Not long ago, I found myself thinking about our old Netflix DVD ratings and wondered what they might still be good for. In a “What would be cool?” kind of way.
So I made a list. A long, all-over-the-place, surprisingly exciting list. It wasn’t a plan or series of to-do’s. Just… a spark.
I’m sharing it here exactly as I wrote it — not because it turned out to be the beginning of something cool: a custom GPT — but because, for me, there’s something fascinating about the messiness of the raw, unpolished, creative process — something that’s worth seeing and sharing.
10 Ways to Use Old Netflix Data
- Surface movies to watch again — say, with friends.
- Identify favorite genres, actors, series, writers, etc., so we can surface more content of the same type or with the same or similar people.
- See what to avoid.
- See what our hit rate has been.
- Get recommendations for content (with help from IMDb and/or MovieLens).
- Look for underappreciated gems.
- See what we’ve watched multiple times.
- See what overlaps with our in-house DVDs.
- See how much we’ve watched over the years.
- Look for connections between titles … or actors, writers, directors. For instance, answer the question, where else have we seen… It would be beautiful to make a graph of those connections and then just fun to trace them.
- Pick out a movie for the next movie night — possibly with user input.
- Mystery movie with Cary Grant.
- Goofball comedy from 50s available on Netflix or Amazon Prime or via our local public library.
- A biopic about Neil Armstrong.
- A nature documentary about Antarctica.
- Can we incorporate Great Courses history / content?
- Create a binge night with Steve Martin comedies.
- Create a binge week with Great Courses lectures, a Netflix TV show, and a movie all that feature whales.
- Create a Bugs Bunny retrospective with best cartoons, commentary, and short movie about a cartoonist or animator or Mel Blanc.
Some of the above sound like prompts for Janet (ChatGPT)…
Could I create a special-purpose GPT to “solve” these requests while satisfying our tastes in movies and TV and documentaries?
Would like to get surprising recommendations — not just replicate IMDB’s suggestions.
With a special-purpose GPT:
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Would like a good hit rate but willing to tolerate some poor ones to help improve the GPT.
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Want to look back as much or more than look forward at new releases. Try to avoid the “tyranny of the new”.
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Want family-friendly, or at least, friend-friendly content.
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Generally want mostly rom-coms, comedies, mysteries, sci-fi, animated, music / musicals, and action flicks.
Plus stand-up, sports, health-fitness, nutrition, biography, heartwarming.
Not much drama, politics, current events, or the like — unless really compelling.
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Could special-purpose Janet recommend a binge, especially a multi-genre one, say with a movie, a TV show, and a Great Courses lecture?
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Theme binges or nights, weeks, or months. For instance, could Janet fill in the blanks below:
- Movie: The Matchmaker (rom-com)
- TV episode: ? (mystery with an Irish component)
- Great Courses: ? (Irish travel, say, a documentary exploring Ireland, specifically the area in the movie)
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And can Janet also suggest a watch order for the above?
Looking back now at this list, I can see the rough outline of what became Maeve, my custom GPT movie and TV maven. And, the list still gets me excited. Not because I crossed everything off — but because I turned personal data that was just laying around into something useful, something I use nearly every day, something that brings just a little more joy and laughter to everyday life.
If you’re anything like me, you’ve got old, personal datasets sitting dormant in forgotten folders: your watchlist from 2011, movie ratings, reading history, a workout log. Dust them off. Make your own list. You might just have the raw material you need for something delightful, and delightfully personal.
In the next post, I’ll show you exactly what I did with my Netflix DVD ratings — how they became a custom GPT maven that knows what we like better than IMDB, Netflix, or Prime. Stay tuned.