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I changed my iPhone’s Action Button from mute/unmute to a menu of shortcuts. When I press it, I can select a shortcut I created called “Expense AI.” Expense AI records my voice, appends the transcript to a prompt, and sends it to ChatGPT. ChatGPT returns a JSON object containing the time, amount, expense description, payment method, and category. The data is then saved to a live Google Sheet. Step-by-step: 1. I changed the iPhone Action Button to display a menu of shortcuts instead of controlling mute/unmute. 2. I selected the “Expense AI” shortcut from that menu. 3. Expense AI recorded my voice and appended the transcript to a prompt. 4. It sent the prompt to ChatGPT, which returned JSON with the time, amount, expense description, payment method, and category. 5. The resulting data was saved to my live Google Sheet.

Several months ago, my dad was in and out of the hospital. My two brothers and I were trying to coordinate his doctor appointments, manage his medications and potential interactions, and keep track of all the other details involved in his care. At one point, up to seven different doctors were seeing him in the hospital on any given day. It became important to track every medication he was taking, what each one was for, and information such as his weight and other vital statistics. When he returned home, we also had to make sure someone checked on him and his wife every day, helped him stay on schedule with his medications, and recorded his diet, mood, and weight. We initially used Apple Notes, a shared iCalendar, multiple text threads, and a weekly call between the three of us. The mental load was huge. If we needed to find information from the previous week, we had to scroll through pages of Apple Notes to locate it. We also struggled to keep the rest of the family updated. Before my dad passed away, I started using Claude Code and Codex to build a simple tool that would keep everything organized and searchable. It also displayed trends in areas such as his mood, appetite, and vital signs, and included a calendar showing who was covering which days and times. The tool was still fairly basic when he passed away. Afterward, we encountered the administrative headaches involved in closing out his estate. It was far more complicated than we expected. We thought having a will, power of attorney, and other documents meant we were prepared, but we were wrong. I began integrating those lessons—and the things we learned not to do—into the final product, Eldercare Log: eldercarelog.com. I built the final tool with Claude helping draft a PRD, which I then handed to Codex for the coding work. It took a few weeks of refining the product with Codex. The tool is hosted on Vercel, with Supabase and Stripe on the backend, and includes the security features I built into it. It is the tool I wish had existed when my brothers and I were going through this journey before my dad’s passing. Step-by-step: 1. I coordinated my dad’s doctor appointments, medications, vital statistics, and other care details with my two brothers while he was in and out of the hospital. 2. We tracked his medications, their purposes, his weight, diet, mood, appetite, and other vital signs while he was at home. 3. We coordinated daily visits and coverage using Apple Notes, a shared iCalendar, text threads, and a weekly call. 4. I used Claude Code and Codex to start building a searchable tool that organized his care information and showed simple trends. 5. I added a calendar to track which family member was covering each day and time. 6. After my dad passed away, I incorporated what we learned from handling his estate, including the things we wished we had known earlier. 7. I used Claude to draft a PRD, then gave it to Codex to handle the coding work and refined the product with Codex over several weeks. 8. I built the final tool with Vercel, Supabase, and Stripe on the backend.

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 built a meal tracker where my only job is to photograph a plate and send it to an agent. Identification, portion estimates, macros, storage, corrections, and the weekly review happen without me. The part worth stealing isn't the food. The agent has its own email address and iMessage thread, so anything I can send from my phone becomes an input. I send a photo through whichever channel takes fewer taps. I send corrections as plain English with `CORRECTION` in the subject, and answer questions in the same thread. There’s no app and no form. Adding a channel took an afternoon and changed what the agent could be pointed at—the tracker is just one use of an agent you can talk to. It polls on a schedule instead of using a webhook because my laptop sleeps and a local gateway would be unavailable half the time. Storage is append-only JSONL. Corrections append a new record with the same ID using last-write-wins, so the stats layer sees one meal while the original estimate stays on disk. Every number can be traced back to the model’s first guess and my override. The accuracy mechanism is a challenge step. Every photo is estimated twice: first by the main model, then by a blind subagent that receives only the photo and the rubric—not the first answer. If it identifies different food, the entry is downgraded to low confidence and becomes a question for me. Testing before making the system autonomous caught bugs that otherwise would have failed silently. The API returns attachments under a different field than I had assumed, and only on the detail endpoint. As a result, a photo email was read as having no attachments, and every meal photo would have been dropped while the daily task logged a tidy “no new meals” and appeared healthy. The send path was separately broken, which would have killed the weekly review on a Sunday even with everything upstream working. A silent no-op that produces a plausible clean run is what this category of build is prone to. The bug that actually cost me was token usage: each blind challenge read used about 44,000 tokens, so a five-photo dinner cost roughly 220,000 tokens for one meal and capped my usage. The fixes, in order of effect, were to run the challenger on a cheap model, skip it once a named product or my confirmation has settled the entry, downscale photos for viewing, and republish the dashboard only when the data changes. Measuring first mattered—I would have blamed photo size, but that was the smaller half. The honest limits are important: calorie estimates from a photo are 20–25% off at best, identification error is a bigger risk than arithmetic error, and alcohol, water, and caffeine are never estimated from photos. The sequence in which I asked for things mattered more than any single instruction: Step-by-step: 1. I gave the goal and the one ingestion mechanic I was sure about, then insisted on an agreed plan before any code. Arguing about storage and failure modes is cheap before code is attached. 2. Before scheduling anything, I processed one real input end to end in front of me, including every outbound path. Inbound gets tested because I use it; the reply and the weekly digest do not. 3. When the agent reported something about my own input that I knew was wrong, I said so and made it re-check. Confidently wrong answers about things I witnessed were the cheapest bugs to find. 4. I asked for an independent second assessment of anything estimated rather than read, and decided up front what level of agreement was enough to accept the result. 5. I asked for a visible audit surface showing every field the agent claims to track, with a correction control on it. 6. I asked the agent to look up anything knowable rather than estimate it. A named product is a lookup; only the unnameable needs a guess. 7. I treated approval friction and token cost as requirements rather than complaints, and measured before changing anything. Almost every rule exists because something went wrong in ordinary use, not because it was designed up front. An agent I can email or text, which keeps records and answers back, is general-purpose; I’ve pointed it at one narrow job. What else would you point it at?

I built a personal daily tracking system called Artifact. It gives me a way to log my days and turn the data into decisions over time. To use it, I copy everything attached—or screenshot the included “green orange red” example—and paste it into my AI assistant, preferably Claude. The assistant briefly interviews me and then builds a personalized daily tracking system.

I recently learned that I have severe osteoporosis. Two doctors highly recommended Evenity, which is prescribed and insurance-approved only for people at severe risk of fracture, so I knew the situation was serious. The decision was difficult because Evenity carries an increased risk of heart attack and stroke. Both conditions run in my immediate family, and one of my arteries has 75% plaque buildup. Ultimately, we agreed that my risk of a life-altering fracture was much greater than my risk of a heart attack or stroke. My father died six weeks after suffering a life-altering fracture, while my mother survived two heart attacks and a stroke, and my brother died from his first heart attack at age 60. That made it even more important to follow recommendations for both heart and bone health, even though the goals for each can be different. Monitoring everything myself would have been overwhelming, so I created a project called "Daily Meals" and prompted Chat to create a chart that tracks my daily intake of protein, carbohydrates, fiber, healthy and unhealthy fats, calcium, cholesterol, and other selected nutrients, along with a goal for each one. I enter everything I eat throughout the day. The chart maintains a running total and displays my goals underneath. When I am about halfway through my calorie goals, Chat recommends foods for the rest of the day to help me stay on target. Within a week, Chat had learned my go-to foods, and I also told it about other foods I like. There is no harsh judgment when I include ice cream. I also added pictures of the labels for the supplements I take each day. In addition to tracking my intake, Chat advises me about combinations that may help or hinder absorption. One important example for me is that vitamin D, which must be taken with fat, helps with calcium absorption, while vitamin K2 activates proteins that help carry calcium to my bones instead of my arteries. Chat also provides guidance about medications. When I was prescribed an antibiotic, I learned that taking it with calcium could greatly reduce the medication's absorption, so I adjusted what I ate accordingly. There is much more benefit than I can fit here. This project may literally be a lifesaver for me. Step-by-step: 1. I created a project called "Daily Meals." 2. I prompted Chat to create a chart for tracking protein, carbohydrates, fiber, healthy and unhealthy fats, calcium, cholesterol, other selected nutrients, and goals for each one. 3. I added pictures of the labels for the supplements I take each day. 4. I enter everything I eat throughout the day so the chart can maintain running totals. 5. I review the totals against my goals, and when I am about halfway through my calorie goals, I ask Chat to recommend foods for the rest of the day. 6. I told Chat about my go-to foods and other foods I like so its recommendations could reflect my preferences, including occasional ice cream. 7. I use Chat's guidance about nutrient combinations, including vitamin D with fat for calcium absorption and vitamin K2's role in helping carry calcium to my bones instead of my arteries. 8. When I was prescribed an antibiotic, I used Chat's medication guidance to learn that taking it with calcium could reduce the medication's absorption, and I adjusted my meals accordingly.

Start with the property, not a generic landscaping template. Upload photos, measurements, site constraints, current problems, future plans, budget limits, and the look you want. Then use AI to build a complete picture of how the property functions today and how it should evolve over time. Next, work area by area. For overcrowded garden beds, map every tree and shrub, then assess mature size, spacing, health, irrigation coverage, sightlines, and maintenance demands. From there, use AI to determine what should stay, what should move, what should be removed, and how each bed should be reshaped and edged. Convert each recommendation into an execution plan that includes: Step-by-step: 1. The target design 2. A step-by-step build sequence 3. Required materials and tools 4. Budget and priority level 5. Seasonal timing 6. A year-by-year maintenance plan Finally, connect every individual project into one master roadmap. Plan the garden beds, trees, lawn, irrigation, drainage, driveway, shelterbelts, recreation areas, and future buildings together so one improvement does not create a problem somewhere else. The result is a living property operating system. Add a new photo, issue, or idea, and the plan updates with the next best action.

I track my recovery daily—mood, energy, focus, activities, medications, and reflections—but typing everything out never lasted more than a few days. So I built a voice-first pipeline: I talk, and it automatically becomes a clean, organized Google Sheet. Step-by-step: 1. I built a free iPhone Shortcut that records my voice when I say, “Hey Siri, Log,” and sends it to the Gemini endpoint. 2. Gemini transcribes the audio and extracts structured details, including wake time; mood, energy, and focus scores; activities with categories and durations; medications; and reflections. 3. Everything is automatically organized in the appropriate place in a Google Sheet: a daily summary, activity log, metrics log, medications log, and clean, human-readable digest for each day. 4. I can say “yesterday” or “on Monday” to log something I forgot, and it files the entry under the correct date. 5. I connected the completed sheet to a separate AI chat scoped specifically to reflection, so I can ask questions such as how my exercise correlates with my mood or how my energy trends over the month. The best part is that the entire workflow runs on free tiers—Google Sheets, Apps Script, iPhone Shortcuts, and Gemini’s free API tier—so there’s zero cost to build or run it. The result is a daily tracker that takes seconds to update from anywhere, requires no typing, and turns my entries into a dataset I can actually learn from—all without writing a traditional app.

I built Show Hole to solve my family’s classic streaming problem: we spend too much time deciding what to watch, and the answer changes depending on who is actually on the couch. We each have different tastes, but the more interesting problem is that our tastes overlap differently in different combinations. I watch one kind of thing with my spouse, my spouse watches something different with our kid, and my kid and I have our own lane too. Most recommendation tools flatten that into one account profile, one watch history, or broad genre buckets, so they miss the real context of a household. Show Hole treats people and viewing contexts as first-class citizens. It recommends titles “in the vein of” something we liked, filters recommendations to the streaming services we actually subscribe to, avoids titles that the people present have already seen or vetoed, and explains why a recommendation fits tonight. Instead of relying mainly on genres such as comedy, drama, or sci-fi, Show Hole looks at more human taste signals: pacing, world building, humor, emotional weight, complexity, tone, and similar dimensions. It also learns from what we actually do after a recommendation: what we watch, skip, save for later, love, and drop. The result is a personal household recommender that understands “who is watching tonight?” as part of the question, instead of pretending one streaming profile can represent everyone. I designed the app using Claude design, then used those designs with Claude Code to build it. Step-by-step: 1. I identified the household viewing problem and accounted for the different combinations of people who might be watching together. 2. I designed Show Hole around people and viewing contexts instead of treating the household as one account profile, watch history, or set of broad genre preferences. 3. I had it recommend titles “in the vein of” something we liked, filter them to the streaming services we subscribe to, exclude titles already seen or vetoed by the people present, and explain why each recommendation fits that night. 4. I used taste signals such as pacing, world building, humor, emotional weight, complexity, tone, and similar dimensions instead of relying mainly on genres. 5. I made the recommender learn from what we watch, skip, save for later, love, and drop after receiving a recommendation. 6. I designed the app using Claude design and used those designs with Claude Code to build it.

“Honey, do you remember where Ryan’s practice is? Do you remember our Wi-Fi password? Do you know my TSA PRE number? Remember who that painter was that we used last year?” If you have a family with children, you may hear questions like these all the time. In a large family, there always seem to be questions about the house, the kids, or technology that could be answered more easily. The information is usually stored in different places: password files, a Rolodex of business cards, or sheets of paper in junk drawers. I set out to build an app where anyone in my family could use voice to save something for the future or retrieve something they had already stored. I integrated AI to understand the meaning of each voice note. It might create a calendar event, store a password in a vault, or simply remember a phone number. Then anyone else in the family could access the information. The app uses multi-factor authentication for anything particularly secret. It is not intended to be a dedicated password protector; I would build in much more security if that were the goal. Instead, it is an everyday-life app for remembering the little things: How long is the warranty on this appliance? Which exact light bulbs did we use here last time? What was our daughter’s email address that we needed to set up on her phone? Being able to use voice to enter information or retrieve it was the key for me. I think that will save people time. As I started adding entries and validating the idea, my wife came up with the name. After a few weeks, I knew she was completely right, because I now use it about 10 times a day—and I hear that exact phrase every time. Step-by-step: 1. I identified recurring family questions about locations, passwords, identification numbers, contacts, warranties, and household items. 2. I built an app that lets family members use voice to save or retrieve information. 3. I integrated AI to interpret the meaning of each voice note and determine whether to create a calendar event, store a password in a vault, or remember a phone number. 4. I made the stored information accessible to other family members. 5. I added multi-factor authentication for information that is particularly secret, while keeping the app focused on everyday memory rather than dedicated password protection. 6. I added entries and validated the idea through regular use, eventually using the app about 10 times a day.

My mother recently moved into a retirement suite, which meant downsizing her possessions. We ended up with dozens of physical items she no longer had space for, and we wanted her children and grandchildren to have an opportunity to sign up for the family items they loved before anything was donated. I needed a way to catalog everything quickly, share the information remotely, and verify that we weren't accidentally giving away items of significant value. I tested a physical-to-digital workflow using Gemini. Instead of putting the items in a pile or typing out endless lists, I drew a numbered grid on a large whiteboard, placed one item in each square, and took a few photos. Gemini identified the objects by grid position, categorized them, estimated their market values, and built a ready-to-use spreadsheet with a signup column. The physical staging took about 30 to 45 minutes, depending on how many items were on that particular board. The entire digital workload—including cataloging, valuing, formatting, and creating the spreadsheet—took under 10 minutes. The process turned an overwhelming chore into something manageable and produced an end product that was easy for everyone to use. Step-by-step: 1. I drew a simple grid on a whiteboard, numbered each box, and placed one item in each square before taking photos. 2. I uploaded the photos to Gemini and prompted it to identify every item by its board name and grid number. 3. I asked Gemini to classify each item into logical groups, such as Glassware, Ceramics, and Woodcraft, and provide quick, realistic resale-market valuations based on visual condition. 4. I asked Gemini to generate a downloadable Excel file formatted as a signup catalog, with columns for Item ID, Grid Number, Description, Category, Estimated Value, and a blank "Claimed / Signed Up By" column for family members to enter their names. 5. I uploaded the file to Google Drive, converted it to Google Sheets, and added a note at the top explaining how tie-breakers would work if two family members wanted the same item.

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.

I regularly come across long articles, reports, papers, ebooks, and other documents that I want to understand but realistically don’t have time to read closely. Instead of letting them pile up in a reading queue, I change the format and turn them into audio I can consume during time that would otherwise be less productive. Step-by-step: 1. I choose a long article, report, paper, ebook, or other document that I want to understand but don’t have time to read closely. 2. I upload it to Google Notebook (formerly NotebookLM) and add it as a source so NotebookLM can work directly from the material. 3. I generate an Audio Overview. NotebookLM turns the source into a conversational, podcast-style discussion that summarizes and explains the major ideas. 4. I listen while walking, driving, working out, doing chores, or running errands. 5. I follow up on what matters. After listening, I know the main ideas, what I want to investigate further, whether the document is worth reading in full, and which sections deserve closer attention. The result is essentially a personal podcast generated from whatever I need to learn. What I like about this workflow is that it doesn’t require me to find more time. It lets me use time I already have differently. Because the material is turned into a conversational discussion rather than simply being read aloud, I find it easier to stay engaged with dense material. I don’t treat the podcast as a replacement for reading the source when the details really matter. I use it as a comprehension and triage layer. Even when I eventually go back and read the original, I’m starting with a mental model of what’s in it rather than approaching it cold. The broader lesson is that AI doesn’t always need to save time by doing the work for you. Sometimes it can save time simply by changing the form of the work so it fits into your life.

I built a family calendar with Lovable and Claude that integrates with our Google Calendars and Todoist projects. My husband, son, dog, and I were struggling to track our schedules in one place. Although we had shared our calendars through Google, we did not have a central view displayed in the house, and the interface became cluttered when we tracked multiple calendars. We had considered buying a Skylight calendar for years, but it costs $300, and we were not convinced we needed all of its extra features. I decided to build my own instead. Step-by-step: 1. I created a product requirements document (PRD) for a family calendar in Claude. It prompted me to choose the available views, decide how to organize each family member’s calendars, support both events and tasks, and add extras such as the day’s weather and a photo background. 2. I shared the PRD with Lovable, which built the first version of the calendar. Lovable helped me integrate my Google Calendars and Todoist projects so that all of our events and tasks appeared in one place. It also helped me integrate Google Photos so our family photos could rotate in the background. 3. I launched the calendar as a password-protected website so any family member could access it from anywhere. 4. I placed the calendar on an iPad in our kitchen and edited it as we used it, learning which features were most and least helpful. I also added calendars for my in-laws when they visited for several months at a time. Claude continues to help me draft new requirements, which I share with Lovable. The calendar is tailored to our needs and costs much less than the Skylight option. The photo feature also makes it feel personal, and I look forward to continuing to evolve it over time.

I spend way too much time staring at screens, so I try to consume my content by listening while I'm out walking (usually podcasts). Lately, I've been digging into more long-form stuff from X articles and old blogs, and ElevenLabs' free app, ElevenReader, turns any wall of text into a mini audiobook using AI-generated voices. It's super simple, but genuinely really useful. Step-by-step: 1. I chose a long-form X article or older blog post that I wanted to consume away from a screen. 2. I added the text to ElevenReader. 3. I selected an AI-generated voice that was comfortable to listen to. 4. I let the app convert the article into a mini audiobook. 5. I listened while walking and kept the workflow as a simple alternative to reading everything on a screen.