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I built an AI-assisted vertical grain supply-chain resilience workflow after discovering that the rye flour we normally use for homemade bread had been discontinued. I started by asking ChatGPT to locate alternative retail and bulk suppliers for rye and spelt flour. We compared package sizes and prices, contacted local mills and bulk-food suppliers directly, and expanded the search from finished flour to locally available whole grain. The workflow then moved offline. ChatGPT helped us identify the resources we already had: agricultural land, grain bins, a cultivator, a seeder, an old but functional industrial grain crusher, and even a combine. We also realized that a friend owns a seed-cleaning plant and that our farming neighbors can connect us with local rye and spelt growers. Our next steps are to test whether a household coffee grinder can produce sufficiently fine flour from grain, source fresh food-grade rye and spelt locally, and investigate growing a small crop ourselves. The biggest lesson was that AI was most useful not because it gave us one answer, but because the conversation kept changing the question. We began with “Where can I buy rye flour?” and ended with “Why are we buying rye flour when we already have most of the infrastructure required to produce it ourselves?” Step-by-step: 1. I asked ChatGPT to find alternative retail and bulk suppliers for rye and spelt flour after our usual rye flour was discontinued. 2. We compared package sizes and prices, contacted local mills and bulk-food suppliers directly, and expanded the search from finished flour to locally available whole grain. 3. We reviewed the resources already available to us, including agricultural land, grain bins, a cultivator, a seeder, an old but functional industrial grain crusher, and a combine. 4. We identified additional local resources: a friend’s seed-cleaning plant and farming neighbors who can connect us with local rye and spelt growers. 5. We plan to test whether a household coffee grinder can produce sufficiently fine flour from grain, source fresh food-grade rye and spelt locally, and investigate growing a small crop ourselves. 6. We reframed the question from where to buy rye flour to whether we could produce it ourselves using the infrastructure already available to us.

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 spent twenty years producing documentary and nonfiction films, all of it the expensive way: crews, schedules, financing, and commissioners saying no. I'm proud of much of that work, and I still think about the films that never got made because the money wasn't there. Generative video pulled me in, but not for the reason people assume. It wasn't the speed or the cost. It was the fact that I could finally finish something without asking anyone for permission. No green light from a major platform. Nobody deciding that a story was too small to deserve a crew. A year later, I could produce this way. It turned out to be a different job rather than the same job with new tools. Then I ran into a problem I hadn't considered: how do you show the work? A finished shot doesn't prove much anymore. A client can look at a beautiful frame and have no way to tell whether it took three weeks or three minutes. If I'm honest, neither would I in their position. The thing every portfolio is built to display had stopped being evidence. I'm not a developer. I produce films. The site is bilingual, has seven sections, includes a case study for each film, plays video on hover, and has a comparison slider that works with a thumb as well as a mouse. I built all of it. I'd wanted this exact site for about fifteen years and had never managed to explain it properly to anyone I paid to make it. This time, I stopped explaining and built it myself. The middle of the page has a slider that you drag between the storyboard panel and the final shot. A basic before-and-after would prove nothing; you can fake that by generating twice. The storyboard is the proof because the framing, eyeline, and decision about what stays outside the frame all existed on paper before any model was asked to generate anything. You drag the handle and watch the intention survive. The rest of the site came from the same place. Video only plays on hover and is silent until you click for sound. Most video portfolios are unreadable because fifteen things start moving at once. The sections are organized by register rather than by client: epic, brand, animation, and lifestyle. A wall of logos answers who has hired you, which nobody is actually wondering. What they want to know is whether you can change tone. Nothing is cropped to fit the grid. A 5:33 film sits in 16:9 next to a 2:42 film in scope, and the layout is lopsided because of it. I nearly made them uniform because it looked tidier, then realized I was about to reformat my own films so a webpage would look neat. Anyone who cuts for a living spots that immediately. Concepts are labeled as concepts. Experiments have their own section instead of being scattered among the client work and hoping nobody asks which is which. Step-by-step: 1. I started from what my client couldn't verify: the craft behind a shot. 2. I found the artifact that proves it. It's usually the ugly thing nobody publishes: the storyboard, the reference sheet, or the version before the good version. 3. I put that artifact next to the finished piece inside one interaction, so nobody has to search for the proof. 4. I made playback deliberate. Video plays on hover, and sound requires a click. 5. I sorted the work by the question being asked rather than by the credentials I wanted to lead with. 6. I didn't reformat the work to fit the layout. I let the grid be uneven. 7. I labeled everything honestly: client, concept, or experiment. One word each can pay for the credibility of the whole page. 8. I wrote a brief for each section as a document first. Arguing with a paragraph is free; arguing with a built page is not. 9. I built the site with an AI coding tool. This was the least interesting step, even though it's the one everyone writes about. If your work can be mistaken for a lucky prompt, stop trying to prove it with the finished piece. The site is at www.termopilas.tv if you want to drag the handle yourself.

I built a custom AI knowledge-transfer GPT designed to prevent institutional knowledge from walking out the door when an experienced employee leaves. The trigger was an experienced operations manager preparing to leave the organization. I realized that while we had procedures, files, emails and account documentation, a huge amount of operational knowledge existed only in that manager’s head: customer preferences, recurring staffing problems, site-specific quirks, key relationships, historical issues, workarounds, lessons learned and the small details that can take a replacement months to discover. My goal was to capture that knowledge and turn it into an interactive resource for the incoming operations manager. First, I conducted and recorded an in-depth interview with the departing manager. Instead of only asking standard turnover questions, I had them walk through the operation as if they were personally training their replacement. We discussed customers, employees, locations, scheduling, recurring problems, escalation procedures, communication preferences, historical decisions and things they believed a new manager might not realize immediately. I then transcribed the conversation and used AI to analyze the interview for knowledge gaps. I asked the AI to approach the transcript from the perspective of someone taking over the job and identify important questions that had not yet been answered. Using those gaps, I had AI create customized knowledge-transfer questionnaires specifically for the departing manager. These asked more targeted questions such as: What problems happen repeatedly? What customer preferences are not documented? What exceptions exist to normal procedures? What mistakes is a new manager likely to make? What information exists only in your memory? What would you make sure your replacement understood during their first 30 days? The departing manager completed those documents, giving me another layer of institutional knowledge that would normally be lost. Next, I organized the interview transcript, completed questionnaires, operational procedures, account information, contacts, historical notes and other relevant documents into a knowledge base. I uploaded that information into a custom GPT and instructed it to function as an operational knowledge-transfer assistant. The GPT was told to base answers on the captured information, not invent answers when information was missing, and clearly tell the user when something needed to be verified. The incoming operations manager can now interact with that knowledge conversationally. Instead of searching through folders or wondering who to ask, they can say things like, “I’m meeting with this customer tomorrow. What should I know?”, “Has this location had staffing problems before?”, “Why do we handle this account differently?”, or “What should I watch for during my first month?” The GPT is now being used by the incoming manager as an ongoing reference tool. The result is essentially a searchable, interactive version of the institutional knowledge that previously would have disappeared with the departing employee. It reduces the “I don’t know what I don’t know” period of starting a new position, shortens the learning curve, improves operational continuity and helps prevent the next person from having to relearn years of lessons through trial and error. The same workflow could be recreated for retiring executives, salespeople, plant managers, administrators, project managers, maintenance supervisors or anyone whose experience contains valuable institutional knowledge. The employee can leave. Their knowledge doesn’t have to.

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’m a very unsophisticated user—and an older man—so my use of ChatGPT is still fairly basic. But it has changed my life. I use ChatGPT as a research assistant for a book I’m writing. In practice, what I do is not much more than an in-depth search, but ChatGPT has taken me places in this journey that I would have thought impossible just three months ago. We’re now deeply engaged in solid research that will likely last about two years before we reach the writing phase. I’m energized by what we’re doing and wanted to share the experience. I believe AI is going to change the way working historians conduct research. Step-by-step: 1. I use ChatGPT as a research assistant for the book I’m writing. 2. I use it for in-depth searching and research. 3. I continue developing the research with ChatGPT over an expected two-year period before beginning the writing phase. 4. I reflect on how this process is changing my research journey and the way historians may conduct research.

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

I 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 SITREP, a self-hosted AI RSS reader that turns roughly 30 articles a day into a single briefing I can act on. It runs in a Docker container on my home NAS, ingests full article text instead of the teasers most feeds provide, and uses OpenAI embeddings to collapse the same story reported by five outlets into one item. Every morning, it writes “The Brief” with the sections Headline, Defense & Aerospace, AI & Tech, and Elsewhere, followed by the two sections I care about most: Implications for my company and Personal Leverage. Each section includes numbered citations linking back to the source articles. SITREP only proposes leverage when the evidence supports it. It also uses a curated, non-sensitive profile of my business lanes and priorities, synced from my Obsidian vault, so its recommendations are specific to my work as a defense-industry VP rather than generic advice. I can select Update Brief at any time during the day to regenerate the briefing in place with a timestamp. Step-by-step: 1. I run SITREP in a Docker container on my home NAS. 2. I have it ingest the full text of roughly 30 articles each day rather than relying on feed teasers. 3. I use OpenAI embeddings to identify and combine the same story when it is reported by multiple outlets, including cases where five outlets cover it. 4. Each morning, SITREP generates “The Brief” with the sections Headline, Defense & Aerospace, AI & Tech, and Elsewhere. 5. It adds Implications for my company and Personal Leverage, using my curated, non-sensitive business profile and priorities synced from Obsidian. 6. It includes numbered citations to the source articles and proposes leverage only when the evidence supports it. 7. I select Update Brief during the day when needed, and SITREP regenerates the briefing in place with a timestamp.

I love reading, but I’m indecisive about what to read next. Lately, I’ve been getting recommendations from ChatGPT, which has led me to keep adding books to my to-read list instead of tackling the books that have been there for years. At the same time, searching through my 200 existing TBR books on Goodreads for one that matches my precise mood would be too time-consuming. To solve this, I exported all the books from my Goodreads bookshelf as a CSV file, uploaded it to ChatGPT, and asked it to recommend books from my “to-read” list that matched a description of what I was currently in the mood to read. For example, I asked it to recommend a book from my TBR list that was published in the 19th or 20th century and featured a strong, unforgettable heroine. It gave me a few options, and I decided to read *O Pioneers!* by Willa Cather next. One note: ChatGPT summarizes the plots of the books it recommends, so I suggest adding “no spoilers, please” to your prompt. Step-by-step: 1. I exported all the books from my Goodreads bookshelf as a CSV file. 2. I uploaded the CSV file to ChatGPT. 3. I described the kind of book I was currently in the mood to read, including publication period and character preferences. 4. I asked ChatGPT to recommend books only from my existing Goodreads “to-read” list. 5. I added “no spoilers, please” to prevent plot summaries from revealing too much. 6. I chose *O Pioneers!* by Willa Cather from the recommendations.

I built a self-hosted wine cellar app that runs in a Docker container on my home NAS and doubles as a personal AI sommelier. I add a bottle by taking a photo of its label with my iPhone. A vision model identifies the wine, vintage, region, and grape varieties, while a second model estimates its market value and writes tasting and pairing notes. The app renders a visual map of my rack, so I can see exactly where each bottle is and pull one without hunting. My ratings feed a taste profile built from my own history, which the AI sommelier chat uses to answer questions such as what to open with dinner or which bottles are drinking at their peak. The feature I use most is Scan & Check. When I’m in a store, I photograph a bottle and get a Collection Fit score based on my profile before buying it. The app also exposes an MCP endpoint, so Claude can search my cellar, score a wine, or list my top-rated bottles from any conversation. I built it almost entirely with Claude Code and use it daily from my phone. Step-by-step: 1. I run the wine cellar app in a Docker container on my home NAS. 2. I photograph a bottle’s label with my iPhone when adding it to the collection. 3. A vision model identifies the wine, vintage, region, and grapes, and a second model estimates market value and generates tasting and pairing notes. 4. I use the app’s visual rack map to locate bottles by slot. 5. I rate wines so the app can build a taste profile from my history. 6. I use the AI sommelier chat to choose what to open, including bottles that are drinking at their peak. 7. I use Scan & Check in stores to photograph potential purchases and review their Collection Fit score. 8. I connect to the app’s MCP endpoint so Claude can search my cellar, score wines, and list my top-rated bottles from a conversation.

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

I use ChatGPT to support a hobby that combines research, negotiation, history, craftsmanship, and family legacy: collecting mechanical watches. The workflow usually begins when I discover a watch that interests me. Rather than simply asking whether it is a good watch, I use ChatGPT as a research analyst. We investigate the exact reference, movement, production history, complications, materials, rarity, manufacturer history, comparable watches, secondary-market pricing, previous sales, and any known weaknesses or servicing concerns. With unusual independent watches, this can become surprisingly deep research. The goal is to answer two separate questions: Is the watch genuinely interesting? And is this particular example worth buying at this price? If I decide to pursue a watch, ChatGPT shifts into the role of negotiation adviser. I share dealer listings, asking prices, trade proposals, emails or text messages, previous offers, comparable sales, and my own walk-away price. We discuss likely dealer economics, negotiating leverage, possible counteroffers, and how aggressively or patiently I should approach the transaction. ChatGPT also helps draft correspondence, but the final decision remains mine. I find the greatest value in having an analytical partner that remembers the research and can challenge my enthusiasm before I spend money. After acquiring a watch, I create an entry in a private document I call my Horological Codex. Each entry includes the technical specifications and historical information, along with why I chose that particular watch, how I acquired it, its price, its appraised value, what was happening in my life at the time, and why it deserves a place in the collection. One early watch commemorates an educational milestone. Another is a one-of-one piece commissioned directly with an independent watchmaker. Other watches connect to family events, travel, my cars, personal interests, or particular periods of my life. The Codex preserves those stories and memories alongside the watches themselves. My original Codex eventually became a large, 70-page formatted document, and maintaining it manually became cumbersome as watches entered and left my collection. My next version will separate the structured information from the presentation layer: I will maintain a master record for each watch, then use AI to generate or update the polished Codex from that underlying data. That way, the collection can evolve without requiring me to rebuild an entire book every time something changes. I keep the full Codex private because it contains family and financial information, but the system itself has become a meaningful way to preserve the history behind the collection. Step-by-step: 1. I identify a watch that interests me and describe the exact reference and available listing information to ChatGPT. 2. I research the watch’s movement, production history, complications, materials, rarity, manufacturer, comparable watches, secondary-market pricing, previous sales, and known weaknesses or servicing concerns. 3. I evaluate both whether the watch is genuinely interesting and whether the specific example is worth buying at the asking price. 4. If I decide to pursue it, I share the dealer’s listing, asking price, trade proposals, correspondence, previous offers, comparable sales, and my walk-away price with ChatGPT. 5. I use that information to discuss dealer economics, negotiating leverage, counteroffers, and the right negotiation approach, then make the final decision myself. 6. After acquiring the watch, I record its technical details, history, acquisition story, price, appraised value, personal significance, and place in the collection in the Horological Codex. 7. I maintain a master record for each watch so AI can eventually generate or update the polished Codex without requiring me to rebuild the entire 70-page document whenever the collection changes.

I use this workflow to handle new business enquiries by connecting AI to the actual business process instead of using AI as a standalone chatbot. The process moves each lead from AI qualification to a CRM update, sales notification, and follow-up. AI extracts the service required, budget, location, and urgency, then routes the lead based on the result: - High intent: Notify sales immediately - Medium intent: Start an automated follow-up - Low intent: Add the lead to a nurture sequence Step-by-step: 1. Capture the new business enquiry. 2. Use AI to extract the service required, budget, location, and urgency. 3. Update the CRM with the qualification details. 4. Route the lead according to its intent level. 5. Notify sales immediately for high-intent leads, start automated follow-up for medium-intent leads, and add low-intent leads to a nurture sequence.

I’m building an AI travel assistant called SundayAtlas, and I wanted a systematic way to keep learning from what travelers are talking about between individual user conversations. Reddit is useful for this because people are unusually candid about trip-planning frustrations, destinations, bad experiences, and what they wish travel products did better. The problem is volume: I didn’t want to manually read hundreds of posts every week, so I built a Reddit Signal Agent that does the first pass and sends me a weekly travel-insights newsletter. Each week, the workflow collects posts from selected travel subreddits and passes them through an LLM-based classification and analysis pipeline. The report is organized around: - New or intensifying signals - Steady baseline themes - Fading signals - Rising destinations - Competitor mentions - Anomalies unusual enough to warrant attention This week, for example, the agent analyzed 77 posts. It surfaced a spike in discussion around short-term rental restrictions in Tokyo, growing payment friction for travelers in Japan, increased interest in quieter alternatives to heavily touristed Asian destinations, and recurring trust issues involving travel platforms. I use the newsletter as one input into product discovery for SundayAtlas. It gives me a weekly pulse on problems and behaviors that may be worth investigating further, rather than relying purely on my own assumptions about what travelers need. Step-by-step: 1. I collect recent posts from a defined set of travel subreddits. 2. I clean and structure the Reddit data for analysis. 3. I run the posts through an LLM using a defined signal taxonomy. 4. I aggregate the classifications across the weekly sample to identify patterns, changes, and anomalies. 5. I generate the report in a consistent newsletter format. 6. I run the full pipeline automatically with GitHub Actions so a new report is produced each week. I built the agent in Node.js and used Claude Code extensively during development. Evaluation ended up being the most important part. Early outputs looked convincing, but I had no objective way to know whether the classifications were actually good. I manually labeled 91 Reddit posts and created a blind golden dataset, then built a deterministic scorer to compare the agent’s classifications with my labels. The first held-out evaluation scored only 0.23, which gave me something concrete to improve against. I iterated on the classification approach and inspected individual failures. Along the way, I found three separate defects in the data collection pipeline. The held-out score eventually improved to 0.61, while the score across the full dataset increased from 0.33 to 0.67. The finished loop is: Reddit conversations → signal classification → trend analysis → weekly insights newsletter → product discovery for SundayAtlas The golden dataset remains underneath the workflow as a regression test, so when I change the agent, I can measure whether I’ve actually improved it.

Most businesses don't need another AI tool. They need to automate the repetitive bottleneck already slowing the team down. A simple example is connecting the tools already in use: Lead → AI qualification → CRM → WhatsApp → follow-up Instead of adding more software, automate the repetitive steps between these tools. The goal isn't more AI; it's less manual work and faster execution. Step-by-step: 1. Identify the repetitive bottleneck slowing the team down. 2. Capture the lead and use AI to qualify it. 3. Send the qualified lead to the CRM. 4. Use WhatsApp for follow-up. 5. Connect the steps so the process requires less manual work and supports faster execution.

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.

We needed legal help with a small-claims case. A company owed us $3,500, and the cost of hiring a lawyer would have far exceeded that amount. I used Grok to explain what to do and how to file the paperwork. Last week, I served three writs of garnishment on banks that might hold the company’s money. Whichever bank has its account will now have to freeze the account and turn that amount over to us. Before AI, I would have had no idea how to do this on my own. Grok scans my completed documents for errors and directed me to the correct county sheriff’s office to have the papers served. It can explain things more simply when I don’t understand them. Need legal help in your state? AI can guide you step by step. Step-by-step: 1. I explained the small-claims situation to Grok, including that the company owed us $3,500 and that hiring a lawyer would cost more than the amount owed. 2. I asked Grok what to do and how to file the paperwork. 3. I used Grok to scan my completed documents for errors. 4. I followed Grok’s direction to the correct county sheriff’s office for serving the papers. 5. I served three writs of garnishment on banks that might hold the company’s money. 6. The bank holding the company’s account will have to freeze the account and turn the amount owed over to us.

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.

I tried using Claude and it lost context of the whole project quickly and became a nightmare of human in the loop meat proxying. It made the exercise far from autonomous.