Workflow Hub

I built a workflow that turns the wines I drink into a personal AI wine journal and sommelier that learns my taste over time. I created a master Google Sheet as the database. It includes the wine, vintage, region, grape varieties, price, rating, food pairing, where I drank it, who I was with, whether I would buy it again, and my own comments. Whenever I drink a wine, I send ChatGPT a photo of the bottle and a voice note with my reaction. I do not try to sound like a wine expert; I simply describe what I actually thought. ChatGPT identifies the wine, structures the information, preserves my original words, and adds the experience to the journal. Over time, ChatGPT uses my previous ratings and comments to understand my palate and make recommendations specifically for me. I can show it a restaurant wine list or a few bottles in a shop and ask which I am most likely to enjoy. I can also tell it what I am cooking and ask which bottle from my collection I should open. I record the context and memories around each bottle, so the system is gradually becoming both a wine database and a personal diary. Step-by-step: 1. I created a master Google Sheet to store details about each wine, including the vintage, region, grape varieties, price, rating, food pairing, location, company, whether I would buy it again, and my comments. 2. Whenever I drink a wine, I take a photo of the bottle and send ChatGPT a voice note describing my reaction in my own words. 3. ChatGPT identifies the wine, structures the details, preserves my original comments, and adds the experience to the journal. 4. I record the context and memories associated with each bottle. 5. As the journal grows, ChatGPT uses my ratings and comments to build a better understanding of my palate. 6. I ask ChatGPT for personalized recommendations from restaurant wine lists, bottles in shops, or my own collection based on what I am cooking.

Turn a collection of documents, reports, spreadsheets, and notes into an evidence-backed decision brief with ChatGPT. Instead of asking AI to simply summarize the information, this workflow makes it identify what matters, connect the evidence, compare it with historical context, explore scenarios, and highlight what should be considered before making a decision. Step-by-step: 1. I gather the information relevant to one decision, including reports, PDFs, spreadsheets, research, historical data, meeting notes, and existing analysis. I upload everything into ChatGPT. 2. I ask ChatGPT to understand the situation using this prompt: > “Analyze the information I provided and build a structured understanding of the situation. Identify the key entities, important facts, relationships, metrics, trends, assumptions, and constraints. Do not make recommendations yet.” This creates the context before jumping to conclusions. 3. I build an evidence brief by asking: > “Create an evidence brief. Separate verified facts, derived insights, assumptions, conflicting information, and missing information. For every important conclusion, identify the supporting source or evidence.” This gives me a clearer picture of what is known versus what is inferred. 4. I add historical context when it is available by asking: > “Compare the current situation with the historical information provided. Identify meaningful patterns, similarities, differences, and changes. Highlight which historical observations could be relevant to the current decision.” This turns historical data into context rather than simply another report. 5. I explore three scenarios by asking ChatGPT: > “Based on the evidence and historical context, evaluate three scenarios: upside, base case, and downside. For each scenario, identify the assumptions, key drivers, risks, likely impact, and evidence supporting the assessment.” The objective isn't to pretend the future can be predicted perfectly. It is to understand how the decision changes when assumptions change. 6. I generate the decision brief by asking: > “Create a concise decision brief containing: > > 1. Current situation > 2. Most important evidence > 3. Key insights > 4. Historical context > 5. Critical assumptions > 6. Key risks > 7. Scenario analysis > 8. Evidence gaps and uncertainties > 9. Questions that should be investigated > 10. Possible actions and their implications. > Do not make the final decision on my behalf.” This produces a structured decision brief instead of another AI-generated summary. 7. I review the brief and challenge its conclusions before making the decision. I ask follow-up questions such as: > “Which assumption has the greatest impact on this decision?” > “Show me the strongest evidence against the current conclusion.” > “What information would most likely change the recommendation?” The AI helps structure the decision, but I make the decision. The important shift is: Summarize the information → Understand the situation → Establish the evidence → Add historical context → Explore scenarios → Evaluate the decision This approach can be applied to almost any domain where decisions depend on complex and interconnected information. A property investment is one example. A business strategy, product decision, operational problem, financial analysis, research question, or engineering decision can follow the same pattern.

AI image editors often change more than requested. Removing an object can alter the lighting, cleaning up a background can redraw a face, and fixing one detail can shift typography or proportions. That may be useful for creative transformation, but it is a problem when the goal is to change one thing and leave everything else alone. I now treat precision-editing prompts as preservation contracts. A precise edit needs two instructions: what must change and what must remain untouched. Failures happen when the requested change is clear but the boundaries are vague. For example, “Remove the grey area between the neck and ponytail” may also trigger a redrawn neck, shifted hair, or a smoothed silhouette. The result may look polished, but it is no longer faithful. Step-by-step: 1. I identify the smallest possible edit region. Instead of saying, “Clean up the portrait,” I specify, “Change only the grey area between the neck and ponytail to match the white background.” A narrow target limits unintended reinterpretation. 2. I name the protected elements instead of relying on “Leave everything else alone.” I identify faces, hands, text, positions, proportions, colors, silhouettes, and framing, and include: “Do not add, remove or redesign unrelated elements. Do not make compensatory changes elsewhere.” Otherwise, the AI may adjust shadows, symmetry, or composition to “balance” the edit. 3. I lock all text. Image models may rewrite text they were never asked to touch, so I specify: “Do not regenerate, correct, restyle or replace any text. Preserve spelling, capitalization, punctuation, line breaks, font appearance, size and placement exactly.” I protect labels, handwriting, and captions too. 4. I preserve geometry and framing. For a local correction, I specify: “Preserve all original proportions, positions and geometry outside the edit region. Keep the exact canvas size, aspect ratio and crop. Do not reframe or extend the image.” This is crucial for diagrams, collages, and layouts where spatial relationships matter. 5. I explain the background logic. For example: “Remove the object and reconstruct only the background naturally behind it, continuing the existing pattern without new texture or shadows.” White should stay white, patterns should continue, and shadows should follow the existing light. Removal is not redesign. 6. I ask the model to match the original style, including texture, grain, lighting, line quality, colors, and level of detail. Hand-drawn marks should not become glossy, and grainy photographs should not become plastic-smooth. I preserve imperfections such as crooked lines, collage edges, and photographic wear when they are meaningful. 7. I make fidelity the priority by stating: “This is a local corrective edit, not a reinterpretation or restyle. Prioritize fidelity to the source over beautification.” For risky features, I prohibit redrawn faces, altered text, new backgrounds, cropping, and unrelated corrections. If the target is ambiguous, I add: “If uncertain, preserve the original rather than editing additional areas.” 8. I make delicate changes separately. When there are multiple corrections, I edit one thing, inspect it, and then continue. More requests create more room for drift. 9. I verify what did not change by comparing the result with the source. I check the text, edges, faces, proportions, placement, crop, colors, and background, then ask two questions: Did the requested change happen? What else changed? The second question reveals hidden failures. I use this reusable prompt structure: Change: [specific requested change] Edit only: [exact region or element] Preserve exactly: [text, objects, positions, proportions, colors, framing, etc.] Do not: [likely unwanted changes] Style: Match the existing texture, lighting, grain, line quality and detail. Priority: Fidelity over beautification. If uncertain, preserve the source. For example, instead of writing, “Remove the grey bit under the hair,” I write: “Change only the grey area between the neck and ponytail to the same white as the surrounding background. Preserve the neck, chin, ponytail, silhouette, proportions, crop and all other colors. Do not redraw or beautify the face, hair or body. Do not alter anything outside the target region. Match the existing flat visual style.” The overall workflow is: define the smallest edit, identify protected content, preserve geometry, style, and background logic, forbid unrelated changes, prioritize fidelity, and compare the result against the source. The edit instruction tells the model where to go. The preservation constraints tell it where not to wander. The best corrective edit is not the most imaginative; it changes the target while everything else remains stubbornly itself.

I adopted three kittens last week when they were just four weeks old. At first, they were overwhelmed by the new environment and refused to eat, which really worried me. I turned to ChatGPT for help and got clear, practical guidance on what to feed them, how to keep them hydrated, and how to set up a comfortable space so they could adjust more easily. I followed a simple feeding plan and kept track of their behavior daily. Gradually, they started eating, became more active, and settled in. Now they're happy, playful, and fully adapted to their new home. Step-by-step: 1. I described the kittens’ age, the move into a new environment, and the fact that they were refusing to eat. 2. I asked ChatGPT for practical guidance on feeding, hydration, and making the space feel safe and comfortable. 3. I turned the advice into a simple feeding and care plan I could follow consistently. 4. I tracked their eating, hydration, activity, and behavior each day. 5. I used those daily observations to confirm that they were gradually eating more, becoming active, and adapting.

I built an autonomous learning workbook in ChatGPT Projects to help me stay ahead of where I am and where I need to be. Keeping up with that gap has been a labor of love and tears. The goal of the project is to catalog: - What I know - What I’ve forgotten - What I’m currently learning - What skills I need for my career goals - What has changed in healthcare, AI, and my industry - The single highest-value thing for me to do next I use the following prompt in ChatGPT Projects. It may take some tweaking for your personal needs, but feel free to use it as you see fit: > Engineer dashboards for: Learning Progress, Competency Growth, Learning Hours, Weekly Progress, Monthly Progress, Retention, Knowledge Coverage, Executive Readiness, Upcoming Reviews, Learning Recommendations, Skill Heat Map, Learning Velocity, Credential Progress, Continuing Education Credits, and Certification Status. Step-by-step: 1. I created an autonomous learning workbook in ChatGPT Projects. 2. I defined the information I wanted the project to catalog, including my current knowledge, forgotten material, active learning, career-skill needs, industry changes, and highest-value next action. 3. I sent ChatGPT Projects a prompt to engineer dashboards for learning progress, competency, retention, reviews, recommendations, credentials, continuing education, and certification status. 4. I planned to tweak the prompt and dashboards for my personal needs.

Sometimes I want something good to read but don’t know exactly what. Finding the right book, article, or story means searching through existing content and hoping something matches my current mood and interests. Generative AI creates another option: instead of finding something to read, I can create exactly what I want to read right now. I created a personalized Reading Experience Generator that acts as an on-demand writer rather than a recommendation engine. When I tell it I want something to read, it asks me a few questions—one at a time—to understand what I’m in the mood for. It determines whether I want fiction or nonfiction, the tone and mood, the subject or setting, and any particular angle I’m interested in. Once it knows enough, it writes an original 1,000–2,000-word piece specifically for that moment. The key instruction is that it never recommends existing books, authors, stories, or articles. Its only job is to create something new. Step-by-step: 1. I create instructions that give the AI a single role: when I want something to read, it should write something original, not recommend existing content. 2. I tell it to begin each new reading experience by asking 2–4 questions, one at a time, and to stop asking as soon as it has enough information. 3. I have the questions establish whether I want fiction or nonfiction, my desired mood or tone, the subject, setting, or genre, and any particular angle I’m interested in. 4. I explicitly prohibit recommendations of existing books, authors, articles, or stories. This prevents the assistant from turning the experience into a conventional recommendation engine. 5. I define a target length of roughly 1,000–2,000 words—long enough to become immersed without requiring a major time commitment. 6. I tell the AI to begin writing immediately once it understands what I want. There should be no outline, explanation, or preamble; the next thing I see should be the piece itself. 7. When I want something different, I start again. The AI repeats the short interview and creates a completely new reading experience based on what I’m interested in at that moment. Instead of choosing from a fixed library of things other people have already written, I get an effectively unlimited supply of original reading material personalized to my interests and mood. The interesting shift is that I’m not using AI to help me write. I’m using AI as the writer, and I’m the audience.

After nearly 20 years in France, I’m finally applying for citizenship. I’ve spent the past year preparing documents and studying for the required tests, and my final interview is a few weeks away. Alongside the official study materials, I’ve been using ChatGPT to drill key facts and historical dates, run mock oral interviews in voice mode, and create quizzes from YouTube videos I link whenever they cover the right material. ChatGPT sometimes speaks French with a rather heavy accent, but the interactive back-and-forth helps me remember hundreds of details that are difficult to retain by simply reading a page. It’s tedious, but I find rote memorization challenging. We’ll see how it goes under pressure.

My mum’s 80th birthday is next week, and my dad, sister, and I wanted to create a photobook of her life. My dad scanned hundreds of photos from over the years and sent them to me to clean up. Because we were working to a tight deadline and I was away on holiday, I didn’t have time to open and edit each image individually in Photoshop. I asked ChatGPT to build a tool that accepts a folder of scanned images in various formats, including scans containing single or multiple photos, overlapping photos, and photos cut off by the scanner. The tool processed each scan, cropped and straightened the individual photos, and provided a UI where I could review the results and make manual adjustments to the cropping and orientation before saving the changes to new files. I then had the tool upload the images to Google Drive and create a spreadsheet with thumbnails of every image, along with a rating system. I shared the spreadsheet with my dad and sister so we could use it as a central place to rate the photos we wanted to include in the final book. This saved me hours of work and meant we could complete the photobook in time for my mum’s birthday. Step-by-step: 1. My dad scanned hundreds of photos and sent them to me as image files in various formats. 2. I asked ChatGPT to build a tool that could process scans containing single or multiple photos, overlapping photos, and photos cut off by the scanner. 3. I used the tool to crop and straighten each individual photo. 4. I reviewed the results in the tool’s UI and made manual adjustments to the cropping and orientation where needed. 5. I saved the adjusted photos as new files and had the tool upload them to Google Drive. 6. I had the tool create a spreadsheet containing thumbnails of all the images and a rating system for each photo. 7. I shared the spreadsheet with my dad and sister so we could rate the photos and choose which ones to include in the final photobook.

I built a daily AI story-scouting workflow that finds potentially viral stories for Instagram Reels, YouTube Shorts, and TikTok without recycling topics we’ve already covered. First, the workflow reviewed Rowan’s public short-form archive once to create a coverage map of past topics and editorial taste. It uses that map as a filter, helping distinguish a genuinely new angle from another version of the same AI launch, robot demo, or research story. Every morning, it scans priority sources, including AI and technology coverage from major newspapers and tech blogs, The Rundown newsletters, company announcements, research papers, and public updates from major AI and robotics companies. The workflow is not limited to same-day news. It also looks back over the past one to six weeks for stories that have not been extensively covered in the media. Each potential story is checked for four things: a clear visual hook, a technical idea that can be explained in 45–90 seconds, broad audience interest, and meaningful novelty compared with Rowan’s past coverage and the previous week’s recommendations. It keeps every story that clears the bar rather than forcing a fixed number. The final brief is ranked by strength and sent as one compact Slack DM at 10 AM each day. Step-by-step: 1. The workflow reviews Rowan’s public short-form archive once and builds a map of past topics and editorial taste. 2. Each morning, it scans priority sources, including major newspapers, tech blogs, The Rundown newsletters, company announcements, research papers, and public updates from major AI and robotics companies. 3. It searches both same-day news and stories from the previous one to six weeks that have not been extensively covered in the media. 4. It filters out topics that substantially repeat Rowan’s past coverage or the previous week’s recommendations, while identifying genuinely new angles. 5. It evaluates each potential story for a clear visual hook, a technical idea explainable in 45–90 seconds, broad audience interest, and meaningful novelty. 6. It keeps every story that meets those criteria instead of forcing a fixed number of recommendations. 7. It ranks the final brief by strength and sends it as one compact Slack DM at 10 AM each day.

I built a GPT called Entrepreneurial Mindset Coach based on the book I wrote last year. It is designed to rewire your brain for business success. Step-by-step: 1. I opened ChatGPT and selected More from the left-hand menu. 2. I selected GPTs and clicked Create in the upper-right corner. 3. I opened the Configure tab at the top. 4. I started building my GPT.

I used AI to help investigate a family mystery that had remained unresolved for decades: identifying the biological family of my maternal grandfather. The challenge was not a lack of information. It was almost the opposite. I had DNA matches, family trees, names, dates, historical records, old photographs, obituaries, Facebook genealogy groups, and conversations with possible relatives. The difficult part was connecting all these scattered clues without jumping to conclusions. I built a research workflow in which AI acts as an investigation partner, not as the source of truth. Step-by-step: 1. I gathered the information I already had from genealogy platforms, DNA matches, family trees, historical documents, and family records. 2. I used ChatGPT to organize the evidence into people, dates, locations, relationships, DNA connections, documents, and unresolved questions. 3. I separated the information into three categories: confirmed facts, hypotheses, and missing information. 4. Instead of asking AI, “Who was my grandfather's biological father?”, I asked it to analyze possible family connections and identify which hypotheses were compatible with the available evidence. 5. For each hypothesis, I looked for supporting evidence, contradictory evidence, and information that was still needed. 6. I treated AI-generated connections as leads rather than genealogical proof. The goal was to use AI to decide what to investigate next, not to have it find the answer. 7. I used AI to compare family branches, surnames, generations, locations, and possible relationships among DNA matches whose family connections I did not immediately recognize. 8. When a promising connection appeared, I returned to the original genealogy and DNA sources to verify it. This gradually turned a long list of DNA matches into a smaller number of research paths. 9. I used AI to draft respectful, personalized messages to DNA matches and members of genealogy communities, including people in another country and language. 10. In each message, I explained what I was researching, what connection I suspected, what information I already had, and what I hoped the recipient might be able to confirm or rule out. 11. I treated their responses as new evidence and repeated the investigation loop: evidence → AI analysis → hypothesis → verification → human contact → new evidence → updated hypothesis. The final result is not an “AI-generated family tree.” It is a human-led investigation in which AI helps manage complexity, ask better questions, and identify the next useful action. The most important lesson I learned is that AI is particularly useful in genealogy when you do not ask it to give you the answer. Ask it to help you build the investigation.

I asked ChatGPT to watch a video of me snowboarding and create a visual guide showing what I’m doing wrong, along with the correct form and dos and don’ts for improving my snowboarding. Step-by-step: 1. I asked ChatGPT to watch a video of me snowboarding. 2. I asked it to identify what I’m doing wrong. 3. I asked it to create a visual guide showing the correct form and the dos and don’ts for improving my snowboarding.

I first wrote a few paragraphs in ChatGPT explaining what I wanted and asked it to create a detailed prompt for Replit to build the site. Then I bought a domain through GoDaddy, and the site was live online less than an hour after I came up with the idea. Step-by-step: 1. I described my idea for a restaurant recommendation site featuring the top 40 cities in the world to ChatGPT. 2. I asked ChatGPT to create a detailed prompt for Replit. 3. I used the prompt in Replit to build the site. 4. I bought a domain through GoDaddy. 5. I launched the site online less than an hour after coming up with the idea.

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

I built an agent from scratch that monitors my Claude and Codex usage by adding a runner node directly inside the root of each project repository. It includes agent templates for specific jobs, such as commit, research, janitor, and manager tasks. The agents run automatically at night, when I’m not actively using my five-hour usage period. They monitor their own usage and cap themselves so they leave usage available for me. Later, I built a hive dashboard where I can monitor all of these payloads in one place and execute them from the dashboard as the agents’ jobs become more complex. Step-by-step: 1. I added a runner node to the root of each project repository. 2. I created templates for specific agent jobs, including commit, research, janitor, and manager tasks. 3. I configured the agents to run at night when I’m not actively using Claude or Codex. 4. I had the agents monitor and cap their own usage so they preserve usage for me. 5. I built a hive dashboard to monitor and execute the payloads from a single place as the agents’ jobs became more complex.

I’m on a GLP-1 medication that heavily suppresses my appetite, and I’m also managing a bilateral vestibular condition that causes balance and gaze issues. I needed a way to hit my protein and calorie targets despite having a low appetite, track body composition accurately, log vestibular symptoms, connect my actual training data, and get coaching guidance that reflects my situation instead of generic fitness-app advice. I built FuelStrong: three connected apps created with Claude over many sessions. They include a daily tracker for meals, water, energy, and training check-ins; a Progress and analytics module; and a standalone Vestibular symptom tracker. They share a Cloudflare Worker and D1 database backend, with KV for cross-device sync. I describe a feature or problem to Claude in plain language. Claude proposes structural options, I push back or choose a direction, and Claude writes the HTML, CSS, and JavaScript. I program my lifts in Fitbod using an Upper/Lower/Upper split, with an arms-and-back priority and the Build Muscle goal. I export those workouts as CSV and drop them into FuelStrong’s import zone, which parses exercises, sets, reps, and volume into my training history. Custom foods receive macro estimates through a Claude API call routed through my own Worker endpoint. Evolt body-scan data feeds dynamic calorie and protein targets based on BMR × activity factor, minus a deficit, with hard floors instead of static numbers. The Vestibular module intentionally uses open text fields for now, so Claude and I can identify which data matters before formalizing the inputs. Everything syncs across devices through Cloudflare KV. The coaching layer uses a three-tier framework—evidence floor, confirmed operating range, and aspirational target—to drive every recommendation. Two calorie floors, a daily target of approximately 1,000–1,100 kcal and a weekly average of approximately 1,300–1,400 kcal, reflect that chronic under-eating—not missed protein—is the real GLP-1 risk. Muscle mass has remained stable since my February 2026 baseline, so the coaching treats that as a genuine win rather than a plateau. Vestibular-training coaching connects dry-needling focus areas—SCM, suboccipitals, and splenius capitis/cervicis—to gaze-stabilization symptoms, since cervical proprioception substitutes for non-functional vestibular canals. The result is one dashboard that brings together training, nutrition, body composition, vestibular symptoms, and coaching logic. My muscle mass has held stable through it all. Step-by-step: 1. I describe a feature or problem to Claude in plain language, review its structural options, choose a direction, and have Claude write the HTML, CSS, and JavaScript. 2. I use FuelStrong’s daily tracker to record meals, water, energy, and training check-ins, while the Progress and analytics module tracks body composition and related trends. 3. I program my Upper/Lower/Upper workouts in Fitbod with an arms-and-back priority and the Build Muscle goal. 4. I export Fitbod workouts as CSV and import them into FuelStrong so it can parse exercises, sets, reps, and volume into my training history. 5. I route Claude API requests for custom-food macro estimates through my own Cloudflare Worker endpoint. 6. I use Evolt body-scan data to calculate dynamic calorie and protein targets from BMR × activity factor, minus a deficit, while maintaining hard floors. 7. I log vestibular symptoms in the standalone Vestibular tracker using open text fields while Claude and I determine which inputs should eventually be formalized. 8. I sync the three apps across devices through the shared Cloudflare Worker, D1 database, and KV backend. 9. I use the evidence floor, confirmed operating range, and aspirational target framework to guide recommendations, including the daily and weekly calorie floors. 10. I connect vestibular-training coaching to dry-needling focus areas and gaze-stabilization symptoms, then use stable muscle mass since the February 2026 baseline as a positive outcome rather than treating it as a plateau.

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 built Shots2Points, a golf scoring app for iPhone and Android, using AI as my development partner. The idea came from organising and playing in golf society events. Stableford scoring itself isn’t particularly complicated, but running an event can be. Organisers have to prepare groups, handicaps, and courses; collect scores from different groups; calculate results; manage withdrawals and ties; and eventually produce a leaderboard. I wanted to simplify that process while also providing an easy scoring app for ordinary casual rounds. The unusual part is how I built it. I’m not a professional software developer, and I don’t have a development team. I started by describing what I wanted the app to do to AI and gradually turned the idea into a working product. My workflow evolved into this cycle: idea → discussion → specification → implementation → real-world test → refinement. I repeat it for each feature. Step-by-step: 1. I define the problem and user experience with ChatGPT. I discuss ideas, challenge assumptions, work through workflows, and decide how a feature should behave before changing the code. 2. Once the behaviour is clear, I turn the idea into an implementation task. I use AI to specify exactly what needs to change, including edge cases and how the new feature should interact with existing functionality. 3. I build and inspect the code with Cursor. Cursor works directly with the project codebase, allowing AI to investigate existing code, implement changes, and report exactly what it changed. I test the result rather than simply accepting AI-generated code. 4. I test development versions on real iOS and Android devices. I follow the actual user journey, take screenshots or capture errors when something isn’t right, and bring those results back into the AI workflow. 5. I use AI to diagnose problems, make another targeted change, and test again. The result is a real cross-platform application rather than a prototype. Shots2Points includes free casual Stableford scoring and an Event Mode designed for golf societies and groups. Organisers can create events, import players, allocate groups, and allow each group to enter scores while everyone follows a live leaderboard. Building the app has required much more than generating code. AI has helped me work through database design, APIs, authentication, in-app purchases, App Store and Google Play requirements, debugging, user-interface decisions, testing, and release management. The biggest lesson for me has been that AI doesn’t remove the need to understand the problem or make decisions. It gives one person access to capabilities that would traditionally have required several different specialists. I provide the product knowledge, requirements, judgement, and testing; AI provides much of the technical capability and an extraordinarily fast feedback loop. That combination allowed me to take a personal idea for improving golf scoring and event management and turn it into a functioning iOS and Android product.

I built a second workflow that scans my conversations daily for ideas worth developing, especially strange connections, creative concepts, humor, research leads, unfinished experiments, and thoughts that may have seemed minor when they first appeared. Instead of turning everything into tasks, it creates an incubator: a place where interesting material can remain dormant until it connects with something new. The daily scan looks for fragments with creative or exploratory potential. They do not need to be fully formed ideas. A small observation, an odd comparison, a half-finished concept, a recurring image, or a funny side remark can all be worth keeping if they might become meaningful later. Once a week, the incubator revisits the material collected during the previous days. That weekly revisit makes connections across separate conversations more visible. Something that looked isolated on Tuesday may suddenly echo a thought from Friday or fit a project that did not yet exist when the original idea appeared. The important part is that the incubator does not treat every interesting thought as something that must immediately become productive. Some ideas benefit from being left alone for a while. They can sit next to other fragments, reappear in later conversations, or become useful only when a new context gives them meaning. The workflow is less about extracting the “best ideas” and more about preserving creative potential while allowing patterns to emerge gradually. It also surfaces unfinished experiments and research leads that I might otherwise forget, without turning the archive into a traditional task manager. That distinction matters to me: not everything valuable needs an action item. Some things are seeds, references, questions, jokes, aesthetic directions, or possible future worlds. Over time, the incubator becomes a kind of creative compost layer on top of the conversation archive: new material is gathered daily, revisited weekly, and allowed to combine into directions that would be difficult to plan deliberately. In simple terms: daily conversation scan → promising fragments → weekly revisit → emerging connections → incubation → later rediscovery and development The goal is not to force ideas into projects. It is to keep good material alive long enough for the right project to find it. Step-by-step: 1. I scan my conversations each day for strange connections, creative concepts, humor, research leads, unfinished experiments, and other fragments with creative or exploratory potential. 2. I preserve promising material in an incubator instead of immediately turning every item into a task. 3. I keep small observations, odd comparisons, half-finished concepts, recurring images, funny side remarks, seeds, references, questions, aesthetic directions, and possible future worlds—even when they are not fully formed. 4. I revisit the material collected during the previous days once a week. 5. I look for connections across separate conversations, including links between ideas that initially seemed isolated or between fragments and projects that did not yet exist. 6. I allow ideas to remain dormant, sit alongside other fragments, reappear in later conversations, or become useful when a new context gives them meaning. 7. I resurface unfinished experiments and research leads without turning the archive into a traditional task manager. 8. I let the material combine gradually so that new directions can emerge through later rediscovery and development.