AI Slop: An Experiment in Discovery Solo Episode Reflection: I'm back behind the mic after about a year-long break. Producing this podcast takes more time than you might imagine, and I was pretty burnt out. The last year brought some major life events, including moving my family back to New Zealand from Denmark, dealing with depression, burying my father, starting a new business with my wife, and having a teenage daughter in the house. These events took up a lot of space. The Catalyst for Return: Eventually, you figure out how to deal with grief, stop mourning the way things were, and focus on the way things could be. When this space opened up in my life, AI came into the picture. AI got me excited about ideas again because for the first time, I could just build things myself without needing to pitch ideas or spend limited financial resources. On "AI Slop": I understand why some content is called "slop," but for those of us who see AI as a tool, I don't think the term is helpful. We don't refer to our first clumsy experiments with other technologies—like our first map or first lines of code—as slop. I believe that if we want to encourage curiosity and experimentation, calling the results of people trying to discover what's possible "slop" isn't going to help. My AI Experimentation Journey My goal in sharing these experiments is to encourage you to go out and try AI yourself. Phase 1: SEO and Content Generation My experimentation began with generating SEO-style articles as a marketing tool. As a dyslexic person, I previously paid freelancers thousands of dollars over the years to help create content for my website because it was too difficult or time-consuming for me to create myself. Early Challenges & Learning: My initial SEO content wasn't great, and Google recognized this, which is why those early experiments don't rank in organic search. However, this phase taught me about context windows, the importance of prompting (prompt engineering), and which models and tools to use for specific tasks. Automation and Agents: I played around with automation platforms like Zapier, make.com, and n8n. I built custom agents, starting with Claude projects and custom GPTs. I even experimented with voice agents using platforms like Vappy and 11 Labs. Unexpected GIS Capabilities: During this process, I realized you can ask platforms like ChatGPT to perform GIS-related data conversions (e.g., geojson to KML or shapefile using geopandas), repro data, create buffers around geometries, and even upload a screenshot of a table from a PDF and convert it to a CSV file. While I wouldn't blindly trust an LLM for critical work, it's been interesting to learn where they make mistakes and what I can trust them for. AI as a Sparring Partner: I now use AI regularly to create QGIS plugins and automations. Since I often work remotely as the only GIS person on certain projects, I use AI—specifically talking to ChatGPT via voice on my phone—as a sparring partner to bounce ideas off of and help me solve problems when I get stuck. Multimodal Capabilities: The multimodal nature of Gemini is particularly interesting; if you share your screen while working in QGIS, Gemini can talk you through solving a problem (though you should consider privacy concerns). The Shift to Single-Serve Map Applications I noticed that the digital landscape was changing rapidly. LLMs were becoming "answer engines," replacing traditional search on Google, which introduced AI Overviews. Since these models no longer distribute traffic to websites like mine the way they used to, I needed a new strategy. The Problem with Informational Content: Informational content on the internet is going to be completely dominated by AI. The Opportunity: Real Data: AI is great at generating content, but if you need actual data—like contours for your specific plot of land in New Zealand—you need real data, not generated data. New Strategy: My new marketing strategy is to create targeted, single-serve map applications and embed them in my website. These applications do one thing and one thing only, using open and valuable data to solve very specific problems. This allows me to rank in organic search because these are problems that LLMs have not yet mastered. Coding with AI: I started by using ChatGPT to code small client-side map applications, then moved to Claude, which is significantly better than OpenAI's models and is still my coding model of choice. Currently, I use Cursor AI as a development environment, swapping between Claude code, OpenAI's Codex, and other models. A Caveat: Using AI for coding can be incredibly frustrating. The quality of the code drops dramatically once it reaches a certain scale. However, even with flaws, it’s a thousand times better and faster than what I could do myself, making my ideas possible. Crucially, I believe that for the vast majority of use cases, mediocre code is good enough. Success Story: GeoHound After practicing and refining my methods, I decided to build a Chrome extension. Every GIS professional can relate to the pain point of sifting through HTTP calls in the developer tools networking tab to find the URL for a web service to use in QGIS or ArcGIS. The Impossible Idea Made Possible: I had pitched this idea to multiple developers in the past, who were either uninterested or quoted between $10,000 and $15,000 to build it. The AI Result: Using AI, I had a minimum viable Chrome extension—GeoHound—that filtered out common geo web services within 3 hours. It took a few days of intermittent work before it was published to the Chrome and Edge web stores. Current Use: GeoHound has thousands of users (my own statistics suggest closer to or over 3,000 users, compared to the 1,000 shown on the Chrome store). While not perfect, it is clearly good enough, and this was something that was impossible for me just six months ago. My Point: Now is the Time to Experiment AI is here, and it will lead to profound change. Experimenting with it is vital because it will: Help you develop the skills and knowledge needed to meet the needs of the people you serve. Help you better understand what is hype and what is not, allowing you to decipher which voices to listen to. We are moving from a world where information is ubiquitous to a world where knowledge is ubiquitous. Now is the time to be making sloppy mistakes. Don't let perfection stop you from learning how to make stuff that is going to be good enough. If your work consists of repetitive tasks that follow step-by-step recipes, that's going to be a tough gig going forward. Long-term, there will be new opportunities, but you need to be experimenting now to be in a position to take advantage of them. Resources Mentioned You will find a list of the tools I've been experimenting with in the show notes. Automation: make.com, n8n, Zapier Voice/Agents: 11 Labs, Vappy, custom GPT (MCP servers) Coding Models: Claude (current choice), OpenAI's Codex, ChatGPT Development Environment: Cursor AI LLMs/Multimodal: Gemini (studio.google.com) Browser Extension: GeoHound (for Chrome and Edge) https://chromewebstore.google.com/detail/nooldeimgcodenhncjkjagbmppdinhfe?utm_source=item-share-cb If you build anything interesting with these tools, please let me know! I'd love to hear about your own experiments.
