We live in a world awash in data, a tidal wave of information that promises to unlock incredible insights and fuel a new generation of AI-powered applications. But as anyone who has ever waded into the deep end of a data-intensive project knows, this abundance can quickly turn into a curse. My own foray into building a podcast recommendation system recently hit a major snag when my meticulously curated dataset went rogue. The culprit? A sneaky infestation of duplicate embedding files, hiding among thousands of legitimate ones, each with “_embeddings” endlessly repeating in their file names. Manually tackling this mess would have been like trying to drain the ocean with a teaspoon. I needed a solution that could handle massive amounts of data and surgically extract the problem files.
Gemini: The AI That Can Handle ALL My Data
Faced with this mountain of unruly data, I knew I needed an extraordinary tool. I’d experimented with other large language models in the past, but they weren’t built for this. My file list, containing nearly 9,000 filenames (about 100,000 input tokens in this case), and proved too much for them to handle. That’s when I turned to Gemini, with it’s incredible ability to handle large context windows. With a touch of trepidation, I pasted the entire list into Gemini 1.5 Pro in AI Studio, hoping it wouldn’t buckle under the weight of all those file paths. To my relief, Gemini didn’t even blink. It calmly ingested the massive list, ready for my instructions. With a mix of hope and skepticism, I posed my question: “Can you find the files in this list that don’t match the _embeddings.txt pattern?” In a matter of seconds, Gemini delivered. It presented a concise list of the offending filenames, each one a testament to its remarkable pattern recognition skills.
To be honest, I hadn’t expected it to work. Pasting in such a huge list felt like a shot in the dark, and when I later tried the same task with other models I just got errors. But that’s one of the things I love about working with large models like Gemini. The barrier to entry for experimentation is so low. You can quickly iterate, trying different prompts and approaches to see what sticks. In this case, it paid off spectacularly.
From AI Insights to Linux Action
Gemini didn’t just leave me with a list of bad filenames; it went a step further, offering a solution. When I asked, “What Linux command can I use to delete these files?”, it provided the foundation for my command. I wanted an extra layer of safety, so instead of deleting the files outright, I first moved them to a temporary directory using this command:
find /srv/podcasts/Invisibilia -type f -name "*_embeddings_embeddings*" -exec mv {} /tmp \;
This command uses the find command, and it uses -exec to execute a command on each found file. Here’s how it works:
- -exec: Tells find to execute a command.
- mv: The move command.
- {}: A placeholder that represents the found filename.
- /tmp: The destination directory for the moved files.
- \;: Terminates the -exec command.
By moving the files to /tmp, I could examine them one last time before purging them from my system. This extra step gave me peace of mind, knowing that I could easily recover the files if needed.
Reflecting on the AI-Powered Solution
In the end, what could have been a tedious and error-prone manual cleanup became a quick and efficient process, thanks to the combined power of Gemini and Linux. Gemini’s ability to understand my request, process my massive file list, and suggest a solution was remarkable. It felt like having an AI sysadmin by my side, guiding me through the data jungle. This was especially welcome for someone like me who started their career as a Unix sysadmin. Back then, cleanups like this involved hours poring over man pages and carefully crafting bash scripts, especially when deleting files. I even had a friend who accidentally ran rm -r / as root, watching in horror as his system rapidly erased itself. Needless to say, I’ve developed a healthy respect for the destructive power of the command line! In this instance, I would have easily spent an hour writing my careful script to make sure I got it right. But with Gemini, I solved the problem in about 10 minutes and was on my way. This sheer amount of time saved continues to amaze me about these new approaches to AI. More than just solving this immediate problem, this experience opened my eyes to the transformative potential of large language models for data management. Tasks that once seemed impossible or overwhelmingly time-consuming are now within reach, thanks to tools like Gemini.
Conclusion: A Journey of Discovery and Innovation
This experience was a powerful reminder that we’re living in an era of incredible technological advancements. Large language models like Gemini are no longer just fascinating research projects; they are becoming practical tools that can significantly enhance our productivity and efficiency. Gemini’s ability to handle enormous datasets, understand complex requests, and provide actionable solutions is truly game-changing. For me, this project was a perfect marriage of my early Unix sysadmin days and the exciting new world of AI. Gemini’s insights, combined with the precision of Linux commands, allowed me to quickly and safely solve a data problem that would have otherwise cost me significant time and effort.
This is just the first in an occasional series where I’ll be exploring the ways I’m using large models in my everyday work and hobbies. I’m eager to hear from you, my readers! How are you using AI to make your life easier? What would you like to be able to do with AI that you can’t do today? Share your thoughts and ideas in the comments below – let’s learn from each other and build the future of AI together!