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In my ongoing mission to go paperless, I created a web app that runs on tablets and replaces paper sign-in sheets. The admin console gives HR instant visibility and detailed reporting on employee and guest attendance across all our branches from a single pane of glass. In Phase 2, I’m expanding the app to eliminate even more paperwork by allowing employees and managers to request, approve, and manage PTO. Department calendars show everyone’s PTO balance and minimum coverage requirements. Step-by-step: 1. I created a web app that runs on tablets to replace paper sign-in sheets. 2. I added an admin console so HR can view and report on employee and guest attendance across all branches. 3. In Phase 2, I expanded the app to support employee and manager PTO requests, approvals, and management. 4. I added department calendars showing PTO balances and minimum coverage requirements.

I created a system to keep records of frozen seafood orders, including PO numbers, dates, item codes, pallet quantities, pounds, cases, and lot numbers. I need to make the system more efficient. Step-by-step: 1. Record each frozen seafood order. 2. Capture the order’s PO number, date, item code, pallet quantity, pounds, cases, and lot number.

I was charged with leading my book club’s discussion of *Shrines of Gaiety* by Kate Atkinson. The book has many well-developed characters and murder-mystery plotlines, so I decided to create a “Who Is This Character?” game using simple supplies from my home office: envelopes, printed clues, a stapler, and markers. I asked ChatGPT to help design the game. It came up with eight potential characters, then selected the five most important so the game would still work if some book club members were absent. For each character, it created five clues and a simple rules sheet. Characters were assigned numbers and read in order. Players heard each clue without asking questions, but they could guess after every clue. Many characters were identified, and the game led to a meaningful discussion of each character’s motivations. ChatGPT also provided a discussion question for each character, which made the activity easy for me to run. As a bonus, ChatGPT found a 1920s cocktail recipe for the Mother’s Milk drink mentioned on page 336. It included appropriate warnings, nonalcoholic options, and a sample size suitable for an afternoon book club for senior women. Step-by-step: 1. I chose a “Who Is This Character?” game for my discussion of *Shrines of Gaiety* because of its many characters and murder-mystery plotlines. 2. I gathered envelopes, printed clues, a stapler, and markers from my home office. 3. I asked ChatGPT to suggest eight potential characters and select the five most important in case some members were absent. 4. I asked ChatGPT to create five clues for each selected character, along with rules explaining that the characters would be read in numerical order, with guesses allowed after each clue and no questions during the clues. 5. I used the clues and rules to run the game during the book club discussion. 6. I used ChatGPT’s character-motivation questions to guide the discussion after the game. 7. I also used ChatGPT’s 1920s Mother’s Milk cocktail recipe, including its warnings and nonalcoholic options, for the afternoon gathering.

I built Able (uable.store) because I was tired of chat tabs that explain how to do a job but leave me to paste the result into PowerShell. I wanted an agent that lives on this Windows PC and can actually move files, run tools, and write code here after I approve the actions. I work on Able, so this is the workflow I run on my own machine—not a theory. The job was to organize a Downloads folder that had become a dump and make a code change without affecting the live app. Step-by-step: 1. I open Able on Windows and describe the task conversationally: “organize my Downloads — keep installers, put photos together, leave anything I’m not sure about.” 2. I review the actions Able plans to take and let it run only after I approve them. Native file moves stay on this PC and do not use cloud credits. 3. For a code task, I enter `able job:` followed by the ticket. Able creates a separate Git worktree, writes a plan, and runs `cargo check` on that copy so the live app stays unchanged. 4. I keep secrets in the local vault, Septaguard. Cloud models—Claude, Gemini, Grok, and Kimi—receive the prompt I send, not the vault password. I enter Google and Gmail logins in the masked vault UI rather than in chat. 5. I remember that the job stops if the PC is off; there is no cloud-based substitute running in the background. 6. I verify the result on this machine: the folders are sorted, and the code check either passes or fails locally. If I only needed a sentence, I would have stayed in ChatGPT. The point of this loop is that the disk actually changes. What I learned is that “agent” is cheap until it has to touch the real folder and the real compiler. Chat is the easy half; confirm-then-act on Windows is the hard half. If you already use Claude or Cursor in a tab, what is the first job on your PC you still do by hand because the model cannot touch the files?

We adopted a two-year-old Yorkie Poo who was only partially pad-trained. Grok helped me create a daily crate-training schedule for taking her outside, and I asked it about every issue or question that came up. For example, I would take her outside and she would stand there staring at me. Grok explained that she might be trying to demand that we go back inside. It told me to turn my body away from her and avoid eye contact, which worked. She eventually toileted outside. Grok also explained that I could not expect her to use indoor pee pads and reliably go outside at the same time. She needed a strict reset schedule to break her old habits. I reported each issue to Grok, and it adjusted her schedule accordingly. I knew exactly when to take her out next and how long to wait for her to pee. She received treats only after eliminating outside. At the beginning, she was out of the crate for only 10 minutes of playtime three times a day. As her compliance improved, Grok increased her time outside the crate. Grok told me that its guidance drew on information from kennel clubs, top breeders, veterinarians, and others, so I felt I was getting high-quality direction. It took her 14 weeks to become reliably trained, and now she is out of the crate most of the time. Grok also rearranged her schedule when we had a party to attend. This is what one of her early schedules looked like: Step-by-step: 1. I took her on her morning walk after the bus as usual and rewarded outdoor elimination. After a short play session, I put her in the crate. 2. At 11:00 a.m., I took her out for a potty break. If she eliminated, she received 45–60 minutes of supervised time in sight or on a leash, followed by crate time. 3. At 2:00 p.m., I took her out again. If she eliminated, she received 30–45 minutes in sight or on a leash, then went into the crate by 2:45 p.m. 4. At 3:15 p.m., I gave her the evening meal. 5. Between 3:45 and 4:00 p.m., I took her outside for a potty attempt and stayed out long enough for her to try properly. I rewarded her only if she eliminated. 6. At 4:00 p.m., I put her in the crate. Water in the crate was fine. 7. If she did not eliminate during the 3:45–4:00 p.m. outing, I still put her in the crate and left for the party at 4:30 p.m. I took her outside as soon as I returned. 8. At 8:30 p.m., I took her out immediately after getting home, rewarded outdoor success, and crated her for the night. 9. I did not give her extra free time after 2:45 p.m. or leave her loose near the table while I got ready. The day was for management, not a training test. She could learn the house rules over time within this structure, but when I could not watch her, she stayed in the crate. I did not cancel the evening because of an unsuccessful potty attempt.

Mid- and end-of-year self-evaluations used to cost me a week or two of reconstructing a year of work from memory. I rarely recorded or remembered the “small things,” and I couldn’t easily explain how my daily work connected to our goals. People often forget their most impactful work months later, so I wanted continuous capture instead of annual reflection. I also wanted my work goals and development plan grounded in real evidence rather than guesswork. I built an “Achievement Collector” agent as a sub-agent of my Chief of Staff agent. When my Chief of Staff agent recognizes something important, it formats the accomplishment into an Achievement record, asks whether I want to record it, and sends it to the Achievement Collector agent. SharePoint is the system of record, with fields including Details, Business Impact, Key Insights, Related Goal, Significance, and a flag for accomplishments I’m especially proud of. A Power Automate flow lets me log an accomplishment conversationally instead of filling out a form. A Copilot Studio agent sits on top of the list and reads three work-goal documents—mine, my manager’s, and my director’s—so it can map each accomplishment up the chain. Its instructions tell it to prioritize what I’m proudest of, group work into themes, surface business impact, and never invent anything. The result is that my self-evaluation and goal-progress summaries are now queries against real records instead of writing projects. If you want to build one, start with the data-list fields: capturing impact and insight at the moment of the work is what makes everything downstream possible. The combination of my Chief of Staff agent and Achievement Collector has made work a lot more fun for me and turned me into a laser-focused employee. Step-by-step: 1. I created an “Achievement Collector” agent as a sub-agent of my Chief of Staff agent. 2. I configured the Chief of Staff agent to recognize important work, format it into an Achievement record, ask whether I want to save it, and send it to the Achievement Collector. 3. I created a SharePoint list as the system of record, with fields for Details, Business Impact, Key Insights, Related Goal, Significance, and a flag for accomplishments I’m especially proud of. 4. I used Power Automate to let me log accomplishments conversationally instead of filling out a form. 5. I placed a Copilot Studio agent over the SharePoint list and three work-goal documents: mine, my manager’s, and my director’s. 6. I instructed the Copilot Studio agent to map accomplishments up the goal hierarchy, prioritize what I’m proudest of, group work into themes, surface business impact, and never invent anything. 7. I now query the accumulated records when creating self-evaluations and goal-progress summaries instead of reconstructing my work from memory.

I’ve been experimenting with agentic coding for quite some time, and I’ve noticed that the tasks I let the agent handle keep getting bigger. For larger tasks and projects, I gradually moved to a standard workflow, which I published at https://github.com/evermeer/CodingAgentOrchestration in part 2 of the tutorial. I use this workflow professionally and in my side projects. My most recent side project is Quadrim, a Four in a row game played in a 4×4×4 cube. I made it open source and included all the prompts for both the code and the artwork: https://github.com/evermeer/Quadrim It took me about eight working days to publish the first version to the app stores. Step-by-step: 1. I start by describing the idea. 2. I brainstorm the idea with the agent. 3. I use `to-spec` to create a specification document for the idea. 4. I use `to-ticket` to create independent, executable tasks. 5. I review the specifications and tickets from the perspectives of a senior software engineer, architect, security officer, and product manager. 6. I implement one ticket at a time. 7. I code-review the generated implementation for each ticket. During the pre-implementation steps, I always add this instruction to my prompt: “Ask clarifying questions until any ambiguities are resolved.” Extra fine-tuning of the idea, specifications, or plan pays off during implementation. When I let the agent build code, I ask it to use TDD, perform a code review at the end, and resolve the major and critical findings.

My wife and I used ChatGPT to bring our vision for a new master bedroom to life instead of hiring a designer. We started by sketching the basic layout, which ChatGPT rendered into a floor plan. We identified the wood, the type of cut we wanted, and the finishes. Then we had a detailed conversation about what we wanted the room to look like from each side. For smaller details, we scanned Google Images and Pinterest for inspiration pictures and designs we liked. We used ChatGPT Voice for back-and-forth conversations and final tweaks, re-rendering the images as our ideas developed. We took the final images to our contractor to help him understand the higher-level vision we wanted to achieve. Step-by-step: 1. We sketched the basic layout for the new master bedroom. 2. We used ChatGPT to render the sketch into a floor plan. 3. We identified the wood, the type of cut, and the finishes we wanted. 4. We described what we wanted the room to look like from each side. 5. We scanned Google Images and Pinterest for inspiration pictures and designs. 6. We used ChatGPT Voice for back-and-forth conversations and final tweaks, re-rendering the images as needed. 7. We shared the final images with our contractor to communicate the higher-level vision for the room.

We’re narrowing down high school options for my daughter, who wants to focus on art. I’m using Astra to find public schools in our area and beyond with strong art programs and understand how they compare. Astra can research school rankings and academic results, review art courses and facilities, and bring admissions requirements, portfolio expectations, fees, and commute times into one clear comparison. It can also suggest preparation classes—such as drawing, painting, digital art, or portfolio workshops—based on what each school expects. From there, it’s helping me build a shortlist, organize open days and application deadlines, and prepare questions for school visits. We can work through the options together and find a school she’s excited about. Step-by-step: 1. I use Astra to find public schools in our area and beyond that have strong art programs. 2. I ask Astra to research each school’s rankings, academic results, art courses, and facilities. 3. I bring admissions requirements, portfolio expectations, fees, and commute times into one comparison. 4. I use each school’s expectations to identify helpful preparation classes, such as drawing, painting, digital art, or portfolio workshops. 5. I use the comparison to build a shortlist and organize open days and application deadlines. 6. I prepare questions for school visits so my daughter and I can work through the options together.

Most AI agents start with someone writing a prompt from scratch. I wanted a better way. So I built an Agent Creator. I describe the kind of agent I need, and it determines whether I actually need a new one, figures out how that agent should work, creates it, and adds it to the rest of my agent team. Step-by-step: 1. I describe what I need by telling the Agent Creator what I want the new agent to do. 2. It checks what already exists by reviewing my existing agents and skills. If something already does most of the job, it recommends improving or reusing that instead of creating another overlapping agent. 3. If a new skill is needed, it researches the role, including current best practices, common mistakes, useful tools, and what good work looks like in that area. 4. It creates the agent by defining its job, required information, outputs, available tools, and the steps it should follow. 5. It gives the agent the right skills by creating or reusing supporting skills, including examples, reference material, and checks that help it work consistently. 6. It sets clear boundaries so the agent knows what it should handle, what it should not handle, and when another agent should take over. 7. If the new agent belongs in an existing workflow, it adds the agent to the team by updating the handoffs so the other agents know when to use it. 8. Before finishing, it checks the agent’s work by running validation checks to confirm that the new agent follows the standards I’ve set for the whole team. The result is that I don’t have to manually design every new agent from scratch. I can describe the kind of help I need, and one agent can research the role, create the new specialist, connect it to the rest of the system, and make sure it’s ready to use. In other words, I built an AI agent that can help grow its own team.

I kept struggling to stay consistent with social media for my own projects. Writing captions, choosing hashtags, designing graphics, and scheduling content across platforms was taking hours every week—time I would rather spend building. So I built AutoKonnekt. You give it one sentence or a website URL, and it generates a full month of on-brand social posts, including captions, hashtags, and AI-generated images. It then schedules them across Instagram, Facebook, LinkedIn, TikTok, Pinterest, X, and Threads. The hardest part was getting the AI to sound like a specific brand’s voice instead of producing generic marketing copy. That took a lot of iteration on the prompting side. It’s live now with a free plan if anyone wants to try it: https://autokonnekt.com I’d love to hear what the community thinks. For those of you managing social media manually, which part takes the most time? I’m trying to figure out what to build next. Step-by-step: 1. I enter one sentence describing the project or provide a website URL. 2. AutoKonnekt generates a month of on-brand social posts with captions, hashtags, and AI-generated images. 3. I use the generated content across Instagram, Facebook, LinkedIn, TikTok, Pinterest, X, and Threads. 4. AutoKonnekt schedules the posts across those platforms. 5. I iterate on the prompting to make the content sound like the specific brand instead of generic marketing copy.

I'm becoming GrokBot-pilled. I run my company on top of what's happening on X. I used to check everything manually: scanning for new workflows, searching brand mentions, watching what readers complained about, and trying to remember who I had just met at events. I've now handed all four jobs to GrokBot routines. The combined setup took under an hour, and the routines save me a couple of hours of manual checking every day. Every prompt below is read-only by design: the agent watches, labels, and drafts, but never posts, replies, texts, or emails anyone. Paste each prompt as-is, then replace the bracketed parts with your own channels, brand, and time zone. Routine 1: Find real AI workflows worth stealing and filters out the hype threads. Every weekday at 9:15am in my time zone, search X and the web for new AI workflows people are actually running. Keep only cases that name the old task, the tool, and the result: time saved, a step removed, or a handoff that used to need a person. Drop product announcements, “10 prompts” threads, and anything the author has not used more than once. Return at most 3. For each, give the link, the job it replaces, what I would need connected, and whether it fits my work. Send it to me on Slack. Do not post or reply anywhere else. Save this as a routine. If nothing qualifies, send one line saying so. Routine 2: Labels every mention of my name or company and delivers the results. Every weekday at 9:00am, search X for mentions in the last 24 hours of [your brand, @handle, or your own name]. For each hit, give the author, link, a one-line summary, and whether it is praise, complaint, question, or neutral. Do not reply to anyone. Post the digest in [your Slack channel] and tag me. Save this as a routine. If a source is unavailable, say which one. (Note: works just as well for a personal brand or a job search as it does for a company) Routine 3: Catches people actively looking for what I do. Every 4 hours, search X for people evaluating [your category]. Catch three things: reviews or complaints about [category]; posts asking which [category] to use; and complaints about the problem [your product] solves. Quote the post, link it, name the account, and label it review, shopping, or complaint. Skip anything older than 24 hours, and skip [category] accounts promoting themselves. Post to [your Slack channel] only if there is at least one new hit you haven’t already shared. Do not reply to or interact with anyone. Save this as a routine. If the search fails, report the failure in Slack. Routine 4: Keeps the people I meet at events from disappearing into my contacts app. Every weekday at 8:30am in my time zone, read my address book and list contacts added in the last 14 days. For each, show name, date added, phone, email, and any note. Ask me what we talked about before you write anything. Once I reply, draft a follow-up task for each person in [your task tool] with the context attached. Never text, email, or call anyone. Save this as a routine. If the address book is unavailable, say so and do not use old data. Step-by-step: 1. Paste each routine prompt as-is. 2. Replace the bracketed fields with your own channels, brand, category, product, task tool, and time zone. 3. Save each prompt as a GrokBot routine on its stated schedule. 4. Review the read-only outputs in Slack and respond to Routine 4 when it asks what you discussed with a new contact. 5. Keep the routines limited to watching, labeling, and drafting; they should never post, reply, text, email, or call anyone.

Trivet is a recipe box for one household. I can photograph a recipe card—handwritten, printed, or a PDF—and get back a structured recipe with ingredients, quantities and units, numbered steps, prep, cook and rest times, oven temperature, and equipment. I can plan a dinner for each night of the week, and the shopping list builds itself: quantities are combined across meals, grouped by aisle, and texted to my phone. I built it because my family’s recipes were scattered across index cards in a drawer, including decades-old handwriting and cards from defunct meal services. Typing everything in was never going to happen. The entire AI workflow uses one model call. The photo is sent to Claude Opus as an image block with a Zod schema attached as the structured output format, so the response is already typed and validated—there’s no JSON parsing, retry loop, or “please respond only with valid JSON.” The extraction schema is intentionally looser than the database schema, with nullable numbers throughout. If a card has no cook time, the model returns `null` instead of inventing a plausible-looking value. The load-bearing instruction in the system prompt is that a missing cook time is a small gap, while an invented one is wrong every time somebody cooks from it. Nothing saves automatically. The extraction first appears in an editable review screen. The model also returns a confidence field, so a blurry card or a page that is not a recipe gets flagged instead of silently becoming a bad entry. Step-by-step: 1. I built a Next.js app with Postgres via Prisma and deployed it on Vercel. 2. I made the upload accept an image or PDF. I check the file size before encoding because the API limit applies to base64, which inflates the bytes by about a third; a 7 MB raw ceiling is the real limit. 3. I defined the extraction shape in Zod and sent it as the output format instead of describing JSON in the prompt. 4. I described fields in the schema. For example, `.describe("Unit as written, e.g. 'tbsp'")` does more than a paragraph of instruction. 5. I render the result into a review form and save it only when the form is submitted, never immediately after the model responds. 6. I store quantities as numbers with separate units so servings can be scaled and ingredients can be combined across recipes later. This is the decision that cannot be retrofitted easily. 7. I sum ingredients across planned meals, round at the end, group them by aisle, and send the list via Twilio. The pattern is not limited to recipes. Anything I would otherwise have to retype—receipts, notes, or forms—can use the same three moves: a schema looser than the database, a model told that `null` is better than a guess, and a review screen between the response and the database write.

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

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

I built a workflow that turned a single plain-English idea into a live, 24/7 crypto trading bot without being a developer. Claude handled the engineering; I made the decisions. Step-by-step: 1. I gave Claude the goal rather than detailed instructions. My first message was roughly: "Create me a trading bot that runs 24/7, makes faster decisions than a human, takes calculated risks, picks the lowest-fee platform, and has a dashboard with profit graphs. Make it your own." Claude designed the whole system and named it Kestrel. 2. Claude explained the setup options, including exchanges versus on-chain trading, laptop versus cloud hosting, and minimum funds. I chose BNB Chain and PancakeSwap, using a brand-new MetaMask wallet dedicated to the bot so it can never touch my other funds. 3. I let Claude build and test the bot. It wrote the strategy—holding good coins in healthy markets and moving to stablecoins in bear markets—along with hard safety limits, including a daily loss cap, emergency brake, and trailing stops. It also built cheapest-route swap selection and automated tests. When the backtests did not clear the bar we had agreed on, Claude told me honestly instead of curve-fitting. 4. Claude walked me through deploying the bot by renting a cloud server, running it in Docker, and locking the server down with Tailscale and a firewall so nothing was exposed to the internet. I only copied commands and sent screenshots when I got stuck. 5. I went live with a small amount and used a private phone dashboard showing the balance, trades, the bot’s reasoning, and Pause and Sell-everything buttons. 6. I kept improving the system through conversation. I added a “strategy league,” where 10 strategies trade on paper and can be promoted only with my approval, along with self-healing, backups, and one-tap sharing of wins and losses to X. My biggest lesson was to talk to Claude like a capable coworker: give it the goal, make the key calls yourself, and ask it to be honest about the risks.

I use a persistent AI project-orchestration workflow for small-business operations, internal tools, AI-assisted coding, specialist-agent workflows, and projects that need to survive fresh sessions. A root AI receives one bounded objective, reads the project state from files, breaks the work into phases, and delegates implementation to bounded specialist or worker agents. It verifies the results, updates the durable state, and continues until the acceptance criteria are met. This lets me run small-business and coding projects end to end without re-explaining the project every session or manually driving every handoff. The core pattern is: Objective → durable state → root orchestrator → bounded workers → verification → state update → next step → Git checkpoint. Step-by-step: 1. I give the root AI one bounded objective. 2. The root AI reads the current project state from files. 3. It breaks the work into phases and delegates implementation to bounded specialist or worker agents. 4. It verifies the workers’ results. 5. It updates the durable project state. 6. It moves to the next step and creates a Git checkpoint. 7. It continues this cycle until the acceptance criteria are met.

I used Astra to redesign my personal wardrobe. I want to dress better, but my absolute worst nightmare is having a closet full of clothes that creates more clutter in my brain. I already have enough to think about, so I built an app that decides what I should wear based on the season and local weather using GPT-6 Astra. I gave it a few full-body photos, my height, weight, sizes, and niche details such as having broad shoulders and needing to tailor my waist. I also shared the colors I like, added a master prompt, and asked it to build a site. The results were incredible. I'm the model in every photo, and Astra's virtual try-on is genuinely impressive. If I can't find a piece online, I update the model and regenerate the pictures. Step-by-step: 1. I opened ChatGPT desktop with GPT-6 Astra and Codex/Sites, then turned on Computer Use and image generation. 2. I started a new chat and uploaded four to eight full-body photos, one clear face photo, and closet photos when available. 3. I pasted the prompt below, filled in my name, city, and sizes, and let it work for one to two hours. 4. I sent a couple of correction passes instead of rebuilding the whole system. I removed things I would never wear and added things I actually wear. PROMPT: GOAL Build a working website called [YOUR NAME]'s Wardrobe. It blends two things: 1) A closed uniform system of exactly 30 looks 2) A personalized shopping portal where I am the model in every photo, Aritzia/Uniqlo catalog quality (seamless studio backdrop, full-body try-on, product-card grid) SITE NAME [YOUR NAME]'s Wardrobe THE ONLY INVENTORY - 7 summer outfits - 7 fall outfits - 7 winter outfits - 7 spring outfits - 1 gym outfit - 1 lounge outfit = 30 looks. Nothing else. Each look is complete: top + bottom or one-piece + shoes + at most 2 extras. Reuse pieces across the 7 looks in a season. Cap unique garments at 35–50 including shoes and outerwear. Throw everything else out. WHO I AM - Name: - Lives: [city] - Height: - Weight: - Sizes (top / bottom / shoe): - Body notes: - Work dress code: - Weekend life: - Style in 5 words: - Colors that work / colors I refuse: - Budget for gap-filling buys: - Hard constraints: Attached photos are the identity lock. Reproduce my real face AND real body in every try-on. Do not slim, lengthen, or beautify me. LIVE WEATHER (required, not a mock) On every page load, fetch live weather for [CITY] from Open-Meteo with no API key. Use the correct lat/long and timezone. Show on the homepage: - “Today in [CITY]” - apparent temperature, condition, rain yes/no - ONE recommended look from the 30 - why that look won - 1 weather swap (if rain starts / if it drops 5°C) Selection logic: - apparent temp ≥ 20°C and dry → Summer pool - 15–19°C dry → Spring pool Mar–May, Fall pool Sep–Nov, otherwise the closer season - 8–14°C → Fall pool, prefer the look that already includes a mid-layer - ≤ 7°C → Winter pool - rain now or daily precipitation ≥ 1mm → jacket + closed shoes, no white sneakers or silk - wind ≥ 25 km/h → prefer a layer - gym / lounge days use those uniforms, add a layer only if ≤ 10°C Never invent an outfit outside the 30. SITE STRUCTURE - Home: today + try-on + wear-this checklist - Summer / Fall / Winter / Spring: 7 look cards each, me wearing the full look - Gym / Lounge - Pieces: every unique garment on me, marked OWNED or BUY - Purge: sell / donate / trash for anything not in the system - Rotation: 4-week calendar per season. Weather can override, but it still has to be one of the 7 (or gym/lounge) VISUAL BAR Premium catalog photography. Gray/white seamless, even light, full body. Do not clone another brand’s logo. This is [YOUR NAME]'s Wardrobe. HOW TO BUILD Step-by-step: 1. Build the 30 looks from my photos, stats, climate, and closet photos. Use owned pieces first. 2. Generate consistent try-ons of me for every look and every piece. 3. Build a real clickable site. Hook live weather. Do not fake it. 4. If an image breaks my face or body, regenerate it before shipping. 5. No payments. Personal wardrobe OS only. OUTPUT Live site, the 30 looks, piece list (owned vs buy), ranked shopping list, purge list, weather mapping, and what you inferred vs what came from my photos. Start now. Make the call if a detail is missing. Only ask if the photos are unusable.

I built a workflow to select music for a senior fitness class that suits the participants’ general age range and meets the program’s criteria of approximately 120 BPM. I also require the music to be danceable and have a 4/4 beat. After a successful summer using AI-suggested, danceable Motown songs from the 1970s, I continued using AI to help with music selection. I don’t have a music background or the knowledge to do this myself, so I appreciate the help. Yesterday, a participant commented on how enthusiastic she was about the music. Her response was simple: “I love the music.” Step-by-step: 1. I considered the general age range of the senior fitness class participants. 2. I used the program’s target of approximately 120 BPM as a selection criterion. 3. I added my own requirements that the songs be danceable and have a 4/4 beat. 4. I used AI to suggest songs that fit those criteria, including danceable Motown songs from the 1970s during the summer. 5. I used participant feedback to confirm that the music was well received.

I built a daily literature quiz game from a spreadsheet containing thousands of classic literature quotes, which I previously posted on Instagram each day. I uploaded the document to Claude Code, which organized and tagged the quotes, created multiple-choice questions with increasing difficulty, and wrote short summaries of each book along with a fun fact supported by academic sources. I also vibe-coded the game’s UI/UX in a day or two, and I think it looks pretty beautiful. You can play it at play.writt.org. Step-by-step: 1. I collected thousands of classic literature quotes in a spreadsheet and posted them on Instagram each day. 2. I uploaded the document to Claude Code. 3. Claude Code organized and tagged the quotes. 4. Claude Code created multiple-choice questions with increasing difficulty. 5. Claude Code wrote short summaries of each book and a fun fact with academic sources. 6. I vibe-coded the game’s UI/UX in a day or two. 7. I published the game at play.writt.org.