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AI is good at generating polished answers, but polished answers are not always your answers. When people ask AI for help with writing, strategy, or difficult decisions, it can jump too quickly to a conclusion and fill in beliefs the user has not fully examined. I created a thought-partner agent that interviews me before producing recommendations or drafts. Its job is to ask probing questions, challenge weak assumptions, surface contradictions, and separate my actual views from ideas suggested by the AI. Instead of writing for me immediately, it helps me clarify my position first. Step-by-step: 1. I give the agent the topic, decision, or idea I want to explore. 2. I tell it not to draft the final output yet. Its first job is to interview me. 3. I have it ask one focused question at a time about my reasoning, evidence, assumptions, audience, and uncertainty. 4. I require it to challenge vague claims and point out contradictions or overlap with my previous thinking. 5. I ask it to clearly separate: - conclusions I stated - ideas the AI proposed - issues that remain unresolved 6. I continue until the central belief, argument, or decision becomes clear. 7. I have the agent create a structured synthesis containing the core thesis, supporting reasoning, counterarguments, open questions, and useful language from the discussion. 8. I pass that synthesis to a writing, planning, or execution agent. I end up with a position that reflects my actual thinking rather than a plausible answer generated by AI. The final writing or strategy is more original, more consistent, and easier for downstream agents to execute.

“Honey, do you remember where Ryan’s practice is? Do you remember our Wi-Fi password? Do you know my TSA PRE number? Remember who that painter was that we used last year?” If you have a family with children, you may hear questions like these all the time. In a large family, there always seem to be questions about the house, the kids, or technology that could be answered more easily. The information is usually stored in different places: password files, a Rolodex of business cards, or sheets of paper in junk drawers. I set out to build an app where anyone in my family could use voice to save something for the future or retrieve something they had already stored. I integrated AI to understand the meaning of each voice note. It might create a calendar event, store a password in a vault, or simply remember a phone number. Then anyone else in the family could access the information. The app uses multi-factor authentication for anything particularly secret. It is not intended to be a dedicated password protector; I would build in much more security if that were the goal. Instead, it is an everyday-life app for remembering the little things: How long is the warranty on this appliance? Which exact light bulbs did we use here last time? What was our daughter’s email address that we needed to set up on her phone? Being able to use voice to enter information or retrieve it was the key for me. I think that will save people time. As I started adding entries and validating the idea, my wife came up with the name. After a few weeks, I knew she was completely right, because I now use it about 10 times a day—and I hear that exact phrase every time. Step-by-step: 1. I identified recurring family questions about locations, passwords, identification numbers, contacts, warranties, and household items. 2. I built an app that lets family members use voice to save or retrieve information. 3. I integrated AI to interpret the meaning of each voice note and determine whether to create a calendar event, store a password in a vault, or remember a phone number. 4. I made the stored information accessible to other family members. 5. I added multi-factor authentication for information that is particularly secret, while keeping the app focused on everyday memory rather than dedicated password protection. 6. I added entries and validated the idea through regular use, eventually using the app about 10 times a day.

I built a website that turns property photos, architectural renderings, or even just an address into cinematic property videos. I’m a civil engineer and photographer/videographer, not a software developer, so I used ChatGPT, Gemini, and Claude Code to help build the site and work through problems as they came up. Much of the process involved translating the way I would personally edit a real estate video into usable software. For example, kitchen and living room shots should usually stay together and have longer durations, bedrooms should stay near their bathrooms, an opening shot should feel different from a detail shot, and every clip should include some movement so the result does not feel like a generic slideshow. I used AI to turn those editing principles into actual logic for the site. The system analyzes and organizes the images, builds a storyboard, ranks scenes to decide which ones to use, creates prompts for video models, generates the clips, and assembles everything into a finished video. Most of the development has been iterative. I test the site with real properties I’ve shot, watch the results, identify what feels off, and then improve the prompts, scene planning, timing, or video assembly process. AI has been useful for much more than generating the final video. I’ve also used it to write code, troubleshoot errors, plan features, improve the user experience, and think through how the product should work from the perspective of its primary user. Step-by-step: 1. I defined the video-editing principles I use for real estate footage, including grouping related rooms, varying shot durations, distinguishing opening shots from detail shots, and adding movement to every clip. 2. I used ChatGPT, Gemini, and Claude Code to translate those principles into software logic. 3. I built the site to accept property photos, architectural renderings, or an address as input. 4. I had the system analyze and organize the images, build a storyboard, rank scenes, and select the footage to use. 5. I used AI to create prompts for video models and generate the clips. 6. I assembled the generated clips into a finished property video. 7. I tested the site with real properties I had shot, reviewed the results, and iteratively improved the prompts, scene planning, timing, and video assembly. 8. I used AI throughout development to write code, troubleshoot errors, plan features, improve the user experience, and think through the product from the primary user’s perspective.

I took a photo of a messy spot at home and asked ChatGPT for ideas on how to organize it. ChatGPT generated several ideas, including a photo showing how to do it. Step-by-step: 1. I photographed the messy spot at home. 2. I asked ChatGPT for ideas on how to organize it. 3. I reviewed the ideas and the photo ChatGPT generated to see how to put them into practice.

I started with a problem: Most people use AI transactionally. They ask a question, get an answer, and leave. That makes AI useful, but it leaves much of its potential untapped. I wanted to find out what would happen if a human and an AI developed an ongoing working relationship—one where context, previous discoveries, disagreements, successes, failures, and the human’s way of thinking accumulated over time. I use ChatGPT, but the goal isn’t to have AI think for me. I remain the decision-maker. The AI’s job is to expand my ability to think: challenge assumptions, identify blind spots, connect seemingly unrelated information, preserve useful context, and sometimes disagree with me. Step-by-step: 1. I established the relationship by telling the AI that I didn’t simply want agreement or answers. I wanted an ongoing thinking partner that could challenge my reasoning while leaving decisions and agency with me. 2. I established operating roles. Over time, ours developed into six modes: Mirror, Builder, Sentinel, Teacher, Witness, and Operator. The AI can reflect my reasoning, help construct something, identify risks or contradictions, teach unfamiliar material, observe patterns across conversations, or help execute a defined task. 3. I separated knowledge from judgment. When we solve difficult problems, we distinguish between facts, reasonable inferences, unknowns, and opinions. This helps prevent a confident AI response from being mistaken for established truth. 4. I let disagreement remain in the system. I correct the AI when it’s wrong, and it challenges me when my assumptions don’t fit the evidence. Instead of treating those moments as failures, I treat them as part of the accumulated context of the relationship. 5. I preserved useful context across different domains. I use the same AI relationship for automotive diagnostics and engineering, business decisions, financial reasoning, writing, research, project planning, and philosophical questions. Something learned in one area can unexpectedly become useful in another. 6. I evaluated the human, not just the AI. The final test isn’t, “Did the AI produce a good answer?” It’s: Did this interaction leave the human better able to understand the problem, make the decision, or solve the next one? After hundreds of conversations, something unexpected happened. The value stopped being any individual prompt or answer. It became the accumulated interaction itself. The AI gained context about how I reason, while I became better at questioning the AI. Previous discoveries started informing new problems, including problems that appeared completely unrelated. Someone can recreate this without special software, coding, or an API. Start with an AI that supports ongoing context or memory, establish the operating principles above, use it consistently across real problems, correct it when it’s wrong, invite disagreement, and allow useful context to accumulate. My original experiment was essentially this: Can an ongoing human-AI relationship make the human more capable rather than more dependent on the AI? Somewhere along the way, I realized we had built a framework for doing exactly that. We gave it a name: Confluxus. The measure of its success isn’t how much the AI can do for me. It’s how much more capable I become because of the relationship.

AI is useful for researching investments, but most workflows stop at “What should I buy?” The harder part comes afterward: Does the idea make sense given what I already own? What would make me add to the position? When should I take profits? What evidence would prove the original thesis wrong? And how do I track all of that without constantly watching the market? I use AI to turn a one-time investment research session into an ongoing decision and monitoring system. I start by asking AI to research the market for potential opportunities. In my case, I look specifically for strong mean-reversion trades, but the same workflow could start with value opportunities, macro themes, sector rotations, individual stocks, crypto, or almost any other investment strategy. Then I give AI my actual portfolio so it can evaluate those ideas in context. After I decide which recommendations I agree with and manually make the trades, I have AI convert each investment thesis into explicit rules for what should happen next. Finally, I turn those rules into automated monitors that periodically check market conditions and alert me only when something happens that warrants another decision. Step-by-step: 1. I define what I’m looking for by asking AI to research potential investment opportunities using criteria I care about, such as mean reversion, valuation, momentum, macro conditions, risk/reward, or another strategy. 2. I have AI investigate current market conditions and rank the opportunities, narrowing a large universe down to a manageable set of ideas worth examining further. 3. I pressure-test each thesis by asking why the opportunity exists, what could drive the expected outcome, what the major risks are, and—most importantly—what evidence would invalidate the thesis. 4. I provide my current holdings so AI can identify overlapping exposures, concentration risks, hedges, or positions that conflict with the new ideas. 5. I ask AI which existing positions the research suggests reviewing and where new exposure might make sense. The goal is a small number of actionable decisions rather than a giant list of interesting trades. 6. I review the analysis and independently decide whether to buy, sell, hold, or do nothing. I keep actual trade execution under human control. 7. Before the market moves, I define the next decision for every position by asking AI to identify conditions that would warrant reviewing whether to: - Add - Take profits - Reduce exposure - Exit - Reconsider the original thesis 8. I turn those conditions into automated monitors. I have ChatGPT periodically check the relevant prices, yields, economic indicators, news, or other variables. Instead of sending routine updates, I tell it to alert me only when a predefined trigger occurs. 9. When a trigger fires, I return to the original thesis with the new information and decide what—if anything—should change. Instead of using AI for isolated investment recommendations, I now have a repeatable loop for managing an investment thesis over time: Find an opportunity → Understand it → Compare it to what I own → Make a decision → Define what would change my mind → Let AI watch for it The most useful part may actually come after the investment decision. By deciding in advance what evidence would make me add, take profits, or reconsider the thesis, I don’t have to start my analysis from scratch every time the market moves. AI becomes a persistent research and monitoring layer while I remain responsible for every investment decision and trade.

I used a simple workflow to save several hundred dollars on veterinary bills while sitting in a veterinary urgent care clinic. This approach could also apply to many other purchases or services where I’m not an expert. I had already been chatting with ChatGPT about my pet’s symptoms and their likely causes. After I arrived at urgent care, I consulted the doctor about the likely diagnosis and the tests and treatment we should pursue. The clinic assistant then brought me a printed, itemized list of all the charges for the proposed plan. I resumed the ChatGPT conversation by dictating what the doctor had told me, photographing both pages of the charge list, and uploading them. I asked whether any charges were very likely unnecessary given the circumstances. ChatGPT identified four of the 24 charges and explained why they appeared to have very limited value. I crossed those four items off the list, handed it back to the assistant, and said, “I don’t think these are necessary at this point.” I could tell from the assistant’s facial expression that no one had done this before. When the doctor returned, I could tell I had caught her off guard. She agreed that two of the four tests likely offered no benefit and explained why we should keep the other two. At that point, I decided to trust the doctor more than the AI agent—perhaps partly to be polite, since I could tell her justification for keeping the tests was weak, although the cost wasn’t significant. Step-by-step: 1. I described my pet’s symptoms and their likely causes to ChatGPT before visiting the clinic. 2. I consulted the doctor about the likely diagnosis and the tests and treatment we should pursue. 3. I photographed the clinic’s two-page, itemized list of 24 charges and uploaded it to the existing ChatGPT conversation. 4. I dictated what the doctor had told me and asked ChatGPT whether any charges were very likely unnecessary in the circumstances. 5. I reviewed ChatGPT’s explanation of the four charges it identified as having very limited value. 6. I crossed those four items off the list and discussed them with the clinic assistant and doctor. 7. I kept two tests after the doctor explained why they should remain, while declining the other two. The process saved me $600.

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

I’m a nerd for deals, so I built a Personal Credit Card Optimizer in ChatGPT. I wanted a simpler way to figure out which store and card would save me the most money on any item I want to purchase. The workflow maintains a database of my credit and debit cards—recording their series, but not their exact numbers—along with their rewards, fees, and benefits. Whenever I want to buy something, I add a product link. It scans retailers for better prices, checks current bank offers against my specific cards, and compares instant discounts, cashback, reward points, and other benefits to determine where I should buy and which card will provide the most value. It’s now an ongoing skill in my ChatGPT project. No more manually checking a dozen bank offers or discovering at checkout that my card isn’t eligible. Step-by-step: 1. I add my credit and debit cards to a database by recording their series, rewards, fees, and benefits without entering their exact numbers. 2. When I want to purchase something, I provide a product link. 3. The workflow scans retailers for better prices on the product. 4. It checks current bank offers against my specific cards. 5. It compares instant discounts, cashback, reward points, and other benefits to determine the best retailer and card combination.

It's finally gardening season in the Midwest, and I wanted to do a big overhaul of my space with a new large in-ground bed and a more organized layout of crops. I first took some images of the space and had ChatGPT / Codex generate some visualizations of what it could look like based on my rambling list of requirements and measurements. After deciding on the vision, I asked for help creating a materials list, a step-by-step guide to building the bed (with instructions on sawing, drilling, etc.). I also provided a list of what we wanted to grow, and was able to get a well-planned map of where to place each plant, optimizing for aspects like shade vs. sun, companion plants, vining, etc. Several trips to Home Depot and a couple of days of work later, we have an awesome, refreshed space and a thoughtful, organized garden for the season! Step-by-step: 1. I photographed the existing garden space and wrote down the measurements, requirements, and rough ideas I had in mind. 2. I gave the images and requirements to ChatGPT and Codex and asked for several visualizations of the finished space. 3. Once I chose a direction, I asked for a materials list and step-by-step instructions for building the in-ground bed. 4. I supplied the full list of plants and asked for a placement map based on sun, shade, companion planting, and vining behavior. 5. I used the plan to buy materials, build the bed, and install a more organized garden layout.

An artist portfolio can feel harder to make than the work itself. My work lives across years, folders, and materials: photographs, drawings, collage, objects, costumes, and digital experiments. Then an application asks me to explain who I am, what I make, and where I want to go. I used AI to help assemble a readable portfolio for HUP03, a Kunstwerkt call. The portfolio included eight works spanning photography, collage, assemblage, performance, and AI-assisted illustration. The goal was to organise the material without flattening it into a generic artist statement. Step-by-step: 1. I started with the actual work: images, titles, dates, and any explanations I already had. For each piece, I established the title, year, medium, whether it was a standalone work or part of a series, what prompted it, how it was made, and which details mattered. I gave AI concrete material rather than asking it to invent my practice. 2. I described each piece in my own words, as I would to someone looking at it beside me. One of my works, *Olifant onder het tapijt*, combined three expressions: the elephant in the room, the elephant in a china shop, and sweeping things under the carpet. The setting became a recognisably Flemish grandmother’s living room, with floral wallpaper, a crucifix, and a decorative cow painting. Those details carry the humour and the observation about hiding uncomfortable things. “An exploration of concealment” would lose much of what makes the piece mine. I asked AI to organise my explanation while preserving its specific ingredients. 3. I gave each work a clear, consistent entry with the title, year, and medium; a short description of the idea; and the relevant visual or material choices. Consistency makes varied work easier to read, even though a photograph, costume, and assemblage may need different explanations. Where relevant, I stated AI involvement clearly, such as “digital illustration developed in collaboration with generative AI.” 4. I selected work for range and relevance. My selection included an older photograph, a costume/performance, collage, recent assemblages, and digital work. I asked what each piece contributed: another method, question, period, or way of seeing. I avoided choosing only the newest work or forcing everything into one visual style. The task was to make the selection understandable. 5. I separated the existing artwork from the proposed project. Existing works show what I have made, while a project proposal explains what I want to investigate next. For this application, my project question centred on human connection: a thread becoming a web, then a garden. AI helped articulate that direction without forcing every earlier piece into the same project. I looked for connections I recognised while leaving room for differences and discoveries. 6. I wrote an accurate artist introduction describing the media I use, what interests me, and my current practice. I checked every biographical claim and distinguished courses attended, qualifications completed, and current activities. In my case, wording around an interior design course needed care so it would not imply that I had completed the programme. AI can make a biography sound polished while quietly making it less true, so I read the facts as closely as the prose. 7. I treated the first draft as material to revise. Useful feedback included: “That sounds more formal than I mean,” “This detail is essential,” “You have made the idea too abstract,” “That suggests a qualification I do not have,” and “This is my observation, not a universal claim.” AI revised the structure and wording, but I decided whether the text still belonged to the work. 8. I checked the portfolio as a whole against the application requirements. I reviewed titles, dates, media, image-text matches, requested sections, and length limits. I removed repetition between the artwork descriptions, biography, and proposal. Someone unfamiliar with me should be able to understand the work without needing the conversation behind the portfolio. The overall process was: gather existing work, explain each piece, verify the facts, build consistent entries, select a meaningful range, describe the proposed direction, revise in my own voice, and check the application. AI was useful as an interviewer, organiser, and editor. The substance came from the work and the person who made it. A portfolio does not need to turn a varied practice into one tidy slogan; it needs to give someone a way into it.

The workflow starts when a project grows beyond one conversation and approved decisions, file versions, or next steps become difficult to track. I begin by creating one active authority document. It is not a transcript of every discussion; it contains only the rules, definitions, methods, and structural decisions that currently control the project. Next, I label project files by status: active, working, superseded, or archived. I also state clearly which file is authoritative instead of expecting the AI to infer it from the filenames. As decisions are approved, I record them in the authority document or a change log. I include what was decided, why it was decided, what it affects, and whether it needs later review. This keeps the chat from becoming the only project record. For large deliverables, I divide the work into named stages. A spreadsheet project might move through architecture, data migration, calculations, validation, and publication. Each stage produces a separate file and has a clear completion check. When a conversation gets too long, I create a handoff prompt for the next chat. It includes: - the project goal; - the active authority files; - completed work; - approved decisions; - unresolved issues; - the next specific deliverable; - anything that should not be redesigned. I start the new conversation with that prompt and only the files needed for the next stage. Finally, I validate the output outside the chat. For Excel files, that means opening them in desktop Excel, checking formulas and errors, saving them, closing them, and reopening them. An AI-generated file is not finished until it passes that check. The core pattern is to record the authority, label the versions, log decisions, work in stages, create a clear handoff, and validate the result. Step-by-step: 1. I create one active authority document containing the project’s current rules, definitions, methods, and structural decisions. 2. I label each project file as active, working, superseded, or archived, and identify the authoritative file. 3. I record approved decisions in the authority document or a change log, including the decision, rationale, effects, and any need for later review. 4. I divide large deliverables into named stages, with a separate file and clear completion check for each stage. 5. I create a handoff prompt when a conversation becomes too long, covering the project goal, authority files, completed work, approved decisions, unresolved issues, next deliverable, and anything that should not be redesigned. 6. I begin the next conversation with the handoff prompt and only the files needed for that stage. 7. I validate the final output outside the chat—for Excel files, by opening them in desktop Excel, checking formulas and errors, saving, closing, and reopening them.

I use reusable AI skills rather than relying on one long prompt or previous chat history. I start by describing the project in plain English and invoking a project-kickoff skill. It creates a live project specification containing the objective, scope, constraints, acceptance criteria, concerns, and next actions. For larger or more ambiguous work, I can opt into a scope-discovery skill. AI guides me through one important decision at a time, explains the trade-offs, recommends a sensible default, and records each accepted decision until the project is ready to implement. When delivery involves several dependent or risky steps, I can use a runbook-design skill. AI converts the agreed scope into a checkpointed implementation plan with validation, rollback, and clear points where my approval or testing is required. AI then executes the runbook, builds the solution, performs automated checks, and records what actually happened. I mainly provide direction, answer business or product questions, and complete the human acceptance checks that AI cannot genuinely perform itself. The skills live inside the project repository alongside the code, decisions, runbooks, and documentation. Codex only links to them at runtime, so the repository remains self-contained and does not depend on my local setup or previous conversations. This also makes the workflow portable. Another capable AI agent or a human engineer can read the repository, understand how the project should be managed, and continue the work without reconstructing everything from chat history. Step-by-step: 1. I describe the project in plain English and invoke the project-kickoff skill. 2. I use the scope-discovery skill for larger or more ambiguous work, working through one decision at a time until the project is ready to implement. 3. I use the runbook-design skill when delivery involves dependent or risky steps, creating a checkpointed plan with validation, rollback, and approval or testing points. 4. AI executes the runbook, builds the solution, performs automated checks, and records what actually happened. 5. I provide direction, answer business or product questions, and complete the human acceptance checks AI cannot genuinely perform. 6. I keep the skills, code, decisions, runbooks, and documentation together in the project repository so the workflow remains self-contained and portable.

During my recent U.S. trip, I had some time to myself and decided to explore solo with ChatGPT as my real-time voice travel guide. I shared my location, what I had already seen, and how much time I had available. ChatGPT built and adjusted my itinerary on the fly, helping me cover more ground, avoid backtracking, and make the most of my time, even when I ran into unexpected car trouble. Chat also pointed out highly rated restaurants with vegetarian options, explained popular menu items I could try, and surfaced deals to pick gifts for my wife. The experience was amazing. The only downside: it drained my phone's battery like crazy. Step-by-step: 1. I opened ChatGPT Voice and shared my current location, how much free time I had, and what I had already seen. 2. I asked it to build a route that would cover the most worthwhile stops without making me backtrack. 3. I kept the voice conversation running and updated it when plans changed, including when I ran into car trouble. 4. I asked for highly rated vegetarian-friendly restaurants and explanations of the menu items worth trying. 5. I also used the same live context to find deals and gift ideas before heading back.

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.

Turn a collection of documents, reports, spreadsheets, and notes into an evidence-backed decision brief with ChatGPT. Instead of asking AI to simply summarize the information, this workflow makes it identify what matters, connect the evidence, compare it with historical context, explore scenarios, and highlight what should be considered before making a decision. Step-by-step: 1. I gather the information relevant to one decision, including reports, PDFs, spreadsheets, research, historical data, meeting notes, and existing analysis. I upload everything into ChatGPT. 2. I ask ChatGPT to understand the situation using this prompt: > “Analyze the information I provided and build a structured understanding of the situation. Identify the key entities, important facts, relationships, metrics, trends, assumptions, and constraints. Do not make recommendations yet.” This creates the context before jumping to conclusions. 3. I build an evidence brief by asking: > “Create an evidence brief. Separate verified facts, derived insights, assumptions, conflicting information, and missing information. For every important conclusion, identify the supporting source or evidence.” This gives me a clearer picture of what is known versus what is inferred. 4. I add historical context when it is available by asking: > “Compare the current situation with the historical information provided. Identify meaningful patterns, similarities, differences, and changes. Highlight which historical observations could be relevant to the current decision.” This turns historical data into context rather than simply another report. 5. I explore three scenarios by asking ChatGPT: > “Based on the evidence and historical context, evaluate three scenarios: upside, base case, and downside. For each scenario, identify the assumptions, key drivers, risks, likely impact, and evidence supporting the assessment.” The objective isn't to pretend the future can be predicted perfectly. It is to understand how the decision changes when assumptions change. 6. I generate the decision brief by asking: > “Create a concise decision brief containing: > > 1. Current situation > 2. Most important evidence > 3. Key insights > 4. Historical context > 5. Critical assumptions > 6. Key risks > 7. Scenario analysis > 8. Evidence gaps and uncertainties > 9. Questions that should be investigated > 10. Possible actions and their implications. > Do not make the final decision on my behalf.” This produces a structured decision brief instead of another AI-generated summary. 7. I review the brief and challenge its conclusions before making the decision. I ask follow-up questions such as: > “Which assumption has the greatest impact on this decision?” > “Show me the strongest evidence against the current conclusion.” > “What information would most likely change the recommendation?” The AI helps structure the decision, but I make the decision. The important shift is: Summarize the information → Understand the situation → Establish the evidence → Add historical context → Explore scenarios → Evaluate the decision This approach can be applied to almost any domain where decisions depend on complex and interconnected information. A property investment is one example. A business strategy, product decision, operational problem, financial analysis, research question, or engineering decision can follow the same pattern.

As a busy father, I was tired of not having the fitness features I needed, so I used Lovable to create an accountability schedule for workouts and nutrition. Step-by-step: 1. I identified the fitness features I needed as a busy father. 2. I used Lovable to build a fitness app around those needs. 3. I created an accountability schedule for working out and nutrition.

I teach and direct a required general education public speaking class at a small college. I’ve built the course around critical inquiry, critical thinking, and public advocacy of a localized problem. Over the years, I’ve noticed that students are increasingly reluctant to engage with the underlying problem. They are often content to identify a problem with enough certainty that they assume their perspective is obviously shared by everyone else. They may also believe that their preferred sources are more certain or credible, leading them to build a case based only on their limited perspectives. With the help of ChatGPT, I built an AI tool that guides students through the problem- and solution-discovery process. It begins with a general question: “what is your topic and what is the problem?” Students often respond with a one-word or incomplete answer, such as “poverty,” “crime,” or “the high cost of education.” These answers do not identify the topic’s deeper dimensions, the extent of the harm, or who is affected by the issue. The AI pushes back on these statements and helps students explore the issue in greater depth. It consistently asks, “what do you mean by x?” I constrained the AI to draw most of its knowledge from the coursepack I wrote for the class, which outlines the assignments, lessons, and instructional content. This keeps the tone of the interaction and the examples provided to students aligned with the overall feel of the course. The AI does not create speeches, make outlines, or find sources for students. Instead, it asks questions that help them refine the direction of their speeches. Whenever possible, the AI also identifies alternative viewpoints from sources traditionally associated with the student’s own perspective. For example, if a student is advocating a progressive viewpoint, the AI may identify statements or research from progressive sources that disagree with that perspective. If the student is advocating a conservative viewpoint, it may identify statements or research from conservative thinkers that challenge the student’s position. This helps students recognize the complexity of ideas and understand that people on the same political, religious, or ideological side do not necessarily agree on every topic. As is often the case when I build GPTs, the 8,000-character limit requires me to move many instructions into a document that I upload to the GPT’s resources. ChatGPT is helpful when I decide which content belongs in the configuration and which content can go in an uploaded document. Step-by-step: 1. I designed the public speaking course around critical inquiry, critical thinking, and public advocacy of a localized problem. 2. I identified a recurring challenge: students often named broad topics such as “poverty,” “crime,” or “the high cost of education” without exploring the depth of the issue, the degree of harm, or who is affected. 3. With help from ChatGPT, I built an AI tool that begins by asking, “what is your topic and what is the problem?” 4. I configured the AI to push back on incomplete answers by repeatedly asking, “what do you mean by x?” 5. I constrained the AI to draw most of its knowledge from my coursepack, including the class assignments, lessons, and instructional content. 6. I instructed the AI to guide students with questions rather than create speeches, make outlines, or find sources for them. 7. I configured it to identify alternative viewpoints, including disagreements from sources traditionally associated with the student’s own political, religious, or ideological perspective. 8. Because of the 8,000-character limit, I moved some instructions into a document uploaded to the GPT’s resources and used ChatGPT to help decide what belonged in the configuration and what belonged in the document.

I built a GPT called Entrepreneurial Mindset Coach based on the book I wrote last year. It is designed to rewire your brain for business success. Step-by-step: 1. I opened ChatGPT and selected More from the left-hand menu. 2. I selected GPTs and clicked Create in the upper-right corner. 3. I opened the Configure tab at the top. 4. I started building my GPT.