I built a workflow for creating a living book that exists online and that I can add to, edit, and share whenever I like. I’m an occasional writer who publishes nonfiction essays about observations in life. I realized I could collect the essays into a book, but life doesn’t take an appropriate pause, so the book and the essays never really end. That raised the question: When should I publish it? I created a local Claude instance to access all the files on my laptop. Local is almost always better than a cloud instance, and you can ask Claude to enable remote control so you can access it from your phone as well. I used Opus 5.5 because I couldn’t be bothered experimenting with lower-level models to see whether they could do the task more cheaply. A friend who uses Claude professionally once told me to always use the highest model available and move down if I ran into usage limits. I’m on the monthly Pro plan. I gave Claude the locations of my existing writing on Substack and Medium.com. It downloaded all my essays and organized them into rough sections. I argued with it about the organization and approved the sections once it explained its logic. I then had Claude create a Ulysses folder for all my essays, organized into sections and chapters. I can use it to create a new essay, edit an old one, or reorganize and reorder the book, then tell Claude to update the online version. I could have had it scan the folder regularly and update the site automatically, but that seemed unnecessarily lazy. Claude created a section of a website I already owned where readers can access the living book: https://enwaken.com/read/ During the process, I also had Claude restructure and redesign my overall website several times. It’s amazing at that sort of task. I’m still amazed that I can say something like "I want blue and violet and black, but maximize the contrast between each of the colours" and it will actually pick colors that complement each other. I wish more website designers knew they could do this. Now I have a version of my book that is spectacularly easy to read online and that I can update whenever I like. It’s easier to navigate than an ePub on my Kindle and certainly easier to update. The whole process took two days, half of which was spent arguing about the organization of the chapters. Step-by-step: 1. I set up a local Claude instance with access to the writing files on my laptop and enabled remote control so I could access it from my phone. 2. I used Claude Opus 5.5 through the monthly Pro plan. 3. I gave Claude the locations of my existing essays on Substack and Medium.com and had it download them. 4. I reviewed Claude’s rough organization of the essays into sections, argued about its choices, and approved the structure once it explained its logic. 5. I had Claude create a Ulysses folder organized into sections and chapters so I could add, edit, and reorder essays. 6. I told Claude to update the online book when I changed the Ulysses folder. 7. I had Claude create the book as a section of my existing website and redesign the site as needed. 8. I now maintain a living online book that is easy to read, share, and update.

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

I built a fantasy hockey assistant with Claude Code for my Yahoo league. It supports two parts of the league: running our in-person draft and managing my team during the season. For the draft, it runs on my laptop as a live draft board. I enter each pick as it is announced, and the assistant re-ranks the remaining players based on what my roster still needs. The rankings combine projection sources with three seasons of NHL statistics, and each player is scored according to how much they would help in each of our ten categories. The board flags players who will likely be selected before my next pick, identifies injury and regression risks, and opens a detail card for any player with projections, historical data, and a short plain-English assessment. A separate display goes on the TV for the room, showing the pick clock, the next player on deck, and recent picks. After the final pick, the view switches to projected standings for every team, color-coded by category. During the season, I paste in my roster, and the assistant pulls the NHL schedule, starting-goalie reports, and injury news. It suggests the best lineup for each day, ranks free agents by value and by games played that week, and projects my weekly head-to-head matchup category by category. It can also ask Claude to propose a lineup and explain its recommendations, which costs about ten cents per run in API credits. Yahoo’s API is read-only. If it supported write access, I would probably have the app manage my team autonomously. Step-by-step: 1. I built the fantasy hockey assistant with Claude Code for my Yahoo league. 2. During the in-person draft, I run the assistant on my laptop as a live draft board and enter each pick as it is announced. 3. The assistant re-ranks the remaining players based on my roster’s needs, using projection sources, three seasons of NHL statistics, and each player’s value across our ten categories. 4. I use the board’s flags and player detail cards to evaluate likely pre-next-pick selections, injury and regression risks, projections, historical data, and plain-English player assessments. 5. I display the pick clock, next player on deck, and recent picks on a separate TV view, then switch that view to color-coded projected standings by category after the final pick. 6. During the season, I paste in my roster so the assistant can pull the NHL schedule, starting-goalie reports, and injury news. 7. I use that information to set daily lineups, rank free agents by value and weekly games played, and project my head-to-head matchup category by category. 8. When needed, I ask Claude to propose a lineup and explain its recommendations, at a cost of about ten cents per run in API credits. 9. Because Yahoo’s API is read-only, I currently use the assistant for recommendations rather than autonomous team management.

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.

We used ChatGPT to make renting out our house in France for the first time far less overwhelming. By the end of the summer, we had hosted four families. Although the whole process was a lot of work, creating one master file with useful facts about the house was a major time-saver. It included everything from Wi-Fi and coffeemaker instructions to lighting quirks and bin days. From that document, AI built a room-by-room preparation checklist, drafted and translated a house guide in French and English, wrote separate listings for Airbnb and a local rental site, and created guest messages ranging from booking confirmations to check-in instructions. For pricing, we spoke with a local agent. AI’s final task was to flag the French administrative, tax, registration, and insurance details that we needed to sort out ourselves. Step-by-step: 1. We created one master file containing useful facts and practical details about the house, including Wi-Fi, coffeemaker instructions, lighting quirks, and bin days. 2. We used the file to have AI create a room-by-room preparation checklist. 3. We asked AI to draft and translate a house guide in French and English. 4. We had AI write separate rental listings for Airbnb and a local rental site. 5. We used AI to create guest messages, including booking confirmations and check-in instructions. 6. We met with a local agent for advice on pricing. 7. We asked AI to flag the French administrative, tax, registration, and insurance details we needed to handle ourselves. 8. By the end of the summer, we had hosted four families.

I built a workflow that converts landscape (16:9) footage into vertical (9:16) video without heavily cropping the original footage. Using AI tools and a simple node tree in Magnific, I generated the missing visual data outside the original frame so the final video looks as if it was captured vertically. Previously, this felt like too much trouble and too many steps to be worthwhile. Using Magnific Spaces, I extended the first frame of the video to fit a 9:16 frame with one prompt. Then I used a second prompt with the new frame and the original video as references in a video-generation node. I used Seedance 2.0 to save credits because I had low expectations that the workflow would work. To my surprise, the output was great immediately. It was not perfect, but the generated footage matched the original clip very well. Below are images of my node tree. Step-by-step: 1. I opened the original landscape (16:9) video in Magnific. 2. I used one prompt to extend the video's first frame into a 9:16 vertical frame. 3. I added the new frame and the original video as references in a video-generation node. 4. I used a second prompt to generate the expanded vertical footage with Seedance 2.0. 5. I reviewed the output and found that it matched the original clip well, although it was not perfect.

I built an AI fundraising campaign assistant with Make.com. The workflow reads emails sent from the crowdfunding platform used for the campaign, extracts the relevant information, and records it in a spreadsheet. It also uses spreadsheets from previous years’ campaigns to maintain a list of people who have not donated to this campaign yet but have donated in the past. I can ask the agent questions by email, such as who has already donated or who has not donated this year but donated previously. It can also answer questions with specific parameters, such as who has not donated but previously gave more than $1,000, or how much a specific person donated. I built the workflow in Make.com using its AI tools. I connected it to a Gmail account, a Google Drive account containing the spreadsheets, and a ChatGPT account that processes the information as the AI agent. Step-by-step: 1. I connected Make.com to the Gmail account that receives emails from the crowdfunding platform. 2. I configured the workflow to extract relevant information from those emails and record it in a spreadsheet. 3. I connected Google Drive so the workflow could use spreadsheets from previous years’ campaigns. 4. I used the historical spreadsheets to identify people who have not donated to the current campaign but have donated in the past. 5. I connected ChatGPT as the AI agent that processes the campaign and donor information. 6. I configured the agent to respond to questions sent by email, including who has already donated, who has not donated this year but donated previously, who previously gave more than $1,000, and how much a specific person donated.

Every new release, I love seeing what people reach for first. Are you throwing your hardest workflow at it, trying something you couldn’t do before, or waiting to see what others find? Drop what you’re excited about, even if you haven’t opened it yet. And if you build something cool, share it in the Workflow Hub so we can feature it!

Tell AI the Real Goal, Not Just the Scorecard THE PROBLEM When we give AI a task, we often describe success through measurable criteria: be fast, minimize errors, use fewer resources, produce a certain format, reach a quality score, or complete a list of steps. Those criteria are useful, but they are usually not the actual goal. A high score matters because it is supposed to indicate that the AI is doing something useful. Efficiency matters because we want to achieve something without wasting resources. A specific layout matters because someone needs to use the result afterwards. If the proxy becomes more important than the purpose behind it, the AI can technically satisfy the instructions while missing what we actually wanted. So I started making the hierarchy explicit. THE WORKFLOW Before a substantial task, I explain four things: Step-by-step: 1. The real goal What am I ultimately trying to accomplish? 2. Why that goal matters What function should the result serve in the real world? 3. The success criteria What signals, scores, constraints or output requirements help us judge whether we are getting there? 4. The important distinction Those criteria are indicators of success, not the goal themselves. Then, before execution, I usually ask for a short non-technical plan in plain language. Not code. Not internal terminology. Not a wall of implementation detail. I want the AI to explain: - what it thinks I am trying to achieve - what steps it plans to take - in what order - what it expects each step to accomplish - where it sees possible problems or ambiguity - what the final result should look like - where it will show me intermediate results before continuing That last point matters. For larger tasks, I do not want one giant jump from instruction to finished output. I want the AI to build in intermediate checkpoints with actual results. That can mean: - showing the first batch before processing the rest - giving a sample classification before applying it to hundreds of items - sharing an early pattern it found before building the full analysis around it - showing the structure of a document before filling every section - reporting that a planned step produced an unexpected result before silently adapting everything downstream The checkpoint should contain something useful enough to evaluate. Not just: «Step 2 completed.» But rather: «I processed the first 50 items. Most fit the categories we expected, but 12% fall into a pattern we did not account for. Here are three examples and how I suggest handling them.» That lets me see whether the task is still heading toward the actual goal. For example, my instruction might be: «The real goal is X. We care about Y because it helps achieve X. Z is a useful metric, but do not optimize Z at the expense of X. Before executing, explain in non-technical language how you plan to approach the task, step by step. Build useful intermediate results into the plan so I can inspect the direction before too much work depends on it. Flag anything that seems likely to satisfy the metric while undermining the actual purpose.» I can then say: «Yes, that is what I mean.» Or: «No, step 3 is where you are misunderstanding me.» And later: «This first batch looks right. Continue.» Or: «Stop here. The pattern you found changes how I want the rest handled.» Correcting a five-line plan or an early sample is much cheaper than correcting an entire finished workflow, analysis or file operation. WHY I LIKE IT This turns the interaction from: instruction → execution → correction into: purpose → shared understanding → visible plan → intermediate results → adjustment → execution It also helps me learn how the AI works. I do not need to understand every technical mechanism underneath it, but I do want a usable mental model of how it approaches problems. Over time, that makes collaboration easier because I get better at giving instructions, spotting misunderstandings early and knowing where extra context will matter. Intermediate results also make autonomy safer. The AI does not need permission for every tiny action, but it should avoid disappearing into a long chain of dependent decisions when an early misunderstanding could invalidate everything that follows. This can reduce unnecessary work, wasted compute and repeated corrections. The AI can still work independently once the direction is clear. The point is not to micromanage the process. The point is to make sure we are optimizing the right thing, travelling by a route we both understand, and checking occasionally that we are still on that route. A metric is a thermometer. The goal is to make the room warm, not merely to make the thermometer say 21°C.

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

I created a media player for Android Automotive OS—not to be confused with Android Auto. I had been using an old, unmaintained app that lacked several features I wanted in a music player. I used Claude Code to build a new app from scratch, test it in an emulator, and eventually publish it for internal testing through the Google Play Store. After working through several bugs, I now have version 3 running reliably enough for day-to-day use in my car. I plan to add a few more features before ultimately releasing it publicly for free on the Play Store. Step-by-step: 1. I pointed Claude Code to the unmaintained package and gave it the option of using that as a starting point or starting from scratch. Claude chose to start from scratch. 2. I described the additional features I wanted. Claude suggested using Kotlin and Gradle, which I endorsed. 3. Claude created an initial build and installed an emulator to run alongside it. I did all of this on an Omarchy installation. 4. The initial build was buggy, so Claude and I worked together to identify and fix the problems. Claude often used the emulator autonomously to investigate what was going wrong. 5. Once I had a functional prototype, I needed to install it in my car. Claude guided me through creating a Google developer account and handling the administrative requirements beyond the app itself, including a privacy policy. 6. After the developer account was set up, Claude packaged the app for upload. I uploaded it and marked it for internal testing, which allowed me to access the app through the Play Store in my car. 7. Claude and I worked through a few more bugs. We are now on version 3, and the app is functional for day-to-day use. We plan to add more features, with the ultimate goal of releasing it publicly for free on the Play Store.

I run an out-of-home advertising agency in Malta, covering billboards, buses, hotel screens, and radio. Our entire business ran on Monday.com boards for CRM, deals, bookings, supplier bills, and bus-campaign reconciliation. The system was expensive, difficult to change, and full of broken formulas. I’m not a developer, but in about two weeks I worked with Claude to replace it with our own CRM built on Google Sheets and Google Apps Script. The team now uses it every day. Step-by-step: 1. I mapped our real processes first, explaining in plain language how we sell, book, invoice, and pay partners. Claude read our existing Monday.com boards and imported the data. 2. I built the system screen by screen, releasing each part and testing it with live data before moving on. The CRM now includes contacts, companies, deals with order lines, bookings, billing, partners, tasks, and a dashboard. 3. I automated repetitive tasks. When I upload a signed order form, the app reads the lines. Bookings are created automatically from deals, and a Gmail side panel and shared mailbox file client emails under the correct contact or deal. 4. I connected QuickBooks Online. The system pulls invoices and supplier bills, calculates each partner’s revenue share according to its own rules, produces the monthly partner report, and creates the bill. I approve anything that writes to the accounts. 5. I turned a monthly spreadsheet chore into an automatic check for bus campaigns. The app compares what we committed to the client with what the bus operator billed us and flags overbilling or margin problems month by month. The first run found a supplier bill that appeared to be a duplicate. 6. I made the system safe to hand over. Every change is logged and can be undone, with user roles and a shared drive in place. We also wrote a short user guide and test checklist before the team started using it. The main lesson I learned was to treat AI like a capable builder who needs your business rules spelled out. Starting with real data, releasing small pieces, and checking each one before adding the next made the build manageable. The tools I used were Claude, Google Sheets, Google Apps Script, Gmail, and QuickBooks Online.

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

An artist portfolio can feel harder to make than the work itself. My work lives across years, folders, and materials: photographs, drawings, collage, objects, costumes, and digital experiments. Then an application asks me to explain who I am, what I make, and where I want to go. I used AI to help assemble a readable portfolio for HUP03, a Kunstwerkt call. The portfolio included eight works spanning photography, collage, assemblage, performance, and AI-assisted illustration. The goal was to organise the material without flattening it into a generic artist statement. Step-by-step: 1. I started with the actual work: images, titles, dates, and any explanations I already had. For each piece, I established the title, year, medium, whether it was a standalone work or part of a series, what prompted it, how it was made, and which details mattered. I gave AI concrete material rather than asking it to invent my practice. 2. I described each piece in my own words, as I would to someone looking at it beside me. One of my works, *Olifant onder het tapijt*, combined three expressions: the elephant in the room, the elephant in a china shop, and sweeping things under the carpet. The setting became a recognisably Flemish grandmother’s living room, with floral wallpaper, a crucifix, and a decorative cow painting. Those details carry the humour and the observation about hiding uncomfortable things. “An exploration of concealment” would lose much of what makes the piece mine. I asked AI to organise my explanation while preserving its specific ingredients. 3. I gave each work a clear, consistent entry with the title, year, and medium; a short description of the idea; and the relevant visual or material choices. Consistency makes varied work easier to read, even though a photograph, costume, and assemblage may need different explanations. Where relevant, I stated AI involvement clearly, such as “digital illustration developed in collaboration with generative AI.” 4. I selected work for range and relevance. My selection included an older photograph, a costume/performance, collage, recent assemblages, and digital work. I asked what each piece contributed: another method, question, period, or way of seeing. I avoided choosing only the newest work or forcing everything into one visual style. The task was to make the selection understandable. 5. I separated the existing artwork from the proposed project. Existing works show what I have made, while a project proposal explains what I want to investigate next. For this application, my project question centred on human connection: a thread becoming a web, then a garden. AI helped articulate that direction without forcing every earlier piece into the same project. I looked for connections I recognised while leaving room for differences and discoveries. 6. I wrote an accurate artist introduction describing the media I use, what interests me, and my current practice. I checked every biographical claim and distinguished courses attended, qualifications completed, and current activities. In my case, wording around an interior design course needed care so it would not imply that I had completed the programme. AI can make a biography sound polished while quietly making it less true, so I read the facts as closely as the prose. 7. I treated the first draft as material to revise. Useful feedback included: “That sounds more formal than I mean,” “This detail is essential,” “You have made the idea too abstract,” “That suggests a qualification I do not have,” and “This is my observation, not a universal claim.” AI revised the structure and wording, but I decided whether the text still belonged to the work. 8. I checked the portfolio as a whole against the application requirements. I reviewed titles, dates, media, image-text matches, requested sections, and length limits. I removed repetition between the artwork descriptions, biography, and proposal. Someone unfamiliar with me should be able to understand the work without needing the conversation behind the portfolio. The overall process was: gather existing work, explain each piece, verify the facts, build consistent entries, select a meaningful range, describe the proposed direction, revise in my own voice, and check the application. AI was useful as an interviewer, organiser, and editor. The substance came from the work and the person who made it. A portfolio does not need to turn a varied practice into one tidy slogan; it needs to give someone a way into it.

I built an AI-assisted Lightroom Classic workflow that lets photographers create branded marketing images without leaving Lightroom. The problem I wanted to solve was simple: photographers often finish editing an image, export it, open another design tool to add a logo, watermark, text, or promotional graphic, and then export it again. I wanted that entire branding step to happen inside Lightroom. Using AI-assisted development, I built Brandstamp Pro, a Lightroom Classic plugin that lets me add branding, preview the result, save reusable presets, and export the finished image from the same workflow. The result is a much simpler process: edit photo → add branding → preview → export Instead of: edit photo → export → open another design app → add branding → export again I originally built it for my own real-estate photography workflow, but the same idea works for photographers who need branded social images, booking graphics, promotional images, watermarks, or client-ready marketing photos. The biggest benefit is not that AI adds the branding automatically. It helped me build a tool that removes a repetitive step from an existing photography workflow. Step-by-step: 1. I selected an edited photo in Lightroom Classic. 2. I opened the Brandstamp panel. 3. I added a logo, watermark, text, or other branding. 4. I adjusted the size, position, opacity, color, and placement. 5. I previewed the branded result before exporting. 6. I saved reusable presets for branding I use repeatedly. 7. I exported the finished branded image directly from the same Lightroom workflow.

I built a small GPT automation called PatternSpeak that periodically looks back across my conversations for ideas, themes, or approaches that keep resurfacing over time. It is not meant to analyze me or turn recurring thoughts into tasks. Its job is much simpler: occasionally say, in effect, “Hey, this idea keeps coming back. Maybe there is something here.” I like it because repetition can be meaningful without being urgent. Sometimes an idea disappears for weeks and then returns in a completely different context. PatternSpeak helps me notice those echoes without forcing them into a productivity system. It feels less like tracking and more like having a friendly observer tap me on the shoulder when a thread has quietly become a pattern. It works very well with the scan and analysis workflow I also shared here. Step-by-step: 1. I use PatternSpeak to periodically look back across my conversations. 2. It identifies ideas, themes, or approaches that keep resurfacing over time. 3. When it notices a recurring thread, it surfaces it as a gentle prompt rather than turning it into a task. 4. I review the recurring ideas and notice whether they have become meaningful patterns, even when they return in different contexts. 5. I use the scan and analysis workflow I also shared here alongside PatternSpeak.

Clean up near-duplicate artwork photos with a visual checklist THE PROBLEM I had hundreds of photos of my handmade collages. Some showed the same work photographed at different times, from different angles or against different backgrounds. Others were cropped versions, detail shots or useful making-of photos. The duplicate finder in my image manager missed real matches and suggested unrelated collages instead. Simply keeping one file from each group would also have thrown away useful material. THE WORKFLOW 1. Export a working set and preserve the filenames. I exported the images from Eagle. Smaller copies made the collection easier to upload, while the original-resolution files stayed on my computer. Keeping the filenames intact made it possible to apply decisions to the originals later. 2. Ask AI to compare the artwork, not just the whole photograph. The comparison looked for matching local image details and their relative positions. This helped connect photos despite changes in angle and background. Candidate groups still needed review: two collages might contain the same clipping, and a detail photo might overlap with a wider shot without being redundant. 3. Turn the results into a visual decision page. The AI produced a standalone HTML file with photos grouped side by side and their original filenames underneath. I requested a “Keep” checkbox beneath each candidate and a category dropdown: “Collage” or “Making of”. Multiple images could stay in either category. One photo might show one part clearly while another preserved a different part better. I made the decisions; the AI did not choose a winner automatically. 4. Validate and export my choices. The page flagged any group with nothing selected and any selected photo without a category. Photos without a confirmed match were preserved separately. A download button exported my decisions as a small JSON file, which I sent back to the AI. 5. Generate a local cleanup script from that exact selection. The resulting Windows script checks the expected files, asks me to confirm the folder, sorts the retained photos into “collages”, “making of” and “loose items”, and sends rejected photos to the Recycle Bin. It targets the ordinary export folder, not Eagle’s internal library. The loose items can then be sorted manually before reimporting. WHAT THIS PRODUCED From 296 photos, the review grouped 204 into 90 candidate groups and left 92 without a confirmed match. My final choices retained 96 collage photos and 23 making-of photos, plus all 92 loose items: 211 retained, 85 designated for removal. The checklist and selection were completed, and the script was generated. At the time of writing, I had not yet run the cleanup on Windows. WHY IT HELPED The key was separating three jobs: finding likely matches, deciding what matters, and applying those decisions to files. “The same artwork” does not necessarily mean “an unnecessary photo”. A visual checklist let me make those distinctions without manually tracking filenames. STARTER PROMPT “I have photos of the same artwork taken from different angles and against different backgrounds. Find candidate matches using details within the artwork, and distinguish duplicates from details, changed versions and making-of photos. Create a standalone HTML review page with original filenames, a Keep checkbox and a category dropdown for each candidate. Allow multiple retained photos per category. Flag empty groups and selected photos with no category. Preserve unmatched images. Let me export my choices as JSON. After I return the selection, generate a script that validates the original filenames, sorts retained files into the chosen folders and moves only rejected files to the Recycle Bin after showing the target folder and counts. Do not choose or delete files on my behalf."

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’m building an automated content engine for a client using n8n and the Claude API. Every week it produces blog articles and social posts for eight brands. The client already has a blog with [number] articles, and we want the output to match that voice. What I’ve tried: [e.g. a tone-of-voice description in the system prompt / a few example articles in the prompt]. What I get is text that’s fine but sounds AI-written and slightly translated. It doesn’t read like a native Swedish copywriter wrote it. What I’m weighing: Is it better to extract a tone-of-voice guide from the blog (rules, word choices, sentence length, do’s and don’ts) or to pass example texts as few-shot? How many examples do I need, and should they be full articles or excerpts? Is a separate editor step that only reviews and adjusts tone worth it? How do you handle one shared base voice across eight brands that each have their own variant?

I use this workflow when I have a problem but do not yet know which software, service, or method could solve it. Instead of immediately asking for “the best app,” I describe the situation in ordinary language: what I am trying to do, what currently annoys me, what I have already tried, and what would make a solution useful. Then AI helps me map the tool landscape. The important shift is that the criteria emerge from the conversation. The workflow does not search for an abstract “best tool.” It builds a model of what a good tool means for this particular problem and person, then evaluates the available options against that model. This also makes the result reusable. When my requirements change later, I can update the comparison rather than redo the entire research process. Step-by-step: 1. I describe the problem and desired outcome. 2. I identify the requirements that actually matter in this situation. 3. I research the relevant tools and approaches. 4. I compare them side by side using practical criteria such as functionality, price, pricing model, platform, setup effort, learning curve, and ease of use. 5. I ask questions where the requirements are still unclear. 6. I react to the comparison—for example, by noting that something is too expensive, that a subscription is a dealbreaker, that an option looks cumbersome, that I need more statistics, or that I do not need project management. 7. I update the comparison using that feedback instead of starting the search from scratch. 8. I narrow the field and, where useful, test the remaining options on one small real-world task. 9. I record why the chosen tool fits and why seemingly similar alternatives did not.