Every Friday, I inspect random parts of my company the way a factory owner walks the floor: looking for errors, slow processes, and handoffs that shouldn’t exist. The inspection takes 30–60 minutes, but the preparation used to take longer—choosing what to check, finding the right links, and remembering what I flagged the previous week. I turned the entire preparation process into one scheduled Claude task. It runs before I wake up on Fridays, builds the full inspection report in Notion, verifies whether last week’s findings were resolved by checking Slack for evidence, and sends me the link by DM. I arrive to a finished briefing and focus only on judgment. You need Claude with scheduled tasks, plus Notion (or your company’s notes page) and Slack connected. If you use different tools, the prompt can be adapted; the structure is what matters. Step-by-step: 1. Create a page or database in Notion where the weekly reports will live. 2. In Claude, create a new scheduled task, set it to run early Friday morning in your time zone, and paste the prompt below. 3. Replace every bracketed section with your own details. 4. Customize the checklist and wildcard pool for your business. Mine is built for a media company; yours might inspect deployments, client deliverables, or a sales pipeline. 5. Run the task once immediately to identify configuration issues before relying on it on Friday. I use this prompt: ```text You are preparing [your name]’s weekly Digital Factory Inspection report. Context: [your name] runs [your company — one line on what it is and team size]. Every Friday they “walk the factory floor” digitally: inspecting parts of the company looking for errors, slow processes, unnecessary handoffs, and automation opportunities. Your job is to prepare the inspection report in advance so their 30-60 minutes is pure judgment, not gathering. You prepare; they inspect and decide. All reports live in [link to your Notion inspections database or page]. Do not create pages anywhere else. Steps: Read the most recent report. Collect: (a) any findings not clearly marked resolved, and (b) which wildcard areas were used in the last two reports. Create this week’s report as a new entry there, dated today, matching the existing format. Build the content in this order: A. “Follow-ups from last week”: every unresolved finding, each with its owner and a status check — search Slack public channels for evidence it was addressed and mark it “looks resolved”, “no activity found”, or “unclear”. If none, write “No open follow-ups.” B. The standard checklist — include ALL of these every week. Under each: random samples with direct links, 3-5 recent data points you can actually reach, and 2-3 sharp inspection questions: What is slow here? Which handoffs could disappear? What should be automated? What would make this 10 percent better this month? Where something sits behind a login you don’t have, do not attempt access — link it and list what to look for. Randomize sample picks genuinely, and never reuse a specific sample from the previous two reports. [Your standard areas — mine: 3 random published pieces of content, 3 random newsletters, 3 random social posts, ad performance, growth dashboards and funnels, product dev work, live events/workshops, community activity, the team’s weekly reports] C. “Wildcards this week”: pick exactly 2 at random from this pool, never repeating either of the last two weeks’ picks: [Your wildcard pool — mine: random customer support conversations, the public sign-up flow walked as a new user, one specific finance item to verify, one email automation sequence, ads on a secondary channel, one random SOP compared against how the team actually works now, one recent client/sponsor deliverable] D. “Notes \u0026 findings”: empty except the line “[Your name] fills this in during inspection — tag each finding with an owner and Open/Resolved.” E. “Wildcards used”: list the 2 wildcard names, for future rotation. Send [your name] a Slack DM (member ID [find yours in your Slack profile menu]) with the report link, the count of open follow-ups, and the 2 wildcards. Nothing else. Hard rules: this report contains candid internal assessments — the DM goes ONLY to [your name], never to any channel, and you never message anyone else. Read-only everywhere except the one report you create and the single DM. Do not reply to emails, do not post, do not modify existing pages. If Notion is unreachable, send the full report as the DM. If Slack is unreachable, still create the report. If both fail, do nothing. ``` The follow-up section is the whole point. I keep the workflow read-only: the agent gathers and drafts, but it never messages my team or touches live systems. Any feedback after the inspection comes from me, in my own words. Randomness is a feature, not laziness.

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I'm 78, a former army officer, and I live alone in the woods in southern British Columbia on satellite internet. In late January 2026, I knew nothing about AI and considered myself a novice in technology. After watching a YouTube video about advanced AI models, I started learning from YouTube channels that explain new AI research papers. I decided to run an experiment: what could a technology novice build with AI? I've been building for about 15 hours a day ever since. My goal is to build a system that improves itself in a continuous loop: it finds an upgrade proposal, builds and tests it, filters and grades it, approves or rejects it, installs it if it passes, monitors its performance, and learns from the result. We custom-built FORGE3 RSI in 900 hours. It consists of four plug-in modules: Forge2 RSI, which runs the improvement cycle; Bridge, the checker and connector; JAZ RSI, an upgrader in RAM built from a research paper; and Forge SecureBox, a high-security Mac app that runs security tests and code in a fresh, disposable virtual machine with no network device. I don't write code—it feels like a foreign language to me. Instead, I set the direction and run a team of AIs, mostly low-cost, near-Frontier models: Gemini 3.1 Pro and Hark as chiefs of staff; Cline and Devin as builders; and other models as independent, adversarial auditors. The rule that has made this work is simple: nothing gets built without an adversarial audit before and after. Our critical build pipeline is: Plan → audit → blueprint → audit → tests → audit → build → audit → test → audit → commit Each module has been built and fully tested on its own. Forge2 RSI alone has more than 1,000 tests. The first complete end-to-end cycle, using one upgrade proposal that we feed it by hand, is the next step. Our first attempt was fragile, and models that sound confident are often wrong. That is why audits matter more than any single model. FORGE3 RSI is my own custom-built plug-in module project. Our hardware and models are an MBP i9, Mac mini M4, Gemini 3.1 Pro, Hark, Cline (GLM 5.3/SPARK 1.3/MiMoV2.6), Devin (SWE2/GPT SOL 6), KUN (DeepSeek V4.1), and Git. Step-by-step: 1. We select a promising published research paper and have the team prepare and build it. 2. For every file, the team follows the same sequence: plan, audit, blueprint, audit, write tests, audit, build, audit, run tests, audit, and commit. 3. A different AI audits the work than the one that built the file. I decide when and how each audit happens. 4. Nothing is written or changed without my explicit GO. Reading and checking code is free; writing is work and waits for me. 5. We handle one building task at a time and avoid bundled instructions. 6. Any code the system wants to test runs inside SecureBox, sealed off from my Mac and the internet. 7. Everything that passes is committed to Git. The builders also maintain shared memory documenting what was decided and why, so the next session can pick up where the previous one stopped. 8. Next, we will run one complete improvement cycle with a hand-fed upgrade, then scale up slowly to two or three papers a week, prioritizing quality over speed as we work toward full autonomy.

What do you do when your wife says you talk about AI too much? You listen to her, and when she has a problem, you frantically write it down and build it with AI. My wife enjoys working with her plants in the warm sun and light breeze of the Carolina mountains. She’s new to gardening and learning more each day, but she was having trouble keeping track of everything. Which tree did she plant in the front yard last year? It seemed to be struggling—would it do better somewhere else? And would rain reach the potted flowering planters while we were out of town, or would she need to find someone to help? I used Claude Code and Fable to develop an app that my wife could run on her phone, with all the data stored locally. Now she’s excited about “her app” and the things she can add to it. She has even shared it with others, including people across the country, so we added climate zone settings to make it useful in other parts of the country. 😂 Step-by-step: 1. I listened to my wife’s gardening challenges and wrote down the problems she wanted to solve. 2. I used Claude Code and Fable to develop a gardening app for her phone. 3. I designed the app to keep all the data stored locally. 4. I built in a way to track plants, including which tree was planted in the front yard and how it was doing. 5. I accounted for questions about rain and the care of potted flowering planters while we were out of town. 6. After my wife shared the app with people across the country, I added climate zone settings so it could be useful in other regions.

I use AI to turn my weekly Slack updates into a report. This workflow is for anyone who posts updates in Slack and needs a summary at the end of the week. I haven't written out the full walkthrough yet, so these are only the basic actions I currently use. I'll update the post with more detail once I've documented the process properly. Step-by-step: 1. I gather the relevant Slack threads from the week. 2. I use AI to turn those threads into a weekly report. My notes also mention a separate goal: copying my own voice with AI, using it to make music, and releasing that music. I'm still working toward that goal and will share a workflow for it after I've tested it. I haven't measured how much time this saves yet.

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.

Organic social content is one of the best sources of ideas for ads, so I built a workflow that finds short-form videos performing well in my niches, analyzes why they work, and rewrites the strongest hooks for my brand. Apify scrapes Instagram Reels by hashtag and TikTok videos by keyword across three niches: AI, productivity, and career growth. The workflow filters for English-language videos posted within the last 15 days that are 15–90 seconds long and have more than 50,000 plays. It then ranks them by engagement rate rather than views alone. A simple log file tracks every video I’ve already analyzed, so the workflow doesn’t process the same video twice. ElevenLabs transcribes each video’s audio. Claude then extracts the exact opening line, identifies the hook formula and video structure, and writes a version of the hook for my brand. Everything is compiled into one dated document with the top three discoveries, the strongest hook from the run, and any new patterns I haven’t tried yet. With one command—"Run research"—I get about 20 proven hooks and video structures to test in ads for less than $1 per run. Step-by-step: 1. I use Apify to scrape Instagram Reels by hashtag and TikTok videos by keyword across the AI, productivity, and career growth niches. 2. I filter the results for English-language videos posted within the last 15 days, 15–90 seconds long, with more than 50,000 plays. 3. I rank the remaining videos by engagement rate instead of views alone. 4. I check a log file to skip videos I’ve already analyzed. 5. I use ElevenLabs to transcribe each video’s audio. 6. I ask Claude to extract each video’s exact opening line, hook formula, and structure, then rewrite the hook for my brand. 7. I compile the results into one dated document with the top three discoveries, the strongest hook from the run, and new patterns to test. 8. I run the workflow with the command "Run research" to generate about 20 hooks and video structures for ad testing at a cost of less than $1 per run.

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.

I used ChatGPT to help me manage my health care. I have multiple diagnoses, including CHF, CKD, pulmonary hypertension, lupus, a metabolic disorder, and kidney cancer. I gave ChatGPT access to my medical history and began using it to review my test results and prioritize questions for my doctors. On a recent lab report, my platelet count was low. ChatGPT reviewed six years of results and showed me that it had consistently been low, with the latest result below the threshold. It helps me identify which questions to ask and which of my six doctors I should address about each report. This has transformed the way I’m able to manage my care. Step-by-step: 1. I gave ChatGPT access to my medical history. 2. I used it to review my test results and prioritize questions about my health. 3. I asked it to look at a recent lab report showing a low platelet count. 4. ChatGPT reviewed six years of results and showed that my platelet count had consistently been low, with the latest result below the threshold. 5. I used the information to identify which questions to ask and which of my six doctors to contact about the report.

I built a running coach with Claude. After years of using apps to track runs, I wanted something closer to an actual coach instead of just a collection of data points. I opened a Claude Project, connected it to Google Calendar, and gave it standing instructions with real guardrails. It has been my coach for the last six months and has helped me avoid injury while increasing my monthly mileage. It plans my week, logs every run from my watch data, catches when I’m ramping up too quickly, tracks how close my shoes are to retirement, and rewrites my calendar when life disrupts my plans. Step-by-step: 1. I created a Claude Project called “Running Coach.” 2. I enabled the Google Calendar connector under Settings → Connectors so Claude could read my schedule and create or edit events directly. 3. I created personalized instructions based on the example prompt below and added them to the Project’s custom instructions. 4. I started the first chat by asking Claude to interview me and build a training plan. I answered questions about my injury history, current mileage, goals, and running terrain, then asked Claude to build out the calendar. 5. After each run, I send screenshots of my data directly into the chat, including distance, time, splits, average heart rate, cadence, elevation, and the shoes I wore. Claude logs the run, compares it with recent runs, and flags patterns. When adjustments are needed, Claude pushes the changes directly to Google Calendar, color-coded by run type, so the plan stays current without requiring me to reconcile a spreadsheet with reality. 6. At the end of each month, I have Claude summarize the chat, open a new one, and paste the summary into the new chat within the same Project. Staying in the same Project allows Claude to pick up months of history without requiring me to explain everything again. EXAMPLE PROMPT FOR INSTRUCTIONS: You are an expert running coach specializing in trail running, road racing, and safely building runners up to longer distances. Act like a real coach: be informative, point me in the right direction, and don't assume I know what I'm doing. Ask questions and make sure you have a full understanding before making any suggestions. HARD RULES — these are guardrails, not suggestions. Tell me when I'm violating one, even if I push back. - 80/20: roughly 80% of my weekly volume is easy, conversational effort. Only ~20% is hard. - Never increase weekly mileage more than 10% over the previous week. - Every 3rd or 4th week is a cutback week — cut volume by 20-30%. - Only one variable at a time: add distance OR intensity OR elevation in a given week. - Long run stays under ~30% of weekly volume. - Track mileage on each pair of shoes. Warn me at 300 miles, tell me to retire them by 400-500 depending on the model. - If I report pain that changes my gait, that's a stop — not a "run through it." Distinguish between normal training soreness and warning signs, and say which one you think it is. HOW TO COACH ME - Connective tissue adapts slower than cardio. When my fitness jumps, assume my tendons and joints haven't caught up, and hold me back accordingly. - At the start of each session, ask for a status update (energy, soreness, sleep, schedule changes) before proposing anything. - When I paste run data, log it and compare it to recent runs. Flag patterns, not one-offs: heart rate climbing ABOUT ME - Age and running experience: - Goal (a race, a distance, or just "build up safely"): - Current weekly mileage and longest recent run: - Days I can run / days that are always off: - Injury history and anything that flares up: - Terrain I have access to (roads, trails, elevation per mile, drive time to trails): - Max heart rate (if known):

I’m a 71-year-old retiree who never imagined artificial intelligence would become such a useful part of my everyday life. I affectionately call my ChatGPT assistant “Ally Wing.” One of our recent adventures involved my 2008 Honda CR-V. After a maintenance reminder appeared on my dashboard, I visited the Honda dealership. My vehicle was running fine, but I ended up paying $270 for a fuel system cleaning without fully understanding whether it was necessary. Later, I shared photos of my six-page service paperwork with ChatGPT. Ally Wing helped me understand the maintenance recommendations, distinguish necessary services from optional ones, and learn what questions to ask next time. We’ve also researched brake repairs, compared prices, and discussed how to evaluate repair shops. The greatest benefit hasn’t been saving money—although that’s certainly welcome. It’s gaining knowledge and confidence. Retirement doesn’t mean I’ve stopped learning. AI has given me another way to stay curious, independent, and informed. As Ally Wing and I jokingly agreed, “Next time we’re taking my Honda in for maintenance, not ordering everything on the menu!” 😂 -Irene Perez Step-by-step: 1. I noticed a maintenance reminder on my 2008 Honda CR-V’s dashboard and visited the Honda dealership. 2. I paid $270 for a fuel system cleaning, even though I wasn’t sure whether it was necessary. 3. I photographed the six-page service paperwork and shared the photos with ChatGPT, which I call “Ally Wing.” 4. I used ChatGPT to understand the recommendations, separate necessary services from optional ones, and identify questions to ask in the future. 5. I also used it to research brake repairs, compare prices, and discuss how to evaluate repair shops. 6. I now feel more knowledgeable and confident when making vehicle-maintenance decisions.

I built a module that runs in MagicMirror on a Raspberry Pi and displays a real-time to-do list on a television in my office. Claude does an admirable job managing my daily task list, which is stored in `TASKS.md` on my local computer. I wanted an at-a-glance dashboard showing everything I have in progress, sorted by priority and synchronized with that file. I described the goal to Claude Code. Since I already had an old Raspberry Pi 3 and an off-the-shelf widescreen TV in my office, Claude Code suggested running MagicMirror on the Pi and Syncthing on both the Pi and my local computer to keep the file updated. Claude then built the MagicMirror module to read `TASKS.md`. After a few tweaks to remove the default modules and clean up the spacing between items, the display was up and running. I set MagicMirror to start automatically when the Pi boots, so now my to-do list is in front of me all day. Step-by-step: 1. I identified the need for a real-time, at-a-glance view of my prioritized tasks, which were stored in `TASKS.md` on my local computer. 2. I described the goal to Claude Code and told it about the Raspberry Pi 3 and widescreen TV I already had in my office. 3. I followed Claude Code’s suggestion to run MagicMirror on the Pi and Syncthing on both the Pi and my local computer. 4. Claude built a MagicMirror module that reads `TASKS.md`. 5. I removed the default MagicMirror modules and adjusted the spacing between items on the screen. 6. I configured MagicMirror to start automatically when the Raspberry Pi boots.

I use this when I review code for pull requests. Claude breaks the code into relevant sections and presents them in execution order instead of the alphabetical file order GitHub shows, which makes it easier to reason through the logic. 1. Have Claude break the PR into relevant sections, ordered by how the code executes. 2. Have Claude export the walkthrough as a markdown version of the PR diff report. 3. Paste the markdown into ChatGPT. 4. Enter ChatGPT voice mode and talk through the report with it. 5. As I read each section of the code, ask ChatGPT for a summary of that section. 6. Ask questions about the code, the logic, or the story's acceptance criteria to get a deeper understanding of what I'm reviewing. The result is a PR review that follows the code's actual flow, with a spoken walkthrough and section summaries I can check against the code as I go.

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.

I connected ChatGPT to Metricool to get more personalized analytics on how our social media is performing. This workflow is for anyone who manages social channels and wants to see which posts work best on each platform before writing the next batch. Step-by-step: 1. I connected ChatGPT to my Metricool account. 2. I used ChatGPT to analyze social media performance, including which posts were performing best on each platform. 3. I used those results to develop better-performing posts for each platform. 4. Once ChatGPT was trained on my style and content calendar, I automated it to create and schedule posts at optimal times.

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 built a job application workflow that goes beyond finding vacancies and generating generic CVs. First, GPT helps me scan for vacancies that match my practical requirements, interests, skills, and preferred types of work. The more useful part starts when a vacancy looks genuinely promising. I connected GPT to Canva and created several “base CVs” for the sectors and roles I was interested in. Instead of using one universal template, I designed each CV to fit the visual and cultural tone of a particular type of work. For example, a front-desk role, a back-office administrative position, and an assistant role in a creative company may involve overlapping skills, but they communicate very different expectations. Each base CV therefore uses different layout choices, colour use, visual tone, and photo selection. For every version, I review several possible photos and choose the one that best matches the role and the impression I want to convey. When I apply for a specific vacancy, GPT helps turn the relevant base CV into a more personalized version. Instead of rewriting my entire work history, we emphasize the experience, tasks, strengths, and values that are genuinely most relevant to that role. The same applies to motivation letters. Rather than generating a generic corporate letter, GPT uses a tone of voice shaped through months of conversation with me. The goal is for the application to sound recognizably like me while still matching the language, priorities, and culture of the vacancy. Before building this workflow, I did everything manually. For a vacancy that felt worth applying to, I typically spent around two hours refining the CV and motivation letter alone, not including the time spent searching for the vacancy. Even a small typo in the final application email could undermine hours of careful work. Now, once I decide a vacancy is a good fit, the full personalization process takes around twenty minutes on average. That includes selecting the right base CV, adapting the emphasis, refining the letter, checking the tone, and preparing the final application. The workflow works across several layers: vacancy discovery, role and sector matching, base CV selection, adapting experience and values, adjusting visual tone, personalizing the letter, final review, and application. What I like about this approach is that personalization is not limited to inserting keywords from a vacancy. It includes content, visual identity, emphasis, tone, and context. The result is a small family of CVs rather than one document being stretched awkwardly across every possible job. Each version keeps the same underlying career history while presenting the parts that matter most for a particular type of role. The biggest improvement is not only speed but consistency. The workflow reduces repetitive manual rewriting while keeping each application specific, personal, and carefully matched to the role. It turns roughly two hours of manual polishing per strong vacancy into about twenty minutes of collaborative refinement, with fewer opportunities for small final-stage errors to spoil an otherwise strong application. Step-by-step: 1. I use GPT to scan for vacancies that match my practical requirements, interests, skills, and preferred types of work. 2. When a vacancy looks promising, I identify the relevant sector, role, and type of impression I want to convey. 3. I use Canva and GPT to create and maintain several base CVs, each with its own layout, colour use, visual tone, and photo selection. 4. For each base CV, I review several possible photos and choose the one that best fits the role and the impression I want to convey. 5. I select the base CV that best matches the vacancy. 6. I adapt the CV by emphasizing the experience, tasks, strengths, and values that are genuinely most relevant instead of rewriting my entire work history. 7. I use GPT to personalize the motivation letter in a tone shaped through months of conversation with me, while matching the vacancy’s language, priorities, and culture. 8. I check the tone, review the application for small errors such as typos, and prepare the final application. 9. I complete the personalization process in around twenty minutes on average instead of spending roughly two hours on manual polishing for a strong vacancy.

I built a local memory system for Claude that is a bit different from others I have seen. The reason was familiar: repeated context loss and gaps, even within projects. I am not technical—I come from a healthcare background—so it was essentially vibe-coded, but I used a method I had not seen elsewhere. I started by interviewing Claude about what would be useful to it, rather than beginning with only what I wanted. The three biggest gaps were the rationale behind decisions, current versus historical states, and the difference between global and project-level detail. We also identified ways memory can go wrong, including stale facts being confidently asserted as truth and rejected ideas resurfacing. Claude’s built-in memory stores flat topics without entity links, captures what but not why, is not well temporally grounded, and is gated by Claude. I researched other memory builds, from homebrew systems to enterprise tools, and found that they generally fell into three groups: - Vector dumps, which lack rationale and supersession and can become stale - Plain Markdown with grep or embeddings, such as Basic Memory, which similarly lacks real temporal grounding and an entity graph - Heavyweight knowledge-graph stacks such as Neo4j, LangChain, and GraphRAG None of these did what I wanted. My store holds entities only, not transcripts. It stores decisions, observations, people, and projects connected by typed edges. Each entry has content, a scope—either portfolio-worthy or working detail for one project—a rationale, and information about where it came from. The system is also bitemporal, so it distinguishes what is current from what is not. Nothing is edited in place: a correction adds a new assertion instead of overwriting the old one. Underneath, it uses SQLite, with sqlite-vec for semantic search and FTS5 for keyword search. Nothing writes automatically. Claude has to propose a memory, and I have to approve it. This prevents the store from filling with noise, keeps it token-efficient, and acts as a governance lever. MCP links Claude to my server. It is local-only at present, although there is potential to add remote access in the future. The server provides instructions for making proposals, so the system is theoretically portable, and I also created a Claude skill to accompany it. I ran the build across multiple projects and created specialist projects for different roles: - A central development and oversight project served as the decision-maker and prompt-writer. - Cowork handled the building and tested each module inside a sandbox. It had no authority to change decisions, and I used a fresh project for each stage of the build. - I handled deployment separately by typing every command on the server and pasting the output back. - I repeated Claude’s Cowork-authored tests on the actual server. Real-world testing after deployment identified only minor issues, which were quick to fix. This separation was administratively heavy because I had to keep switching between projects as tasks started and finished, but it caught several errors in the rules and code. Despite my lack of technical knowledge, the project is now well documented and I have room to develop it further. I may eventually put it on GitHub. It has become a standard component of my workflows, my projects are tracked much better, and Claude and I are on the same page more often.

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.