crystallizing that lead to more predictable outcomes, even if those outcomes are still far from reliable.
Texty
Texty, built in January 2025 before coding agents in their current form were a thing, was my first entirely vibe coded project. I’m dyslexic, so my grammar and spelling is just horrendous. I wanted a simple tool that works in the apps I use to write text: my browser and mobile apps.
All Texty does is show a tiny button next to whatever text editing field has focus. Click it, and it extracts the text, lets you spell and grammar check it via an LLM, then puts the corrected text back into the field. I built this as both a browser extension and an Android app. The Android version uses the Accessibility Service so it can observe all running apps, inspect their UI, and interact with UI components.
I used this for a few months until I got fed up with the workflow. It was just too cumbersome, and the accessibility service makes apps noticeably slower.
The real benefit of building Texty for me was that it was my first foray into "agentic engineering" (aka LLM take the wheel). Before Texty, I would use Cursor just for tab completion and sometimes for rewriting functions. With Texty, I wanted to see how far an LLM could go building a project with minimal human intervention. While I had to fix up a bunch of things manually, the simplicity of the extension and app allowed the LLM to actually succeed in building the thing I wanted. I hadn’t taken a look at the fantastically complex accessibility service API, and I’m hopeful I never will.
Hacking Claude Code
While I was a heavy Claude Code user during my Claude Code phase, I got curious about how it actually works. So I did what any reasonable person would do: I patched the binary to disable its anti-debugging checks, monkeypatched fetch to intercept all API calls, and started poking around.
Some fun findings (for the Claude Code version back then):
- Claude Code uses Haiku to generate those little whimsical "please wait" messages. For every. token. you. input.
- It also uses Haiku to check if a bash command contains injection attacks. One LLM judging whether another LLM’s output is dangerous. What could go wrong?
- The
/costcommand is disabled for Max plan users because apparently we don’t need to know numbers. Numbers are bad for us. I patched that too. - The system prompt told Claude to clean up test files, which explained why it kept deleting things it shouldn’t. They removed that.
- They added emoji restrictions three separate times across different tools because everybody hates when LLMs inject emojis everywhere.
I wrote up the full details in two blog posts: Hacking Claude Code and Claude Code Anti-debugging. I also built cchistory.mariozechner.at where you can see diffs between any two Claude Code versions’ system prompts and tool definitions.
Before Claude Code had hooks and other extensibility features, I built a bunch of tools to help with my workflows: claude-trace to record all API requests and see everything Claude hides (system prompts, tool outputs, raw API data), claude-bridge to use other LLM providers with Claude Code, cc-antidebug to disable anti-debugging and enable /cost for Max users, cccost for accurate real-time cost tracking, and claude-notify to get system notifications when Claude needs input.
I’ve since abandoned Claude Code and written my own coding agent harness, which has served me well over the past months. More on that later.
Exploring and Abandoning MCP
I might be losing my hacker cred, but I actually like VS Code for code exploration, viewing diffs, and other navigational tasks. When working with Claude Code, I wanted it to be able to open, highlight, and diff files for me in VS Code as it works through things. Back in April, there wasn’t a Claude Code VS Code extension yet. The only way to make Claude Code interact with VS Code back then was by building an MCP server. So I built vs-claude, a VS Code extension with an integrated MCP server. That allowed any Claude instance on my machine to access any VS Code window, open files, highlight lines, and show diffs. It worked okayish.
Building that extension and associated MCP server gave me a pretty good handle on what’s possible. So naturally I started building more MCP servers, like mailcp for Gmail integration, which all things considered was pretty fucking terrible. It basically just shat a bunch of JSON into the context of the agent. And while it worked, it was definitely far from being ideal.
That was also the project that made me realize that MCP server outputs can only be transformed, filtered, or composed by the LLM inside its context. Which is fucking terrible, especially compared to the kinds of composability and context space preservation you can achieve by chaining multiple commands via its bash tool.
After this realization, I started writing more CLI tools the LLM can invoke and easily compose via bash. The first baby steps were lsp-cli, which extracts symbol information from codebases using Language Server Protocol. I used this as part of an agent-based workflow in a brownfield project where it was quite effective. But it’s not the LLM that’s invoking it directly. It’s a little CLI that prepares context for the LLM to work off of. If you want to know more, check out the Prompts are Code blog post.
In general, I haven’t found LSP servers to be all that helpful to an LLM, be it for code exploration or reporting errors. I found setting up forced type checking, linting, and what have you as part of a commit hook to be far more effective at making the agent fix its errors, without filling up your context window with possibly irrelevant LSP diagnostics.
In the summer of 2025, AI social media was full of MCP talk, which didn’t align with my own experience with MCP and CLI tools. That prompted me to see what actually performs better with the current gen of agents. I tried to numerically assess that with a very non-scientific evaluation. The result: it doesn’t matter so much for many use cases whether it’s MCP or CLI, especially those where composability doesn’t play a role. What matters more is that whatever tool you use, it doesn’t shit a ton of tokens into your context. Since CLI tools are significantly easier to create, maintain, and debug, I eventually abandoned all MCP servers and shared my conclusions in What if you don’t need MCP?
I also built a bunch of Google CLI tools so my agent can access Gmail (gmcli), Calendar (gccli), and Drive (gdcli), which entirely replaced mailcp and other ad hoc tools I wrote for those use cases.
Yakety
Yakety promotional material generated by Veo 3
Peter introduced me to the wonderful world of instructing your coding agent by voice. Back when I started doing this, there were already a handful of options on the market, such as Whisperflow. But all of them require very intrusive permissions on macOS, such as key logging and permanent access to your audio input devices. While I’m sure that the products available on the market aren’t nefarious, they would be an excellent vector to exfiltrate tons of useful data from you.
So I figured, since I had previous experience with Whisper, I’d just build my own little voice transcription app. And that’s what Yakety is. It runs fully local, you can select whatever Whisper model you want, and it only records while you hold down the hotkey. Press FN (macOS) or Right Ctrl (Windows), speak, release, and the transcription gets pasted into whatever app has focus.
I originally planned for this to be a commercial product with a pay once, get updates for life kind of policy, including the source code. That idea kind of fizzled out as I got distracted by other things. I’ve since stopped using voice dictation for working with my coding agent. Most of the time I need to specify file paths and other stuff to build up the context, and voice transcription doesn’t lend itself well to that work. Also, I type very fast. I still use Yakety to this day, but not for coding. I mostly use it for writing, like this very blog post. Here’s a YouTube video of the first one and a half hours of me writing this blog post.
Is it as fancy as the others? No. But I know exactly what it’s doing, and starting today it’s open source and probably full of bitrot. Enjoy!
VibeTunnel
Dorks
Sometime in June, Peter invited Armin and myself to his beautiful flat in Vienna, where we set out to build a thing within 24 hours. We didn’t have any idea what to build initially, so we just took to a whiteboard and started throwing ideas at it. The one thing that stuck was: we would all like to be able to control Claude Code, the coding agent we all used back then, through our mobile phones on the go. There wasn’t really anything like that on the market, either as a product or open source. So we thought, how hard can it be if the three of us just sit together for 24 hours with infinite numbers of Clawds working for us. And so VibeTunnel was born.
The whole thing was an experiment in ad hoc vibe coding. We worked like a Skunk Works team, assigning different parts of the project to each of us. Details are blurry, but I think Armin was assigned the backend, Peter was assigned a dedicated macOS app, and I was assigned the frontend, the thing I’m least capable of. While Peter and Armin worked on their respective parts, I threw together a backend so my frontend had something to work with, especially the terminal streaming. Armin was writing the backend in Rust, naturally, only to find that the Node.js version actually worked better. We ended up with a Frankenstein that worked but was basically unmaintainable.
Despite that, it was an extremely fun experiment. The three of us had a great time hanging out in real life, as we usually just shoot the shit on the internet. After we dissolved, Peter tried to keep VibeTunnel running, turned it into an open source project, but at that point the project was probably too complex for anyone to jump on board. Peter has since created Clawdis, which is just a much better version of VibeTunnel that directly integrates with WhatsApp and other chat apps. It too is a spaceship, and I haven’t dared looking into the repository yet, but it seems to work a lot better than VibeTunnel, at least for him.
Sitegeist and Cellgeist
Sitegeist using Google to search cookie recipes, then creating a calculator (real-time) Claude for Chrome trying to do the same OpenAI Atlas also failing
Sometime in October I started working on a browser extension called Sitegeist. Its purpose is twofold. For one, it’s a deep research agent that allows me to research companies and persons or find information on the web on specific topics by directly using Google and interacting with websites.
It’s also a great scraping companion for one-off scrapes for which I don’t want to write a program myself. You can also use it for shopping if that’s your thing, which is the use case Google and all the others always demo. I think that’s a complete waste of the technology. There are better uses for browser agents.
The benefit over something like Chrome DevTools Protocol or Playwright is that I get a full trace of the research process, so I can verify the research more easily. Sitegeist has full session management so you can go back and continue a research session if you want. It also has multi-provider, multi-model support, so you can use any kind of model. Empirically the Claude models have performed the best so far.
Sitegeist also comes with artifacts, which you might know from Anthropic’s web UI or OpenAI’s Canvas feature. Sitegeist can create in-memory HTML, Markdown, Excel, PowerPoint, Word docs, and so on, which I can also download. This lets Sitegeist create interactive dashboards containing the research results, which I can iterate on.
For better automation of sites you visit regularly, Sitegeist comes with a skills system where Sitegeist just writes itself a bunch of JavaScript that gets executed on a page to do something. For example, write a new email in Gmail, add a calendar entry on Google Calendar, interact with Google Sheets, and so forth.
Under the hood, Sitegeist does a bunch of interesting things to interact with websites. The basic gist is that it writes JavaScript that gets executed in the context of the website. The naive version of this is just slamming a script tag into the website with the code the model generated. The problem is that depending on the site’s content security policy, that may or may not work. For most popular sites it doesn’t work at all. The alternative is to use user scripts, an extension API that allows you to inject JavaScript into a page without CSP restrictions. The nuclear option is directly using the debugger. With the debugger, the model actually has access to everything in the website, including HTTP-only cookies. I use this functionality a lot to write scrapers for websites with paywalls.
Of course, with great power comes great exfiltratability via prompt injection. It’s surprisingly easy to just have some hidden text on a website and get the model to exfiltrate data to some endpoint. Paired with access to the debugger, the model can easily exfiltrate your auth credentials and browser-side secrets. None of the big lab browser extensions have a solution for this. And neither do I. I did some experiments isolating the JavaScript execution environment in a more controlled way (see the JailJS blog post). Ultimately, that too cannot cover all attack surfaces. I personally think that Anthropic putting Claude for Chrome into the hands of normies is a really bad idea, specifically since it seems that the model can only interact with a website through the debugger, which means the model potentially has access to all those secrets needed to impersonate a normie on the web.
On the other hand, I don’t see a lot of people actually using browser agents, just because it takes some technical ability to make them useful. So maybe the great prompt injection and exfiltration crisis is averted simply by the fact that these agents aren’t fit for the general populace yet.
Given how much better Sitegeist performs compared to other offerings, I originally planned on commercializing it. But then I realized that for normies I would have to become a token gateway as well. And for techies, it just feels wrong to ask for money for such a tool. So I’ll open source Sitegeist probably in a few weeks when I clean it up and switch it over to pi as the underlying agent engine.
As a fun little experiment, I also built Cellgeist, which is the same exact thing, just living in your Excel.
Cellgeist analyzing data and creating charts in Excel
This too I will open source, but it’s really just a proof of concept, while Sitegeist is actually quite robust and useful already.
pi coding agent
pi exploring its own codebase
Anthropic kept adding features to Claude Code and would also mess with the toolset and system prompt constantly. This fucked with my workflows and I got more and more annoyed over the months. So I eventually set out to write my own little coding agent that works exactly the way I want it to work. That meant stripping away all the stuff I don’t need and that I consider to be an anti-pattern. No MCP support, no built-in sub-agents, no plan mode, no to-do tool. I found that the models are really effective if you give them just a minimal set of tools: read, write, edit, bash. Anything else on top of that should be an extension of this minimal core and not a built-in thing. As Armin put it, pi is "the Hacker’s Open Source choice." It’s infinitely extensible without having to touch the core itself, allowing you to create entirely different agents like Peter’s Clawdis, a personal agent that lives in your chat app of choice with computer access.
While the core is minimal, pi comes with all the same creature comforts you’re used to from Claude Code, Codex, Amp, opencode, or what have you. It’s just that you can easily write your own features on top of it, down to custom compaction. This also includes hooks and custom tools being able to contribute custom UI to pi, a feature I haven’t seen in any of the other coding agent harnesses.
pi is also multi-provider and multi-model, so you can use your GitHub Copilot subscription, your Claude Max plan, Cerebras, OpenRouter, local models, whatever. You can also switch models within the same session, which may or may not be a good idea. It also includes cost tracking, something other coding agent harnesses do a bad job of.
I’ve written extensively about how and why I built pi in What I learned building a minimal LLM coding agent, so I won’t repeat all of that here. Since I wrote that blog post, pi has seen a lot of third-party contributions from really nice people.
I’m kinda glad I’m back in the open source game with it. It brings me great joy to sit down at night after everyone’s asleep and just collaborate with people on GitHub or tune tiny little things I’ve found to be lacking. Pi has been my exclusive daily driver for the past two months.
Teaching My Linguist Wife Claude Code
My lovely SO Steffi (@steedl.bsky.social) is a linguist who usually does her data analysis by hand in Excel. While she has a minor technical background (a little HTML, JavaScript, and some Python), she’s definitely not a programmer. As she worked on her last paper, I looked over her shoulder to see what she was doing and realized that Claude Code could help her with some of that. So we set out to teach her Claude Code in two short sessions over two nights.
Her research project involved analyzing Austrian dialect verb prefixes across 32 speakers, each with two conversation settings. She had folders full of Excel files with 18,000+ rows of linguistic annotations. The manual workflow would have been: open each file, split a column containing four values separated by semicolons into four separate columns, filter out certain forms marked with asterisks, then aggregate the results per speaker and setting.
Instead, Claude Code wrote Python scripts for each step. One script converted all Excel files to CSV with the right delimiter. Another split the annotation column into four columns. A third filtered out the irrelevant forms and wrote them to separate files. Then came the analysis scripts that counted the three possible values of her linguistic phenomenon per speaker and setting, followed by summary statistics comparing settings and analyzing factors, clustering analysis, and finally a visualization script that generated charts.
The beautiful thing about this workflow is reproducibility. When we found that 10 out of 18,000 data points had errors in the source database, she fixed them in the cloud database, re-exported, and just re-ran the three Python scripts. Everything regenerated automatically. She can attach the raw data and the scripts to her paper as supplementary material, and anyone can reproduce her results.
What I found interesting was that just by telling her that Claude can write code for her, she developed an intuition of what’s possible and what’s hard rather quickly. By giving her some idea about how to structure her work (building a pipeline made of small scripts that take files as inputs and generate files as outputs that feed into the next little program), she was able to run with Claude Code on her own in pretty much no time. She cannot judge the Python code Claude generated for her. But she can judge the outputs of each individual pipeline stage and verify their correctness.
This is a repeating theme with agents: they’re really only effective in the hands of domain experts. Another repeating theme is that domain experts don’t yet understand how these tools can actually help them automate verifiable tasks, so they have more time to focus on the actual work. While the experiment with Steffi was a great success in terms of increasing her productivity, I still struggle to come up with teaching materials that could be deployed to a bigger audience of non-technical domain experts so they too can benefit from these kinds of tools. Sitting down with everyone for two nights is not a scalable approach. I think the biggest problem is that the baseline knowledge each domain expert comes with, with respect to technical things, is different. So while they learn these tools, the questions will also differ. Which kind of requires a very personal tutor to help them.
The videos are in German: Part 1, Part 2, Part 3, Part 4. The code is on GitHub.
Investigating Slop in the Wild
Since the release of ChatGPT, I’ve been hard on organizations and companies that use LLMs irresponsibly or in ways that are actually detrimental. You can read some of that in my two years in review blog post. This kind of topic plays well on social media and allows me to create long threads explaining different aspects of LLMs and their use and why certain uses are not a great idea. At the end of these threads, I usually beg for donations for our association, which explains why I put so much time into this kind of "work." Here’s a selection of fun little explorations I did over the past year.
Austrian Media LLM Usage
Commander Raika posted a screenshot on Bluesky showing an OE24 article containing unedited ChatGPT artifacts. That’s what we in my industry call nerd sniping.
Austrian newspapers are heavily subsidized, both with government ads and direct funding. One of these funding programs is called "Fonds Digitale Transformation," where newspapers can hand in shoddy project descriptions that lay out digitalization efforts, for which they then get thousands to hundreds of thousands of euros. Most of these projects are just bullshit. So when I saw Commander Raika’s screenshot, I thought this is a good time to investigate how this money is spent. Here’s a table for OE24 showing some of their funded "Digital Transformation" projects.
So how could I numerically investigate whether OE24 was using LLMs to write articles? We already had direct evidence by them leaving traces of ChatGPT output in their online articles. But that could be just a single person at the newspaper fucking up. I needed more data.
The basic idea was this: LLMs love inserting em-dashes and en-dashes everywhere. The hypothesis is that after the introduction of ChatGPT, (plus some time for the technology to actually reach newsrooms), we would see an increase in em-dash or en-dash use over time. That increase can obviously also be explained by other causes, such as changed style guides, new authors coming into the team, or changes in the CMS. But given enough data, we might be able to differentiate between these causes.
So I scraped OE24’s sitemap, downloading thousands of articles for each October for the years 2021 to 2025, and counted dashes normalized by article length. The result: stable dash usage from 2021-2024, then a massive spike in 2025. Categories like Shopping, Tierschutz, and lifestyle content went from almost no dashes to being full of them.
Since the spikes weren’t uniform across categories, I would assume this wasn’t a style guide or CMS change that caused the spike. It is also unlikely that they switched out the entire team for these categories with authors who have writing styles with more en-dashes. So I would count this as direct evidence that at least for these categories, OE24 now lets LLMs write the articles. It kind of makes sense because this is the kind of content that LLMs excel at and don’t necessarily need a human in the loop. I made a YouTube video and wrote a Bluesky thread explaining the whole thing.
For the second investigation, I was joined by my linguist friend Jenia. We expanded to Exxpress, Heute, Krone, and Falter. Falter and Krone also showed rising dash usage:
Falter 
Krone
While our biases would lead us to believe that’s expected for Krone, we didn’t expect it for Falter, which is generally considered a high quality weekly. Did they start using LLMs too?
Not necessarily. Many CMS systems auto-convert <space><hyphen><space> to <space><en-dash><space>. So a CMS update could explain the pattern. The solution: differential analysis. If dashes go up while hyphens go down proportionally, that’s a CMS doing find-and-replace on existing content. If dashes explode while hyphens stay flat, that’s new dash-heavy content being added, which means LLM generation. Falter and Krone showed the CMS pattern. Exxpress, OE24, and Heute showed the LLM pattern:
OE24 
Heute
For the final investigation, we added Kurier and Kleine Zeitung. Both showed selective LLM adoption in specific verticals like lifestyle and entertainment, while keeping hard news human-written.
This correlates nicely with RTR (Austrian Regulatory Authority) public funding. OE24’s parent company received €287,000 in 2024 for "KI Bilderkennung/Datenbanken/Korrektur/Creation." Heute got €140,876 for "KI-gestützter Journalismus." Austrian taxpayers funded these AI deployments, yet none of the publications disclose LLM usage to readers.
As a byproduct, I now have a scraper for all major Austrian news websites. I’m working on turning that into a media monitoring tool and a public search engine across all news articles published in the past 30 days. Here’s a demo tracking Mahrer coverage across publications after a ZIB 2 interview:
Searching Austrian news sites for Mahrer coverage
For shits and giggles, Jenia and I had Claude write up a full methodology paper with all the charts and details if you want to dig deeper.
fobizz School AI Tools
LLMs in schools and universities are reality now. Whether that’s good or bad is yet to be seen. Political decision makers love nothing more than stuffing AI tools into schools, pretending they increase some made-up metric. I truly hate that. I believe when used appropriately, LLMs can actually help with learning. But the things I see in the wild are just mindless GPT wrappers that actually hinder learning.
One of these tools is called fobizz, and you should definitely watch this 38C3 talk by Rainer Mühlhoff and Marte Henningsen. They investigated fobizz’s automated homework correction tool and found it produces essentially random grades. They also interviewed teachers about AI in classrooms and discuss how dystopian it is to fix social problems in education with tech tools. Multiple German states have bought licenses for all their teachers from fobizz, and some Austrian schools use it too. The talk is in German, but well worth your time.
I went through fobizz’s own training course for teachers and documented what I found. The training videos use AI-generated voiceover pretending to be a real person. Tools like quiz generators and lesson planners have prominent PDF download buttons, encouraging teachers to use the output directly without verification. There are almost no warnings about checking LLM output for correctness.
They claim "highest security standards, TÜV certified" for GDPR compliance, but also tell users "please don’t enter sensitive data." Their transcription tool scrapes YouTube and Vimeo videos, which violates both platforms’ terms of service. They suggest teachers use random custom GPTs from unknown creators.
The cherry on top: historical figure roleplay chatbots. Including a Sophie Scholl bot. When asked "Welches Unrecht ist dir widerfahren?" (What injustice happened to you?), the bot replied "Mir persönlich ist kein Unrecht widerfahren" (No injustice happened to me personally). Sophie Scholl was executed by the Nazis. This is what schools are paying for with taxpayer money.
From April 2024 to September 2025, 114 Austrian schools participated in an AI tools pilot project with "scientific accompaniment" by KF Uni Graz. The final report is basically a glorified link collection. I went through it and found they unreflectively recommend fobizz (including the Sophie Scholl bot), list Perplexity as providing "fact-based answers," and include tools like LaLeTu that collect children’s full PII (name, birthday, native language, gender) plus audio recordings and send them to their servers. No real evaluation of any of these tools, no guidance for teachers on how to assess them in practice. Schools are left to figure it out on their own.
Facial Recognition Hiring Paper
The Economist published an article about a paper claiming facial analysis can predict personality traits for hiring decisions. The article read like the author was getting off on the idea of this being used in production. I read the actual paper and it’s methodological garbage.
The paper tries to establish a connection between facial features and personality traits by citing genetics research. But the cited papers only say personality traits are partially genetic. They do NOT say you can infer personality traits from facial features. The authors then build on a method from a previous paper that was already torn apart in Cell as "the reanimation of pseudoscience in machine learning."
The data comes from Revelio, a company that generates much of its data using AI. The salary data is model-generated. The "race" classification is based on names and locations, plus image recognition. They use a model trained on self-reported personality traits, which any psychologist will tell you is unreliable.
The paper even admits its predictive power is weak (low R²), but claims the method is "new." The robustness checks are laughable: they tested "calm" versus "happy" facial expressions on 28 women and 37 men, all with identical lighting and hairstyles. No variation in any real-world conditions.
The paper asks whether it’s "ethical to screen out individuals whose faces predict less desirable personalities." This isn’t a challenging question because the method doesn’t work. The challenging question is why pseudoscience like this gets media coverage in outlets like The Economist. This is phrenology with a fresh coat of AI paint.
Other Investigations
Government Chatbots: I used Sitegeist to find and test chatbots on Austrian government websites. Kremsi (Kremsmünster) is built by schorn.ai and trivially jailbreakable. Its privacy policy claims EU-only data processing, but it’s actually hosted on Vercel in the US. It won a Verwaltungspreis. Feldi (Feldkirchen) is built by AI Concierge, the same company behind the AMS Berufsinfomaten. KLARA (Klagenfurt) uses the same platform. The Telfer Copilot (Telfs) is hosted on Azure Ireland and can actually read Gemeinderatsprotokolle, which is genuinely useful. I did a YouTube livestream showing the whole process.
Clinara Medical Transcription: Someone posted about Clinara, an Austrian startup selling AI transcription for doctors. I tested it by reading a Standard article in Hochdeutsch and analyzed the word error rate. Whisper-based transcription has around 5-10% word error rate, meaning roughly 5-10 words per 100 are wrong. For medical documentation, that’s concerning. Bonus: their web app sends the system prompt from the client side, so I could use their server as a free GPT API endpoint.
StoryOne Book Generator: The FAZ published a puff piece about StoryOne, an Austrian startup claiming to revolutionize non-fiction book writing with AI. I did a self-test: gave it one of my blog posts and had it generate a "fact-based" book. The result was 75 pages (18 of them blank "for layout"), no actual fact enrichment despite their "neurosymbolic AI" claims, just hallucinated LinkedIn slop. All parties involved should be ashamed, including the FAZ.
Other Notable Projects
Parliament Watch: @alltagsprolet.at asked me if I could build something to track who’s absent from parliament sessions. "Give me 10 minutes," I said. It took 2 hours. The site scrapes stenographic protocols and shows absence rankings by party and individual MPs.
Carinthian Honors Dashboard: Kathrin Stainer-Hämmerle posted that Carinthia awards about 50 state honors per year but claims they can’t determine how many go to women. Armin Wolf suggested someone count them manually. I used Sitegeist to scrape 3 years of honors data and had an LLM classify gender from names. Built a dashboard in a few hours.
Coalition Protocol Leaks: In February, someone leaked the FPÖ/ÖVP coalition negotiation protocol. I hosted the 223-page PDF so people could read it themselves. Then a little bird also sent me the ÖVP/SPÖ/NEOS protocol from the failed negotiations. Made both available and archived them on the Wayback Machine. Martin Thür asked if anyone could find identical formulations between the two documents. I suggested using LLMs to compare thematically similar chapters.
The reaction from the journalist bubble was interesting. I got quite a few DMs congratulating me on actually leaking the thing. Others were upset because it torpedoed their own reporting. I even got a call from a prominent Austrian journalist who wanted me to expose my sources. That was fun.
WaveMind: I started scraping Austrian audio news sources and transcribing them with AI. It began with Falter Radio, where I transcribed all 437 episodes going back to 2023. Then I added Ö1 Journale, ORF TV news, Dunkelkammer, and some other podcasts. Everything is searchable on transcript level, behind closed doors. Closed doors because I don’t want to get sued by the biggest Austrian broadcaster. I did give access to a couple of journalists. They found it mildly useful.
Cremer Archive: Matthias Cremer has a photo blog on Der Standard documenting Austrian politics through images. I scraped and archived the whole thing. This was a nice little project, and Matthias sent me his book as a thank you. That made my day.
Talks
Vienna Tech Soirée panel discussion
2025 was the year I resumed giving talks. From 2010 to 2016 I was on the conference circuit a lot, both for my open source work and my money-making work. Then I stopped. Really enjoyed getting back into it.
It started with an invitation to Vienna Tech Soirée where I was asked to jump in for a deeply sick Peter Steinberger. It was a panel discussion with Armin Ronacher of Flask and Sentry fame, and Nicolay Gerold who’s working on AMP. The panel’s topic was "How to vibe code to a billion dollars." Funnily enough, none of us panelists actually agreed that’s a thing yet. The full panel discussion is available on YouTube if you want to see me make an ass of myself.
It was an enjoyable experience with great organization from Andreas Klinger and others. After the panel, I had a lot of interesting discussions with people I’d only known from the internet up until that point.
Peter then set up the first Claude Code Anonymous meetup in Vienna, where I turned my Hacking Claude Code blog posts into a five-minute talk. The event was hosted at Felix Krause’s Context offices. Everything was last minute and makeshift and I loved it. Again, I met a few people I’d so far only known from the internet, as well as old friends like Christian Cito, my partner in crime in scraping grocers.
I gave the same talk at the [LLMs in Real Software meetup in Graz](https://www.meetup.com/llms-in-real-software-meetup-group-graz/events