Workflow Hub

My car recently lost a small part near the bonnet. I didn’t know what it was called, and even the official workshop couldn’t help because the model was no longer in production. I gave ChatGPT my car details and a photo showing where the part was missing. It identified the part, helped me cross-check the specifications and compatible part numbers, and found sellers that had it available. I found the right part and had it delivered within a week. Step-by-step: 1. I shared my car model and a photo of where the part was missing. 2. I used ChatGPT to identify the part and its specifications. 3. I cross-checked compatible part numbers and alternatives. 4. I searched for sellers carrying the correct part. 5. I compared the options and ordered the right one.

I built a workflow to evaluate whether communication with a legal counseling service was as slow and incomplete as it felt. Email was my primary—and necessary—communication channel with them. I had become increasingly dissatisfied with delayed replies, partial answers, and questions that seemed to remain unresolved. Rather than relying only on memory or frustration, I asked GPT to review the relevant Gmail correspondence and turn it into a structured communication inventory. The workflow identified my outgoing questions, their replies, and whether each question had been fully answered, partially answered, or left open. From that inventory, we could calculate concrete indicators such as the median response time, the longest delay between a question and a substantive reply, and the number of questions that remained unresolved or were only partly addressed. This was useful because long email threads can create a distorted sense of what happened. A few frustrating exchanges can dominate memory, while other delays or omissions disappear into dozens of messages. Structuring the correspondence made the pattern measurable. The analysis was not meant to decide whether the counselors were “good” or “bad.” It was meant to answer narrower questions: How quickly were questions usually answered? Which ones were not answered? Were replies resolving the issues raised, or only responding to part of them? Afterward, we created a clear list of the questions that were still open. I used that list as a set of dossier questions when moving the case to another counselor, turning the analysis into practical continuity rather than just a complaint about the past. In simple terms: Gmail correspondence → question-and-response inventory → response-time and completeness analysis → unresolved-question list → handover to another counselor What I liked about this workflow is that it turned a vague feeling that “this communication is not working” into a documented overview I could actually use. Step-by-step: 1. I gathered the relevant Gmail correspondence with the legal counseling service. 2. I asked GPT to identify my outgoing questions, the counselors’ replies, and the status of each question. 3. I classified each question as fully answered, partially answered, or left open. 4. I calculated indicators including the median response time, the longest delay before a substantive reply, and the number of unresolved or partly addressed questions. 5. I created a clear list of the questions that remained open. 6. I used that list as dossier questions when handing the case over to another counselor.

When medical care, psychosocial support, and administrative follow-up are spread across services, emails, documents, and people over many years, there can be plenty of information but very little overview. A new provider sees snapshots, old decisions hide in email threads, and referrals resurface months later. Reconstructing the full story from memory every time requires almost a second brain. So I built a living chronological timeline. I did not want a summary of what my situation was “more or less.” I wanted: `date → event → action taken` For example: `14 March 2026 — Symptoms discussed with service X — Service refers me to Y; appointment requested.` This shows not only what happened, but what followed. My timeline runs from 2009 to the present and is divided into five documents covering meaningful periods. A new document begins when a new phase in life or support begins. Older periods stay stable, while almost all active maintenance happens in the latest document. I reopen historical files mainly when a missing record resurfaces. Step-by-step: 1. I collect existing traces, including emails, appointments, referrals, medical or administrative documents, formal decisions, and earlier notes. A sent email is often only half an event, so I also search for replies and follow-up. 2. I search by trajectory rather than by one name. I use names, spelling variants, organisations, domains, senders, recipients, subject words, and attachment terms. 3. I read the full conversation. A search result is not yet a timeline item, so I establish what happened, when it happened, who was involved, and what action or response followed. 4. I separate evidence levels. I distinguish between my own report at the time, a response or confirmation, an official decision or document, and an event that seems real but still lacks confirming evidence. I do not replace a missing document with an assumption; the absence itself becomes part of the record. 5. I track people and organisations separately. “Contact with organisation X” can hide that different people suggested, revisited, or formally communicated different things. 6. Before adding a new event, I compare it with the existing timeline. It may add information by filling a gap, replace an incomplete entry, clarify an existing event, or mark a missing record while preserving uncertainty. This prevents duplicate versions of the same event. 7. For substantial historical work, I first create a temporary review document using `date — event — action taken`. It can contain open issues, missing documents, and uncertainties. Only after review do I update the main timeline. For ordinary new events, I skip this step when the source and date are clear. 8. I make uncertainty visible by grouping research notes into “Clearly found,” “Possible identity confusion,” and “Not found or insufficiently documented.” 9. I maintain the living timeline using the structure `year → month → chronological events`. Older information is preserved, duplicates are merged, uncertainties stay visible, and open trajectories remain open. In practice, the five documents behave like version control: the first four are mostly archive, while the fifth is alive. 10. I reuse the timeline for thematic analysis. Chronology is the factual foundation, but events can also be regrouped to reveal patterns in functioning, work capacity, transport, medical developments, administrative burden, communication needs, and realistic conditions for participation. Two workflows emerged: - Historical reconstruction: collect sources → reconstruct trajectory → determine evidence status → review → update the correct historical period - Ongoing follow-up: new event → verify source → record date and action → add to the active document Most records are organised around institutions. A GP sees one part, a psychologist another, and an agency only its own procedure. Emails are chronological within a mailbox, but not across a life. A personal timeline turns that model around. The person becomes the organising axis, not the institution. That lets administratively separate events appear next to each other in time, revealing patterns such as repeated referrals, waiting times, administrative burden, or support disappearing when the need for it increases. The workflow in one line is: `Collect sources → reconstruct trajectories → connect events to actions → distinguish evidence status → compare → review → integrate → maintain → analyse thematically.` The result is not a perfect record, but a steadily improving map of what actually happened, with mainly the newest edge continuing to grow while the past stays calmly in place.

Sometimes the best AI workflow starts without a workflow at all. This one began almost accidentally. During an ordinary conversation about my cat, AI offered to write a haiku. I had not set out to create a poetry project; it was simply a small creative response to a moment. I liked it, so another moment with the cat became another haiku, and then another. Over time, those isolated poems started to feel less like chat fragments and more like a collection. The workflow emerged afterward: Step-by-step: 1. I notice something small from everyday life, such as a pose, habit, expression, interaction, or tiny domestic scene. 2. I tell AI about it in ordinary conversational language. 3. When the moment has the right texture, it becomes a short poem. 4. I react to what works, whether that is the humor, tenderness, rhythm, imagery, absurdity, or the particular way the cat was captured. 5. Those reactions gradually shape the voice of later pieces. 6. Instead of forcing every moment into the format, I keep the ones that feel worth preserving. 7. The poems accumulate over time. 8. Eventually, a spontaneous haiku becomes part of a coherent collection documenting a relationship and a period of life. The interesting part is that the collection was not designed from the top down. There was no initial brief, content calendar, or plan to “write a book of cat poetry.” Continuity emerged because the same subject kept returning, the format was small enough to revisit casually, and previous pieces gave later ones a growing creative context. AI therefore acted less like a writing tool and more like a creative companion with continuity: noticing when an everyday moment might be worth turning into something. The end result is more than a stack of generated poems. It is a record of tiny observations that probably would never have been written down otherwise.

I built a suite of teaching tools that work independently and together. Chalkline supports maths, while Penline supports literacy. Both tools provide quick access to the New Zealand curriculum for planning and feed information directly into my planning utility, Planline, making teacher planning faster. Planline integrates with Google Drive and is set up with Stripe billing for subscriptions. The suite also includes Compass World News, a news aggregator with a freemium model and a pro unlock. The apps are designed without user tracking. Findaroo is a freemium word-find game with ads. Slashkit helps Minecraft players quickly create commands to use in games. Everything is built into my website, www.danielsoutham.co.nz, which is hosted on Cloudflare. I push updates and changes through GitHub, so I can make changes—big or small—from anywhere, on any device. Changes are then pushed and merged in one prompt. Step-by-step: 1. I built Chalkline for maths and Penline for literacy, with each tool able to work independently. 2. I connected both tools to the New Zealand curriculum so teachers can quickly access it for planning. 3. I connected Chalkline and Penline to Planline so they can feed information into the planning utility. 4. I integrated Planline with Google Drive and set up Stripe billing for subscriptions. 5. I added Compass World News as a freemium news aggregator with a pro unlock and no user tracking. 6. I added Findaroo as a freemium word-find game supported by ads. 7. I built Slashkit to help Minecraft players quickly create commands for use in games. 8. I brought all of the apps together on www.danielsoutham.co.nz and hosted the website on Cloudflare. 9. I use GitHub to push and merge updates, allowing me to make changes from anywhere, on any device, in one prompt.

Earlier in my career, I spent a significant amount of time delivering security risk assessments. These engagements typically took 8–12 weeks from the initial client meeting to the final report, with complex projects often stretching to 3–4 months. Today, I can produce reports of comparable quality in 1–2 days. The automation runs end-to-end in about 15 minutes; I spend the remaining time reviewing and validating the output. The workflow starts with a structured questionnaire and uses ChatGPT, Perplexity, Claude, Make.com, Google Drive, and Gamma.ai to research, draft, format, and deliver the report. This compresses the delivery timeline while preserving quality and lets me focus on expert judgment, validation, and client communication instead of manual report production. Step-by-step: 1. I have the client visit a web page and complete a structured questionnaire built on Lovable.dev. 2. The submitted information triggers an automated Make.com workflow. 3. ChatGPT analyzes the client’s responses. 4. Perplexity conducts targeted research tailored to the client’s industry, context, and risk profile. 5. Claude drafts the report and saves it to Google Drive. 6. The draft is passed to Gamma.ai, which generates the final client-ready report. 7. I receive the report by email, perform a thorough review, and send it to the client once I approve it. 8. I use the time saved to focus on expert judgment, output validation, and client communication.

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 needed to create documentation for an insulation company, so I put together a set of images that people could understand within three to five seconds. The materials gave salespeople something to leave behind to help customers make a decision and understand how they could save money through rebates and tax incentives. Before this, salespeople were mainly dropping off the contract, and they were closing two out of 10 deals. Now they’re closing six out of 10. I think that number will go even higher as people get more time to organize the rebates and bids. Step-by-step: 1. I created documentation for the insulation company using images that people could understand within three to five seconds. 2. I made the materials useful for salespeople to leave behind after meeting with customers. 3. I included information to help customers understand potential savings from rebates and tax incentives. 4. I compared the results with the previous approach, when salespeople mainly dropped off the contract and closed two out of 10 deals. 5. After using the new materials, the sales team increased its close rate to six out of 10 deals. 6. I expect the close rate may increase further as customers have time to organize the rebates and bids.

Copy and paste the ChatGPT prompt. A screen video is included.

I use ChatGPT as a label-reading filter for packaged snacks, uploading product photos and asking it to flag hidden sugars, oils like palm, and preservatives, then compare options against a strict checklist of clean ingredients, minimal processing, and decent macros. It's especially useful for decoding lesser-known, jargon-heavy terms on the back label, translating things like INS numbers, stabilizers, and emulsifiers into plain English so it's clear what's actually being consumed. Instead of trusting front-of-pack claims like "multigrain" or "sugar-free," it breaks down what's inside, surfaces trade-offs, and narrows choices to the best options available online Step-by-step: 1. I photographed the ingredient labels on the packaged snacks I was considering. 2. I gave ChatGPT a strict checklist covering hidden sugars, oils such as palm oil, preservatives, processing, and macros. 3. I asked it to translate ingredient jargon such as INS numbers, stabilizers, and emulsifiers into plain language. 4. I had it compare the products based on what was actually inside rather than the claims on the front of the package. 5. I used the trade-offs it surfaced to narrow the list to the best available options.

I recently learned that I have severe osteoporosis. Two doctors highly recommended Evenity, which is prescribed and insurance-approved only for people at severe risk of fracture, so I knew the situation was serious. The decision was difficult because Evenity carries an increased risk of heart attack and stroke. Both conditions run in my immediate family, and one of my arteries has 75% plaque buildup. Ultimately, we agreed that my risk of a life-altering fracture was much greater than my risk of a heart attack or stroke. My father died six weeks after suffering a life-altering fracture, while my mother survived two heart attacks and a stroke, and my brother died from his first heart attack at age 60. That made it even more important to follow recommendations for both heart and bone health, even though the goals for each can be different. Monitoring everything myself would have been overwhelming, so I created a project called "Daily Meals" and prompted Chat to create a chart that tracks my daily intake of protein, carbohydrates, fiber, healthy and unhealthy fats, calcium, cholesterol, and other selected nutrients, along with a goal for each one. I enter everything I eat throughout the day. The chart maintains a running total and displays my goals underneath. When I am about halfway through my calorie goals, Chat recommends foods for the rest of the day to help me stay on target. Within a week, Chat had learned my go-to foods, and I also told it about other foods I like. There is no harsh judgment when I include ice cream. I also added pictures of the labels for the supplements I take each day. In addition to tracking my intake, Chat advises me about combinations that may help or hinder absorption. One important example for me is that vitamin D, which must be taken with fat, helps with calcium absorption, while vitamin K2 activates proteins that help carry calcium to my bones instead of my arteries. Chat also provides guidance about medications. When I was prescribed an antibiotic, I learned that taking it with calcium could greatly reduce the medication's absorption, so I adjusted what I ate accordingly. There is much more benefit than I can fit here. This project may literally be a lifesaver for me. Step-by-step: 1. I created a project called "Daily Meals." 2. I prompted Chat to create a chart for tracking protein, carbohydrates, fiber, healthy and unhealthy fats, calcium, cholesterol, other selected nutrients, and goals for each one. 3. I added pictures of the labels for the supplements I take each day. 4. I enter everything I eat throughout the day so the chart can maintain running totals. 5. I review the totals against my goals, and when I am about halfway through my calorie goals, I ask Chat to recommend foods for the rest of the day. 6. I told Chat about my go-to foods and other foods I like so its recommendations could reflect my preferences, including occasional ice cream. 7. I use Chat's guidance about nutrient combinations, including vitamin D with fat for calcium absorption and vitamin K2's role in helping carry calcium to my bones instead of my arteries. 8. When I was prescribed an antibiotic, I used Chat's medication guidance to learn that taking it with calcium could reduce the medication's absorption, and I adjusted my meals accordingly.

I have been looking around at new apartment development projects, looking to potentially buy in the next two years. I used ChatGPT Deep Research to help compare the cost, the quality of the appliances and materials the builders are using, and chart out the overall cost. Step-by-step: 1. I listed the apartment developments I was considering and the roughly two-year purchase horizon. 2. I asked ChatGPT Deep Research to compare the purchase cost of the options. 3. I had it research the quality of the appliances and materials each builder was using. 4. I asked it to chart the overall cost so the differences were easier to see. 5. I used the combined research and visuals to compare projects on more than the advertised price.

I built a Driver Management System with ChatGPT and Google. It stores client and driver information, including links, and lets me schedule jobs through a one-click booking wizard. The system also provides reports, helps me search for available drivers by licence type and other criteria, tracks available jobs, and includes a dashboard showing unassigned drivers. This is my first project with AI, and I love what I’ve been able to build. Step-by-step: 1. I used ChatGPT and Google to build a Driver Management System. 2. I added client and driver information, including links. 3. I created a one-click booking wizard for scheduling jobs. 4. I added reports and search tools for finding available drivers by licence type and other criteria. 5. I set up job tracking and a dashboard showing unassigned drivers.

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 built a workflow to examine complaints I had about a local bureaucratic service and check whether my account of what happened was supported by the written record. I was concerned about delays, incomplete handling, and the way my case progressed after I gave feedback about a harmful aspect of the service and asked for a different person to handle the dossier. That request was refused. Afterwards, I felt that the process had become slower, more confusing, and more prone to mistakes. Rather than asking GPT to confirm that the service had handled things badly, I used it to test my own interpretation against the evidence. I asked GPT to review the relevant emails and dossier communications, reconstruct the chronology, and identify requests, replies, delays, unresolved issues, and changes in how the case was handled over time. We then compared the periods before and after my feedback and request for reassignment. The central questions were deliberately neutral: Were my complaints grounded in the correspondence? Did the handling of the dossier objectively become slower or more incomplete afterwards? Were there concrete errors or unanswered questions in the record? Or was I remembering the experience as worse than the documentation supported? This distinction mattered to me. The workflow was not designed to produce a verdict or turn frustration into evidence after the fact. It was designed to challenge my assumptions and separate what I felt from what could actually be demonstrated. Where the documentation supported a complaint, we could point to the relevant chronology, delays, unanswered questions, or inconsistencies. Where the evidence was incomplete or ambiguous, that was noted too. The result was a more grounded account of the case: not “I know this was handled terribly,” but “these are the parts of my experience that are supported by the written record, these are the parts that remain uncertain, and this is where the timeline changed.” In simple terms, the workflow was: emails and dossier communications → chronology reconstruction → before-and-after comparison → review of delays, errors, and unresolved issues → evidence check against my complaints → a grounded account of what can and cannot be supported. What I liked about this workflow was that it used AI as a reality-checking tool rather than an agreement machine. It helped me test whether my criticism was actually anchored in the record before I relied on it in further communication. Step-by-step: 1. I gathered the relevant emails and dossier communications about my case. 2. I asked GPT to reconstruct the chronology and identify requests, replies, delays, unresolved issues, and changes in how the case was handled. 3. I compared the period before and after my feedback about a harmful aspect of the service and my request for reassignment, which was refused. 4. I examined whether the record showed that the process became slower or more incomplete, and whether it contained concrete errors or unanswered questions. 5. I separated points supported by the documentation from points that remained incomplete or ambiguous. 6. I used the resulting chronology and evidence check to create a grounded account of what my complaints could and could not demonstrate.

I regularly use ChatGPT for lesson planning and materials development with students ranging from EFL learners to UK undergraduates. I keep a separate anonymous thread for each student, so GPT can track the context and suggest the best direction for the next session. I also contribute my experience as a tutor while using AI to create fresh worksheets based on typical materials from various UK exam boards. Today, I had a successful interview for a lecturer role. Gemini helped me design and plan my demo seminar, and it looks like I’ve landed the job. Step-by-step: 1. I create a separate anonymous thread for each student. 2. I use ChatGPT to track each student’s context and suggest the best direction for the next session. 3. I combine AI-generated ideas with my experience as a tutor when planning lessons and developing materials. 4. I use typical materials from various UK exam boards as the basis for creating fresh worksheets. 5. For my lecturer interview, I used Gemini to design and plan my demo seminar. 6. I had a successful interview, and it looks like I’ve landed the job.

This year, we only have one day—eight hours—at the Fringe, so we needed to decide what to see. We first scanned the roughly 800 available shows and made a shortlist of 38. After a second round, we narrowed it to 21. I gave the shortlist to ChatGPT, including the show times and locations, and asked it to create five itineraries that fit in as many shows as possible, allowed enough time to travel between venues, and finished with a particular show: a Johnny Cash tribute. The five options quickly became two. After one small tweak, we had our plan: five shows, minimal walking, and time to eat in George Square. Step-by-step: 1. I scanned the roughly 800 available shows and created a shortlist of 38. 2. I reviewed the shortlist and narrowed it to 21 shows. 3. I gave ChatGPT the remaining shows, including their times and locations. 4. I asked ChatGPT to create five itineraries that fit in as many shows as possible, allowed time to travel between venues, and finished with the Johnny Cash tribute. 5. I narrowed the options to two, made one small tweak, and chose an itinerary for five shows with minimal walking and time to eat in George Square.

I created a skill that helps me submit a garment reimbursement every month. It handles the long forms and the upload of thousands of different documents required for the reimbursement. Step-by-step: 1. I created a skill for submitting my garment reimbursement. 2. I use it every month to complete the required long forms. 3. I upload the different types of documents needed for the submission.

子どもが気に入って毎日読んでいる絵本の付属品が、破れたり紛失したりしてボロボロになっていました。そこで、付属品を写真に撮ってChatGPTに送り、似たものを複製してもらいました。 さらに、もともとは付属していなかったものの、子どもがあったら喜びそうな付属品も追加で作ってもらいました。完成したものをハサミで切ってラミネート加工し、絵本と一緒に渡しました。 ポイントは、絵本のページをいくつか写真でChatGPTに送り、同じようなデザインやテイストのイラストを生成してもらうことです。そうすることで、絵本の世界観を崩さずに付属品を作れます。 Step-by-step: 1. 破れたり紛失したりした絵本の付属品を写真に撮る。 2. 絵本のページをいくつか写真に撮り、ChatGPTに送る。 3. 絵本と同じようなデザインやテイストで、付属品を複製してもらう。 4. 子どもが喜びそうな、もともとは付属していなかった付属品も追加で作ってもらう。 5. 作成した付属品をハサミで切ってラミネート加工し、絵本と一緒に渡す。

Granola AI has become my meeting recorder outside of just Zoom meetings. I recently had a 3-hour meeting with legal for business structuring, and I turned on Granola AI on my phone (with other parties' consent), and it picked up the entire transcript nearly word for word (for over 3 hours!), which I later pasted into ChatGPT to go back and forth on things that I didn't fully grasp in the moment. Step-by-step: 1. I obtained consent from the other participants before recording the in-person legal meeting. 2. I turned on Granola on my phone and used it to capture the full three-hour conversation. 3. I took the resulting transcript and pasted it into ChatGPT. 4. I asked follow-up questions about the business-structuring concepts I had not fully understood in the room. 5. I used the transcript as durable context for learning and clarification after the meeting.