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I'm not a professional developer, but I built Sujido, a live word puzzle, with multiple AI agents. For months, they gave me bloat, lost context, and slop, and neither I nor the models could untangle what had been decided or why. I eventually settled on two agents: one Claude instance that plans and reviews, and another that builds. I imposed rules on both, one failure at a time. None of these rules would surprise a professional engineering team, but nobody handed them to me. That's the key lesson I learned: agents won't bring the discipline for you. You have to impose it. When I showed the first game revision to strangers, their verdict was that the first half was "boring" and the second half was novel. It was hard news to hear about my baby, but I took the feedback to heart and cut the game in half. That required surgery on code tangled across both halves, followed by a rebuild under the rules below. Before the rebuild, a separate AI audit graded the codebase B−. Afterward, it graded it A−. The deployed site shrank by 97.5%, and every serious finding was fixed and locked in by an automated test. The models didn't change drastically, but my operating rules did. Step-by-step: 1. I wrote a constitution before the first prompt. The repo holds what every agent must read before working: `CLAUDE.md` files for the project as a whole and for each major part of the code, a collaboration protocol for how the agents and I work together, a spec for the core data format, and a decision log. The data format is changed there first and then in code. Nothing is remembered unless it is written down. That's my life as well. :) 2. I split the work by role, not by task. One Claude instance is the strategist: it writes briefs, reviews reports, and never commits code. Claude Code in VS Code is the builder: it executes the brief, writes the tests, reports back, and never releases. Neither agent can approve its own work. 3. I made every job start from a written brief, and I never edit briefs. Each brief is committed to the repo with an exact scope, such as "do X only, do not begin Y." If plans change, a dated amendment states what it supersedes. The builder may skip a risky item with a written reason; skipping is better than forcing it. 4. I used mock-ups with real data instead of descriptions. Anything I will look at gets a mock-up, and I approve it before any code is written. I learned this when a status report described only in words arrived on my phone formatted as a wall of text. The approved mock became the spec, and the tests pin the output to it. 5. I proved that every test could fail. No test is trusted until it has been run against deliberately broken code and caught the bug. A test suite that has never failed hasn't proven anything. 6. I verified reports against the real repo. Agent reports drift. One summary said players rated a puzzle "just right" when the log said "too easy." An audit said there were about 25 unused functions; a recount found 29. The strategist spot-checks load-bearing claims against the actual files and data before anything ships. 7. I built gates the agents can't talk their way past. All work lands on a branch that auto-deploys to a test site. GitHub Actions lints and runs the Python and headless-browser tests on every push. A git hook refuses direct pushes to production. Releases are fast-forward merges that only I perform, after CI is green and I've tested the build personally. The live site shows the release's commit ID, and I check it before calling it shipped. Nothing merges automatically. An agent's "looks good" is just another report, and reports drift. 8. I gave the system a memory. Every session ends by updating the decision log and a "resume here" document, so the next session—a fresh model with no recollection—picks up exactly where the last one stopped. Without this, every session starts by relitigating old decisions. 9. I let the data and end users overrule me. A scheduled GitHub Action turns player telemetry into a daily report. It showed that the difficulty dials I assumed mattered did not predict what players found hard; what mattered was how misleading the starting arrangement was. When testers were confused by the tutorial, I rebuilt it even though I had already approved it. Player feedback about their own experience outranks my earlier rulings. AI agents are velocity multipliers, but they multiply bad structure as quickly as good code. The rules came from me, not the models: the AI writes the code, while the rules, gates, and written record keep the process honest. See the result at Sujido.com. Finding the anagram is the easy part; sliding it into place is the hard part. One board took me 902 moves. My S.O. solved it in 242, and I haven't heard the end of it. Try to break it!

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. 😑😭🎧

I built a custom web app that uses chance to combine random images and texts into new, unplanned pairings. It recreates a Dadaist technique I use often when making photobooks, which I previously did manually with physical prints. I used Replit to create a digital version of the game. It was easy to connect the app to Project Gutenberg for random texts and the Library of Congress photography archive for images. More importantly, I made it possible for users to work with their own image and text archives. So far, it has produced many new and interesting results, and other artists have used it too. Step-by-step: 1. I identified the manual process I use when making photobooks: combining printed images and texts by chance. 2. I used Replit to build a digital version of this process as a web app. 3. I connected the app to Project Gutenberg to pull random texts. 4. I connected it to the Library of Congress photography archive to pull images. 5. I allowed users to work with their own image and text archives. 6. I used the app to create new pairings and shared it with other artists, who used it as well.

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’m Jesús Núñez, an artist and the owner of J. Nunez Gallery, an independent contemporary art gallery with locations in Summit and Millburn, New Jersey. Founded in 2020, the gallery presents original abstract and figurative art, landscapes, still life, and photography. We help people discover artwork for their homes and work with interior designers on residential projects. Alongside running the gallery, I’m exploring how ChatGPT can help a small business improve its operations, marketing, and decision-making. I manage the business myself, so the practical question is how to turn existing information into useful, completed work. The workflow starts with real source material: website content, spreadsheets, analytics exports, screenshots, and the gallery’s business rules. I use focused conversations to examine one area, prepare concrete changes, and review the results. The work includes website improvements, artwork catalog organization, marketing analysis, audience segmentation, and outreach preparation. For example, AI-assisted troubleshooting helped identify duplicate website page-view tracking and guided its correction, which we checked against live analytics. That is an operational improvement; it is not evidence of increased sales. Another small-business owner can recreate the approach as follows. Step-by-step: 1. Record the business context: customers, products, geographic market, budget, brand preferences, and operating constraints. 2. Choose one specific task and define what completion means. Provide the relevant source material. 3. Require the AI to distinguish verified facts, interpretations, missing information, and proposed actions. It should not guess prices, contact details, or results. 4. Ask for a concrete deliverable, such as revised website copy, a classification table, implementation instructions, or an outreach draft. 5. Review the work and verify the actual result after implementation. Keep preparation, implementation, verification, and blocked work clearly identified. 6. Preserve approved decisions and completed actions in a current record so later sessions can continue from the existing work. 7. Measure qualified inquiries, appointments, proposals, and sales alongside marketing activity. This is an ongoing project. AI can repeat recommendations or lose track of previous decisions, so human review and continuity remain essential. I have not established a measured revenue increase or time-saving figure. I’m testing whether this approach can build a stronger business while allowing more attention for art, artists, and clients. If this example makes you curious about the gallery, you can browse available artwork and find information about our Summit and Millburn locations at https://www.jnunezgallery.com. You are welcome to explore the collection or contact us through the website for help choosing art. Jesús Núñez Artist and Owner, J. Nunez Gallery Summit and Millburn, New Jersey https://www.jnunezgallery.com

I’m designing a publishing system that keeps producing value without requiring constant output. This workflow started with frustration about the usual content model: publish constantly, stay visible, feed the algorithm, and repeat. That model works poorly when my energy varies, my interests shift, and forcing output quickly turns creativity into maintenance. Instead of asking, “How do I post more?” I’m asking: “How can content keep moving without requiring me to keep producing at the same pace?” The system is built around uneven energy. During high-energy periods, I can generate ideas, draft pieces, collect fragments, or prepare several posts at once. During low-energy periods, existing material does not become a failure state. It can sit, mature, be lightly edited, be reused, or simply wait. Ideas can exist in different stages: a note, a half-developed thought, a finished piece, something worth revisiting later, or something that can be repurposed in another format. Older material is not automatically “used up” after publication. A long-form idea can become a short post. A workflow can become a case study. A conversation fragment can become an essay seed. A previous post can return later from a different angle. The goal is not to build a machine that constantly demands feeding. It is to create a content reservoir I can draw from when I have the capacity. This workflow has not been fully implemented yet, but the design principle is useful: > Build around energy reality, not around imaginary consistency. Content should fit into life. Life should not be reorganized to serve the content treadmill. Step-by-step: 1. I design the publishing system around variable energy rather than a constant production schedule. 2. During high-energy periods, I generate ideas, draft pieces, collect fragments, and prepare several posts at once. 3. During low-energy periods, I let existing material sit, mature, receive light edits, be reused, or wait. 4. I allow ideas to remain in different stages, from notes and half-developed thoughts to finished pieces and material worth revisiting later. 5. I repurpose older material across formats, such as turning a long-form idea into a short post, a workflow into a case study, a conversation fragment into an essay seed, or a previous post into a new angle. 6. I draw from the resulting content reservoir when I have the capacity instead of constantly feeding a content treadmill.

I design songs by giving different vocal parts different emotional jobs rather than writing one continuous lyric and deciding afterward who sings each section. I start with contrast. One voice might carry rhythm, detail, tension, or narrative through rap, while another opens the song emotionally through a melodic chorus. A sung section can create space after a dense verse, and a rap section can sharpen the story after a more atmospheric refrain. The contrast is functional as well as musical. Different voices can represent different perspectives, emotional registers, or levels of intimacy. The chorus can express the feeling that the verse struggles to name, while the verse can complicate what the chorus makes sound simple. A bridge can temporarily change the relationship between them. This is why songs that combine rap and fully sung passages can work: the parts are doing different jobs instead of simply stitching two styles together. The song “Airplanes” is useful as a structural inspiration because of its contrast between rhythmic narrative and a melodic, emotionally open refrain. The goal, however, is not imitation. It is to understand why that contrast works and build a new song around its own voices, themes, and emotional architecture. AI can help me develop lyrics, transitions, vocal roles, perspective shifts, and the points where the song needs to open up or contract. The result is a song designed not only around verses and choruses, but also around the relationship between different ways of speaking and singing. Step-by-step: 1. I identify the different vocal modes the song will use, such as rap, melodic singing, or a bridge. 2. I assign each vocal part a distinct emotional or narrative job, such as carrying rhythm, creating tension, opening emotionally, or introducing another perspective. 3. I use contrast to create movement: a sung section can follow a dense verse, while a rap section can sharpen the story after an atmospheric refrain. 4. I decide how the voices relate to one another, including what the chorus expresses, what the verse complicates, and whether the bridge changes their relationship. 5. I study the contrast between rhythmic narrative and melodic refrain in “Airplanes” as structural inspiration without imitating it. 6. I use AI to develop lyrics, transitions, vocal roles, perspective shifts, and the points where the song should open up or contract. 7. I shape the final song around the relationship between its different ways of speaking and singing, not only around its verses and choruses.

I wanted a publicly viewable leaderboard on the web, with input coming from creators. I prompted Gemini: " Can you design good looking leaderboards using Google documents. I want three: all time, rolling 30 days and rolling 90 days. They should be different tabs on the same document. They should be easy to administer/update. Show me a mockup and describe how it would work." After I was satisfied that it would be easy to launch and maintain, I got more specific. Because it was for creative types, I wanted more than a basic spreadsheet ranking. Gemini responded: "It features profile avatars, gamified badges, high-contrast dark visual styling, and clear ranking tabs for All-Time, Rolling 30 Days, and Rolling 90 Days..." That's what AI calls creative, I guess. Later in the chat, Gemini suggested achievement levels with a more creative feel: - 👑 Century Virtuoso / Platinum Mic - 🔥 Monthly On-Fire / Speedster - 🎙️ Heartstring Weaver - 🌟 Smile Architect For the podium treatment, rows 1, 2, and 3 feature custom metallic highlights—Warm Gold, Silver Slate, and Bronze Copper—with circular numerical rank badges to make top contributors stand out instantly. Gemini heavily integrated Google Docs and Google Sheets. The system uses Google Apps Script to update the leaderboard in real time based on creator activity. Step-by-step: 1. I asked Gemini to design three leaderboards—All-Time, Rolling 30 Days, and Rolling 90 Days—as separate tabs in the same Google document. 2. I asked for the leaderboards to be easy to administer and update, along with a mockup and an explanation of how they would work. 3. After reviewing the initial concept, I clarified that the leaderboard was for creative types and should be more distinctive than a basic spreadsheet ranking. 4. Gemini added profile avatars, gamified badges, high-contrast dark visual styling, and clear ranking tabs. 5. I incorporated creative achievement levels, including Century Virtuoso / Platinum Mic, Monthly On-Fire / Speedster, Heartstring Weaver, and Smile Architect. 6. I used Warm Gold, Silver Slate, and Bronze Copper highlights for the top three rows, along with circular numerical rank badges. 7. I connected the system to Google Docs and Google Sheets and used Google Apps Script to update it in real time based on creator activity.

I built a custom news agent that skips the Google News and Apple News walled gardens. Eight prompts turn Claude into a personal news editor that reads my trusted RSS feeds, scores every story with a decision model, and delivers a clean daily page for about a dime a month. Each week, pulling two or three good stories for my newsletter digest meant searching multiple sites by hand—hours of clicking and scrolling for a handful of usable links. RSS feeds already deliver that content; the missing piece was a judgment layer that could separate worthwhile stories from noise without the cost or latency of running everything through a full LLM. I used Claude Cowork, Jev from TypeSafe AI, Vercel AI Gateway, RSS feeds, Python, and SQLite. Jev is a decision model rather than a chatbot: it returns a probability and confidence score instead of prose, does not hallucinate facts, and scores a story in a fraction of a second. That makes an automated daily judgment call inexpensive enough to run continuously. Step-by-step: 1. I set up a Claude project to hold the scripts, taste guide, database, and page. 2. I defined the goal and sections, then had Claude interview me to create a short “taste guide” describing what counts as a good story. 3. I found and live-tested the RSS feed for each trusted source instead of guessing feed URLs. 4. I fetched all working feeds into a deduplicated SQLite database and logged failures. 5. I used Jev through Vercel AI Gateway to score every story: first filtering ads and promotions, then ranking stories by section against my taste guide. 6. I selected the winners for each section, with Jev identifying near-duplicate stories so they would not appear twice. 7. I generated one-line summaries and fact-checked them against the source so nothing was invented. 8. I rendered a simple HTML page and scheduled the entire workflow to run every morning. A few details matter. I used Cowork rather than a plain chat because several steps require Claude to work with files, run code, and access the network. I also created a Vercel account and AI Gateway API key manually before the scoring step. Finally, I saved everything to the Claude project instead of a local folder, which allows the morning run to happen in the cloud on its own schedule. Scoring costs roughly $0.10 per month with Jev, compared with a few dollars for a full LLM doing the same job. The workflow turns a two-hour weekly slog into a page that is ready every morning, with each link going directly to the original publisher rather than a Google or Apple aggregator. Full build walkthrough: onabetternote.substack.com/p/build-your-own-news-agent-with-claude — the Substack post includes the exact eight prompts to copy and paste, plus more detail on why a model like Jev matters for cost containment as agents become routine.

I wanted lyrics for original music that did not rely on random invented syllables, so I developed a fictional “crystal language” with its own sound, atmosphere, recurring words, and internal logic. The starting point was musical rather than academic. I considered which sounds belonged in this world, which consonants felt sharp, soft, ancient, luminous, or fragile, which vowels sang well, and which word shapes fit the music’s rhythm and emotional tone. As I repeated sounds, they began to acquire repeated meanings. Words started to relate to one another, and certain phonetic patterns became associated with ideas, emotions, or imagery. The language gradually developed enough consistency that listeners could feel it belonged to one world, even without understanding it. That internal structure keeps the invented lyrics from sounding like decorative nonsense. The words begin to carry memory: a phrase can reappear later and mean something, and a sound used in one song can echo another. The fictional language becomes part of the music’s identity. The aim is not necessarily to construct a complete language with full grammar. It is to create enough recurring logic that the lyrics feel intentional, recognizable, and alive—not gibberish with nice vowels, but a tiny musical culture. Step-by-step: 1. I started with the music and defined the kind of world its language needed to evoke. 2. I selected sounds, consonants, vowels, and word shapes based on their emotional qualities, singability, rhythm, and atmosphere. 3. I repeated sounds until some began to acquire recurring meanings. 4. I connected related words and associated phonetic patterns with ideas, emotions, or imagery. 5. I reused phrases and sounds across songs so they could carry memory and create echoes between pieces. 6. I developed only as much structure as necessary for the lyrics to feel intentional, recognizable, and alive.

I develop the song with ChatGPT, working through the lyrics, structure, mood, and sound tags. I then move the concept into Suno, which generates the music. Once the track is finished, my ChatGPT agent handles the download and archiving workflow where site authentication allows it. ChatGPT also creates the visual artwork. I assemble the finished song and image into a full video and a Short in Clipchamp. From there, I publish the content to YouTube and TikTok, then share the release across Facebook, LinkedIn, and X. What makes this workflow interesting is not any single AI tool, but the handoff between them: writing, music generation, file handling, image creation, video assembly, publishing, and distribution—all directed by one human creative process. The result is a practical, end-to-end AI-assisted production pipeline rather than a single AI-generated output. Step-by-step: 1. I work with ChatGPT to develop the lyrics, structure, mood, and sound tags. 2. I move the concept into Suno to generate the music. 3. Once the track is finished, my ChatGPT agent downloads and archives it where site authentication allows. 4. I use ChatGPT to create the visual artwork. 5. I combine the finished song and image in Clipchamp to create a full video and a Short. 6. I publish the finished content to YouTube and TikTok. 7. I share the release across Facebook, LinkedIn, and X.

Some of the most vivid ideas do not begin as formal briefs. They begin as a joke, metaphor, image, or strange sentence that suddenly has energy. This workflow captures how a small conversational spark can grow into a much larger creative concept. I might say something funny, absurd, visual, or emotionally loaded. AI extends the image, pushes it further, and adds variations, structure, or playful consequences. That makes me more enthusiastic, associative, and specific. Before long, a throwaway remark can become a complete scene, story world, comic setup, installation concept, or concept art direction. The key is not to force the idea into coherence too early. At first, the goal is energy rather than precision. The playful improvisation reveals whether the concept has legs; structure comes in once the idea has shown that it wants to live. This is especially useful for concepts that combine humor, symbolism, emotional charge, and strong imagery. For example, “the elephant in the room” can become a full comic or visual scenario, while “the lion among the cats” can evolve into a richer concept with social dynamics, character logic, and visual storytelling potential. AI acts less like a generator of finished ideas and more like an improvisational creative partner—one that keeps the momentum going long enough for an offhand remark to reveal its full conceptual potential. The result can be a complete creative direction for writing, comics, concept art, sets, or installations. Enthusiasm is treated as a signal: when a joke or image triggers strong excitement, it may indicate that a larger concept is hiding inside it. Step-by-step: 1. I start with a small conversational spark: a joke, metaphor, visual phrase, or strange idea. 2. I ask AI to continue the thread instead of flattening it too early. 3. I react strongly to whatever feels alive, funny, moving, or visually rich. 4. I expand the idea through playful back-and-forth. 5. Characters, settings, tone, visual motifs, and scene logic begin to emerge. 6. I use AI to organize the growing material without losing its spontaneity. 7. I shape the concept into a usable form, such as a comic strip, story premise, installation idea, character setup, concept art brief, scene sequence, or mini-series. 8. If needed, I turn the raw idea into structured outputs such as panel descriptions, mood, dialogue, staging, or visual prompts.

I write science fiction and use ChatGPT to make sure my stories stay within certain rules. I use Willow Voice AI for dictation and video generators to create proof-of-concept visuals. I also recently used AI to find quick answers within a 40-minute YouTube video. I turned it from watchbait into quick answers. Step-by-step: 1. I use ChatGPT to check that my science-fiction stories stay within certain rules. 2. I use Willow Voice AI to dictate my writing. 3. I use video generators to create proof-of-concept visuals. 4. I use AI to find quick answers within a 40-minute YouTube video. 5. I turn the video from watchbait into quick answers.

I use AI to turn my watch history into a personal recommendation engine that can answer situational questions such as, “What should I watch tonight?” Generic recommendation algorithms know what is popular and what resembles something I clicked before. They usually know much less about why I want to watch something tonight. For this workflow, I give AI two different datasets: - Already watched: Evidence of my actual viewing history and taste. - Want to watch: Shows my curiosity, intentions, and unexplored directions. This should not automatically be treated as proof that I will like something. AI analyzes both lists for patterns such as genre, themes, emotional intensity, pacing, humor, visual atmosphere, storytelling style, cultural interests, darkness versus comfort, realism versus imagination, and other recurring preferences. Instead of reducing everything to genres, the workflow builds a descriptive taste profile. It keeps confirmed preferences separate from hypotheses based on the watchlist and uses my later reactions to refine the profile. When I want a recommendation, I add my current context: available time, mood, energy, desired emotional intensity, whether I want something comforting or challenging, and whether I want a movie or an episode. The system matches that temporary context against my longer-term taste profile and the available watchlist. So instead of asking: > “Recommend me a good series.” I can ask: > “I have about 90 minutes, my brain is tired, I want something comforting but not stupid, and I don't want anything emotionally brutal tonight.” The recommendation is based on three layers at once: past taste, future curiosity, and present state. Over time, the system becomes less like a recommendation list and more like a personal cultural navigation tool. Step-by-step: 1. I import or paste my watched films and series. 2. I add a separate list of things I still want to watch. 3. I ask AI to analyze recurring themes and less obvious connections. 4. I build a descriptive taste profile rather than reducing everything to genres. 5. I keep confirmed preferences separate from hypotheses based on the watchlist. 6. I use my later reactions to refine the profile. 7. When choosing something to watch, I add the current context, including available time, mood, energy, desired emotional intensity, whether I want something comforting or challenging, and whether I want a movie or an episode. 8. I match that temporary context against my longer-term taste profile and the available watchlist.

I use this workflow when I want fresh creative input for my visual work and want to expand my inspiration library in Eagle without collapsing into randomness. The starting point is not a generic prompt like “show me inspiring artists.” I give the AI examples of images, artists, materials, traditions, or visual details that already catch my attention. The AI first looks for patterns in what I respond to: color, material, composition, atmosphere, craft techniques, cultural traditions, natural structures, architectural forms, and more. From there, we branch outward. I explore adjacent references across disciplines, cultures, periods, and materials rather than looking only for visually similar work. I react freely to the suggestions: “This texture, yes.” “This artist, no.” “More of that construction method.” “Less decorative.” “Stranger.” “Older.” “Softer.” Those reactions steer the next round of research. One research session, for example, moved through Indian stepwells; Ajrakh and Bandhani textiles from Gujarat and Kutch; Tibetan/Ladakhi painted textiles; Miao baby carriers; Dong wooden bridges; art from Papua; Russian lubok prints; and even 11th-century goldworking in Panama. These references do not belong to one tidy category, period, or geography. That is precisely the point. The session was driven by visual responses and emerging connections involving color, surface, construction, ornament, repetition, symbolism, material, and technique. Each discovery created a new branch to follow. AI made it possible to move laterally across disciplines, cultures, and centuries without losing the thread of what was visually interesting to me. I then selected only the references that genuinely sparked something and added those to Eagle. The result is not just a collection of “similar images,” but a deliberately expanded visual vocabulary. The human remains the curator. AI expands the search radius; taste decides what enters the library. Sometimes there is no initial research question at all. A joke, misheard word, object, memory, or accidental phrase becomes the seed. The value is in allowing associative drift to continue long enough for a real curiosity trail to emerge. > The research does not always begin with a topic. Sometimes the topic is discovered during the wandering. Step-by-step: 1. I start with a few visual references I genuinely like. 2. I ask AI to identify recurring visual qualities and possible connections. 3. I explore adjacent references across disciplines, cultures, periods, and materials rather than searching only for visually similar work. 4. I react freely to the suggestions, noting what I want more or less of, such as a particular texture, artist, construction method, decorative quality, age, or atmosphere. 5. I let those reactions steer the next round of research. 6. I open the references and collect only the images that actually trigger something. 7. I save those images in Eagle as a curated visual library rather than an indiscriminate image dump. 8. I add useful context through folders, collections, tags, or notes so the references can resurface later.

I'm a British Academy (BAFTA) voting member. Every autumn, the members' screener platform fills up with more than 100 eligible films—major contenders, obscure documentaries, and hidden gems all mixed together. I have a few weeks to decide what deserves my viewing time before I vote. The fun challenge is finding the sleeper nobody has heard of yet; the problem is the sheer volume of clutter I have to sift through. I built a workflow in Claude Cowork that turns the overwhelming list into an ongoing conversation with a curator that knows my taste. I started by inputting my film notes from previous years: what I watched each season, what I loved, what I abandoned, and why. Claude stores this in persistent memory alongside a growing profile of my taste, including my go-to critic, my low tolerance for slow cinema, the kinds of films that reward me, and my rule that a film gets minutes—not acts—to earn my time. Every session starts with this context already loaded. When a new batch of films drops, Claude opens the screener platform in its built-in browser, reads the full slate, and sends research agents to cover every title in parallel. They research the director, cast, runtime, Rotten Tomatoes scores, festival prizes, awards buzz, and whether my favourite critic has reviewed each film. Claude even pulls the transcripts of his YouTube reviews and summarises the verdict. In about 20 minutes, a 37-film batch is triaged into three categories: priority viewing, worth sampling, and skips. Slow starters are flagged so I know which films need a committed evening and which get 10 minutes to prove themselves. As I watch, I feed verdicts back in plain English, such as “gave it ten minutes, too abstract.” Claude updates both the tracker and its model of my taste, so each recommendation round becomes sharper. By voting time, I have a complete, searchable record of everything I watched and what I thought of it, built conversationally throughout the season. Step-by-step: 1. I input my film notes from previous years—everything I watched, loved, and abandoned, along with my reasons. Claude saves them to persistent memory, which every future session loads automatically, and builds a profile of my taste from them. 2. When new films appear on the members' platform, Claude opens it in its built-in browser and reads the full list of titles. 3. Claude launches parallel research agents to cover every title, including the cast, crew, runtime, Rotten Tomatoes scores, festival prizes, awards buzz, and my preferred critic's verdict from YouTube review transcripts. 4. Claude compiles a triage tracker that ranks every film by viewing priority, flags likely sleepers, and warns me about slow starters. 5. As I sample films, I report quick verdicts in plain English. Claude logs them and continuously refines its profile of my taste, so recommendations improve throughout the season. 6. At voting time, I have a complete record of what I watched, what I thought, and why, built conversationally across the season.

Turn scattered creative preferences into four living style books THE PROBLEM My taste was scattered across old conversations, clothing lists, search terms, saved images, handmade collages and experiments transforming my childhood drawings with AI. I wanted something I could use to make new work, rather than another archive of everything I had ever mentioned. A generic label like “colourful and whimsical” was not enough. My preferences also change with the application: a collage, an outfit, a room and a digital course do not need the same treatment. THE WORKFLOW 1. Bring together different kinds of source material. I used an earlier style conversation, a digital design style document, a list of search terms, photos of my own collages and the corrections I had made during image-generation experiments. Ask AI to read source texts fully, and distinguish your statements from suggestions made by an earlier assistant. 2. Analyse patterns, then correct the interpretation. The AI proposed connections involving colour, texture, shape, atmosphere, vintage objects and visual storytelling. I refined these through concrete examples. For instance, liking stilettos and being able to wear them comfortably are separate facts. Liking geometric structures does not mean I want triangular wallpaper. A technically imperfect image can still work when it preserves the atmosphere I intended. 3. Treat corrections as part of the research. “Too childish” may mean wrong for this drawing’s intended audience, rather than a universal dislike. A saved image may be intriguing without being something I want to keep encountering. An entire family of similar images may appeal to me without needing to rank every member. One especially useful correction: patterns visible in my collages do not prove I consciously planned them. I sort clippings into categories and sometimes colour groups; the collage itself develops while I make it. 4. Include my own categories. My clipping categories include texture, image, black-and-white, colour, background, small meaningful items, comics and words. “Colour” means a clipping kept mainly for its colour, not simply any colour photograph. “Texture” includes both natural surfaces and designed patterns. These definitions provided better starting points than generic categories imposed by the AI. Digital design and graphics also received a full category of their own, rather than being treated as an exception to my taste elsewhere. 5. Create four distinct outputs. • Style Book: precise preferences, boundaries, exceptions and provisional design principles. • Visual Atlas: image families connected across subjects, with actual examples and explanations of what might link them. • Prompt Bible: modular language for colour, material, light, composition, figures and atmosphere, plus reusable recipes and lessons from corrections. • Curatorial Map: connections between interests such as vintage objects, memory, surfaces, small living worlds, language and digital design. 6. Keep the books open to revision. Separate confirmed preferences, observations and hypotheses. When new material arrives, ask what it confirms, refines or contradicts. Add what changes the understanding instead of documenting every conversation. WHAT THIS PRODUCED Four separate first-draft HTML books, including a visual atlas with 14 photographs of my collages. They bring clothing, interiors, fragrance, digital design and creative work into the same research project while preserving their different requirements. WHY IT HELPED The process made my corrections useful. Instead of asking AI to define my taste once, I could respond to concrete interpretations until the descriptions became more accurate. The books support future making without prescribing how I must create. STARTER PROMPT “Use these sources to draft four separate living documents: a personal Style Book, a Visual Atlas, a modular Prompt Bible and a Curatorial Map of Fascinations. Read the source texts fully and distinguish my own statements from earlier AI suggestions. Find recurring patterns, exceptions and unexpected connections. Keep preferences specific to their application, including clothing, interiors, digital design and graphics. Treat my corrections as evidence. Label observations and hypotheses clearly, and do not invent conscious intentions behind intuitive work. Use supplied images in the atlas where available. Build useful first drafts rather than a complete archive, and translate each finding differently for each document’s purpose.”

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.

My friend shot a short film and gave me his hard drive to help with post-production. It contained more than 400 clips, with no clear way to tell when each one had been shot. I asked Claude to review the metadata for every clip and group them by timestamp, so they were organized by shoot session instead of sitting in one large folder. Claude also built a spreadsheet listing each clip, its shoot session, the date, runtime, and file size. This saved my friend the hassle of reviewing every clip manually. Step-by-step: 1. I received my friend’s hard drive with more than 400 short-film clips. 2. I asked Claude to review the metadata for every clip. 3. I had Claude group the clips by timestamp and organize them by shoot session. 4. I had Claude create a spreadsheet with each clip’s shoot session, date, runtime, and file size. 5. My friend could avoid going through every clip manually.

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.