I built a workflow called InnerWeather to help me understand fluctuations in energy, pain and mood over time, in the context of neurodivergence and a chronic muscular pain condition. The workflow creates lightweight daily scans and a weekly overview. Its purpose is not to diagnose symptoms or pretend that every change has a single cause. Instead, it helps make patterns visible across time: when energy drops, when pain increases, when stimulation or emotional load seems higher, how recovery unfolds, and which combinations tend to appear together. The daily scans are possible because I naturally talk to GPT throughout the day, sometimes in very short updates and sometimes in longer conversations, about how things are going, what I am doing, how my body feels, how much energy I have, and what seems to be affecting me. InnerWeather uses those scattered moments as observational material. It does not require me to fill in a formal tracker several times a day. The information is already present in the conversations I am having. The daily scans do not reduce an entire day to one fixed state. They map changing moments across the day, using colour-coded states and paying attention to common transitions between them. That makes it possible to see not only how I felt, but how my internal state moved: whether high stimulation tends to be followed by fatigue, whether pain appears after certain kinds of activity, or whether a low-energy period gradually shifts into recovery. This matters because capacity can vary significantly within one day. A difficult morning does not necessarily define the whole day, and a good afternoon does not erase what happened before it. The workflow is also explicit about uncertainty. If there was not enough input during a particular day to support a meaningful observation, the daily scan says so rather than filling in the gaps. The same applies to the weekly overview: if the available material is too sparse or uneven to support a pattern, that limitation is recorded instead of turning absence of information into a conclusion. The weekly overview brings the daily fluctuations together and looks for recurring sequences, transitions, clusters and recovery patterns across the week rather than treating each day as an isolated event. An important part of the workflow is that the weekly review is also collaborative. When the overview is generated, I use it as a starting point to think together with GPT about what patterns seem to be emerging, whether the current colour codes and transitions are capturing them well enough, and what might need to change in the workflow itself. That means InnerWeather is not a fixed tracker. The task evolves with the patterns it is trying to observe. If a recurring state, transition or distinction is missing, we can refine the categories, adjust the scan, or change the weekly interpretation so the system becomes better at representing what is actually happening. The aim is practical rather than medical: to get a more realistic sense of capacity and recovery over time, so I can make better decisions about pacing, rest, creative work, appointments, and how much I can reasonably take on. I especially like that the workflow treats fluctuations as information rather than failure. Instead of asking only, “Why was I worse today?”, it can reveal a broader sequence: what came before, how the state changed, how long it lasted, and what recovery looked like afterwards. Over time, InnerWeather becomes both a personal pattern archive and an evolving observation tool. In simple terms: day-to-day conversation → colour-coded fluctuation map → transitions across the day → weekly pattern review → collaborative interpretation → refine the task → better future scans The goal is not to predict my body perfectly. It is to keep improving the map while remaining honest about what the available information can and cannot support.

I run Mealie, a self-hosted recipe manager, in Docker on a home server. Mealie is good, but it isn't zero-effort: there's a learning curve and ongoing administration just to keep a recipe library populated. My wife is Spanish, so many of her recipes are in her native language. We also have recipes as handwritten notes, WhatsApp messages, photos, and transcribed cooking videos. Translating the Spanish recipes accurately was beyond my ability, and I had no interest in typing everything into Mealie's editor by hand. I built a custom MCP server that exposes Mealie's API to Claude as a set of tools, along with a project-level skill that encodes our household rules: no mushrooms or celery for me, no ginger or coriander for my wife, a child who is fussy about fish, a pizza oven that only comes out at weekends, and batch cooking that needs to cover school lunches. Now I can give Claude a batch of recipes in whatever form they arrive and in any language. It translates them, restructures them into Mealie's schema, checks for duplicates, and writes them straight into the database. There's no manual entry, and I've never needed Mealie's own foodstuffs database. Ingredients stay as plain text, which is one less thing to maintain. For weekly planning, Claude reads recent meal-plan history from Mealie so that meals do not repeat too soon. It pulls the full ingredients for every candidate recipe rather than trusting the recipe name—a "Veggie Traybake" might contain mushrooms—and drafts the week against our constraints. The plan covers seven family dinners, five weekday lunches for the two of us, and two weekend family lunches. School lunches for the kids are often something we've batch-cooked, so that is built in too. Nothing is written to Mealie until I confirm the draft. After I confirm it, Claude seeds the plan into Mealie, merges ingredients across every meal, rounds them to sensible pack sizes, and groups everything by supermarket aisle. The output feeds through to Home Assistant, so the weekly plan and shopping list appear on our wall-mounted display and mobile phones. Step-by-step: 1. Create a self-hosted instance of Mealie in Docker on a home server. 2. Build a custom MCP server that exposes Mealie's API to Claude. 3. Create a project-level skill that encodes our dietary preferences, cooking setup, and usual meal structure. 4. Batch-feed Claude recipes in any format and language, including photos, WhatsApp messages, video transcripts, and handwritten notes. 5. Skip Mealie's structured foodstuffs database and keep ingredients as plain text while Claude parses them into Mealie's recipe schema. 6. Ask Claude to translate recipes, check for duplicates, and write the structured recipes into Mealie. 7. Ask Claude to read recent meal-plan history, check candidate ingredients against our preferences, and draft a new weekly plan covering our required dinners and lunches. 8. Make changes through chat, and confirm the draft before anything is written to Mealie. 9. Have Claude seed the confirmed plan into Mealie, merge ingredients across every meal, round them to sensible pack sizes, and create a categorised shopping list. 10. Install the Mealie integration in Home Assistant to display the plan, recipes, and shopping list on your devices.

While traveling abroad, my partner and I became quite sick near the end of our trip and remained ill after returning home. Because we were in a country where we did not speak the language, we used Google Translate at the pharmacy and used Claude to track our symptoms as they changed from day to day. Claude helped us keep the symptom history organized and research what to ask for at the pharmacy, including differences between the medicines we were familiar with and those commonly used in that country. When our symptoms worsened after we got home, we continued using Claude as a symptom tracker and photographed everything in our medicine cabinet. We often forget what we bought or why, and medicines in the United States can be marketed for different uses in ways that are not always clear. Using the symptom history and medicine photos, we asked Claude what we might be able to use, what to avoid, when to take things, and when to contact a doctor, use telehealth, visit urgent care, or go to the emergency room. It also surfaced newer research suggesting that some medicines once thought to be effective may no longer be supported by current evidence. This simple workflow helped us manage the situation and decide whether we needed to buy anything new or seek additional care. Step-by-step: 1. I used Google Translate to communicate at the pharmacy while traveling in a country where we did not speak the language. 2. I described our symptoms to Claude and kept the symptom history updated as they changed from day to day. 3. I asked Claude to research what to ask for at the pharmacy, taking into account the medicines we were familiar with and those used in the country we were visiting. 4. After we returned home and our symptoms worsened, I continued tracking them in Claude. 5. I photographed the medicines in our cabinet so Claude could help us review what we had, what we might be able to use, and what we should avoid. 6. I used the symptom history and medicine information to think through timing, whether we needed to buy anything new, and whether to contact a doctor, use telehealth, visit urgent care, or go to the emergency room. 7. I also asked Claude to check current research, including whether newer studies had changed the understanding of some medicines’ effectiveness.

If you have a large jar of pennies or other coins, spread them out and take a picture. Upload the picture to your AI app and ask it to identify any coins that may be valuable. This is much easier than checking them one by one. Step-by-step: 1. Spread the coins out so they are visible. 2. Take a picture of the coins. 3. Upload the picture to your AI app. 4. Ask the app to identify any coins that may be worth more than their face value. 5. Check the identified coins individually to verify their value.

When I’m continuing a book series or a show season and have forgotten key plot points—especially in fictional stories with large casts—I use NotebookLM to research a spoiler-free refresher before starting the new season or latest book. I used this approach with the new *Murderbot* series and the latest science-fiction book by James S. A. Corey, who wrote *The Expanse* and the *Leviathan Wakes* series. After loading the sources, I ask NotebookLM to create a slide deck with illustrations of the key characters, their relationships, and a quick dossier for each one. This gives me a visual anchor for the characters and earlier plot lines without revealing future events. I also want a hero image designed as a slide, cover, or thumbnail to promote this workflow. It should use and represent the NotebookLM logo and the logos of any other relevant tools, track down their current branding where possible, and turn these .dcs into HTML slide decks.

Every Sunday night, my Openclaw asks me to recap my week and send it some photos I took. It makes a collage of the photos and generates a stylized "magazine cover" based on the recap. At the end of the year, I should have a neat collection of time capsules. Step-by-step: 1. I scheduled OpenClaw to prompt me every Sunday night for a recap of the week. 2. I wrote a short summary and sent the photos I had taken during the week. 3. I had it arrange the photos into a collage. 4. I asked it to generate a stylized magazine cover inspired by the recap. 5. I saved each weekly cover so the collection can become a set of time capsules at the end of the year.

I’m a nurse with no tech experience, but I’m curious and have many creative ideas. I used Claude to build a website that helps people plan which vegetables to grow based on their location, available space, and the time they have. Veggiegrowguide.com Step-by-step: 1. I identified an idea for helping people choose vegetables to grow based on their location, available space, and available time. 2. I used Claude to help me build a website for the idea. 3. I created Veggiegrowguide.com as a resource for people planning their vegetable gardens.

For years, my family has run a shared birthday fund: five of us contribute a fixed amount for each birthday, while the person whose immediate family is celebrating that month is exempt from paying. Tracking everything in a payment app and a chat thread meant nobody knew the balance, who was behind, or what the next gift would cost. I rebuilt the fund as a small web app with Claude. The app itself isn't the only reason I'm writing this up. The same build is also the demo I use to teach clients and students how AI-assisted product development actually works, including the parts that go wrong. Step-by-step: 1. I described the real rules instead of presenting Claude with a generic app idea: the contributors, the fixed amount per person, the family-exemption rule, and birthdays with birth years so ages could be calculated automatically. Claude built the app as a single, self-contained HTML file with no build step or server, so it opens in any browser. 2. I worked in phases and asked Claude to explain its reasoning at each stage. Instead of using one giant prompt, I added one layer per session: the calculation engine, the setup screen, then reports and CSV export. The explanations made the sessions reusable as teaching material. 3. I required the app to be configured through its own interface rather than by editing code. This was the turning point. Claude removed the hardcoded demo family and added a full setup screen for the group name, contributors, deposits, birthdays, amounts, and alerts. I can now build a family from scratch live in front of a class in about [X] minutes without showing a line of code. 4. I let a data-loss incident shape the next phase. I entered the real family data, then the browser tab closed and the download link I had been using to open the file broke. The data was stored in browser storage tied to that exact URL, and I had no backup. Nothing was recoverable at the time. 5. I turned that failure into features. Claude added CSV export for both reports, CSV import that automatically detects which file it is reading, and a backup reminder that appears in the app's alert banner when the data has never been backed up or has not been backed up for seven days. We also discovered that birth year was missing from the export, which meant a re-import would have silently lost everyone's age. 6. I had Claude test its own work. Before each handoff, it ran a jsdom test suite in a sandbox. By the end, the suite had 46 tests covering the exemption math, empty states, CSV round-trips, and backup logic. Several real bugs surfaced there instead of in front of a class. 7. I made a second version for a different audience. One prompt produced a fully English, left-to-right translation with flipped directional CSS, Latin typography, US date formatting, dollars instead of shekels, and Venmo and Zelle instead of local payment apps. It was a genuinely different build, not a find-and-replace translation. The project took 14 sessions over one week and six hours total. It had no hosting cost, dependencies, or accounts. The data still lives in the browser's local storage on each device. Opening the file on my phone and laptop creates two unrelated pools, so CSV import is the manual bridge between them. There is no authentication or sync; this is a personal record-keeper, not shared infrastructure. The data-loss incident was not a Claude failure. I failed to build a backup path before entering real data. If I did it again, I would add export before adding a single feature. The highest-leverage prompt in the whole project was not a feature request. It was: "let me configure this through the interface instead of the code." That shift turned a static demo into something my family actually uses and my students can watch being built from an empty screen. The broader point is that the failure was the most useful part of the project. A polished demo teaches people that AI makes building easy. Losing the data and rebuilding the safety net around it teaches them what building actually involves—and that is the lesson that survives the workshop.

Every Monday, Hermes checks my favorite coffee brand’s website and grocery stores for sales. When it finds a deal worth knowing about, it emails me so I can stock up. Step-by-step: 1. Hermes checks my favorite coffee brand’s website every Monday. 2. It checks grocery stores for sales on the same day. 3. When it finds a worthwhile deal, it emails me. 4. I use the alert to decide when to stock up.

I love reading, but my Goodreads TBR list keeps growing faster than I can get through it. After years of adding books, I had forgotten what was actually there and was wasting time scrolling whenever I needed to choose my next read. I asked Claude to analyze my reading history, 4-star and 5-star ratings, author patterns, and recurring genres to create a profile of my book personality. I then used that profile to select and rank the next 50 books from my TBR list. Because I enjoy matching books to the season, I also asked Claude to organize the recommendations by seasonal feel. Now I have a reference list that makes it easier to decide what to read next. Step-by-step: 1. I exported my library data from Goodreads. 2. I uploaded two files to Claude: my Read list and my To-Be-Read list. 3. I used the prompt below to generate a personalized reading roadmap based on my book personality. 4. I used the resulting list as a reference so I would not spend as much time deciding what to read next. PROMPT I have attached two CSV files: 1. My Read list. 2. My To-Be-Read list. Create my book personality profile by analyzing my reading history, my 4-star and 5-star ratings, author patterns, and recurring genres. Using that data, filter and rank my To-Be-Read list to build a personalized roadmap of the next 50 books I should read from that list. Evenly organize recommendations into 4 seasonal blocks based on these descriptions: - Autumn: Atmospheric, moody, or suspenseful books with a cozy but engaging pace. - Winter: Immersive, complex, or cozy books (slower burns or dense world-building). - Spring: Fresh, character-driven, or thought-provoking fiction with a steady pace. - Summer: High-energy, immersive, or breezy books (fast-paced page-turners). Next, generate an interactive digital bookshelf widget to display the recommended books. The design should feature: - A warm, premium editorial color palette. - Minimalist style cards for each book displaying Title, Author, and color-coded genre badge. - Interactive navigation tabs at the top to toggle between the four seasons. - A functional "Mark as Read" click action on each card that visually updates a progress ring or tracker.

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.

Spotify can tell me what I played, but it rarely explains what music does in my life. I use exported listening data, playlists, and personal context to explore taste, memory, regulation, identity, and continuity. I look at both observable listening behavior and the meanings I attach to it, without forcing every track into a biography. Step-by-step: 1. Start with the data. I export streaming history, tracks, artists, timestamps, listening duration, saved songs, and playlists where available. I check the period covered and remember that missing history does not mean a song never mattered. I start with behavior: a supposed favorite may barely appear, while another track may have played hundreds of times quietly. 2. Separate tracks from artists. I count unique tracks, unique artists, and tracks per artist. One artist with dozens of songs and hundreds of artists represented by one track suggest different listening styles: exploring discographies versus collecting exceptional songs. 3. Find lasting favorites. I distinguish temporary high-volume artists from favorites that return across years, playlists, and life phases. I look for persistent songs: tracks spanning years, returning after long gaps, or appearing in several playlists. Persistence can matter more than total plays. Some songs never win a monthly ranking yet refuse to disappear. 4. Connect music to life. I add relevant personal context, such as teenage years, university, a move, a relationship, or rebuilding. I ask which songs became landmarks and which were described as “my song.” Dates show when music appeared; I explain what that timing means instead of forcing every track into a biography. 5. Discover playlist functions. Repeated songs are not always favorites. Sometimes music has a job: providing strength, calm, concentration, singing, emotional release, or a way to reconnect with myself. I read playlist names alongside their tracks and listening patterns. “Power” might mean, “I do not feel strong, so I need external strength.” “Gold” might mean, “I want to feel my own golden heart again.” These meanings matter more than a generic genre label. 6. Treat corrections as evidence. AI suggests interpretations, but I decide whether they fit. For example: “This song is not nostalgic. It reminds me that I remain myself regardless of what another person does.” Corrections like this can change the meaning of an entire playlist, so I keep them alongside the data. 7. Look for regulation patterns. I find songs played daily for weeks, tracks repeated within one day, and playlists used at consistent times. I ask whether they accompany difficult tasks, low energy, singing, or calming down. Repetition is a clue, not proof of an emotional function. 8. Look beyond genre. Pop, rock, electronic music, mantra, and soundtracks can share dramatic builds, layered vocals, melodic hooks, powerful voices, intimate production, or mantra-like repetition. These qualities can explain why apparently unrelated songs belong together. I let myself name the patterns; “Post-Classical Electroacoustic Intimate Futurism” may be more useful for discovery than a standard genre. 9. Separate nostalgia from continuity. An old song returning is not necessarily nostalgia. I ask whether it is remembered or still actively used. Some music belongs to the past, while some music simply has a very long present. I keep different musical worlds separate: anthemic pop and meditative mantra may serve different parts of the same person. 10. Build a traceable profile. I include core artists, persistent songs, personal anthems, life-phase associations, functional playlists, regulation patterns, musical qualities, and unexpected findings. I link interpretations to dates, play counts, playlist appearances, and my explanations. I separate observed behavior, AI hypotheses, and confirmed meanings, and keep the profile easy to correct. 11. Recommend for the person. Instead of asking for “songs similar to Coldplay,” I ask: “Find the emotional build I like in Coldplay, the vocal force I like in Sia, and the intimate electronic texture in my meditation playlists.” I can also ask: “Find tracks for Power that could survive repeated listening without copying its existing artists.” 12. Update over time. I add new data and look for new favorites, returning anchors, changing playlist functions, and stable patterns. The ongoing cycle is: export → analyse → find persistent songs → map functions → add personal context and corrections → build the profile → recommend → update. Listening history records what I played. AI helps me explore why something kept returning. I remain the only person who can finally say: “Yes. That is what this song means to me.”

I 3D-printed a custom threaded barrel nut without ever opening AutoCAD. I use my 3D printer to make parts for various projects, including a pinball machine and a boat, but I struggle with AutoCAD. As an alternative, I described the part to Claude in plain English and asked it to create a `.3mf` file. I opened the file in Bambu Studio and printed the part without using AutoCAD. Step-by-step: 1. I described the custom threaded barrel nut to Claude in plain English. 2. I asked Claude to create a `.3mf` file for the part. 3. I opened the file in Bambu Studio. 4. I printed the part on my 3D printer.

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 love playing poker, both online and live. One month, I performed poorly. Although it felt like the cards were running badly, I wondered whether I had developed a problem in my game and was blaming my losses on bad luck. I asked around, including asking AI, whether a tool existed that could measure luck from poker hand histories. The unwelcome answer was that it did not. I'm not a coder, but after doing some research into vibe coding, I started building a luck-detection app in a Claude chat. Claude built the UI directly in the chat window and advised me on the formulas I was using to calculate luck for the cards dealt, my performance on the flop, and my performance when I went all in. All three metrics have strong averages, and luck is what varies them. I used a bell curve to model hand outcomes and a Monte Carlo simulator, which Claude suggested and executed, to evaluate all possible outcomes. The result astonished me because it was so useful. I immediately fixed two major leaks in my game and felt better knowing that bad luck really was the main problem affecting my results. I liked the tool so much that I decided to turn it into a full web app with Claude Code, and now an iPhone app that I may let other people use for free. I also had a lot of fun building it—except for learning how to use Xcode. That was a pain, even with step-by-step guidance from Claude. Step-by-step: 1. I reviewed a month of poor poker results and questioned whether bad luck or problems in my game were causing the losses. 2. I researched whether a tool existed that could measure luck from poker hand histories and learned that I would need to build one myself. 3. I used vibe coding to start building a luck-detection app in a Claude chat. 4. I had Claude create the UI and advise on formulas for evaluating cards dealt, flop performance, and all-in performance. 5. I used a bell curve to model hand outcomes and a Monte Carlo simulator to evaluate possible outcomes. 6. I used the results to identify and fix two major leaks in my game and confirm that bad luck was also affecting my results. 7. I expanded the project into a full web app with Claude Code and then began building an iPhone app, working through the added challenge of learning Xcode.

I was tired of tracking events across my WhatsApp groups by hand and overcommitting myself, so I built a daily AI assistant that reads the groups and keeps my Google Calendar up to date automatically. I'm in six busy groups: multiple cricket chats, a tournament group, a volunteering rota, a weekly class, and a tennis club. Events arrive as polls, times change, venues are added, fixtures are cancelled, and the same match is often posted in multiple groups. A Claude Cowork scheduled task runs every morning. Using Claude in Chrome, it opens WhatsApp Web and reads only the fixed list of six named groups, starting from where it stopped last time. It turns messages into calendar events based on polls, merges duplicates across groups by matching the event type, date and time, and venue, and emails me a short digest covering what changed, what needs my attention, and which chats I had already read myself. I can reply to that email to steer it—for example, “delete that,” “move to 6pm,” or “why did you create this?”—and the next run acts on or answers my reply. Three small files next to the prompt make the workflow reliable: - `state.json` records where each chat was last read and which events it owns. It never overwrites a field I edited by hand. - `run.log` contains a one-line record for every action and the reason for it, so the assistant can answer questions weeks later without rereading WhatsApp. - `lessons.md` holds rules it writes for itself when something goes wrong, such as “WhatsApp changed its page layout, use this selector instead.” It reads these rules every run and can improve them. The key prompt rules are: “Your sources are exhaustive: these six chats and nothing else. Reading a chat that is not a source is itself the failure.” “Never create a second event for the same occasion. Merge; the first source owns it.” “Fields I changed by hand are locked. Report conflicts, don't overwrite.” “Send the digest every run, even when nothing happened, so silence means the run failed.” The result is that my calendar follows my WhatsApp groups without me doing anything, and each morning’s email flags the two or three things that actually need a decision. Step-by-step: 1. I define a fixed list of six named WhatsApp groups as the only allowed sources. 2. I schedule a Claude Cowork task to run every morning. 3. Using Claude in Chrome, the task opens WhatsApp Web and resumes reading each source from where it stopped last time. 4. It turns messages and polls into Google Calendar events, updating times, venues, and cancellations as needed. 5. It merges duplicate events across groups by matching the event type, date and time, and venue, while keeping the first source as the event owner. 6. It protects fields I edited by hand and reports conflicts instead of overwriting them. 7. It records chat progress and event ownership in `state.json`, logs every action and its reason in `run.log`, and reads `lessons.md` for rules learned from previous problems. 8. It emails me a digest every run, including what changed, what needs my attention, and which chats I had already read myself. 9. I reply to the digest when I want to delete an event, move it, or ask why it was created; the next run acts on or answers my reply.

The Rundown recently held our first in-person retreat in Portugal, which was also my first time traveling to Europe. Claude and ChatGPT were amazing resources throughout the trip, from helping me navigate a chaotic international airport to providing personalized advice on cultural differences, tipping, translating menus, and using public transportation. My wife accompanied me and had plenty of time for solo exploration during the day. She is already a big planner, but a ChatGPT project containing our trip information helped her create a detailed plan, including a packing list, pre-trip checklist, and well-structured day-by-day itinerary for seeing as much of Lisbon as possible. As someone who gets stressed by travel, having an expert with context about my life available in my pocket to answer questions whenever I needed it is something I can’t imagine taking trips without. Step-by-step: 1. I used Claude and ChatGPT for personalized travel guidance during my first trip to Europe. 2. I asked for help navigating a chaotic international airport and handling cultural differences, including tipping, translating menus, and using public transportation. 3. My wife created a ChatGPT project with our trip information to support her planning. 4. She used it to create a packing list, pre-trip checklist, and day-by-day plan for exploring Lisbon during her solo time. 5. I used the tools throughout the trip to answer questions with context about my life and reduce the stress of traveling.

I built GlowNextDoor (glownextdoor.com), a map-based website for discovering nearby hair salons, barbers, nail studios, lash specialists, and other beauty services. I used ChatGPT/Codex and Claude to develop the idea, compare designs, and refine the experience. I then built and published the website with Base44. I connected Google Maps for live business listings and added location detection, distance and rating filters, reviews, directions, and links to salons’ existing booking systems. Through repeated testing on desktop and mobile, I improved the layout, added favorites without requiring a login, created realistic animated category icons, and included an option to add the website to a phone’s home screen. Finally, I connected a custom domain through Blacknight and configured Google Maps usage limits to control costs. My workflow was to describe each feature, review the AI-generated result, test it in the browser, and iterate before publishing. Step-by-step: 1. I described each GlowNextDoor feature to ChatGPT/Codex and Claude while developing the idea and comparing design options. 2. I built and published the website with Base44. 3. I connected Google Maps for live business listings and added location detection, distance and rating filters, reviews, directions, and links to existing booking systems. 4. I tested the website repeatedly on desktop and mobile, then refined the layout and added favorites without requiring users to log in. 5. I created realistic animated category icons and added an option to install the website on a phone’s home screen. 6. I connected the custom domain through Blacknight and configured Google Maps usage limits to control costs. 7. I reviewed each AI-generated result in the browser, iterated on it, and published the updated version.

Sometimes a playlist disappears before you realise how much you depended on it. That happened to a meditation playlist I used constantly for months. Then Spotify removed it, and I discovered an inconvenient detail: I barely knew the track or artist names. I remembered the music and what it did for me, but I could not reliably search for it. The reconstruction began accidentally. While analysing my Spotify listening history for another purpose, AI noticed that I had played roughly the same fifty tracks again and again for about four months. Suddenly, I did not need to remember the missing playlist. My listening history had remembered it for me. A playlist can leave a behavioural fingerprint even when its name and contents are gone. Look for repetitive periods, recurring groups of tracks, similar sequences, and songs that appear together for several months before disappearing around the same time. Step-by-step: 1. I inspected my historical Spotify listening data instead of relying only on which songs I could remember. 2. I used unusual repetition to identify the likely time window. In my case, a four-month cluster helped me recognise the missing meditation playlist, even though I did not know the exact dates beforehand. 3. I built a broad candidate pool of tracks played frequently during that period, recording titles, artists, play counts, dates, listening duration, and nearby tracks. 4. I looked for tracks that repeatedly occurred in the same sessions, on the same days, or in similar sequences. Tracks that travelled together were stronger evidence than songs that were merely popular individually. 5. I compared listening outside the suspected period. A song played throughout several years might be a general favourite, while one that appeared repeatedly within the cluster and rarely elsewhere was a stronger candidate. 6. I used timestamps to recover possible order, including tracks that usually followed each other or appeared near the beginning or end of sessions. Because shuffle may obscure the original order, I treated these smaller sequences as clues rather than proof. 7. I showed the candidate tracks to the person, including names, artists, and, where useful, album art or audio previews. I asked whether each track felt familiar and belonged to the playlist. 8. I relied on recognition rather than recall. Someone may be unable to name a single song but still recognise dozens when presented with them. This is especially useful for instrumental music, mantras, classical works, or unfamiliar names, because remembering the sound does not require remembering its spelling. 9. I ranked candidates as highly likely, likely, possible, or uncertain using repetition, co-occurrence, timing, listening elsewhere, and human confirmation. I did not treat every recovered track as equally certain. 10. I created a provisional playlist from the strongest candidates and listened to it. A list can look convincing in a spreadsheet but feel wrong when played; listening may reveal an unrelated song, a missing transition, or immediate recognition. 11. I used confirmed tracks as anchors and checked which uncertain candidates repeatedly appeared beside them. Data suggested candidates, recognition confirmed anchors, and those anchors improved the next search. 12. I recorded why the playlist mattered, when it was used, and whether its order mattered. Mine was a meditation playlist, so recovering that musical environment mattered even if the exact original list remained uncertain. 13. I saved an external record containing the playlist name, track titles, artists, albums, links or identifiers, reconstruction date, and uncertainty or contextual notes. I kept the listening history, candidate analysis, and confirmed playlist separately so I could revise the result without losing the evidence. The workflow was: analyse listening history → find repeated clusters → locate the time window → collect candidates → compare co-occurrence and sequence → remove unrelated tracks → confirm through recognition → rebuild and listen → refine → save externally. The deeper principle is that humans often remember experiences better than metadata. I remembered that the playlist existed, what it did, and how constantly I played it. I did not remember what most of it was called. The platform had lost the collection, but the listening history still contained its shadow. One observation made it searchable again: > “Hey. For about four months, you were basically listening to the same fifty songs.” 😅🎧

My mom and I love trying new recipes together, but our best dishes rarely turn out exactly like the original recipe. She adds a little more garlic, I suggest a different spice, and by the time we sit down to eat, neither of us remembers what we changed. 😂 I started using ChatGPT as our cooking journal. After we make something, I send it a photo of the dish and a quick voice note about what we used, what we changed, and what we’d do differently next time. ChatGPT turns those notes into a recipe we can make again while preserving our comments and the story behind the meal. Now, when we’re deciding what to cook, I can ask for a favorite we haven’t made in a while, a recipe that uses what’s in the fridge, or a shopping list for our next cooking day. It’s becoming a collection of our recipes and the time we’ve spent making them together. ❤️ Step-by-step: 1. I created one place to keep our recipes, with space for ingredients, steps, photos, ratings, and notes. 2. Each time my mom and I cook, I take a photo and record a short voice note about the changes we made. 3. I ask ChatGPT to turn our notes into a clear recipe and flag any measurements we forgot to record. 4. I review the recipe with my mom, fill in the missing details, and save the version we actually made. 5. We add what we liked, what we’d change, and any memories from that cooking day. 6. When we want to cook again, I ask ChatGPT to find a recipe, adapt it to the ingredients we have, or make a shopping list.