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After nearly 20 years in France, I’m finally applying for citizenship. I’ve spent the past year preparing documents and studying for the required tests, and my final interview is a few weeks away. Alongside the official study materials, I’ve been using ChatGPT to drill key facts and historical dates, run mock oral interviews in voice mode, and create quizzes from YouTube videos I link whenever they cover the right material. ChatGPT sometimes speaks French with a rather heavy accent, but the interactive back-and-forth helps me remember hundreds of details that are difficult to retain by simply reading a page. It’s tedious, but I find rote memorization challenging. We’ll see how it goes under pressure.

I built a workflow to examine complaints I had about a local bureaucratic service and check whether my account of what happened was supported by the written record. I was concerned about delays, incomplete handling, and the way my case progressed after I gave feedback about a harmful aspect of the service and asked for a different person to handle the dossier. That request was refused. Afterwards, I felt that the process had become slower, more confusing, and more prone to mistakes. Rather than asking GPT to confirm that the service had handled things badly, I used it to test my own interpretation against the evidence. I asked GPT to review the relevant emails and dossier communications, reconstruct the chronology, and identify requests, replies, delays, unresolved issues, and changes in how the case was handled over time. We then compared the periods before and after my feedback and request for reassignment. The central questions were deliberately neutral: Were my complaints grounded in the correspondence? Did the handling of the dossier objectively become slower or more incomplete afterwards? Were there concrete errors or unanswered questions in the record? Or was I remembering the experience as worse than the documentation supported? This distinction mattered to me. The workflow was not designed to produce a verdict or turn frustration into evidence after the fact. It was designed to challenge my assumptions and separate what I felt from what could actually be demonstrated. Where the documentation supported a complaint, we could point to the relevant chronology, delays, unanswered questions, or inconsistencies. Where the evidence was incomplete or ambiguous, that was noted too. The result was a more grounded account of the case: not “I know this was handled terribly,” but “these are the parts of my experience that are supported by the written record, these are the parts that remain uncertain, and this is where the timeline changed.” In simple terms, the workflow was: emails and dossier communications → chronology reconstruction → before-and-after comparison → review of delays, errors, and unresolved issues → evidence check against my complaints → a grounded account of what can and cannot be supported. What I liked about this workflow was that it used AI as a reality-checking tool rather than an agreement machine. It helped me test whether my criticism was actually anchored in the record before I relied on it in further communication. Step-by-step: 1. I gathered the relevant emails and dossier communications about my case. 2. I asked GPT to reconstruct the chronology and identify requests, replies, delays, unresolved issues, and changes in how the case was handled. 3. I compared the period before and after my feedback about a harmful aspect of the service and my request for reassignment, which was refused. 4. I examined whether the record showed that the process became slower or more incomplete, and whether it contained concrete errors or unanswered questions. 5. I separated points supported by the documentation from points that remained incomplete or ambiguous. 6. I used the resulting chronology and evidence check to create a grounded account of what my complaints could and could not demonstrate.

Michigan Campaign Finance Explorer turns public Michigan campaign-finance records into a user-friendly, searchable research tool. It automatically collects and validates official filings, then lets users compare candidates, trace transactions, explore races on a map, and visualize how money moves between campaigns, PACs, donors, and vendors—all with links back to the original records. The current public system only allows you to look up records one at a time, and you have to know what you’re looking for. I built a tool called Filing Radar to make that process more useful. Every 20 minutes, it searches all filings using Python’s built-in `urllib.request`. It compares filing IDs with those saved during the previous check. If a filing is new, it downloads it; otherwise, it moves on. For each new download, a parser built with `pypdf` processes the PDF and sends the data to a local SQLite database. I was concerned about overloading the public website with requests, so I also built a circuit breaker and added a limit on requests per second. I also built another tool called Vendor Resolver. Campaigns often record the same vendors in slightly different ways, such as “Little Caesars” and “Little Caesar’s.” Vendor Resolver groups transactions that are likely associated with the same entity and assigns a confidence score. It then ranks the groups by impact so the most useful matches are easier to review manually. Together, these tools make the site more useful when I’m trying to determine which PACs are connected to particular campaigns. Step-by-step: 1. I searched all filings every 20 minutes with Python’s built-in `urllib.request`. 2. I compared the filing IDs with those saved during the previous check and downloaded only new filings. 3. I processed each new PDF with `pypdf` and stored the results in a local SQLite database. 4. I used a circuit breaker and a requests-per-second limit to avoid overloading the public website. 5. I grouped differently named vendors, such as “Little Caesars” and “Little Caesar’s,” and assigned confidence scores to likely matches. 6. I ranked those matches by impact so I could review the most useful ones manually. 7. I used the resulting data to identify connections between PACs and campaigns.

Over the past year, I've relied heavily on LLMs to support my French citizenship application— including drafting required letters and tracking paperwork. Although I'm already fluent, I used ChatGPT to prep for the required language exam to get familiar with the test format for 100% confidence. Step-by-step: 1. I listed the letters, forms, and paperwork required for the French citizenship application. 2. I used ChatGPT to draft the required letters and improve the wording. 3. I created a process for tracking what had been completed and what was still outstanding. 4. I asked ChatGPT to reproduce the format and expectations of the required language exam. 5. I practiced against that format until I felt fully confident despite already being fluent.