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.

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 use ChatGPT to support a hobby that combines research, negotiation, history, craftsmanship, and family legacy: collecting mechanical watches. The workflow usually begins when I discover a watch that interests me. Rather than simply asking whether it is a good watch, I use ChatGPT as a research analyst. We investigate the exact reference, movement, production history, complications, materials, rarity, manufacturer history, comparable watches, secondary-market pricing, previous sales, and any known weaknesses or servicing concerns. With unusual independent watches, this can become surprisingly deep research. The goal is to answer two separate questions: Is the watch genuinely interesting? And is this particular example worth buying at this price? If I decide to pursue a watch, ChatGPT shifts into the role of negotiation adviser. I share dealer listings, asking prices, trade proposals, emails or text messages, previous offers, comparable sales, and my own walk-away price. We discuss likely dealer economics, negotiating leverage, possible counteroffers, and how aggressively or patiently I should approach the transaction. ChatGPT also helps draft correspondence, but the final decision remains mine. I find the greatest value in having an analytical partner that remembers the research and can challenge my enthusiasm before I spend money. After acquiring a watch, I create an entry in a private document I call my Horological Codex. Each entry includes the technical specifications and historical information, along with why I chose that particular watch, how I acquired it, its price, its appraised value, what was happening in my life at the time, and why it deserves a place in the collection. One early watch commemorates an educational milestone. Another is a one-of-one piece commissioned directly with an independent watchmaker. Other watches connect to family events, travel, my cars, personal interests, or particular periods of my life. The Codex preserves those stories and memories alongside the watches themselves. My original Codex eventually became a large, 70-page formatted document, and maintaining it manually became cumbersome as watches entered and left my collection. My next version will separate the structured information from the presentation layer: I will maintain a master record for each watch, then use AI to generate or update the polished Codex from that underlying data. That way, the collection can evolve without requiring me to rebuild an entire book every time something changes. I keep the full Codex private because it contains family and financial information, but the system itself has become a meaningful way to preserve the history behind the collection. Step-by-step: 1. I identify a watch that interests me and describe the exact reference and available listing information to ChatGPT. 2. I research the watch’s movement, production history, complications, materials, rarity, manufacturer, comparable watches, secondary-market pricing, previous sales, and known weaknesses or servicing concerns. 3. I evaluate both whether the watch is genuinely interesting and whether the specific example is worth buying at the asking price. 4. If I decide to pursue it, I share the dealer’s listing, asking price, trade proposals, correspondence, previous offers, comparable sales, and my walk-away price with ChatGPT. 5. I use that information to discuss dealer economics, negotiating leverage, counteroffers, and the right negotiation approach, then make the final decision myself. 6. After acquiring the watch, I record its technical details, history, acquisition story, price, appraised value, personal significance, and place in the collection in the Horological Codex. 7. I maintain a master record for each watch so AI can eventually generate or update the polished Codex without requiring me to rebuild the entire 70-page document whenever the collection changes.

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.

I built a workflow for turning large ChatGPT conversation exports into a usable personal and creative archive instead of simply storing them as backups. The archive is processed incrementally. The inventory is built offline, and conversations that have already been indexed are not needlessly reanalyzed on every run. Each new export is compared with the existing archive, and only newly added conversations or conversations that have been revisited, extended, or otherwise changed are processed again and updated in the inventory. This keeps the workflow lightweight while allowing the archive to evolve over time. A conversation can remain stable for months, then become relevant again and receive new material without forcing the entire archive back through analysis. This matters because many of my conversations are long, layered thinking sessions: creative explorations, project development, research, problem-solving, or extended reflection. Without an inventory, the depth inside those individual conversations and thinking processes becomes difficult to retrieve later. The workflow makes long-form analysis and creative thought processes findable and reusable without flattening them into a few generic summaries. On top of the inventory, I use lightweight “blubscans” (analysis to improve retrieval): small, human-readable summaries that capture what mattered during a day or period without replacing the original conversations. They act as a navigational layer between thousands of raw messages and the things I may want to find, understand, revisit, or continue later. The important principle is that compression never becomes deletion. The raw conversations remain the source of truth, the inventory provides structure, and the scans provide context and tone. The result is more than a backup system. It becomes working creative memory: something I can preserve, search, revisit, connect across time, and reuse for projects, research, writing, pattern-finding, and future creative work. In simple terms, the structure is: raw exports → offline incremental inventory → blubscans/context layer → retrieval and reuse for later projects and creative work That way, the archive stays deep without becoming heavy, and useful without constantly reprocessing everything that was already understood. Step-by-step: 1. I collect large ChatGPT conversation exports as the raw source material for the archive. 2. I build and maintain an offline inventory of the conversations that have been indexed. 3. With each new export, I compare the conversations against the existing archive. 4. I process only newly added conversations and conversations that have been revisited, extended, or otherwise changed. 5. I update the inventory with the results while leaving stable conversations untouched. 6. I create lightweight “blubscans” with small, human-readable summaries of what mattered during a day or period. 7. I use the raw conversations as the source of truth, the inventory for structure, and the scans for context and tone. 8. I retrieve and reuse the archive for projects, research, writing, pattern-finding, and future creative work.

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.” 😅🎧

I love reading, but I’m indecisive about what to read next. Lately, I’ve been getting recommendations from ChatGPT, which has led me to keep adding books to my to-read list instead of tackling the books that have been there for years. At the same time, searching through my 200 existing TBR books on Goodreads for one that matches my precise mood would be too time-consuming. To solve this, I exported all the books from my Goodreads bookshelf as a CSV file, uploaded it to ChatGPT, and asked it to recommend books from my “to-read” list that matched a description of what I was currently in the mood to read. For example, I asked it to recommend a book from my TBR list that was published in the 19th or 20th century and featured a strong, unforgettable heroine. It gave me a few options, and I decided to read *O Pioneers!* by Willa Cather next. One note: ChatGPT summarizes the plots of the books it recommends, so I suggest adding “no spoilers, please” to your prompt. Step-by-step: 1. I exported all the books from my Goodreads bookshelf as a CSV file. 2. I uploaded the CSV file to ChatGPT. 3. I described the kind of book I was currently in the mood to read, including publication period and character preferences. 4. I asked ChatGPT to recommend books only from my existing Goodreads “to-read” list. 5. I added “no spoilers, please” to prevent plot summaries from revealing too much. 6. I chose *O Pioneers!* by Willa Cather from the recommendations.

I often lose track of movies and TV shows I want to watch, especially when they are announced months before release. To solve this, I use a free API from watchmode.com. I asked Hermes Agent—although ChatGPT Work could probably do the same—to create a scheduled task that checks the API once a week and notifies me when a title becomes available to stream. I also gave it a simple instruction: whenever I send an IMDb link for a movie, TV show, or specific season, it should automatically add it to my watchlist. Since I prefer to binge-watch, it only notifies me when an entire season is available. The next step is to make it automatically start tracking the following season once the current one has been fully released. Step-by-step: 1. I use the free API from watchmode.com to check streaming availability. 2. I asked Hermes Agent to create a scheduled task that checks the API once a week. 3. I configured the task to notify me when a movie or TV show becomes available to stream. 4. I instructed it to add a movie, TV show, or specific season to my watchlist whenever I send an IMDb link. 5. I configured notifications for TV shows to wait until the entire season is available. 6. My next step is to have it automatically start tracking the following season after the current one has been fully released.

I asked Claude to build me an artifact that works as my own personal book tracker. It includes separate sections for books I want to read, books I’m currently reading, and books I’ve read. I also asked Claude to collect specific data points about each book and create an insights tab that can gather information about my likes and dislikes over time, then make future recommendations. I personally dislike Goodreads’ UI and don’t use its social media features, so I wanted a personalized alternative housed within Claude alongside all my other workflows—a one-stop shop. Step-by-step: 1. I asked Claude to build an artifact for tracking my books. 2. I organized the tracker into books I want to read, books I’m currently reading, and books I’ve read. 3. I specified the data points I wanted to collect for each book. 4. I asked Claude to add an insights tab to gather data about my likes and dislikes. 5. I set it up to use those insights for future recommendations and keep the entire workflow within Claude.

I recently had an Evolt body scan. I've uploaded my results to Claude, shared my daily habits (sleep, diet, schedules, etc.), and asked it to create a full workout and diet plan based on the areas I want to improve. I was also able to include my recent Oura ring report to see if there's anything wrong with me. Will I follow it? We'll see.... Step-by-step: 1. I uploaded my Evolt body-scan results to Claude. 2. I added context about my sleep, diet, schedule, daily habits, goals, and the areas I wanted to improve. 3. I included my recent Oura report so the analysis could consider recovery and other wearable data alongside the scan. 4. I asked for a complete workout and diet plan tailored to the combined information. 5. I reviewed the plan as one organized starting point instead of trying to reconcile each source manually.

I built a self-hosted wine cellar app that runs in a Docker container on my home NAS and doubles as a personal AI sommelier. I add a bottle by taking a photo of its label with my iPhone. A vision model identifies the wine, vintage, region, and grape varieties, while a second model estimates its market value and writes tasting and pairing notes. The app renders a visual map of my rack, so I can see exactly where each bottle is and pull one without hunting. My ratings feed a taste profile built from my own history, which the AI sommelier chat uses to answer questions such as what to open with dinner or which bottles are drinking at their peak. The feature I use most is Scan & Check. When I’m in a store, I photograph a bottle and get a Collection Fit score based on my profile before buying it. The app also exposes an MCP endpoint, so Claude can search my cellar, score a wine, or list my top-rated bottles from any conversation. I built it almost entirely with Claude Code and use it daily from my phone. Step-by-step: 1. I run the wine cellar app in a Docker container on my home NAS. 2. I photograph a bottle’s label with my iPhone when adding it to the collection. 3. A vision model identifies the wine, vintage, region, and grapes, and a second model estimates market value and generates tasting and pairing notes. 4. I use the app’s visual rack map to locate bottles by slot. 5. I rate wines so the app can build a taste profile from my history. 6. I use the AI sommelier chat to choose what to open, including bottles that are drinking at their peak. 7. I use Scan & Check in stores to photograph potential purchases and review their Collection Fit score. 8. I connect to the app’s MCP endpoint so Claude can search my cellar, score wines, and list my top-rated bottles from a conversation.

Sometimes I remember something perfectly badly: not its title, creator, or where I found it, but a sound, a color, a vague cover, a sentence, or a period in my life. I know it exists because I loved it, but I cannot name it. I use AI and my digital traces to recover songs, books, videos, articles, and quotes. Memory is not a catalog; it stores traces. AI can turn those traces into searchable hypotheses. Step-by-step: 1. I dump every surviving clue without waiting for a proper search query. The clues can be as rough as “The title sounded like nu nau tau,” “It was ambient or humming,” “The cover was maybe a green desert?” or “I knew it around 2019.” Another example is: “A book about makers and Disney. White 3D-printed figures, tiny people, and bears on a blue cover.” This is raw memory evidence. 2. I separate certainty from guessing by labeling each clue as certain, fairly sure, possible, or probably distorted. I keep uncertain clues, but do not treat them as facts. A wrong attribution or outdated cover does not invalidate everything else. 3. I reconstruct the original encounter. I ask where I was and whether I Shazamed it, saved it, sent it to someone, screenshotted it, or bought it at a book fair. In one case, I remembered hearing a song in someone’s car and Shazaming it because I could not parse the pronunciation. Remembering that capture action pointed to a retrieval source. 4. I search my own collections first: playlists, liked songs, Shazam history, saved videos, old posts, messages, bookmarks, notes, photos, receipts, and reading lists. The object may already be there under metadata I no longer remember. I start with the smallest reliable fragment. 5. I search for “nu” inside my playlists after remembering “nu nau tau.” That recovered Man O To — Nu. The artist and title had blended into one phrase, and the song was sitting in several playlists, including my Shazam collection. A clue does not need to be complete; it only needs to be distinctive enough inside the right collection. 6. I allow memory to scramble metadata. An artist and title may merge, album art may become song art, and a quotation may survive with the wrong poet attached. I split remembered phrases, swap artist and title, try different word boundaries, consider older releases, and search phonetically using similar syllables, vowel sounds, homophones, alternate spellings, or other languages. I also ask what the cover looked like when I knew it, because artwork changes. 7. I use context and cross-reference traces. A year, trip, relationship, or platform can narrow the search more than a half-correct title. Spotify might preserve the song, Shazam the discovery moment, and an old message the recommendation. Old posts are accidental archives, and dead links may still leave names, dates, or descriptions. If memory fails completely, I inspect behavior from the relevant period, such as repeated listening, watch history, purchases, or library loans. 8. I let AI propose candidates and compare them against several independent clues, including sound, subject, period, cover, and personal context. One matching detail is not confirmation. I show titles, covers, creator names, and short descriptions because recognition may succeed where recall fails. Rejections also help: “No, but the cover was more minimal,” “The title was shorter,” or “It was fiction, not nonfiction.” Each correction sharpens the search. 9. I pay attention to the strangest clue. The blue cover with white figures, bears, and a makers-and-Disney theme led to *Makers* by Cory Doctorow. 10. Once I confirm the object, I save the title, creator, link, why it mattered, and how I recovered it. For example: - Clue: “nu nau tau” - Encounter: heard in someone’s car and Shazamed - Search: Spotify/Shazam collection → “nu” - Result: Man O To — Nu - Distortion: artist and title blended together This becomes a retrieval playbook: which clues survive, where traces live, and how my memory distorts them. The overall workflow is: collect fragments → mark uncertainty → reconstruct the encounter → search personal traces → try phonetic, visual, and contextual clues → compare candidates → recognize and confirm → save the result and retrieval path. I do not need to remember the database fields. I need enough fragments to rebuild the trail. Sometimes the best clue is what I did when I first encountered something. Sometimes it has been sitting in five of my own playlists while I spend years wondering what it was called. 😑😭🎧

My car recently lost a small part near the bonnet. I didn’t know what it was called, and even the official workshop couldn’t help because the model was no longer in production. I gave ChatGPT my car details and a photo showing where the part was missing. It identified the part, helped me cross-check the specifications and compatible part numbers, and found sellers that had it available. I found the right part and had it delivered within a week. Step-by-step: 1. I shared my car model and a photo of where the part was missing. 2. I used ChatGPT to identify the part and its specifications. 3. I cross-checked compatible part numbers and alternatives. 4. I searched for sellers carrying the correct part. 5. I compared the options and ordered the right one.

I live in a state experiencing extreme drought conditions, and my water provider recently notified us of new fees for excess usage. That prompted me to dictate nearly 10 years of monthly water bills to Manus by reading each date and gallon total aloud. I then asked it to cross-reference my usage with local temperature and precipitation records for Eden, Utah. The analysis found that temperature explains 51% of the variation in my water usage, while precipitation explains 19%. That suggests roughly 30% is driven by my own choices—a significant opportunity for conscious water stewardship. It took about an hour of conversation to build the entire dataset from scratch. Step-by-step: 1. I read the date and gallon total from each monthly water bill aloud to Manus. 2. I asked Manus to organize nearly 10 years of usage data into a dataset. 3. I asked it to cross-reference my usage with local temperature and precipitation records for Eden, Utah. 4. I reviewed the analysis showing that temperature explained 51% of usage variation, precipitation explained 19%, and roughly 30% was associated with my own choices. 5. I identified that remaining 30% as an opportunity for more conscious water stewardship.

I'm knitting a sweater for my baby nephew, and accidentally knitted extra rows, but didn't notice until I was 30 rows past it. I dropped the pattern I was using into ChatGPT and asked it to revise it so the stripes on the front & back aligned. Saved me from undoing my work and made sure the sweater stayed cute! Step-by-step: 1. I gave ChatGPT the original knitting pattern and explained where I had accidentally added the extra rows. 2. I told it how far I had continued knitting before noticing the mistake. 3. I asked it to recalculate the remaining pattern instead of telling me to undo 30 rows of work. 4. I checked that the revised stripe counts would align across the front and back. 5. I continued knitting from the corrected instructions while preserving the completed work.

I used AI to develop a simple daily care plan for my above-ground pool. I copied the links to the pool I bought online and asked which chemicals would be best and whether I should make any upgrades right away. Then I asked AI to create a daily routine to keep the pool sparkling throughout the summer. When the water started looking cloudy, I took pictures of the water tests I was running and shared them so AI could help me get the pool back where it needed to be. Step-by-step: 1. I copied the online links for the above-ground pool I bought. 2. I asked AI which chemicals would be best for the pool and whether I should make any upgrades. 3. I asked AI to create a daily pool-care routine for the summer months. 4. When the water became cloudy, I took pictures of the tests I was running on the water. 5. I shared the pictures with AI to get guidance on bringing the water back to the right condition.

BiteTrack makes calorie tracking simple with AI. Step-by-step: 1. Log a meal using a photo, voice, text, barcode, or food search. 2. BiteTrack’s AI identifies the food and estimates its calories, protein, carbohydrates, and fats. 3. Track daily nutrition, weight, and progress in one place. 4. Receive personalized nutrition recommendations based on health and fitness goals. 5. Monitor progress over time with detailed analytics to stay on track. Whether you're trying to lose weight, gain muscle, or maintain a healthy lifestyle, BiteTrack helps you make smarter nutrition decisions every day.

I kept forgetting who I had lent books to, and spreadsheets felt like overkill. So I built Runo, a book-lending app for friends, without traditional coding. Runo (runo.club) is a web app where I can catalog my home library, get a unique shareable link, and let friends browse my books and request to borrow them. I can approve or decline each request with one click. Step-by-step: 1. I used Claude to think through the feature set, data model, and UX flow before writing a single prompt. This helped me avoid building the wrong thing first and gave me a clear blueprint for the Lovable prompts that followed. 2. I used Claude to create precise, narrowly scoped prompts for Lovable, with each prompt focused on a single change so existing functionality would be less likely to break. This significantly conserved Lovable credits. The React, TypeScript, and Supabase stack came out of the box. 3. I added two ways to scan books instead of requiring users to type titles manually: - Barcode scanner: Point the camera at an ISBN barcode to autofill the title, author, and cover using the Open Library API. - Cover photo scanner: Photograph the cover so Gemini 2.5 Flash can extract the title and author in under 2 seconds. 4. I routed the cover scanner through a Supabase Edge Function using Lovable’s AI Gateway, so the API key never touches the client bundle. 5. For every bug fix and feature, I followed the same iteration loop: describe the problem to Claude, get a precise Lovable prompt, push the changes to GitHub, and let Lovable auto-sync them. Claude and Lovable’s bidirectional GitHub sync made the process feel like pair programming. Tools used: Lovable, Claude (Sonnet), Gemini 2.5 Flash (via Lovable AI Gateway), Supabase, and GitHub. Live at: runo.club — public beta and free to use. Feedback welcome. #appbuilding #vibecoding #buildinpublic #lovable #nocode

I used ChatGPT + Button to understand my dog’s blood work in language I could follow. Every couple of weeks, I uploaded all of my dog’s blood work PDF files and provided the information verbally that wasn’t included on the forms. It helped me understand which markers were important and compare the results from week to week, including where values were changing. That gave me a better foundation for conversations during veterinary appointments. During a difficult time, it also gave me peace of mind in a way nothing else could. It feels strange to say that, but it’s true. Step-by-step: 1. I uploaded all of my dog’s blood work PDF files to ChatGPT + Button every couple of weeks. 2. I provided the relevant information verbally when it wasn’t included on the forms. 3. I used it to identify which markers were important. 4. I compared the results from week to week and reviewed where the values were changing. 5. I used that understanding to have better conversations with the veterinarian.

AI can make a child’s drawing look more polished, but it can also erase exactly what made it interesting. A yellow tree, a rabbit larger than a person, a horse with impossible legs, or a tiny floating object may look like mistakes to a model—but they may be the whole point. I use the child’s drawing as art direction: preserve its visual logic, then build outward. The child supplies the invention; AI helps develop the surroundings. Step-by-step: 1. I read the drawing before transforming it by making a neutral description of what is visible: placement, relative sizes, colours, expressions, unusual shapes, and small details. For example: “A large yellow tree stands on the left. Two tiny figures sit near its trunk. A blue shape fills the lower right.” 2. I avoid deciding what an unclear shape represents. “An oval object with three legs” is more faithful than “a rocket” unless the child confirms it. If possible, I ask the child what is happening, since a strange proportion or colour may have a specific story behind it. 3. I separate essentials from flexible details in two short lists. Essentials might include the yellow tree, oversized rabbit, horse’s pose, characters’ arrangement, and red object in the corner. Flexible details might include lighting, textures, materials, environmental detail, and depth. 4. I make clear that the original is the structural source, not just a mood reference. This gives the model room to work while defining the boundaries. 5. I choose a target style, such as a storybook illustration, watercolour, digital painting, comic panel, or 3D scene. 6. I explicitly ask the model to preserve unusual proportions, unexpected colours, expressions, and relationships between characters. I identify details that must remain, even if they appear anatomically impossible or visually awkward. I avoid vague instructions such as “make it better,” because they leave the model to decide what counts as improvement. 7. I use a starting prompt such as: “Use this drawing as the structural blueprint for a storybook illustration. Preserve the composition, relative sizes, poses, expressions and original colour choices. Keep the yellow tree and the rabbit larger than the person. Do not normalize unusual anatomy or remove unexplained objects. Add texture, lighting and environmental depth around the existing scene.” 8. I treat comparison as part of the workflow because specific instructions do not guarantee a faithful result. I expand the world around the invention: a blank background can become a forest, a blue patch can become a pond if that interpretation fits, and two skiing figures can gain a mountain landscape. 9. I keep the original spatial relationships: who stands beside whom, what dominates the scene, which way characters face, and where empty space matters. I give colour the same care, so a yellow tree does not quietly become green because the model expects green trees. 10. I place the source and result side by side and check whether a character disappeared, the rabbit shrank, the palette changed, or the model invented a focal point, “fixed” an unusual shape, or altered the story. I also watch for generic cuteness—big eyes, pastel colours, and perfect symmetry—which can make very different drawings converge into the same image. I ask whether the result still feels like an expansion of this exact drawing. 11. If most of the result works, I request a targeted edit, such as: “Keep this version, but restore the original hair shape.” “Make the rabbit larger again.” “Return the tree to yellow.” 12. I check the whole image after each local edit because changes can affect other areas. I keep useful versions instead of repeatedly starting over. 13. I treat handwritten text separately. I do not guess unclear wording, correct spelling, or invent labels. If the tool cannot reproduce the text faithfully, I retain the original lettering through a separate editing step. 14. I save the original photo or scan alongside the result, instructions, and useful intermediate versions. I also keep a brief note about protected details to make future transformations easier. 15. Once I have a faithful version, I explore alternatives such as a space setting, surreal painting, or cinematic scene, keeping them as branches from the original. Across several drawings, I preserve recurring colours, shapes, and characters without forcing every image into one rigid style. The child has already decided who exists, what matters, how large things are, and what belongs together. Those decisions are creative direction. A drawing does not need to become less itself to become more finished. The impossible colour may be the atmosphere, and the awkward shape may be the character. The question I return to is: “How can I help this drawing become more fully what it already is?”