Fien De Waele Tongeren, Belgium 2H AGO

I built a workflow called PeopleSparkles for researching people connected to creative fields, schools, collectives, residencies, local art scenes, and cultural networks. The workflow starts from a defined group of people, for example teachers, alumni, artists, current or former members of an association, residents, or associates connected to a specific academy, organization, or cultural scene. A typical research request can be as simple as: “Look at this art website and identify all current and former residents and associates. Build a list. Then research them in batches of ten, checking their websites and other relevant sources, and write a short description of their work. Deliver the results in a format that can be imported into my local database.” Before each new research run, I provide GPT with a ZIP containing the current state of the PeopleSparkles database. This means the research starts from the existing corpus rather than from scratch. New people can be added, existing entries can be expanded, and previously researched people can be recognized before new material is prepared for import. The actual database lives locally on my computer. Apart from the initial development of the code and the research needed to create new lists of people, the database itself runs locally. Once a new batch has been researched and imported, browsing, organizing, scoring, annotating, and using the material does not require the whole corpus to be sent back for analysis. The database also generates a human-readable HTML version with a designed layout, so the research is not trapped inside raw data or spreadsheets. I can browse the people and their notes visually as a small personal research publication, while the underlying structured data remains available for future additions and processing. The “Sparkles” part is personal. I can add my own notes and scores to each person to capture whether their work sparked something in me, and if so, how. That might be curiosity, recognition, inspiration, aesthetic attraction, a strong question, a surprising association, or simply the desire to look again. This means the database does not only record who someone is and what they make. It also records my evolving relationship to their work. Over time, that creates a second layer on top of the research corpus: not just a map of creative people, but a map of resonance. The database also includes a “Surprise me” function that brings up a person from the collection without me choosing them deliberately. This helps break habitual search patterns and allows older, less obvious, or previously overlooked entries to resurface. Someone I barely noticed months ago can suddenly become relevant in a completely different creative context. The purpose is not to create conventional biographies. I am interested in the sparks around a person: what they make, the media and themes they work with, the organizations or people they connect to, and which traces may lead somewhere unexpected. This is particularly useful for creative ecosystems where information is fragmented across artist websites, academy pages, exhibition archives, old posters, association websites, interviews, catalogues, and small cultural organizations. The workflow gathers those fragments into a cumulative research corpus. It also allows the research to grow organically. One artist may lead to a collective, a teacher to a former student, an exhibition to another maker, or an old membership list to someone whose work would never have appeared in a conventional search. In simple terms: existing local database → new source or people list → GPT-assisted discovery and research in batches → import-ready structured data → local database → HTML browsing, notes and scores → surprise rediscovery → new creative connections The result is a living creative research database that combines external research with personal resonance, so I can not only discover people, but also trace which work actually sparks something in me over time.