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I used AI to help investigate a family mystery that had remained unresolved for decades: identifying the biological family of my maternal grandfather. The challenge was not a lack of information. It was almost the opposite. I had DNA matches, family trees, names, dates, historical records, old photographs, obituaries, Facebook genealogy groups, and conversations with possible relatives. The difficult part was connecting all these scattered clues without jumping to conclusions. I built a research workflow in which AI acts as an investigation partner, not as the source of truth. Step-by-step: 1. I gathered the information I already had from genealogy platforms, DNA matches, family trees, historical documents, and family records. 2. I used ChatGPT to organize the evidence into people, dates, locations, relationships, DNA connections, documents, and unresolved questions. 3. I separated the information into three categories: confirmed facts, hypotheses, and missing information. 4. Instead of asking AI, “Who was my grandfather's biological father?”, I asked it to analyze possible family connections and identify which hypotheses were compatible with the available evidence. 5. For each hypothesis, I looked for supporting evidence, contradictory evidence, and information that was still needed. 6. I treated AI-generated connections as leads rather than genealogical proof. The goal was to use AI to decide what to investigate next, not to have it find the answer. 7. I used AI to compare family branches, surnames, generations, locations, and possible relationships among DNA matches whose family connections I did not immediately recognize. 8. When a promising connection appeared, I returned to the original genealogy and DNA sources to verify it. This gradually turned a long list of DNA matches into a smaller number of research paths. 9. I used AI to draft respectful, personalized messages to DNA matches and members of genealogy communities, including people in another country and language. 10. In each message, I explained what I was researching, what connection I suspected, what information I already had, and what I hoped the recipient might be able to confirm or rule out. 11. I treated their responses as new evidence and repeated the investigation loop: evidence → AI analysis → hypothesis → verification → human contact → new evidence → updated hypothesis. The final result is not an “AI-generated family tree.” It is a human-led investigation in which AI helps manage complexity, ask better questions, and identify the next useful action. The most important lesson I learned is that AI is particularly useful in genealogy when you do not ask it to give you the answer. Ask it to help you build the investigation.

I built a workflow for turning large ChatGPT conversation exports into a usable personal and creative archive instead of simply storing them as backups. The archive is processed incrementally. The inventory is built offline, and conversations that have already been indexed are not needlessly reanalyzed on every run. Each new export is compared with the existing archive, and only newly added conversations or conversations that have been revisited, extended, or otherwise changed are processed again and updated in the inventory. This keeps the workflow lightweight while allowing the archive to evolve over time. A conversation can remain stable for months, then become relevant again and receive new material without forcing the entire archive back through analysis. This matters because many of my conversations are long, layered thinking sessions: creative explorations, project development, research, problem-solving, or extended reflection. Without an inventory, the depth inside those individual conversations and thinking processes becomes difficult to retrieve later. The workflow makes long-form analysis and creative thought processes findable and reusable without flattening them into a few generic summaries. On top of the inventory, I use lightweight “blubscans” (analysis to improve retrieval): small, human-readable summaries that capture what mattered during a day or period without replacing the original conversations. They act as a navigational layer between thousands of raw messages and the things I may want to find, understand, revisit, or continue later. The important principle is that compression never becomes deletion. The raw conversations remain the source of truth, the inventory provides structure, and the scans provide context and tone. The result is more than a backup system. It becomes working creative memory: something I can preserve, search, revisit, connect across time, and reuse for projects, research, writing, pattern-finding, and future creative work. In simple terms, the structure is: raw exports → offline incremental inventory → blubscans/context layer → retrieval and reuse for later projects and creative work That way, the archive stays deep without becoming heavy, and useful without constantly reprocessing everything that was already understood. Step-by-step: 1. I collect large ChatGPT conversation exports as the raw source material for the archive. 2. I build and maintain an offline inventory of the conversations that have been indexed. 3. With each new export, I compare the conversations against the existing archive. 4. I process only newly added conversations and conversations that have been revisited, extended, or otherwise changed. 5. I update the inventory with the results while leaving stable conversations untouched. 6. I create lightweight “blubscans” with small, human-readable summaries of what mattered during a day or period. 7. I use the raw conversations as the source of truth, the inventory for structure, and the scans for context and tone. 8. I retrieve and reuse the archive for projects, research, writing, pattern-finding, and future creative work.

Planning a trip often meant piecing together information from a dozen different places. I might save inspiration from Instagram Reels and carousels, bookmark useful links in my browser, receive booking confirmations as PDFs from different platforms, and have tickets spread across emails and travel apps. By the time the trip got closer, I had all the information I needed—but no easy way to see it together or turn it into a coherent plan. I recently started using an AI travel tool called Mindtrip to solve this. It’s free and brings my saved content, links, bookings, PDFs, and tickets into one place. Mindtrip uses AI to understand the context and organize everything into a single trip, including the tips and hacks in Instagram Reels. It then creates a day-by-day itinerary based on what I’ve saved, liked, and booked. I have one place to see, manage, and adjust my entire trip instead of constantly switching between different sources. It also provides reminders and checklists based on my plans, along with a map view of where I’m supposed to be going. Step-by-step: 1. I saved travel inspiration from Instagram Reels and carousels, bookmarked useful links, and collected booking confirmations, PDFs, and tickets from different platforms, emails, and travel apps. 2. I brought the saved content, links, bookings, PDFs, and tickets into Mindtrip. 3. Mindtrip used AI to understand the context and organize everything into a single trip, including tips and hacks from Instagram Reels. 4. I used the resulting day-by-day itinerary, based on what I had saved, liked, and booked. 5. I used Mindtrip’s reminders, checklists, and map view to manage and adjust the trip in one place.

I’m on a GLP-1 medication that heavily suppresses my appetite, and I’m also managing a bilateral vestibular condition that causes balance and gaze issues. I needed a way to hit my protein and calorie targets despite having a low appetite, track body composition accurately, log vestibular symptoms, connect my actual training data, and get coaching guidance that reflects my situation instead of generic fitness-app advice. I built FuelStrong: three connected apps created with Claude over many sessions. They include a daily tracker for meals, water, energy, and training check-ins; a Progress and analytics module; and a standalone Vestibular symptom tracker. They share a Cloudflare Worker and D1 database backend, with KV for cross-device sync. I describe a feature or problem to Claude in plain language. Claude proposes structural options, I push back or choose a direction, and Claude writes the HTML, CSS, and JavaScript. I program my lifts in Fitbod using an Upper/Lower/Upper split, with an arms-and-back priority and the Build Muscle goal. I export those workouts as CSV and drop them into FuelStrong’s import zone, which parses exercises, sets, reps, and volume into my training history. Custom foods receive macro estimates through a Claude API call routed through my own Worker endpoint. Evolt body-scan data feeds dynamic calorie and protein targets based on BMR × activity factor, minus a deficit, with hard floors instead of static numbers. The Vestibular module intentionally uses open text fields for now, so Claude and I can identify which data matters before formalizing the inputs. Everything syncs across devices through Cloudflare KV. The coaching layer uses a three-tier framework—evidence floor, confirmed operating range, and aspirational target—to drive every recommendation. Two calorie floors, a daily target of approximately 1,000–1,100 kcal and a weekly average of approximately 1,300–1,400 kcal, reflect that chronic under-eating—not missed protein—is the real GLP-1 risk. Muscle mass has remained stable since my February 2026 baseline, so the coaching treats that as a genuine win rather than a plateau. Vestibular-training coaching connects dry-needling focus areas—SCM, suboccipitals, and splenius capitis/cervicis—to gaze-stabilization symptoms, since cervical proprioception substitutes for non-functional vestibular canals. The result is one dashboard that brings together training, nutrition, body composition, vestibular symptoms, and coaching logic. My muscle mass has held stable through it all. Step-by-step: 1. I describe a feature or problem to Claude in plain language, review its structural options, choose a direction, and have Claude write the HTML, CSS, and JavaScript. 2. I use FuelStrong’s daily tracker to record meals, water, energy, and training check-ins, while the Progress and analytics module tracks body composition and related trends. 3. I program my Upper/Lower/Upper workouts in Fitbod with an arms-and-back priority and the Build Muscle goal. 4. I export Fitbod workouts as CSV and import them into FuelStrong so it can parse exercises, sets, reps, and volume into my training history. 5. I route Claude API requests for custom-food macro estimates through my own Cloudflare Worker endpoint. 6. I use Evolt body-scan data to calculate dynamic calorie and protein targets from BMR × activity factor, minus a deficit, while maintaining hard floors. 7. I log vestibular symptoms in the standalone Vestibular tracker using open text fields while Claude and I determine which inputs should eventually be formalized. 8. I sync the three apps across devices through the shared Cloudflare Worker, D1 database, and KV backend. 9. I use the evidence floor, confirmed operating range, and aspirational target framework to guide recommendations, including the daily and weekly calorie floors. 10. I connect vestibular-training coaching to dry-needling focus areas and gaze-stabilization symptoms, then use stable muscle mass since the February 2026 baseline as a positive outcome rather than treating it as a plateau.

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 am a Mohs surgeon who built and shipped an iOS app without formal software engineering training. The surprising part was not only getting AI to write the code; it was getting AI to remember what it had already done. I built ErgoSherpa because up to 90% of surgeons in my field report musculoskeletal symptoms, while our training fails to address them. I wanted to help surgeons improve their health easily between cases. It is free on the Apple App Store and at ergosherpa.com. The bottleneck was maintaining coherence over time. Many sessions seemed to start from zero: I would re-explain the architecture, then watch a fix quietly undo something I had solved earlier. Three habits fixed that. First, I created persistent project documentation: a `MEMORY.md` index file plus separate topic files for architecture and business decisions. I update them at the end of every session so a new session can read the files first and pick up where the last one stopped. Second, I stopped handing one model the whole job. I use three models and match them to the task. Claude Fable audits only. I open a separate session, point it at the codebase, and require a prioritized checklist with the file path, the problem, and the fix in one sentence—without writing code. A model that did not write the code and has no memory of the project reviews it more honestly than the session that built it. I paste that checklist into a Claude Opus session, which handles the codebase repairs. Claude Sonnet handles routine work such as content updates, image processing, and scheduled maintenance, often from handoffs written by Opus or Fable. Nothing gets implemented on the auditor’s word alone: I have Opus flag any decision that needs human input. This workflow caught a deep-link handler that accepted authentication tokens from any URL and a database policy missing its write-side check. Third, I verify against production, not just the code. Some of the most frustrating parts of the project involved fixes that were correct in the file but wrong on the user’s screen because of a cached asset, a stale database row, or an iOS process that needed a force-quit. I no longer consider anything fixed until I have checked the live app. Step-by-step: 1. I keep a project memory directory with a `MEMORY.md` index and separate topic files for architecture and business decisions, updating them at the end of every session. 2. I open a separate Claude Code session running Claude Fable and prompt it to audit the codebase and return only a prioritized checklist: file path, line number, the problem in one sentence, the fix in one sentence, with no code or commentary. 3. I keep the auditor in its own session with no project history so it reviews the code independently instead of defending work it wrote. 4. I paste the checklist verbatim into a Claude Opus session and have it work from the top down, reading each file and making each change. 5. I tell Opus to flag anything it believes is a false positive instead of implementing it, keeping a human in the loop on key findings. 6. I use Claude Sonnet for routine work such as content updates, image processing, and scheduled maintenance, often from handoffs written by Opus or Fable. 7. I verify every change against the live production site rather than the local files because cached assets and stale database rows can make correct code behave incorrectly for users. 8. I append confirmed lessons and architectural decisions to the project memory so the next session starts from the project’s current state.

A reusable Claude Skill called `trust-due-diligence`, packaged as a `.skill` file. It is not a single report; it is a methodology made up of a `SKILL.md` file and three reference documents that teach Claude a repeatable process for investigating a named person, company, coach, or offer before you commit money or trust to them. Once installed, it activates automatically whenever you ask something like “deep dive on X” or “is this legit,” so you do not need to explain the process each time. Step-by-step: 1. Package the `trust-due-diligence` Claude Skill as a `.skill` file. 2. Include a `SKILL.md` file and three reference documents. 3. Use the files together to teach Claude a repeatable due-diligence methodology. 4. Install the Skill so it activates automatically for prompts such as “deep dive on X” or “is this legit.” 5. Use it to investigate a named person, company, coach, or offer before committing money or trust.

A while ago, my dad told me he wanted to use AI to analyze World Cup matches. He had never coded before, and his AI experience was mostly limited to occasionally asking Gemini a question. I installed Claude Code for him and gave him a few prompting tips. He ended up building a full football analytics site himself and deploying it on Vercel so he could show it to his friends. Getting started with AI is easier than people assume. Sometimes, you just need a project you genuinely want to make. Now that the Premier League season has kicked off, he’s already reworking the site for it. Step-by-step: 1. I helped my dad choose a football analytics project he genuinely wanted to build for analyzing World Cup matches. 2. I installed Claude Code for him, since he had never coded before. 3. I gave him a few prompting tips and let him use AI to help build the site. 4. He built a full football analytics site himself. 5. He deployed the site on Vercel so he could show it to his friends. 6. After the Premier League season kicked off, he began reworking the site for the new competition.

My mum’s 80th birthday is next week, and my dad, sister, and I wanted to create a photobook of her life. My dad scanned hundreds of photos from over the years and sent them to me to clean up. Because we were working to a tight deadline and I was away on holiday, I didn’t have time to open and edit each image individually in Photoshop. I asked ChatGPT to build a tool that accepts a folder of scanned images in various formats, including scans containing single or multiple photos, overlapping photos, and photos cut off by the scanner. The tool processed each scan, cropped and straightened the individual photos, and provided a UI where I could review the results and make manual adjustments to the cropping and orientation before saving the changes to new files. I then had the tool upload the images to Google Drive and create a spreadsheet with thumbnails of every image, along with a rating system. I shared the spreadsheet with my dad and sister so we could use it as a central place to rate the photos we wanted to include in the final book. This saved me hours of work and meant we could complete the photobook in time for my mum’s birthday. Step-by-step: 1. My dad scanned hundreds of photos and sent them to me as image files in various formats. 2. I asked ChatGPT to build a tool that could process scans containing single or multiple photos, overlapping photos, and photos cut off by the scanner. 3. I used the tool to crop and straighten each individual photo. 4. I reviewed the results in the tool’s UI and made manual adjustments to the cropping and orientation where needed. 5. I saved the adjusted photos as new files and had the tool upload them to Google Drive. 6. I had the tool create a spreadsheet containing thumbnails of all the images and a rating system for each photo. 7. I shared the spreadsheet with my dad and sister so we could rate the photos and choose which ones to include in the final photobook.

I am 65 and recently retired, and I have been asking AI for advice to keep my plants alive. It tells me whether they need more or less water, more or less light, or soil amendments such as perlite or coconut.

We built an evidence-driven Agentic AI workflow for extracting trustworthy investment intelligence from messy real-estate documents. The problem wasn't simply getting an LLM to read PDFs. Real-estate investment information can be distributed across reports, underwriting documents, valuation materials, rent schedules, spreadsheets, tables, and multiple versions of the same information. A metric such as IRR can also appear several times with different scenarios, dates, classifications, or meanings. Instead of building another "chat with your documents" agent, we designed a controlled agentic workflow around one principle: Don't make the agent smarter. Make the workflow harder to fool. Step-by-step: 1. I start with the business question. The agent receives a request for a specific investment metric for an asset and determines the business context instead of immediately searching for matching words. 2. I resolve the entity by normalizing the asset or entity using aliases, identifiers, relationships, and hierarchy information. This prevents ambiguous names from sending retrieval in the wrong direction. 3. I build a metric-specific plan using governed definitions for important metrics. A definition can include the metric's business meaning, terminology, preferred sources, classifier information, negative cues, and extraction rules. The agent starts with a contextualized retrieval and extraction plan rather than a vague instruction such as "find IRR." 4. I discover the right documents by narrowing candidate source documents with metadata and path-level information before searching the entire corpus semantically. The goal is: Find the right document before finding the right chunk. 5. I retrieve evidence within the selected documents. Only when scoped retrieval is insufficient does the workflow fall back to broader semantic retrieval, keeping the agent's search controlled and auditable. 6. I inspect structured information when necessary. Important investment information frequently lives in tables rather than paragraphs, so the workflow escalates to table-aware processing to inspect rows, columns, schedules, and structured financial evidence. 7. I extract a structured result instead of a long free-form answer. The result preserves the metric, value, unit or context, source document, page or location, and citation information. 8. I validate the evidence by checking the extracted value against the metric definition and relevant validation rules. Depending on the metric, these checks can include unit, scenario, chronology, plausibility, and table-to-text consistency. 9. I resolve conflicts explicitly. If multiple plausible values are found, the agent does not simply select the first result. The workflow applies rules for source precedence, chronology, scenario classification, and evidence strength. If a conflict cannot be safely resolved, the ambiguity is preserved rather than hidden. 10. I produce an evidence-backed result containing the selected metric, supporting evidence, context, and lineage. The result can then become a structured business artifact for downstream analytics, reporting, or decision-support workflows. This is not simply Question → RAG → Answer. The workflow is Question → Understand → Ground → Plan → Retrieve → Inspect → Extract → Validate → Resolve → Evidence-backed output. Retrieval is one capability inside the workflow. The agent coordinates the process, chooses the appropriate tools, follows the retrieval policy, handles structured evidence, and moves the result through validation and resolution. The biggest improvement did not come from giving the model more freedom. It came from giving the model better boundaries, better domain knowledge, better tools, and explicit decision rules. This pattern can be applied beyond real estate to financial research, insurance, compliance, legal documents, due diligence, and other enterprise workflows where an answer needs to be not only useful, but defensible and traceable.

I built Shots2Points, a golf scoring app for iPhone and Android, using AI as my development partner. The idea came from organising and playing in golf society events. Stableford scoring itself isn’t particularly complicated, but running an event can be. Organisers have to prepare groups, handicaps, and courses; collect scores from different groups; calculate results; manage withdrawals and ties; and eventually produce a leaderboard. I wanted to simplify that process while also providing an easy scoring app for ordinary casual rounds. The unusual part is how I built it. I’m not a professional software developer, and I don’t have a development team. I started by describing what I wanted the app to do to AI and gradually turned the idea into a working product. My workflow evolved into this cycle: idea → discussion → specification → implementation → real-world test → refinement. I repeat it for each feature. Step-by-step: 1. I define the problem and user experience with ChatGPT. I discuss ideas, challenge assumptions, work through workflows, and decide how a feature should behave before changing the code. 2. Once the behaviour is clear, I turn the idea into an implementation task. I use AI to specify exactly what needs to change, including edge cases and how the new feature should interact with existing functionality. 3. I build and inspect the code with Cursor. Cursor works directly with the project codebase, allowing AI to investigate existing code, implement changes, and report exactly what it changed. I test the result rather than simply accepting AI-generated code. 4. I test development versions on real iOS and Android devices. I follow the actual user journey, take screenshots or capture errors when something isn’t right, and bring those results back into the AI workflow. 5. I use AI to diagnose problems, make another targeted change, and test again. The result is a real cross-platform application rather than a prototype. Shots2Points includes free casual Stableford scoring and an Event Mode designed for golf societies and groups. Organisers can create events, import players, allocate groups, and allow each group to enter scores while everyone follows a live leaderboard. Building the app has required much more than generating code. AI has helped me work through database design, APIs, authentication, in-app purchases, App Store and Google Play requirements, debugging, user-interface decisions, testing, and release management. The biggest lesson for me has been that AI doesn’t remove the need to understand the problem or make decisions. It gives one person access to capabilities that would traditionally have required several different specialists. I provide the product knowledge, requirements, judgement, and testing; AI provides much of the technical capability and an extraordinarily fast feedback loop. That combination allowed me to take a personal idea for improving golf scoring and event management and turn it into a functioning iOS and Android product.

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.

Management pushed for an AI workflow to handle a massive daily headache: staff were manually searching for, copy-pasting, and moving hundreds of files listed in Excel. I was assigned to train the team to use the AI workflow. As soon as training started, though, it became clear that this was the wrong tool for the job. Forcing non-technical staff through a lengthy process of opening browsers, writing prompts, uploading spreadsheets, and dealing with token friction created more work than the manual process. It also introduced token costs, speed bottlenecks, and hallucination risks involving local file paths. The data was already structured. It did not need semantic intelligence; it needed deterministic speed. So AI got fired from running the task. Instead, I used AI as the developer. In less time than it would have taken to train one person, I had Gemini code a standalone Python desktop app and compile it into a simple executable. Now, with zero training required, staff drag and drop their Excel list into the app, choose a destination folder, and click Run. The app executes the transfers for hundreds of files, verifies every arrival on disk, and logs missing files in seconds. The result: $0 in API tokens, two hours recovered each day, zero path errors, and 100% team adoption. A two-second drag-and-drop will always beat a multi-step prompt-engineering exercise. As a side effect, the experience also supported AI adoption. Staff are now seeing more ways AI can help rather than hinder their work. Step-by-step: 1. I evaluated the proposed AI workflow for manually searching, copy-pasting, and moving hundreds of files from Excel lists. 2. I identified that training non-technical staff to open browsers, write prompts, upload spreadsheets, and manage token friction added more work, costs, bottlenecks, and local-file-path hallucination risks. 3. I used Gemini as the developer to create a standalone Python desktop app and compile it into a simple executable. 4. I had staff drag and drop their Excel list into the app, select a destination folder, and click Run. 5. The app transferred hundreds of files, verified each arrival on disk, and logged missing files in seconds. 6. I measured the results: $0 in API tokens, two hours recovered daily, zero path errors, and 100% team adoption.

My friend shot a short film and gave me his hard drive to help with post-production. It contained more than 400 clips, with no clear way to tell when each one had been shot. I asked Claude to review the metadata for every clip and group them by timestamp, so they were organized by shoot session instead of sitting in one large folder. Claude also built a spreadsheet listing each clip, its shoot session, the date, runtime, and file size. This saved my friend the hassle of reviewing every clip manually. Step-by-step: 1. I received my friend’s hard drive with more than 400 short-film clips. 2. I asked Claude to review the metadata for every clip. 3. I had Claude group the clips by timestamp and organize them by shoot session. 4. I had Claude create a spreadsheet with each clip’s shoot session, date, runtime, and file size. 5. My friend could avoid going through every clip manually.

I had a bad workplace experience, so I built Doris: a saucy but loving anonymous AI aunt who remembers the tea, protects storytellers, and warns others. I built Spill Tea with Doris, an anonymous AI workplace confessional for conversations people cannot really have on LinkedIn: the bad manager, the inexplicable reorg, the coworker who somehow survives every layoff, and the meeting that should probably be entered into evidence. Doris does something more interesting than simply listen. She remembers the tea—and, carefully, spills it. The problem I wanted to solve was not really “chat with an AI.” Most AI conversations are disposable, but workplace stories are not. They accumulate companies, people, reorganizations, layoffs, recurring behaviors, management decisions, and institutional weirdness. At the same time, people are understandably reluctant to talk openly about their employers because a sufficiently specific story can identify its author. I designed Doris around a different idea: retain the knowledge without retaining the storyteller’s identity. Someone visits [spillteawithdoris.com](https://spillteawithdoris.com) and tells Doris what happened at work. The application is built in Next.js and deployed through Vercel. The conversation goes to an AI model with Doris’s personality and behavioral rules. Redis handles temporary conversational context, while Neon Postgres and Prisma maintain the structured, longer-lived pieces of the story—companies, people, events, and their relationships. Rather than treating every conversation as one giant transcript, the application extracts useful information and connects it to the larger story of a company. That creates the second half of the experience. When another visitor asks Doris, “Have you heard anything about working at Company X?”, she can draw upon what previous visitors have told her. But she does not simply retrieve someone’s confession and repeat it. The system separates what is useful about a story from what could identify the person who told it. Names, exact teams, precise dates, unusual job titles, and other unnecessarily identifying details do not need to travel with the underlying observation. Doris can instead recognize that she has heard several stories involving reorganizations, unusual management turnover, or a particular cultural complaint. Then Doris tells the story herself, in Doris’s voice. She might say that she’s “heard some tea” about a company, explain the general pattern, distinguish something she’s heard once from something that appears repeatedly, and avoid pretending anonymous reports are established facts. Visitors get useful institutional memory without being handed the breadcrumbs needed to identify an individual employee. Public information can provide a second layer of context. If appropriate, Doris can search for publicly available information about a company and compare it with what people have privately described. Those sources remain conceptually separate: what Doris can verify publicly, what Doris has heard privately, and what Doris herself infers should never become the same thing. The result is deliberately a little strange. It is an anti-LinkedIn. LinkedIn is where thousands of individual experiences are polished until every company sounds wonderful and every departure is an exciting new chapter. Doris works in the opposite direction. One anonymous story may just be a story. Ten people independently telling Doris versions of the same story start to describe a workplace. And Doris remembers. She just doesn’t need to remember who told her. Step-by-step: 1. I built a Next.js application and deployed it through Vercel at spillteawithdoris.com. 2. I defined Doris’s personality and behavioral rules for the AI model. 3. I added Redis to manage temporary conversational context. 4. I created a Neon Postgres database and used Prisma to model Company, Person, Story, and Event relationships. 5. I built an extraction layer that converts conversations into structured observations, removes unnecessary identifying information, and associates the knowledge with the appropriate company. 6. I built retrieval so Doris can find relevant prior observations when someone asks about a company. 7. I had the AI synthesize those observations in Doris’s voice instead of quoting or exposing the original submissions. 8. When appropriate, I let Doris search publicly available company information while keeping public sources, private reports, and Doris’s inferences conceptually separate.

I teach AI in Business at Western Washington University, and I built a free, case-based AI textbook with Claude and GitHub Pages: https://prof-califf.github.io/ai-in-business/ The 11-chapter digital textbook includes seven real company cases—EveryCure, Netflix, Spotify, Uber, Waymo, Airbnb, and Epic—plus chapters on AI’s environmental cost, ethics, regulation, and the future of work. Seven chapters include hands-on Python labs, and Chapter 8 has an interactive calculator that models the energy and water footprint of a reader’s own AI usage. The textbook is free, has no publisher, and costs $0 to host. The problem was that AI textbooks are stale before they ship. An 18-month publishing cycle means students can pay $200 for a book that is already two model generations behind. Textbooks also tend to teach theory first and postpone business relevance until much later. My students do not need to derive backpropagation. They need to understand why Spotify built a recommender, how it works, what broke, and what it cost—then build one themselves. My stack is Claude, GitHub Pages, VS Code, Google Colab for the labs, and n8n for the Chapter 7 agentic lab. The project started when I was assembling a reading list and could not find anything usable—only outdated textbooks and paywalled cases. I already had years of lecture notes scattered across documents. Step-by-step: 1. I locked in a framework first. I use the AI Factory model—Data → Model → Prediction → Decision → Value → loop—and run every company through it. This is the step people skip: it builds transferable skills for students and gives Claude a stable structural contract across every chapter. 2. I set up the repository before writing. I created a new repo, added `index.html`, opened Settings → Pages, and confirmed that the site deployed. Five minutes up front was better than debugging after 40,000 words. 3. I built one chapter completely and used it as the template. Each chapter is a single, self-contained HTML file with no build step or dependencies. Then I prompted Claude: "Here's my finished Chapter 1 as the format reference. Here are my notes on Uber. Draft Chapter 4 in the same structure and voice." A finished exemplar worked better than an abstract description. 4. I started from my existing material instead of using a blank prompt. Claude structured and clarified my notes, but it did not decide what I think. I rewrote anything that did not sound like me. That distinction preserved the resource’s voice and avoided generated filler. 5. I made the labs builds rather than exercises. Students reconstruct each system in Google Colab using Claude as a coding partner, then publish their work to their own GitHub repositories. They finish with artifacts. 6. I shipped an incomplete version and update it like software. I published with fewer chapters, then edit and push updates when regulation changes so students automatically get the current version.

I built a digital Second Brain after trying several approaches, including OpenBrain and LLM Wiki. OpenBrain and LLM Wiki are useful frameworks for building a digital brain. The theory is solid: flat Markdown files, AI-first conventions, and an ingestion pipeline that turns raw inputs into searchable knowledge. But when applied in practice, the process can be bumpy and may require adjustments—or an entirely different approach. I adapted the ideas to fit how I actually think and work. I kept what worked, discarded what didn’t, and built my own digital brain. The result is documented in a single file containing everything an AI needs to understand, maintain, or rebuild the system from scratch. Step-by-step: 1. I tried several digital-brain frameworks, including OpenBrain and LLM Wiki. 2. I evaluated their approaches, including flat Markdown files, AI-first conventions, and an ingestion pipeline for turning raw inputs into searchable knowledge. 3. I identified where the frameworks were difficult to apply in practice and adjusted my approach. 4. I kept the ideas that worked for me, discarded what didn’t, and built a digital brain suited to how I think and work. 5. I documented the system in a single file so an AI can understand, maintain, or rebuild it from scratch.

My company takes cybersecurity seriously, and part of our new-hire onboarding process is a tour of the IT department to meet the team. Our walls have several screens displaying metrics and information from our security tools, firewalls, and other systems. It looks like a scene from a movie. By far, the most popular display is our custom-made Cyber Threat Map, which shows real-time attempts to infiltrate our network, malicious emails, and other security threats we're blocking. The map drives home the reality of what we're facing 24/7 while protecting our employees, vendors, and customers. I built it using Claude Code. It polls a range of devices and systems through APIs, aggregates the data on one screen, and displays attack vectors, location information, and other details from the last 30 days. Step-by-step: 1. I identified the security tools, firewalls, devices, and systems that provide relevant threat data. 2. I used Claude Code to build a custom Cyber Threat Map. 3. I connected the map to the devices and systems through their APIs. 4. I aggregated the data on a single screen, including attack vectors, location information, and other details from the last 30 days. 5. I displayed the map during IT department tours so new hires could see the real-time threats our company is blocking.