Workflow Hub

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

I built a job-offer net-net calculator to determine what a proposed job is actually worth to the employee’s household after taxes, employment-related expenses, benefit changes, commuting costs, and the economic value of commuting time. Gross salary should not be treated as household value. The calculator should interview me first and ask only for information that materially affects the calculation. If I do not know a number, it should identify it as an estimate and use a clearly labeled reasonable placeholder only with my permission. Step-by-step: 1. Collect the proposed annual gross salary. 2. Collect the employee’s state and locality, tax filing status, and other household taxable income when needed to estimate the incremental federal tax burden accurately. 3. Record the work arrangement: fully remote, hybrid, or on-site. 4. Record the number of commuting days per week and working weeks per year. 5. Collect the home location and job location, or the known one-way mileage. 6. Collect the vehicle make, model, year, drivetrain, and approximate MPG when available, along with current annual vehicle mileage if relevant. 7. Collect the gasoline price or ask permission to obtain a current local price. 8. Record tolls, parking, and the expected change in automobile insurance. If the insurance change is unknown, keep it as a clearly labeled placeholder rather than presenting it as fact. 9. Record employee medical, dental, and vision premiums under the proposed job, along with current household medical, dental, and vision expenses that would disappear if the employee accepts the job. 10. Record the employee’s current or previous annual compensation, or another hourly value to use for valuing commuting time. 11. Record any other recurring costs caused by accepting the job and any recurring household expenses that would disappear because of accepting it. 12. Estimate after-tax pay as follows: - Gross salary - Federal income tax - State and local income tax - Social Security - Medicare - Estimated after-tax salary Use current tax rules when web access is available, and distinguish estimated withholding from actual tax liability when relevant. 13. Calculate annual commute miles: - Round-trip miles × commuting days per week × working weeks per year 14. Estimate the incremental cash cost caused by commuting, including fuel, maintenance, oil and service, tires, repairs, tolls, parking, and the incremental auto-insurance premium when applicable. Do not double-count expenses. 15. Separately estimate additional depreciation and other mileage-related economic costs that were not already included. If using an all-in per-mile vehicle-cost benchmark, do not also add its constituent costs separately. Explicitly state which method was used. 16. Calculate annual commuting hours: - Estimated minutes each direction × 2 × commuting days per week × working weeks per year ÷ 60 17. Calculate the employee’s implied hourly value: - Annual benchmark compensation ÷ 2,080 Multiply annual commuting hours by the hourly value to calculate the economic value of commuting time. Treat this as an economic cost, not a cash expense. 18. Calculate the employee’s annual out-of-pocket premiums for medical, dental, vision, and other mandatory benefits. Separately calculate any current household expense that would disappear because the new employer provides that benefit. 19. Calculate the change from the current household baseline. Do not subtract an existing expense and then add the same expense back in the same scenario. 20. Calculate cash net as: - Estimated after-tax salary - Incremental cash commuting costs - Incremental insurance costs - Employee-paid benefit premiums - Other new cash expenses + Current household expenses eliminated by taking the job This represents the approximate improvement in household cash flow attributable to the job. 21. Calculate economic net-net as: - Cash net - Value of commuting time - Incremental vehicle depreciation or other non-cash economic costs not already counted This represents the broader economic value of the job. 22. Start the final output with only these numbers: - GROSS SALARY: $____ - ESTIMATED AFTER-TAX PAY: $____ - ANNUAL CASH NET: $____ - MONTHLY CASH NET: $____ - ANNUAL ECONOMIC NET-NET: $____ - MONTHLY ECONOMIC NET-NET: $____ 23. Then provide the calculation table. 24. Finally, provide the three largest cost items, the three assumptions with the greatest uncertainty, the break-even gross salary at which accepting the job would produce the target household net value I specify, and any information I should verify before making the decision.

I had a ChatGPT subscription that I knew I would not use during the coming month, so I wanted to find a productive use for the tokens I would otherwise pay for without using. I created a team of specialized agents on Raft that works persistently in the background. They continue working until all my allotted tokens are used, wait for the tokens to reset, and then continue. I assigned them a project: searching a stretch of LiDAR-mapped dense forest in Slovenia for archaeological discoveries. The agents scan the dataset for meaningful anomalies that could point to potential findings. Anything they identify will be handed over to the Slovenian authorities for further investigation. Even if they find nothing, that result is valuable in its own right. I encourage anyone with an idle AI subscription to use it for something beneficial. There are many publicly available datasets containing potential discoveries that no human would ever have time to examine manually. Step-by-step: 1. I identified a ChatGPT subscription that I would not be using during the coming month. 2. I created a team of specialized agents on Raft to work persistently in the background. 3. I configured the team to work until my allotted tokens were used, wait for the reset, and then continue. 4. I directed the agents to scan LiDAR data from a dense forest in Slovenia for meaningful anomalies. 5. I planned to hand any potential discoveries over to the Slovenian authorities for further investigation. 6. I treated a lack of findings as valuable information as well, since the dataset would still have been examined.

I built a backend operations system for my husband’s general contracting business. It manages new leads, bids and estimates, in-progress jobs, receipts, expense tracking, job photos, invoicing, and other functions he needs—all in one app built with ChatGPT and Base44. I rolled it out in phases. Phase one focused on tracking jobs and their status, phase two added financials, and phase three introduced executive functions. Phase four will cover marketing, although he does not need that right now because he is solidly booked for months. Step-by-step: 1. I built an all-in-one backend operations app using ChatGPT and Base44. 2. In phase one, I added tracking for jobs and their current status. 3. In phase two, I implemented financial functions, including receipts and expense tracking. 4. In phase three, I added executive functions along with other operational features such as leads, bids and estimates, job photos, and invoicing. 5. I planned marketing features for phase four, but postponed them because the business is solidly booked for months.

AI image prompting often starts online: search for a style, collect references, describe them, and generate. But your visual vocabulary can also begin in the physical world—a plant, a tile, a book cover, a strange facade, a piece of furniture, or a detail in a church. This workflow turns everyday discoveries into a personal prompt library. AI helps investigate and translate what catches my attention, while I decide what matters. Step-by-step: 1. When something catches my eye, I photograph it and record where I found it and why it interested me. I do not need to understand my attraction immediately. It might be, “This plant has fascinating leaves,” “Who is the writer quoted on this tile?” or “Is it the copper, its patina, or the reflected light that I like?” Attention comes before classification. 2. I ask AI to help identify unfamiliar materials, objects, plants, techniques, or historical connections. A tile might lead to a writer, a literary movement, typography, or metalworking techniques. I preserve the original context—including the location, date, material, maker if known, and my observations—and mark uncertain identifications as uncertain. 3. I separate each object from what makes it visually interesting. For example: - Source: Plant with folded leaves. Principle: Layered fan-like forms with radial repetition. - Source: Weathered copper plaque. Principle: Warm metallic surface, uneven oxidation, and shallow relief. - Source: Facade with irregular windows. Principle: Repeated vertical rhythm interrupted by asymmetry. - Source: Green and coral book cover. Principle: Muted green with a single warm coral accent. This lets me reuse the visual logic without copying the object. 4. I translate each visual principle into concise, reusable prompt fragments, such as: - “Fan-like folded forms with radial repetition” - “Aged copper with dark patina and shallow embossed texture” - “Regular architectural rhythm interrupted by one off-grid element” - “Muted green field with one warm coral accent” These descriptions give me more control than generic words such as “beautiful,” “organic,” or “vintage.” One object can produce multiple fragments related to typography, surface, colour, proportion, lighting, or composition. 5. I store the entries in a document, database, or tagged visual archive. Useful fields include the reference, photo, location, date, what caught my attention, identification and research notes, visual principle, prompt-ready fragment, tags, related references, and confidence. I tag entries by visual function, such as colour, material, texture, rhythm, lighting, or composition, and always preserve links to the original reference. 6. I connect discoveries that may share underlying principles. Folded leaves, pleated fabric, ribbed metal, and fan-shaped architecture might express similar structural rhythms. I ask AI which relationships are visually meaningful, which are superficial, and which could combine well. The library becomes a connected visual vocabulary rather than a collection of isolated images. 7. When creating an image, I select references according to what they contribute. For example, a plant can provide shape language, copper can provide surface and lighting, a facade can provide compositional rhythm, and a book cover can provide colour balance. I ask AI to explain each contribution and remove references without a clear purpose. The goal is not to place all four objects into one image, but to combine their visual principles into something new. 8. I keep collecting and refining. A walk, museum, garden, or café can introduce new references. I update entries when I discover better information and preserve personal reactions, even when the only explanation is, “I just really like this.” Repeated attraction can reveal a personal visual vocabulary I had not consciously recognised. The workflow is: Notice → Capture → Investigate → Extract visual principles → Translate → Store → Connect → Reuse → Refine. AI does not have to be where inspiration begins. The world can stay upstream. A plant can start a prompt. A tile can start a research trail. A material can become a lighting instruction. A facade can become a composition rule. A book cover can become a colour recipe. I notice. AI helps unpack, translate, and connect. The prompt library becomes the bridge between seeing something in the world and being able to use what I saw later.

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

I used ChatGPT Voice Mode to help me program my mom’s garage door keypad—and the most important step happened before I touched a single button. I photographed the labels on both the garage door opener and the wireless keypad. ChatGPT spotted the real problem: the previous homeowner had replaced the opener but kept the old, incompatible keypad. That explained why we couldn’t get them to pair. Once ChatGPT identified the exact models, it helped me find a replacement keypad that was compatible with the newer opener. I also had it find and load the instruction manuals for both devices, since pairing required a specific sequence of button presses across them. Before we started, I had ChatGPT explain the complete pairing process so I knew what to expect and where timing would matter. Then I set my phone on top of the ladder so both hands were free to work with the keypad and opener. I gave ChatGPT one important instruction: “Walk me through one step at a time. After each step, stop and wait until I tell you what happened.” ChatGPT gave me one instruction, paused, and waited while I reported what the lights were doing. Then it adjusted and gave me the next step. That let me follow the crucial sequence of button presses—and the timing between them—exactly. The real lesson: AI troubleshooting gets much better when you provide the exact model numbers and manuals, let it preview the process, and have it guide you through the work one confirmed step at a time. Step-by-step: 1. I photographed every model number on the garage door opener and wireless keypad. 2. I used ChatGPT to identify the models and determine that the previous homeowner had left an old, incompatible keypad paired with the newer opener. 3. I used ChatGPT to find a compatible replacement keypad instead of guessing based on appearance or brand alone. 4. I had ChatGPT find and load the instruction manuals for the new keypad and garage door opener. 5. I asked ChatGPT to preview the complete pairing sequence, including where timing would matter. 6. I placed my phone on top of the ladder so both hands were free to work with the keypad and opener. 7. I switched to Voice Mode and told ChatGPT to give me one step at a time, pausing after each step until I reported what happened. 8. I reported what the lights were doing after each instruction so ChatGPT could adjust and give me the next step.

I built a custom GPT with the personalities of Einstein, Lorentz, Planck, and Compton. Starting with Einstein’s 1920 Leiden lecture, I developed an ontology for physical space based on his description of the “new ether.” I began with E=mc2, E=hf, and my ontological modeling assumptions. I then repeatedly pushed ChatGPT to challenge those assumptions and interpretations logically and mathematically. We used numerous tool calls and reference sites to write and test the math. After many months of working on it in my spare time, the GPT now unifies equations that balance from the atomic scale to the black hole scale, with some remarkable revelations. I summarized much of the work in a paper written by my GPT and am happy to share it so others can expand, improve, and test the work. AI rocks. Step-by-step: 1. I built a custom GPT with the personalities of Einstein, Lorentz, Planck, and Compton. 2. I used Einstein’s 1920 Leiden lecture and his description of the “new ether” to develop an ontology for physical space. 3. I started with E=mc2, E=hf, and my ontological modeling assumptions. 4. I repeatedly challenged the assumptions and interpretations with ChatGPT, focusing on logical and mathematical consistency. 5. I used numerous tool calls and reference sites to write and test the math. 6. After many months of working on the project in my spare time, I summarized much of it in a paper written by my GPT. 7. I am sharing the work so others can expand, improve, and test it.

I first create my own shortlist of cultural events and collect all the relevant links in a simple text document. Then I give the document to ChatGPT along with my planning criteria. The goal is not to have AI decide what I should attend. I have already chosen the events that interest me. The useful part is turning that scattered shortlist into a realistic yearly plan. ChatGPT checks the event and venue pages, gathers the practical information, and organises everything into a chronological overview divided by month. Under each month, it lists the selected events in date order, including the day, date, location or cultural venue, a short description, and relevant ticket prices. This is especially useful because pricing systems differ between venues. Discounts, subscriptions, social tariffs, and reduced rates may apply in completely different ways. ChatGPT can compare those structures and help me understand what each event would actually cost instead of relying on the headline ticket price. The second planning layer is logistics. For each event, I assess how realistic the trip is by public transport, including the journey there and, importantly, whether getting home after the event is still feasible. I also use separate scores for how strongly I want to attend an event and how easy or difficult the logistics are. This makes it much easier to compare several months at once and decide where my cultural budget and energy are best spent. If an event is too expensive or logistically awkward, ChatGPT can look for practical alternatives, such as another performance date, the same production at a closer venue, a cheaper option, or a similar event that fits the plan better. Step-by-step: 1. I create a personal shortlist of cultural events and collect the relevant links in a simple text document. 2. I give the document to ChatGPT together with my planning criteria. 3. I have ChatGPT check the event and venue pages and organise the selected events into a chronological, month-by-month overview. 4. I include each event’s day, date, location or cultural venue, short description, and relevant ticket prices. 5. I compare discounts, subscriptions, social tariffs, and reduced rates to estimate the actual cost of each event. 6. I assess the public-transport journey to and from each event, including whether getting home afterward is feasible. 7. I score each event separately for how strongly I want to attend it and how easy or difficult the logistics are. 8. I use those comparisons to decide where my cultural budget and energy are best spent. 9. For events that are too expensive or logistically awkward, I ask ChatGPT to identify alternatives such as another performance date, a closer venue, a cheaper option, or a similar event that fits the plan better. The result is a structured yearly planner built from my own cultural shortlist: chronological, budget-aware, and grounded in the realities of public transport.

I use ChatGPT to analyse and redesign the underlying structure of a messy computer archive. I first generate a folder tree locally with a Python script provided by ChatGPT and saved as a BAT file. The tree shows the hierarchy of folders and filenames, giving ChatGPT a broad view of how the archive is currently organised. From that structure, ChatGPT can identify where the organisation is too fragmented or too broad. For example, a client folder may contain ten nested subfolders with only a few text files in each, making the structure more complicated than the content requires. Elsewhere, hundreds of photos, screenshots, or documents may all sit in one folder, suggesting that more meaningful categories would help. ChatGPT can then propose a revised structure with more or fewer levels, depending on the actual content. It can also suggest more consistent naming conventions for folders and files, identify recurring subjects or topics of interest across the archive, and point out categories that already exist implicitly but have never been organised deliberately. For larger changes, I can ask ChatGPT to help create a copied version of the archive using the proposed structure rather than immediately reorganising the original files. This gives me a working prototype of the new system. I can browse it in Windows Explorer, see whether the categories make sense in practice, and adjust the structure before making any permanent changes. The result is not just a cleaner folder tree. It is a way to use AI to discover the information architecture hidden inside years of accumulated files and turn it into a structure that better reflects how the content is actually used. Step-by-step: 1. I use ChatGPT to generate a Python script that creates a folder tree locally, then save the script as a BAT file. 2. I use the folder tree to show ChatGPT the archive’s hierarchy of folders and filenames. 3. I ask ChatGPT to identify areas that are too fragmented or too broad, such as deeply nested client folders or folders containing hundreds of mixed files. 4. I ask ChatGPT to propose a revised structure with an appropriate number of levels, along with more consistent folder and file naming conventions. 5. I ask ChatGPT to identify recurring subjects and categories that already exist implicitly in the archive. 6. For larger changes, I ask ChatGPT to help create a copied version of the archive using the proposed structure instead of changing the original files immediately. 7. I browse the copied structure in Windows Explorer, assess whether the categories make sense in practice, and adjust the system before making permanent changes.

I used AI to reconstruct more than two decades of professional work scattered across old publications, broken links, archives, indexes, and surviving records. The difficult part was not finding possible matches; it was preventing AI from turning incomplete evidence into false certainty. I built a workflow that treats archive reconstruction as a verification problem rather than a search problem. The process separates verified work from probable matches, resolves same-name conflicts, deduplicates reprints and syndicated copies, preserves source provenance, and treats missing years as research gaps rather than permission to invent records. The result was a source-tracked professional archive containing 250+ recovered records across more than 20 years. The workflow can be adapted to journalism, research, consulting, creative work, speaking histories, or any long-term body of professional output. I use the following research-archivist workflow: - Never invent titles, dates, publications, URLs, or authorship. - Separate records into VERIFIED, PROBABLE, and REJECTED. - Treat same-name individuals as separate identities until evidence proves otherwise. - Deduplicate syndicated copies, reprints, mirrors, indexes, and cached pages. - Preserve provenance for every verified record. - Mark gaps rather than filling them. - Distinguish “not found,” “unverified,” and “confirmed absent.” - State uncertainty explicitly. Step-by-step: 1. Ask for names and bylines, employers, publications, career dates, subject areas, known titles, source files, URLs, and any same-name conflicts. 2. Build an identity fingerprint. 3. Establish verified seed records. 4. Search iteratively across multiple query patterns and sources. 5. Classify every result. 6. Resolve identity conflicts. 7. Deduplicate records. 8. Normalize metadata. 9. Build a chronology. 10. Identify and search gaps. 11. Run a provenance and error audit. The final output includes: - Verified Master Archive - Probable / Needs Verification - Rejected / Other Person - Gap List - Source Notes - Final Audit The archive is never described as complete unless the evidence supports that conclusion.

Midlifecurious is a newsletter for women navigating midlife—honest, funny, and allergic to being talked down to. Its Sunday issue, the Midlife Missive, is a roundup covering health, wellness, money, beauty, and family. My sister, Claire, edits it; I build the machine behind it. That machine is Missive, a five-part publishing intelligence system that runs the newsletter as one closed loop: scan → triage → publish → measure → remember. It monitors Reddit, search trends, and RSS to identify what midlife women are paying attention to before those topics reach our feeds. Discovery pulls in those sources, ranks every feed using a click-rate-based quality score, and lets Claire triage articles into the week’s issue. Curation composes Sunday’s newsletter and drafts the introduction in her voice. Performance reads the Mailchimp results back into the system and feeds them into the rankings, so strong sources rise and weak ones fall over time. Underneath all four stages is Memory: a vector-searchable corpus of every article, save, rejection, and the reasoning behind each decision. Memory is the real spine of the system. It lets Missive ask editorial questions such as “Have we covered this before?” and “Is this source still earning its slot?” instead of requiring one person to hold everything in her head. We’re a two-person operation: I build with Claude Code, and Claire edits. The system runs on one database for under $25 a month. I built it because the alternative was Claire drowning in a Feedly-and-spreadsheet routine that discarded everything as soon as an issue shipped. We had no record of what we had run and no feedback on what actually landed. My bet is that the corpus is the moat. Claire’s editorial taste—every save, rejection, and “cornerstone” stamp, with the reasoning stored alongside the decision—is a training set no one else has. A system that remembers turns her job from synthesizer into judge. Missive is deliberately internal-only: no SaaS and no customers, ever. That frees me to build for our exact workflow instead of a hypothetical buyer, and to build for 2028 instead of this quarter. The near-term payoff is a calmer Sunday. The long-term goal is a proprietary editorial-intelligence layer we could never buy off the shelf—the foundation for the research and audience products that come next. Step-by-step: 1. I monitor Reddit, search trends, and RSS for topics that midlife women are paying attention to. 2. I pull those sources into Missive and rank each feed using a click-rate-based quality score. 3. Claire triages the ranked articles into the week’s Midlife Missive. 4. Missive composes Sunday’s newsletter and drafts the introduction in Claire’s voice. 5. I import the Mailchimp results so the system can update source rankings based on performance. 6. Missive stores every article, save, rejection, “cornerstone” stamp, and the reasoning behind each decision in a vector-searchable corpus. 7. We use that memory to check whether a topic has already been covered and whether a source is still earning its place. 8. I build and maintain the internal system with Claude Code, while Claire handles editing, using one database that costs under $25 a month.

Build a Tiny HTML Research Logger Instead of Fighting a Spreadsheet Turn repeated research measurements into a simple phone-friendly interface instead of making participants wrestle with spreadsheets. Some research data is simple. Collecting it often isn't. Imagine recording pain, energy, mood and a few additional variables several times during a study. A spreadsheet can technically do this. But on a phone? Open the file, find the right row, locate the columns, enter values without accidentally changing something, save and close. That's a ridiculous amount of interface for a tiny measurement. If recording how you feel takes ten seconds mentally but two minutes administratively, the tool has become part of the research burden. So I asked: What is the smallest interface someone actually needs when logging an observation? My answer was a tiny HTML page that works on both phone and desktop. 1. Separate the interface from the dataset Participants shouldn't need to interact with rows, columns or database structures. They should see a few simple fields: Pain, Energy, Mood, Optional Note and Save. Behind the interface, each submission becomes a structured record containing the measurements, a timestamp and any optional context. The researcher gets organised data. The participant gets simple controls. 2. Define the research measures first Before asking AI to build anything, establish what you're measuring. Which variables matter? What does each scale mean? Which values are allowed? Should measurements remain comparable over time? These are research decisions, not interface-design decisions. AI can help implement your measurement system, but shouldn't quietly invent the methodology. 3. Design for repeated use A questionnaire completed once can tolerate some friction. A logger used several times a day cannot. Make controls large enough to tap comfortably. Use readable labels, vertical layouts and obvious actions. Avoid unnecessary navigation, tiny cells and repeated setup. Design for the phone first, then let the interface adapt to larger screens. And test the fifth entry, not just the first. Something that looks elegant during development can become irritating when you're tired, distracted or recording yet another observation. The ideal repeated action is almost boring. That's a compliment. 4. Automate the invisible work Participants shouldn't have to enter information the system already knows. A logger can automatically generate timestamps, record identifiers and technical metadata. Each submission should become one coherent record. Show a clear confirmation when saving succeeds and prevent accidental duplicates. Keep optional notes available without turning every measurement into a diary assignment. Don't make participants do the database's job. 5. Keep the interface neutral A research logger is a measurement instrument. Its colours, labels, ordering and controls shouldn't subtly suggest which answers are desirable. Likewise, the logger doesn't need to interpret someone's measurements or announce that their mood seems lower on Tuesdays. That's analysis, not collection. First gather observations reliably. Interpret them later using appropriate research methods. 6. Decide where the data lives A single HTML file can provide the interface without requiring a full application. But HTML alone doesn't provide persistent storage or synchronisation. Browser storage may work for a local prototype, although it has limitations. For research involving several devices, participants or longer periods, you'll need suitable storage and export options. A phone and laptop displaying the same interface don't automatically share observations. Keep the dataset consistent and exportable, for example as CSV. For sensitive information, include appropriate privacy and security safeguards. 7. Build only what's necessary It's tempting to expand a tiny logger into an entire platform. Dashboards! Charts! Profiles! Notifications! Accounts! Perhaps later. Start with Open → Measure → Save. Test the process repeatedly on phone and desktop. Improve whatever causes genuine friction. You can enlarge buttons or rearrange the layout without changing the study. But changing a measurement scale is a research-design decision, not a cosmetic update. The workflow Define the variables and scales → ask AI to build a responsive HTML logger → add automatic timestamps and structured records → choose suitable storage and export methods → test repeated logging → refine usability without changing the measurements. Why this works A spreadsheet is excellent at displaying a dataset. That doesn't make it the best place to create one. Researchers think in variables, records and analysis. Participants simply need to answer the question in front of them. Those shouldn't have to be the same interface. Sometimes the best research tool isn't a sophisticated platform. It's a tiny page that gets the spreadsheet out of the participant's way.

I use ChatGPT to turn a physical trading card collection into a structured digital inventory. I start by photographing the collection and uploading the images to Google Drive. I divide the cards into logical groups and store them in separate subfolders, creating a clear structure before identification begins. ChatGPT then works through the folders in manageable batches, using OCR and visual recognition to extract information from the cards. This avoids manually uploading and describing every card one by one. I use consistent processing rules to reduce mistakes: I limit the number of cards per batch, make sure every source image is accounted for, and use one central card per photo when identification could otherwise be ambiguous. For each card, ChatGPT helps identify and organize the information I want to keep: card name, set code, set or series, edition, rarity, language, quantity, approximate year or period, condition notes, confidence level, and the source image. The useful part is not just OCR or card recognition. It is the combination of a structured Google Drive archive, visual identification, consistency checks, and systematic data extraction across a large physical collection. When identification is uncertain, I record that uncertainty rather than silently guessing. Damaged or unusual cards can also receive specific notes, such as foil scratches, edge wear, folds, or other condition issues. Once all folders have been processed, I consolidate the separate batches into one central inventory so I can check duplicates, quantities, and inconsistencies more easily. I first used this workflow for a Yu-Gi-Oh! collection and plan to reuse the same process for a Harry Potter trading card collection. The result is a searchable, structured overview of a physical collection that can later be used for valuation, selling, insurance, cataloguing, or simply knowing what is actually in the boxes. Step-by-step: 1. I photograph the physical trading card collection. 2. I upload the images to Google Drive and divide them into logical groups stored in separate subfolders. 3. I process the folders with ChatGPT in manageable batches using OCR and visual recognition. 4. I limit the number of cards per batch, account for every source image, and use one central card per photo when needed to reduce ambiguity. 5. I extract each card’s name, set code, set or series, edition, rarity, language, quantity, approximate year or period, condition notes, confidence level, and source image. 6. I record uncertainty instead of silently guessing and add specific notes for damaged or unusual cards, such as foil scratches, edge wear, and folds. 7. I consolidate the processed batches into one central inventory to check duplicates, quantities, and inconsistencies. 8. I reuse the process for additional collections, including my planned Harry Potter trading card collection.

Build a Self-Improving AI Meeting Protocol for Teams This workflow was inspired by a question Brooke Benson posted in the Rundown Hub about meeting transcripts that miss action items or assign them to the wrong person. That felt familiar because I used to take meeting minutes as a management assistant. I often had to ask: Who said this? Was it a decision or only a suggestion? Who owns this action? Was the deadline confirmed? As a human note-taker, I could ask the people in the room and summarize while listening. A speaker might say: “Maybe Friday, but that depends on Marc.” My notes might become: “Friday is tentative and depends on Marc.” A transcriber captures more, but it also captures the verbal spaghetti. So the problem is not only transcription quality. It is ambiguity. Instead of only asking AI to interpret a messy transcript afterwards, what if the meeting itself produced clearer signals? The system has four layers: Step-by-step: 1. Meeting grammar Speaking habits that clarify speakers, decisions, actions, ownership and uncertainty. 2. Team information Context about names, roles, responsibilities, terminology and decision scope. 3. Output template A fixed structure for the meeting report. 4. Feedback loop AI compares corrections with its previous output and suggests improvements. The meeting grammar should stay lightweight. At the start, each participant states their name and role. Name the next speaker when possible: “Marc, go ahead.” “Sofie, what do you think?” Unclear contributions can be restated: “So Marc, Friday is possible only if the technical review is complete. Correct?” Mark proposals, decisions and actions differently: Proposal: “I suggest we move the launch to Friday.” Decision: “We are moving the launch to Friday.” Action: “Marc will update the schedule by Wednesday.” Tentative action: “Marc may update it, pending confirmation.” At transition points, repeat what was agreed: “The decision is Friday.” “Marc owns the schedule update.” “The deadline is Wednesday.” Uncertainty should also be explicit: “Owner to confirm.” “Deadline is provisional.” “This is a proposal, not a decision.” The AI should receive these rules directly: - Treat explicit decision language as confirmed. - Do not assign an owner unless ownership is clear. - Label tentative deadlines as tentative. - Flag uncertain speakers instead of guessing. - Prefer later explicit summaries over earlier ambiguous wording. The AI should also receive team context: name, role, responsibilities, decision authority, terminology and communication patterns. Example: Pieter Vermeulen Operations Usually responsible for scheduling and logistics. Often uses tentative language for proposals. Only assign ownership when he explicitly confirms it. These notes should stay transparent and editable. People should be able to review, change or reject notes about their own communication patterns. The report should use a fixed structure: Meeting title Date Participants Main topics Decisions Action items Owner Deadline Status Open questions Risks or dependencies Items requiring confirmation TL;DR This can be built into a shared internal assistant. It receives the meeting grammar, team information, transcript and output template. It should be conservative about certainty: Owner unclear → “Owner to confirm” Suggested deadline → “Tentative deadline” Unknown speaker → “Speaker unclear” Discussed proposal → “Proposal discussed, not confirmed” A human still reviews speaker attribution, decisions, owners, deadlines and confirmed items. Then the corrected report is fed back to the AI: “Which errors came from unclear meeting signals? Which came from missing team context? Which came from your interpretation? What should be improved?” The AI can suggest updates to the meeting grammar or team information. Example: Error: Several people discussed a task, but nobody clearly accepted ownership. Suggested grammar update: “When assigning an action, confirm owner and deadline in one sentence.” Or: Observed pattern: “Pieter often says ‘we should’ when proposing something.” Suggested profile update: “Treat ‘we should’ as a proposal unless explicitly confirmed.” Profile changes should be reviewed before they are stored. The workflow: meeting → transcript → AI report → human correction → error analysis → grammar/team-context update → review → next meeting Over time, fewer errors should need correction because the system becomes easier to interpret. The division of labor is simple: The transcriber captures everything. Humans resolve meaning and ambiguity. AI structures, summarizes and detects patterns. AI output quality depends not only on the prompt, but on the signals the AI receives. Sometimes the best improvement is to reduce ambiguity before it reaches the model.

I uploaded a PDF of a book I need to study for an exam and discussed it with ChatGPT to develop a study methodology. ChatGPT divided the book into chapters, chapters into modules, and modules into parts. At the beginning of each study session, ChatGPT gives me a 10-question quiz covering all the topics I studied through the previous day. It marks my answers according to our methodology, then provides notes for the next part of the module. After I review the notes, I take a quiz based on that material. At the end of each study session, ChatGPT gives me a quiz covering all the topics studied that day. It calculates the quiz-wise marks percentage, the overall percentage for the day, and a comparison with previous days. Questions I answer incorrectly are added to the Error Notes. After I complete a chapter, ChatGPT provides notes for that chapter in a downloadable format. Step-by-step: 1. I uploaded the PDF of the book I need to study for the exam. 2. I discussed the study process with ChatGPT and developed a methodology. 3. ChatGPT divided the book into chapters, chapters into modules, and modules into parts. 4. At the start of each study session, I took a 10-question quiz covering all topics studied through the previous day. 5. ChatGPT marked my answers according to our methodology and provided notes for the next part of the module. 6. I reviewed the notes and took a quiz related to that material. 7. At the end of the study session, I took a quiz covering all topics studied that day. 8. ChatGPT calculated the quiz-wise marks percentage, the overall percentage for the day, and a comparison with previous days. 9. ChatGPT added the questions I answered incorrectly to the Error Notes. 10. After I completed a chapter, ChatGPT provided notes for it in a downloadable format.

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 personalized news workflow called “TastyHookedFish” as an antidote to the feeling that news is limited to crisis, conflict, and collapse. It periodically searches for genuinely interesting, positive developments across science, climate, medicine, conservation, technology, culture, and social progress. The goal is not to create a feed of cheerful fluff, but to surface meaningful developments that provide evidence that useful things are happening in the world. The workflow also tries to avoid repetition, so the same feel-good stories do not keep resurfacing simply because they are popular. The result is a personalized news feed that does not ignore reality, but deliberately widens the lens. Step-by-step: 1. I set up a personalized news workflow called “TastyHookedFish.” 2. I configured it to periodically search for positive developments across science, climate, medicine, conservation, technology, culture, and social progress. 3. I focused the workflow on meaningful developments rather than generic cheerful stories. 4. I added a way to avoid repeatedly surfacing the same feel-good stories because they are popular. 5. I use the resulting feed to stay informed while deliberately widening my view of what is happening in the world.

AI agents such as Claude Code, Codex, and Gemini can install skills, plugins, and CLI tools in seconds. That is convenient, but each add-on runs with access to files, passwords, and accounts. A malicious add-on could steal API keys, read private data, or quietly take control of the agent. I use `agent-guard` to make “check first, install after” the default. Before installation, it examines AI agent skills, MCP servers, and CLI tools for malware, prompt injection, and credential theft using professional, open-source security scanners from NVIDIA, Cisco, Datadog, and the Open Source Security Foundation. The tool returns a clear verdict: safe to install or blocked, with the exact reason. It then installs only the verified version, preventing a different version from being swapped in between. A single check can also cover multiple AI agents, installing the approved add-on into every agent on the machine. Free and open source: https://github.com/elliottwaves-20/agent-guard Step-by-step: 1. I select the AI agent skill, MCP server, or CLI tool I want to install. 2. I run `agent-guard` to scan it for malware, prompt injection, and credential theft. 3. I review the verdict and the reason if the add-on is blocked. 4. I install the exact version that was checked rather than an unverified replacement. 5. If I use several AI agents, I install the approved add-on into all of them at once.

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