- Built a windows desktop app that scans local folders for videos and automatically transcribes the audio, summarises the content into a structured JSON format based on screenshots and subtitles, and automatically categorises each video. I used it on my PC to scan a couple of TB of videos. Has a relatively nice interface for browsing videos and searching and stores everything locally in SQLite. Did this in C# & Avalonia - which I've never used before. AI wrote about 75% of the code (about 28k LOC now).

- Built a custom throw-away migration tool to export a customers data from one CRM to import into another. Windows app with basic interface.

- Developed an AI process for updating a webform system that uses XML to update the form structure. This one felt like magic and I initially didn't think it would work, but it only took a minute to try. Some background - years ago I built a custom webform/checklist app for a customer. They update the forms very rarely so we never built an interface for making updates but we did write 2 stored procs to update forms - one outputs the current form as XML and another takes the same XML and runs updates across multiple tables to create a new version of the form. For changes, the customer sends me a spreadsheet with all the current form questions in one column and their changes in another. It's normally just wording changes so I go through and manually update the XML and import it, but this time they had a lot of changes - removing questions, adding new ones, combining others. They had a column with the label changes and another with a description of what they wanted (i.e. "New Question", "Update label", "Combine this with q1, q2 and q3", "remove this question"). The form has about 100 questions and the XML file is about 2500 lines long and defines each form field, section layout, conditional logic, grid display, task creation based on incorrect answers etc, so it's time consuming to make a lot of little changes like this. With no expectation of it working, I took a screenshot of the spreadsheet and the exported XML file and prompted the LLM to modify the XML based on the instructions in the spreadsheet and some basic guidelines. It did it close to perfect, even fixing the spelling mistakes the customer had missed while writing their new questions.

- Along with using it on a daily basis across multiple projects.

I've seen the stat that says developers "...thought AI was making them 20% faster, but it was actually making them 19% slower". Maybe I'm hoodwinking myself somehow, but it's been transformative for me in multiple ways.

Though, now that I'm thinking about it, you could probably do this locally and just look at the part of the image that has the current score, do some local OCR on it to check if the score has changed each frame, if it has, store the timestamp and then use ffmpeg to extract the correct parts. Probably wouldn't need an LLM at all.

As for editing, one thing I do in my videos is audio keywords so my app can do specific things. For example, I can say "AI, mark what I just said as important." Then when it transcribes the audio and the LLM processes it, it will mark that part as a Distinct Moment with a start and end timestamp, a title and description that will show in my app as a clickable link to that part of the video. I'm thinking of adding more commands for more complex editing too.

I also have the whole Aider/Claude prompt history in the repo too, as I started this on a platform & framework I'd never used, and used the AI to scaffold much of the app at the beginning. Thought that might be useful to go back and see what worked the best when AI programming.

In the app you pick a folder with videos in it and it stores the path, metadata, extracts frames as images, uses a local whisper model to transcribe the audio into subtitles, then sends a selection of the snapshots and the subtitles to an LLM to be summarised. The LLM sends back an XML document with a bunch of details about the video, including a title, detailed summary and information on objects, text, people, animals, locations, distinct moments etc. Some of these are also timestamped and most have relationships (i.e this object belongs to this location, this text was on this object etc). I store all that in a local SQLLite database and then do another LLM call with this summary asking for categories and tags, then store them in the DB against each video. The App UI is essentially tags you can click to narrow down returned videos.

I plan on adding a natural language search (Maybe RAG -- need to look into the latest best way), have half added Projects so I can group videos after finding the ones I want, and have a bunch of other ideas for this too. I've been programming this with some early help from Aider and Claude Sonnet. It's getting a bit complex now, so I do the majority of code changes, though the AI has done a fair bit. It's been heaps of fun, and I'm using it now in "production" (haha - on my PC)