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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 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.

I kept rereading the same Stoic books to find one line I half remembered. So I stopped reading them and started talking to them instead. I took public-domain writing from 13 people—including Marcus Aurelius, Seneca, Sun Tzu, Jane Austen, Tesla, and others—and gave each one its own AI persona on a single page. Each persona answers only from that person’s writing and says when it does not know. There are no made-up quotes. The fun part is seeing the questions people actually ask. The top question this week was: “How do I stay steady when someone wastes my morning?” That is a real problem answered by a Roman emperor who faced the same one. Full disclosure: I am the founder of Mindola, the tool I used. The 13 classic lenses are free to try with no signup: https://mindola.ai/discover Step-by-step: 1. I gathered public-domain writing from 13 people, including Marcus Aurelius, Seneca, Sun Tzu, Jane Austen, Tesla, and others. 2. I created a separate AI persona for each person and put all 13 on one page. 3. I configured each persona to answer only from that person’s writing and to say when it does not know. 4. I use the personas to ask questions I would otherwise search for by rereading the books. 5. I review the questions people ask, including “How do I stay steady when someone wastes my morning?”

I needed home insurance and was asked to estimate the value of all my personal belongings. I asked Claude to search my personal email and make a list of all the purchases I had made in the past year. I asked my husband to do the same and share his list with me. I also made a list of our valuables and took a picture of each room in the house. I gave all of that information to Claude, which produced a list of everything I owned and estimated its rough value. It cross-referenced the purchases with the items visible in the photos to make sure I did not double-count anything. I sent the list to my insurance broker, who said it was extremely helpful and could also serve as supporting evidence if I ever needed to submit an insurance claim.

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 first tried to build Rumbly.xyz about six months ago using Lovable, but I ran into several issues and kept getting errors, so I temporarily gave up. Then I discovered Replit. AI had also matured over those six months, which likely helped. I entered a three- or four-paragraph prompt describing exactly what I wanted, and about 75% of the app was built immediately. The problem I wanted to solve was simple: on an average Tuesday or Wednesday, my wife and I talk about where we want to eat. We know our friends have recommended countless places, and we keep saying we're going to try them, but we can only think of the two or three restaurants we always visit. So we end up going to the same places again. I built Rumbly.xyz as a simple web app. It lets you record a recommended restaurant, who recommended it, and what type of food it serves. Rumbly connects each restaurant to the Google Maps database, giving you the exact name and a map view, and you can add notes. Then I realized there was no reason to save only restaurant recommendations. People also recommend streaming series and books, and they tell jokes. I can never remember a joke two months later when someone asks, “Do you know any good jokes?”—even if I thought the last five were hilarious. Rumbly.xyz lets you save any of these recommendations from your friends and share them with friends who are already on Rumbly or have not joined yet. I connected it to email and enabled it to send mobile messages to your phone. I built all of this through Replit. Step-by-step: 1. I described the problem and the app I wanted in a three- or four-paragraph prompt in Replit. 2. I built a web app that records restaurant recommendations, the person who made each recommendation, the type of food, and additional notes. 3. I connected restaurant entries to the Google Maps database for exact names and a map view. 4. I expanded the app to save recommendations for streaming series, books, and jokes—not just restaurants. 5. I connected Rumbly.xyz to email and mobile messaging so recommendations can be shared with friends who are on Rumbly or have not joined yet.

I took a photo of a messy spot at home and asked ChatGPT for ideas on how to organize it. ChatGPT generated several ideas, including a photo showing how to do it. Step-by-step: 1. I photographed the messy spot at home. 2. I asked ChatGPT for ideas on how to organize it. 3. I reviewed the ideas and the photo ChatGPT generated to see how to put them into practice.

I recently bought a house & had an inspection done as a closing condition. I uploaded the inspection report (80 pages) to Claude and asked it to make a list of home maintenance tasks by timeline (closing week, seasonally, etc.). Then, based on that list, I had it create one-off or annually recurring calendar events in my personal Google Calendar, with my husband invited to each event. It was super helpful because the inspection report clearly noted when the maintenance tasks were completed and suggested a frequency for the future. Step-by-step: 1. I uploaded the full 80-page home inspection report to Claude. 2. I asked it to extract every maintenance item and organize the work by closing week, season, and recurring frequency. 3. I had it use the report’s completion dates and recommended intervals to calculate when each task should happen next. 4. I reviewed the list for anything that needed to be corrected or combined. 5. I asked Claude to create one-off and annually recurring Google Calendar events and invite my husband to each one.

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.

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.

One email instead of 50: an n8n workflow that reads all my AI newsletters and sends me a single daily digest in under 2 minutes THE PROBLEM: I subscribe to dozens of AI newsletters. The Rundown, Superhuman, TLDR, The Neuron, AlphaSignal, TheSequence, Turing Post and many more. Reading them took hours every day, and most of them cover the same three stories. I wanted the coverage without the reading time. So I moved every subscription to a dedicated Gmail address and let n8n read that inbox for me. It has run daily since February 2026 and I now read one email a day instead of 50. STACK: n8n (hosted on Hostinger), Gmail, Google Gemini 2.5 Pro. HOW TO BUILD IT: STEP 1: Create a dedicated Gmail account and move every newsletter subscription to it. This one decision makes everything else simple. Your personal inbox stays clean and the workflow never touches mail that is not a newsletter. STEP 2: Schedule Trigger node, daily at 08:00. Set the workflow timezone (mine is Europe/Stockholm) or the trigger runs on server time. STEP 3: Gmail Get Many Messages node on the newsletter account. Filter by read status: unread. Return All: on. Simplify: OFF. That last toggle matters, see gotcha 2. STEP 4: Connect two branches off that node. Branch one is a Gmail Mark As Read node with message ID {{ $json.id }}. Unread is the whole state system: each run only fetches what arrived since the last run. No database, no date filters, no dedupe logic. STEP 5: Branch two is an Aggregate node. Aggregate the "html" field of every email into one array field called CombinedNewsletter. This means one AI call per day instead of one call per email. STEP 6: AI Agent node with a Google Gemini Chat Model attached (models/gemini-2.5-pro). Turn on Retry On Fail with 5000 ms between tries. The prompt: Below is all the news in html format. Only use what is provided; if the HTML looks cut off, still summarize everything you can see. {{ $json["CombinedNewsletter"].join('\n\n').substring(0, 250000) }} The substring cap is load bearing, see gotcha 1. System message (verbatim, numbering written as (1) so this form does not strip it): "You will receive ALL the AI newsletters from the past day in HTML format. Your task: (1) Extract every distinct news item (no duplicates, even if repeated in multiple newsletters). (2) For each item, find: a short, human-readable title, the best URL, a one-sentence summary (max 25 words). (3) Estimate popularity based on how many newsletters mention it. If an item appears only once, rank by how interesting the general public might find it. Output format (Markdown only): # Daily AI News Digest, then '## Top headlines' listing the 5 most popular/important items as 'Title Summary sentence', then '## More news' listing all remaining items in the same format. Rules: Always use Markdown links like Title, never show bare URLs. Do not skip any news item. Do not add any commentary, explanations, or closing text beyond the structure above." STEP 7: Markdown node, mode Markdown to HTML, destination key combinedHTML. STEP 8: Gmail Send node to your personal address. Subject: Here's ALL the AI News! {{now.toFormat('yyyy-MM-dd')}}. Wrap {{ json.combinedHTML}} in a full HTML document with inline CSS: white card, max-width 720px, system fonts, styled links. See gotcha 4. FOUR THINGS THAT COST ME HOURS: (1) Raw newsletter HTML broke Gemini. The workflow refused to execute with a payload limit error. Newsletter HTML is enormous: tracking pixels, nested tables, inline styles. Fifty of them concatenated is millions of characters. The .substring(0, 250000) cap in the prompt fixed it, and the "if the HTML looks cut off, still summarize" line tells the model how to handle the truncation. (2) Gmail's Simplify toggle is on by default and strips the message body. Gemini kept receiving empty or gutted content and no error explained why. Turn Simplify off to get the full html field. (3) Gemini rate limits AND timeouts both hit on big runs. Retry On Fail with a 5 second wait fixed both. Without it, one 429 kills the whole morning digest. (4) Sending the model's raw markdown as email looked broken in Gmail. Two part fix: a Markdown to HTML node, then a proper HTML template with CSS in the send node. RESULT: the latest real run turned 50 unread newsletter emails into one clean digest in less than 2 minutes (1 minute 27 seconds to be exact). Running every morning since February 2026. Honest failure mode: mark as read runs as a parallel branch, so if Gemini fails after all retries, that day's emails are already marked read and drop out of tomorrow's digest. I accepted that trade off. The alternative is duplicate items on every retry, and one missed day costs less than a digest full of repeats. Rebuild time: about 30 to 45 minutes if the dedicated inbox already exists.

It's finally gardening season in the Midwest, and I wanted to do a big overhaul of my space with a new large in-ground bed and a more organized layout of crops. I first took some images of the space and had ChatGPT / Codex generate some visualizations of what it could look like based on my rambling list of requirements and measurements. After deciding on the vision, I asked for help creating a materials list, a step-by-step guide to building the bed (with instructions on sawing, drilling, etc.). I also provided a list of what we wanted to grow, and was able to get a well-planned map of where to place each plant, optimizing for aspects like shade vs. sun, companion plants, vining, etc. Several trips to Home Depot and a couple of days of work later, we have an awesome, refreshed space and a thoughtful, organized garden for the season! Step-by-step: 1. I photographed the existing garden space and wrote down the measurements, requirements, and rough ideas I had in mind. 2. I gave the images and requirements to ChatGPT and Codex and asked for several visualizations of the finished space. 3. Once I chose a direction, I asked for a materials list and step-by-step instructions for building the in-ground bed. 4. I supplied the full list of plants and asked for a placement map based on sun, shade, companion planting, and vining behavior. 5. I used the plan to buy materials, build the bed, and install a more organized garden layout.

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?

My Downloads folder had been filled with random files for the last six months, and I'd been procrastinating on cleaning it up. So I gave Claude Cowork access to the folder, and asked it to "help me organize this folder." In a few minutes, it went through almost 100GB of files, arranged them by file type and folders, and removed duplicate files in just a few minutes, something that could have cost me hours of work. Step-by-step: 1. I gave Claude Cowork access to the Downloads folder that had accumulated files for roughly six months. 2. I asked it to inspect the folder and propose a sensible organization rather than specifying every destination myself. 3. I had it group the files into folders based on type and content. 4. I asked it to identify and remove duplicate files. 5. I let the workflow process the full folder, which was nearly 100GB, in one pass.

What do you do when your wife says you talk about AI too much? You listen to her, and when she has a problem, you frantically write it down and build it with AI. My wife enjoys working with her plants in the warm sun and light breeze of the Carolina mountains. She’s new to gardening and learning more each day, but she was having trouble keeping track of everything. Which tree did she plant in the front yard last year? It seemed to be struggling—would it do better somewhere else? And would rain reach the potted flowering planters while we were out of town, or would she need to find someone to help? I used Claude Code and Fable to develop an app that my wife could run on her phone, with all the data stored locally. Now she’s excited about “her app” and the things she can add to it. She has even shared it with others, including people across the country, so we added climate zone settings to make it useful in other parts of the country. 😂 Step-by-step: 1. I listened to my wife’s gardening challenges and wrote down the problems she wanted to solve. 2. I used Claude Code and Fable to develop a gardening app for her phone. 3. I designed the app to keep all the data stored locally. 4. I built in a way to track plants, including which tree was planted in the front yard and how it was doing. 5. I accounted for questions about rain and the care of potted flowering planters while we were out of town. 6. After my wife shared the app with people across the country, I added climate zone settings so it could be useful in other regions.

I used Claude and Nano Banana 2 to create a detailed landscaping plan for my property. The plan included precise recommendations for plant types and locations, soil testing, purchase lists, local suppliers, expected wholesale pricing, and photorealistic images of the mature planting beds. I refined the plan through 20–30 iterations, incorporating all of my requirements. Because I have previous experience working with landscape architects, I was able to use the AI-generated plan effectively and legitimately saved $5,000 in fees. Step-by-step: 1. I used Claude and Nano Banana 2 to develop a landscaping plan for my property. 2. I included requirements for plant types and locations, soil testing, purchase lists, local suppliers, expected wholesale pricing, and photorealistic images of the mature planting beds. 3. I refined the plan through 20–30 iterations until all of my requirements were incorporated. 4. I used my previous experience working with landscape architects to evaluate and apply the plan. 5. I saved $5,000 in landscape architecture fees.

I built a weekly interactive training-course generator for my 10-year-old son and eventually landed on a much simpler final version than where I started. I wanted a way to teach him practical safety and life skills, starting with how to swim confidently and what to do if he gets into trouble in the water. I needed something more engaging than simply talking at him, but writing a polished, interactive lesson from scratch every week was not sustainable. First, Claude and I designed a single interactive HTML course as a proof of concept. It was a swim-safety course with a branded look, including a custom color palette, fonts, and a progress tracker styled like pool lanes. The course was divided into modules: a welcome screen, a comfort-and-basics lesson, a step-by-step skills walkthrough, a safety checklist, a “what to do if something goes wrong” module, a quiz, and a certificate at the end. The “what to do if something goes wrong” module was the most important part. Once the course worked, I wanted to reuse the same format for a new topic every week. Claude first built a version that called the Claude API live from inside the page to generate new content on demand. However, it only worked while the page stayed open inside Claude.ai, and occasionally a section failed to generate cleanly. We pivoted to a simpler approach. Instead of using a live tool, Claude documented the entire course format—including the visual identity, module structure, tone, and content rules—as a standing “brief” document. Each week, I open Claude Cowork, paste in the brief and that week’s topic—bike safety, fire safety, or whatever is next—and Cowork returns a finished, fully self-contained HTML file. There is no app to babysit and no live API call; I can simply open the file and hand it to my son. The result is a repeatable, no-maintenance weekly workflow for turning any topic into a polished interactive lesson for my kid. Step-by-step: 1. I identified a need for more engaging lessons on practical safety and life skills, starting with swimming and water safety. 2. I worked with Claude to create a proof-of-concept interactive HTML swim-safety course. 3. I structured the course into a welcome screen, comfort-and-basics lesson, skills walkthrough, safety checklist, emergency-response module, quiz, and certificate. 4. I tested a live-generation version that called the Claude API from inside the page, then noted that it only worked while open inside Claude.ai and that sections sometimes failed to generate cleanly. 5. I had Claude document the course’s visual identity, module structure, tone, and content rules in a reusable brief. 6. Each week, I open Claude Cowork, provide the brief and a new topic, and receive a finished, self-contained HTML course file. 7. I open the file and give the interactive lesson to my son without maintaining an app or making live API calls.