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I took a picture of my wife's spice rack, uploaded it to Grok, and asked it to organize the contents into three categories: Must Have, Keep, and Discard, based on age or lack of common usage. Step-by-step: 1. I took a picture of my wife's spice rack. 2. I uploaded the picture to Grok. 3. I asked Grok to categorize the contents as Must Have, Keep, or Discard based on age or lack of common usage.

AI chatbots have limitations around memory, numbers and types of attachments, connectors, storage, and other resources. The underlying LLMs also change over time, and data leakage is a concern. Code-based versions such as Claude Code and Grok Build can store essentially unlimited files on your computer, organize them with databases and files, connect to public and private MCPs, and even host interfaces for reviewing results. You can choose between a hosted LLM such as Claude, Grok, or ChatGPT and a local LLM if you prefer. The best part is that these code-based versions have no memory between sessions: everything is stored on your computer, assuming a basic subscription. You start by prompting the system about the domain, what you want to store, how you want to access it, and other requirements. Then it creates the system for you. I started with my own personal health archive. I'm comfortable using the terminal interface for Claude Code and Grok Build, so I use that directly. When I built one for my wife, she couldn't use the terminal, so I had it build a basic website with Q&A interfaces, data access, and upload functionality. I hosted it on my Mac, and I wrote no code. We loaded hundreds of documents in various formats into each of our instances without any trouble. When we have new files, we can simply drop them in. For specific questions or health events we're tracking, the system has perfect memory and no memory limits within the archive. For my archive, I use a Garmin watch and a food tracker. Because public MCPs weren't available for those services, I had the system build local MCP servers by reverse-engineering them. My health tracker now has a continual feed of my food, fitness, sleep, and other data. My wife has some chronic health issues, and we had reached a point where the standard chatbots were hitting their limits. We were struggling and didn't know what to do, so I discussed it with Claude, and we came up with this approach. The best part, besides getting a better handle on our health, is that I can launch any LLM within the project—Claude, Grok, or others—and use it for that session. The project isn't tied to or limited to a single LLM. That lets me manage costs, switch to better LLMs as they improve, and use specialized models for deep research, all while working with one data set. Step-by-step: 1. I prompted a code-based AI about the health domain, the information I wanted to store, and how I wanted to access it. 2. I created a personal health archive and loaded hundreds of documents in various formats into it. 3. I built a second archive for my wife with a basic website, Q&A interfaces, data access, and upload functionality, then hosted it on my Mac. 4. I added new files by dropping them into the appropriate archive. 5. I had the system build local MCP servers for my Garmin watch and food tracker by reverse-engineering those services when public MCPs weren't available. 6. I used the resulting health tracker to maintain a continual feed of food, fitness, sleep, and other health data. 7. I launched different hosted or local LLMs within the same project as needed for each session, including using specialized models for deep research.

I took pictures of the labels on the back of my TV and my old Sennheiser headphones. I submitted both pictures to Grok and asked how my old RCA-connected headphones could work with my new Vizio TV, which did not have an RCA output. Grok directed me to Amazon to buy a $15.00 DAC converter. Following its instructions, I plugged the optical lead supplied with the DAC into the TV, made one adjustment to the audio settings, and voilà—my old headphones worked again. I didn't need to provide descriptions of my products; the pictures of the labels were enough. Step-by-step: 1. I took pictures of the labels on the back of my Vizio TV and old Sennheiser headphones. 2. I submitted both pictures to Grok and explained that the TV did not have an RCA output. 3. I followed Grok's recommendation to buy a $15.00 DAC converter from Amazon. 4. I connected the optical lead supplied with the DAC to the TV. 5. I made one adjustment to the TV's audio settings and used my old headphones again.

My girlfriend has a professional camera and lens for photographing birds. Because the camera captures an enormous number of images per second, sorting through them and keeping only the good ones after a day at the wetlands took a very long time. She showed me the two tools she was using to tag, crop, view, and zoom into photos, inspect focal points, and view the color histogram. I decided to vibe-code a C# tool using Cursor, Grok, and Claude Opus. The tool reads the EXIF and MakerNotes metadata in each picture and lets her scroll through all the photos in fullscreen with the mouse. It displays the histogram in the top-right corner and useful information—aperture, shutter speed, focal length in millimeters, and ISO—in the top-left, with color coding from red to green. It also shows the Nikon focus zone. Clicking the left mouse button zooms to 75%, allowing us to pan, while clicking the right mouse button zooms to 100%. Once we decide visually on the best crop and judge the photo’s potential, pressing a keyboard key instantly crops the image at the current view, saves a copy, tags the photo, and adds her copyright in the bottom-right corner. This makes the process much faster because she can view, tag, crop, and add copyright at the same time. It took about two hours of working with her to iterate on the tool and find the right balance of features and ease of use. Step-by-step: 1. I observed the two tools she was using to tag, crop, view, and inspect bird photographs. 2. I used Cursor, Grok, and Claude Opus to vibe-code a C# application for her workflow. 3. I made the tool read each photo’s EXIF and MakerNotes metadata. 4. I added fullscreen mouse scrolling, a histogram in the top-right, and camera information in the top-left, including aperture, shutter speed, focal length, and ISO. 5. I added red-to-green color coding and a display of the Nikon focus zone. 6. I set up left-click zoom to 75% with panning and right-click zoom to 100%. 7. I added a keyboard shortcut that crops the image at the current view, saves a copy, tags the photo, and adds her copyright in the bottom-right corner. 8. I iterated on the tool with her for about two hours until the features and ease of use were balanced for her needs.

I’m retired and in my early 70s. I never got into RPGs growing up, but I decided to try a tabletop RPG with my wife. The game I purchased included a rulebook and a PDF of the book. I wanted my wife and me to play as the player characters without either of us having to be the game master, so I tested whether AI could take on that role. I used ChatGPT to parse the PDF and walk us through the process. It helped us create our characters, Heisenberg and Felicity, and then started the game as the game master. We’ve only just started, but it seems like this is going to work well. The AI can keep secrets to itself, roll the dice when needed, and guide us through the adventure. Step-by-step: 1. I purchased a tabletop RPG that included a rulebook and a PDF of the book. 2. I used ChatGPT to parse the PDF and walk us through the process. 3. ChatGPT helped my wife and me create our player characters, Heisenberg and Felicity. 4. We had ChatGPT start the game as the game master so neither of us had to fill that role. 5. We began playing while ChatGPT kept secrets to itself, rolled the dice when needed, and guided us through the adventure.

This World Cup 2026 poetry generator scraped the web for match information and called LLMs to help users generate match-specific poems. 1. A daily schedule triggered web scraping to collect match information. 2. When a user requested a poem, the generator used that match information to create a new match-specific poem with help from LLMs.

I have been using Grok in my Tesla, and it is a fun way to navigate and find new places to try around me. You can ask it to map multiple locations now, and ask it things like "after my first destination, I want to get coffee from a local place, not a chain". It's given some unique, interesting options I never would've explored otherwise. Step-by-step: 1. I asked Grok in the Tesla to map the first destination I wanted to visit. 2. I added the other stops I was considering so it could plan a multi-location route. 3. I gave it preference constraints, such as finding a local coffee shop instead of a chain. 4. I let it suggest options that fit the route after the first destination. 5. I used those conversational recommendations to discover places I would not have found on my usual route.