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I built a family calendar with Lovable and Claude that integrates with our Google Calendars and Todoist projects. My husband, son, dog, and I were struggling to track our schedules in one place. Although we had shared our calendars through Google, we did not have a central view displayed in the house, and the interface became cluttered when we tracked multiple calendars. We had considered buying a Skylight calendar for years, but it costs $300, and we were not convinced we needed all of its extra features. I decided to build my own instead. Step-by-step: 1. I created a product requirements document (PRD) for a family calendar in Claude. It prompted me to choose the available views, decide how to organize each family member’s calendars, support both events and tasks, and add extras such as the day’s weather and a photo background. 2. I shared the PRD with Lovable, which built the first version of the calendar. Lovable helped me integrate my Google Calendars and Todoist projects so that all of our events and tasks appeared in one place. It also helped me integrate Google Photos so our family photos could rotate in the background. 3. I launched the calendar as a password-protected website so any family member could access it from anywhere. 4. I placed the calendar on an iPad in our kitchen and edited it as we used it, learning which features were most and least helpful. I also added calendars for my in-laws when they visited for several months at a time. Claude continues to help me draft new requirements, which I share with Lovable. The calendar is tailored to our needs and costs much less than the Skylight option. The photo feature also makes it feel personal, and I look forward to continuing to evolve it over time.

I'm not a professional developer, but I built Sujido, a live word puzzle, with multiple AI agents. For months, they gave me bloat, lost context, and slop, and neither I nor the models could untangle what had been decided or why. I eventually settled on two agents: one Claude instance that plans and reviews, and another that builds. I imposed rules on both, one failure at a time. None of these rules would surprise a professional engineering team, but nobody handed them to me. That's the key lesson I learned: agents won't bring the discipline for you. You have to impose it. When I showed the first game revision to strangers, their verdict was that the first half was "boring" and the second half was novel. It was hard news to hear about my baby, but I took the feedback to heart and cut the game in half. That required surgery on code tangled across both halves, followed by a rebuild under the rules below. Before the rebuild, a separate AI audit graded the codebase B−. Afterward, it graded it A−. The deployed site shrank by 97.5%, and every serious finding was fixed and locked in by an automated test. The models didn't change drastically, but my operating rules did. Step-by-step: 1. I wrote a constitution before the first prompt. The repo holds what every agent must read before working: `CLAUDE.md` files for the project as a whole and for each major part of the code, a collaboration protocol for how the agents and I work together, a spec for the core data format, and a decision log. The data format is changed there first and then in code. Nothing is remembered unless it is written down. That's my life as well. :) 2. I split the work by role, not by task. One Claude instance is the strategist: it writes briefs, reviews reports, and never commits code. Claude Code in VS Code is the builder: it executes the brief, writes the tests, reports back, and never releases. Neither agent can approve its own work. 3. I made every job start from a written brief, and I never edit briefs. Each brief is committed to the repo with an exact scope, such as "do X only, do not begin Y." If plans change, a dated amendment states what it supersedes. The builder may skip a risky item with a written reason; skipping is better than forcing it. 4. I used mock-ups with real data instead of descriptions. Anything I will look at gets a mock-up, and I approve it before any code is written. I learned this when a status report described only in words arrived on my phone formatted as a wall of text. The approved mock became the spec, and the tests pin the output to it. 5. I proved that every test could fail. No test is trusted until it has been run against deliberately broken code and caught the bug. A test suite that has never failed hasn't proven anything. 6. I verified reports against the real repo. Agent reports drift. One summary said players rated a puzzle "just right" when the log said "too easy." An audit said there were about 25 unused functions; a recount found 29. The strategist spot-checks load-bearing claims against the actual files and data before anything ships. 7. I built gates the agents can't talk their way past. All work lands on a branch that auto-deploys to a test site. GitHub Actions lints and runs the Python and headless-browser tests on every push. A git hook refuses direct pushes to production. Releases are fast-forward merges that only I perform, after CI is green and I've tested the build personally. The live site shows the release's commit ID, and I check it before calling it shipped. Nothing merges automatically. An agent's "looks good" is just another report, and reports drift. 8. I gave the system a memory. Every session ends by updating the decision log and a "resume here" document, so the next session—a fresh model with no recollection—picks up exactly where the last one stopped. Without this, every session starts by relitigating old decisions. 9. I let the data and end users overrule me. A scheduled GitHub Action turns player telemetry into a daily report. It showed that the difficulty dials I assumed mattered did not predict what players found hard; what mattered was how misleading the starting arrangement was. When testers were confused by the tutorial, I rebuilt it even though I had already approved it. Player feedback about their own experience outranks my earlier rulings. AI agents are velocity multipliers, but they multiply bad structure as quickly as good code. The rules came from me, not the models: the AI writes the code, while the rules, gates, and written record keep the process honest. See the result at Sujido.com. Finding the anagram is the easy part; sliding it into place is the hard part. One board took me 902 moves. My S.O. solved it in 242, and I haven't heard the end of it. Try to break it!

I built WarrantKit, an open-source (Apache-2.0) kill switch for AI agents. Each agent run receives a Warrant: a typed contract that specifies what the agent may do, for how long, and under which policy. A gateway checks every action before it reaches the outside world. When containment triggers, the runtime’s epoch advances, immediately invalidating every Warrant issued under the old epoch instead of allowing it to linger until a timer expires. Evidence of what happened remains separate from authority, and receipts can be verified offline with a standalone Python script. I designed the architecture and threat model, then used AI coding assistants for most of the implementation. I reviewed everything against an adversarial test suite and a real-host enforcement demo that others can run themselves. Before writing the mechanisms, I wrote tests for the attacks: replayed runtime reports, tampered event sequences, evidence attempting to upgrade itself into authority, and authority carried across execution boundaries. The suite has 202 passing tests, and several core behaviors exist because a test caught the system trusting too much. One example is a recovered runtime carrying a stale report into a new epoch. I then proved enforcement on a real Linux host with a cgroup-v2 real-kill demo before claiming it. The no-root demo is simulated, and the README says so. It also lists what WarrantKit is not. The architecture is layered: WarrantKit owns the authority lifecycle and verification; AgentContainment owns the enforcement boundary; and Warden, DProvenanceKit, and ClaimProofKit record evidence independently. Step-by-step: 1. I defined a Warrant as a typed contract specifying an agent’s permitted actions, duration, and governing policy. 2. I placed a gateway in front of the outside world so it checks every agent action before allowing it through. 3. I made containment advance the runtime’s epoch, invalidating every Warrant issued under the previous epoch immediately. 4. I kept evidence separate from authority and made receipts verifiable offline with a standalone Python script. 5. I wrote adversarial tests for replayed runtime reports, tampered event sequences, evidence attempting to become authority, authority crossing execution boundaries, and stale reports carried into a new epoch. 6. I implemented the system with help from AI coding assistants and reviewed the implementation against the test suite and a real-host enforcement demo. 7. I validated enforcement on a real Linux host with a cgroup-v2 real-kill demo, while clearly labeling the no-root demo as simulated in the README. 8. I kept authority lifecycle, enforcement, and evidence recording in separate architectural layers using WarrantKit, AgentContainment, Warden, DProvenanceKit, and ClaimProofKit.

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 regularly come across long articles, reports, papers, ebooks, and other documents that I want to understand but realistically don’t have time to read closely. Instead of letting them pile up in a reading queue, I change the format and turn them into audio I can consume during time that would otherwise be less productive. Step-by-step: 1. I choose a long article, report, paper, ebook, or other document that I want to understand but don’t have time to read closely. 2. I upload it to Google Notebook (formerly NotebookLM) and add it as a source so NotebookLM can work directly from the material. 3. I generate an Audio Overview. NotebookLM turns the source into a conversational, podcast-style discussion that summarizes and explains the major ideas. 4. I listen while walking, driving, working out, doing chores, or running errands. 5. I follow up on what matters. After listening, I know the main ideas, what I want to investigate further, whether the document is worth reading in full, and which sections deserve closer attention. The result is essentially a personal podcast generated from whatever I need to learn. What I like about this workflow is that it doesn’t require me to find more time. It lets me use time I already have differently. Because the material is turned into a conversational discussion rather than simply being read aloud, I find it easier to stay engaged with dense material. I don’t treat the podcast as a replacement for reading the source when the details really matter. I use it as a comprehension and triage layer. Even when I eventually go back and read the original, I’m starting with a mental model of what’s in it rather than approaching it cold. The broader lesson is that AI doesn’t always need to save time by doing the work for you. Sometimes it can save time simply by changing the form of the work so it fits into your life.

I’m Jesús Núñez, an artist and the owner of J. Nunez Gallery, an independent contemporary art gallery with locations in Summit and Millburn, New Jersey. Founded in 2020, the gallery presents original abstract and figurative art, landscapes, still life, and photography. We help people discover artwork for their homes and work with interior designers on residential projects. Alongside running the gallery, I’m exploring how ChatGPT can help a small business improve its operations, marketing, and decision-making. I manage the business myself, so the practical question is how to turn existing information into useful, completed work. The workflow starts with real source material: website content, spreadsheets, analytics exports, screenshots, and the gallery’s business rules. I use focused conversations to examine one area, prepare concrete changes, and review the results. The work includes website improvements, artwork catalog organization, marketing analysis, audience segmentation, and outreach preparation. For example, AI-assisted troubleshooting helped identify duplicate website page-view tracking and guided its correction, which we checked against live analytics. That is an operational improvement; it is not evidence of increased sales. Another small-business owner can recreate the approach as follows. Step-by-step: 1. Record the business context: customers, products, geographic market, budget, brand preferences, and operating constraints. 2. Choose one specific task and define what completion means. Provide the relevant source material. 3. Require the AI to distinguish verified facts, interpretations, missing information, and proposed actions. It should not guess prices, contact details, or results. 4. Ask for a concrete deliverable, such as revised website copy, a classification table, implementation instructions, or an outreach draft. 5. Review the work and verify the actual result after implementation. Keep preparation, implementation, verification, and blocked work clearly identified. 6. Preserve approved decisions and completed actions in a current record so later sessions can continue from the existing work. 7. Measure qualified inquiries, appointments, proposals, and sales alongside marketing activity. This is an ongoing project. AI can repeat recommendations or lose track of previous decisions, so human review and continuity remain essential. I have not established a measured revenue increase or time-saving figure. I’m testing whether this approach can build a stronger business while allowing more attention for art, artists, and clients. If this example makes you curious about the gallery, you can browse available artwork and find information about our Summit and Millburn locations at https://www.jnunezgallery.com. You are welcome to explore the collection or contact us through the website for help choosing art. Jesús Núñez Artist and Owner, J. Nunez Gallery Summit and Millburn, New Jersey https://www.jnunezgallery.com

I'm 78, a former army officer, and I live alone in the woods in southern British Columbia on satellite internet. In late January 2026, I knew nothing about AI and considered myself a novice in technology. After watching a YouTube video about advanced AI models, I started learning from YouTube channels that explain new AI research papers. I decided to run an experiment: what could a technology novice build with AI? I've been building for about 15 hours a day ever since. My goal is to build a system that improves itself in a continuous loop: it finds an upgrade proposal, builds and tests it, filters and grades it, approves or rejects it, installs it if it passes, monitors its performance, and learns from the result. We custom-built FORGE3 RSI in 900 hours. It consists of four plug-in modules: Forge2 RSI, which runs the improvement cycle; Bridge, the checker and connector; JAZ RSI, an upgrader in RAM built from a research paper; and Forge SecureBox, a high-security Mac app that runs security tests and code in a fresh, disposable virtual machine with no network device. I don't write code—it feels like a foreign language to me. Instead, I set the direction and run a team of AIs, mostly low-cost, near-Frontier models: Gemini 3.1 Pro and Hark as chiefs of staff; Cline and Devin as builders; and other models as independent, adversarial auditors. The rule that has made this work is simple: nothing gets built without an adversarial audit before and after. Our critical build pipeline is: Plan → audit → blueprint → audit → tests → audit → build → audit → test → audit → commit Each module has been built and fully tested on its own. Forge2 RSI alone has more than 1,000 tests. The first complete end-to-end cycle, using one upgrade proposal that we feed it by hand, is the next step. Our first attempt was fragile, and models that sound confident are often wrong. That is why audits matter more than any single model. FORGE3 RSI is my own custom-built plug-in module project. Our hardware and models are an MBP i9, Mac mini M4, Gemini 3.1 Pro, Hark, Cline (GLM 5.3/SPARK 1.3/MiMoV2.6), Devin (SWE2/GPT SOL 6), KUN (DeepSeek V4.1), and Git. Step-by-step: 1. We select a promising published research paper and have the team prepare and build it. 2. For every file, the team follows the same sequence: plan, audit, blueprint, audit, write tests, audit, build, audit, run tests, audit, and commit. 3. A different AI audits the work than the one that built the file. I decide when and how each audit happens. 4. Nothing is written or changed without my explicit GO. Reading and checking code is free; writing is work and waits for me. 5. We handle one building task at a time and avoid bundled instructions. 6. Any code the system wants to test runs inside SecureBox, sealed off from my Mac and the internet. 7. Everything that passes is committed to Git. The builders also maintain shared memory documenting what was decided and why, so the next session can pick up where the previous one stopped. 8. Next, we will run one complete improvement cycle with a hand-fed upgrade, then scale up slowly to two or three papers a week, prioritizing quality over speed as we work toward full autonomy.

I'm becoming GrokBot-pilled. I run my company on top of what's happening on X. I used to check everything manually: scanning for new workflows, searching brand mentions, watching what readers complained about, and trying to remember who I had just met at events. I've now handed all four jobs to GrokBot routines. The combined setup took under an hour, and the routines save me a couple of hours of manual checking every day. Every prompt below is read-only by design: the agent watches, labels, and drafts, but never posts, replies, texts, or emails anyone. Paste each prompt as-is, then replace the bracketed parts with your own channels, brand, and time zone. Routine 1: Find real AI workflows worth stealing and filters out the hype threads. Every weekday at 9:15am in my time zone, search X and the web for new AI workflows people are actually running. Keep only cases that name the old task, the tool, and the result: time saved, a step removed, or a handoff that used to need a person. Drop product announcements, “10 prompts” threads, and anything the author has not used more than once. Return at most 3. For each, give the link, the job it replaces, what I would need connected, and whether it fits my work. Send it to me on Slack. Do not post or reply anywhere else. Save this as a routine. If nothing qualifies, send one line saying so. Routine 2: Labels every mention of my name or company and delivers the results. Every weekday at 9:00am, search X for mentions in the last 24 hours of [your brand, @handle, or your own name]. For each hit, give the author, link, a one-line summary, and whether it is praise, complaint, question, or neutral. Do not reply to anyone. Post the digest in [your Slack channel] and tag me. Save this as a routine. If a source is unavailable, say which one. (Note: works just as well for a personal brand or a job search as it does for a company) Routine 3: Catches people actively looking for what I do. Every 4 hours, search X for people evaluating [your category]. Catch three things: reviews or complaints about [category]; posts asking which [category] to use; and complaints about the problem [your product] solves. Quote the post, link it, name the account, and label it review, shopping, or complaint. Skip anything older than 24 hours, and skip [category] accounts promoting themselves. Post to [your Slack channel] only if there is at least one new hit you haven’t already shared. Do not reply to or interact with anyone. Save this as a routine. If the search fails, report the failure in Slack. Routine 4: Keeps the people I meet at events from disappearing into my contacts app. Every weekday at 8:30am in my time zone, read my address book and list contacts added in the last 14 days. For each, show name, date added, phone, email, and any note. Ask me what we talked about before you write anything. Once I reply, draft a follow-up task for each person in [your task tool] with the context attached. Never text, email, or call anyone. Save this as a routine. If the address book is unavailable, say so and do not use old data. Step-by-step: 1. Paste each routine prompt as-is. 2. Replace the bracketed fields with your own channels, brand, category, product, task tool, and time zone. 3. Save each prompt as a GrokBot routine on its stated schedule. 4. Review the read-only outputs in Slack and respond to Routine 4 when it asks what you discussed with a new contact. 5. Keep the routines limited to watching, labeling, and drafting; they should never post, reply, text, email, or call anyone.

Today's Rundown reported that Anthropic spent the past year consulting religious scholars from different faiths to help shape Claude's morals. I use a different approach for Bible study, research, and writing Bible study books with Claude. I wrote a set of Bible study principles that Claude reads before any work begins and follows throughout the process. The main tenets are: - The Bible is the sole authority. No commentary, tradition, or scholar outranks the text. - Scripture interprets Scripture. When a passage is unclear, the answer comes from another passage, not from a theological system. - First ask what the text actually says before asking what it means. - Ask who is speaking, to whom, and under what circumstances. - Quote every verse word for word. Never paraphrase it. - Separate what the text states from what we infer, and say "I don't know" where the Bible is silent. The difference shows in the outcome. The books contain no man-made doctrines, denominational vocabulary, or creeds—only what the Bible says, with the passage shown for every claim. When a draft goes beyond the text, these principles catch it. My experience is that the moral standard an AI needs is already written down, and an AI can be held to it. The principles, along with 23 free books and studies, are available at noblemind.study. Step-by-step: 1. I wrote a set of Bible study principles for my own Bible study, research, and Bible study books. 2. I have Claude read the principles before any work begins. 3. I require Claude to follow the principles throughout the work. 4. I use the principles to keep claims grounded in the biblical text, quote verses word for word, and distinguish statements from inferences. 5. I review drafts for man-made doctrines, denominational vocabulary, creeds, and claims that go beyond the text.

I built a custom news agent that skips the Google News and Apple News walled gardens. Eight prompts turn Claude into a personal news editor that reads my trusted RSS feeds, scores every story with a decision model, and delivers a clean daily page for about a dime a month. Each week, pulling two or three good stories for my newsletter digest meant searching multiple sites by hand—hours of clicking and scrolling for a handful of usable links. RSS feeds already deliver that content; the missing piece was a judgment layer that could separate worthwhile stories from noise without the cost or latency of running everything through a full LLM. I used Claude Cowork, Jev from TypeSafe AI, Vercel AI Gateway, RSS feeds, Python, and SQLite. Jev is a decision model rather than a chatbot: it returns a probability and confidence score instead of prose, does not hallucinate facts, and scores a story in a fraction of a second. That makes an automated daily judgment call inexpensive enough to run continuously. Step-by-step: 1. I set up a Claude project to hold the scripts, taste guide, database, and page. 2. I defined the goal and sections, then had Claude interview me to create a short “taste guide” describing what counts as a good story. 3. I found and live-tested the RSS feed for each trusted source instead of guessing feed URLs. 4. I fetched all working feeds into a deduplicated SQLite database and logged failures. 5. I used Jev through Vercel AI Gateway to score every story: first filtering ads and promotions, then ranking stories by section against my taste guide. 6. I selected the winners for each section, with Jev identifying near-duplicate stories so they would not appear twice. 7. I generated one-line summaries and fact-checked them against the source so nothing was invented. 8. I rendered a simple HTML page and scheduled the entire workflow to run every morning. A few details matter. I used Cowork rather than a plain chat because several steps require Claude to work with files, run code, and access the network. I also created a Vercel account and AI Gateway API key manually before the scoring step. Finally, I saved everything to the Claude project instead of a local folder, which allows the morning run to happen in the cloud on its own schedule. Scoring costs roughly $0.10 per month with Jev, compared with a few dollars for a full LLM doing the same job. The workflow turns a two-hour weekly slog into a page that is ready every morning, with each link going directly to the original publisher rather than a Google or Apple aggregator. Full build walkthrough: onabetternote.substack.com/p/build-your-own-news-agent-with-claude — the Substack post includes the exact eight prompts to copy and paste, plus more detail on why a model like Jev matters for cost containment as agents become routine.

I built my agent fleet around one question: could a single assistant become a coordinated team that keeps useful work moving even when its main machine goes dark? I didn't want a pile of chatbots. I wanted specialists with clear jobs, a shared board, a second set of eyes, and a real failover drill. Here's how it works. THE STACK Cloud: GrokBot runs as Fleet Commander. It routes work, runs routines, and stays with me on my phone. Specialist GrokBots take focused research, design, and budgeting assignments. Mac Studio (M2 Max, 64 GB) with two Studio Displays: home base. It runs Codex (my Chief of Staff), Hermes (my local operator), ChatGPT Dot, and Hindsight shared memory. Hermes runs Qwen3.6-35B-A3B, a 4-bit quantization served by llama.cpp with a 256K-token context. With about 3B active parameters per token, it stays responsive on Apple silicon. NVIDIA DGX Spark (128 GB unified memory): my independent compute node. It runs Qwen3.5-122B-A10B in NVFP4, with about 10B parameters active per token, and the model can read images. It also hosts a backup Hermes operator. Windows PC with a GeForce RTX 5090: dedicated GPU capacity for image work. MISSION CONTROL Mission Control is a Kanban board on a private GitHub repository. Every card opens with the question, deadline, evidence links, and done criteria. Labels route ownership. An urgent label fires a webhook that wakes the reviewing agent, and a needs-me label is the one that pulls me in. Agents hand off, report, and raise concerns in card comments, so the work and its evidence stay together. The roles are simple. GrokBot owns routine board moves. Codex checks evidence and records verification. Hermes handles local execution and machine-level evidence. Dot watches for new work and changes. Hindsight holds the shared operating rules, and the jobs themselves stay on the board. A REAL DEADLINE CROSS-CHECK Right before a major application package went out, Dot ran an independent, read-only cross-check of the narrative, the budget justification, and the walkthrough across connected email and Drive. It found three places where the documents contradicted each other and quoted the exact sentences, so each conflict was easy to find. Fleet Commander fixed all three before submission. That's the job of a watcher: spot the mismatch early, show the evidence, and hand whoever owns the work a clear fix. THE FAILOVER DRILL I wanted to know what happens if the Studio's primary Hermes gateway pauses. On October 5, I ran a controlled, reversible soft-pause to find out. During the pause, Hermes on the Spark stayed active through its own Telegram connection and answered a one-shot request with the local Qwen3.5-122B-A10B NVFP4 model. From that worker, I listed a Mission Control issue, verified service-account access and a vault credential check without exposing a secret, and ran the Studio health probe. The probe flagged the Studio Hermes gateway as paused and reported the other checked Studio services as passing. So I had an evidence-backed result: the independent worker could respond, use its local model, read the work queue, and report the primary gateway's health during a real, reversible pause. THE PATTERN Give each agent a narrow job. Put the work on one shared board. Require evidence before anything counts as done. Then pause one piece on purpose and record exactly what the backup can do. That's how capable models turn into dependable operations. The fleet catches contradictions before they get submitted, keeps routine work moving, and shows me exactly where its limits are. I set the goal. The fleet does the work. The evidence shows what actually happened.

I’m designing a publishing system that keeps producing value without requiring constant output. This workflow started with frustration about the usual content model: publish constantly, stay visible, feed the algorithm, and repeat. That model works poorly when my energy varies, my interests shift, and forcing output quickly turns creativity into maintenance. Instead of asking, “How do I post more?” I’m asking: “How can content keep moving without requiring me to keep producing at the same pace?” The system is built around uneven energy. During high-energy periods, I can generate ideas, draft pieces, collect fragments, or prepare several posts at once. During low-energy periods, existing material does not become a failure state. It can sit, mature, be lightly edited, be reused, or simply wait. Ideas can exist in different stages: a note, a half-developed thought, a finished piece, something worth revisiting later, or something that can be repurposed in another format. Older material is not automatically “used up” after publication. A long-form idea can become a short post. A workflow can become a case study. A conversation fragment can become an essay seed. A previous post can return later from a different angle. The goal is not to build a machine that constantly demands feeding. It is to create a content reservoir I can draw from when I have the capacity. This workflow has not been fully implemented yet, but the design principle is useful: > Build around energy reality, not around imaginary consistency. Content should fit into life. Life should not be reorganized to serve the content treadmill. Step-by-step: 1. I design the publishing system around variable energy rather than a constant production schedule. 2. During high-energy periods, I generate ideas, draft pieces, collect fragments, and prepare several posts at once. 3. During low-energy periods, I let existing material sit, mature, receive light edits, be reused, or wait. 4. I allow ideas to remain in different stages, from notes and half-developed thoughts to finished pieces and material worth revisiting later. 5. I repurpose older material across formats, such as turning a long-form idea into a short post, a workflow into a case study, a conversation fragment into an essay seed, or a previous post into a new angle. 6. I draw from the resulting content reservoir when I have the capacity instead of constantly feeding a content treadmill.

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.

The Rundown recently held our first in-person retreat in Portugal, which was also my first time traveling to Europe. Claude and ChatGPT were amazing resources throughout the trip, from helping me navigate a chaotic international airport to providing personalized advice on cultural differences, tipping, translating menus, and using public transportation. My wife accompanied me and had plenty of time for solo exploration during the day. She is already a big planner, but a ChatGPT project containing our trip information helped her create a detailed plan, including a packing list, pre-trip checklist, and well-structured day-by-day itinerary for seeing as much of Lisbon as possible. As someone who gets stressed by travel, having an expert with context about my life available in my pocket to answer questions whenever I needed it is something I can’t imagine taking trips without. Step-by-step: 1. I used Claude and ChatGPT for personalized travel guidance during my first trip to Europe. 2. I asked for help navigating a chaotic international airport and handling cultural differences, including tipping, translating menus, and using public transportation. 3. My wife created a ChatGPT project with our trip information to support her planning. 4. She used it to create a packing list, pre-trip checklist, and day-by-day plan for exploring Lisbon during her solo time. 5. I used the tools throughout the trip to answer questions with context about my life and reduce the stress of traveling.

“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’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.

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

Spotify can tell me what I played, but it rarely explains what music does in my life. I use exported listening data, playlists, and personal context to explore taste, memory, regulation, identity, and continuity. I look at both observable listening behavior and the meanings I attach to it, without forcing every track into a biography. Step-by-step: 1. Start with the data. I export streaming history, tracks, artists, timestamps, listening duration, saved songs, and playlists where available. I check the period covered and remember that missing history does not mean a song never mattered. I start with behavior: a supposed favorite may barely appear, while another track may have played hundreds of times quietly. 2. Separate tracks from artists. I count unique tracks, unique artists, and tracks per artist. One artist with dozens of songs and hundreds of artists represented by one track suggest different listening styles: exploring discographies versus collecting exceptional songs. 3. Find lasting favorites. I distinguish temporary high-volume artists from favorites that return across years, playlists, and life phases. I look for persistent songs: tracks spanning years, returning after long gaps, or appearing in several playlists. Persistence can matter more than total plays. Some songs never win a monthly ranking yet refuse to disappear. 4. Connect music to life. I add relevant personal context, such as teenage years, university, a move, a relationship, or rebuilding. I ask which songs became landmarks and which were described as “my song.” Dates show when music appeared; I explain what that timing means instead of forcing every track into a biography. 5. Discover playlist functions. Repeated songs are not always favorites. Sometimes music has a job: providing strength, calm, concentration, singing, emotional release, or a way to reconnect with myself. I read playlist names alongside their tracks and listening patterns. “Power” might mean, “I do not feel strong, so I need external strength.” “Gold” might mean, “I want to feel my own golden heart again.” These meanings matter more than a generic genre label. 6. Treat corrections as evidence. AI suggests interpretations, but I decide whether they fit. For example: “This song is not nostalgic. It reminds me that I remain myself regardless of what another person does.” Corrections like this can change the meaning of an entire playlist, so I keep them alongside the data. 7. Look for regulation patterns. I find songs played daily for weeks, tracks repeated within one day, and playlists used at consistent times. I ask whether they accompany difficult tasks, low energy, singing, or calming down. Repetition is a clue, not proof of an emotional function. 8. Look beyond genre. Pop, rock, electronic music, mantra, and soundtracks can share dramatic builds, layered vocals, melodic hooks, powerful voices, intimate production, or mantra-like repetition. These qualities can explain why apparently unrelated songs belong together. I let myself name the patterns; “Post-Classical Electroacoustic Intimate Futurism” may be more useful for discovery than a standard genre. 9. Separate nostalgia from continuity. An old song returning is not necessarily nostalgia. I ask whether it is remembered or still actively used. Some music belongs to the past, while some music simply has a very long present. I keep different musical worlds separate: anthemic pop and meditative mantra may serve different parts of the same person. 10. Build a traceable profile. I include core artists, persistent songs, personal anthems, life-phase associations, functional playlists, regulation patterns, musical qualities, and unexpected findings. I link interpretations to dates, play counts, playlist appearances, and my explanations. I separate observed behavior, AI hypotheses, and confirmed meanings, and keep the profile easy to correct. 11. Recommend for the person. Instead of asking for “songs similar to Coldplay,” I ask: “Find the emotional build I like in Coldplay, the vocal force I like in Sia, and the intimate electronic texture in my meditation playlists.” I can also ask: “Find tracks for Power that could survive repeated listening without copying its existing artists.” 12. Update over time. I add new data and look for new favorites, returning anchors, changing playlist functions, and stable patterns. The ongoing cycle is: export → analyse → find persistent songs → map functions → add personal context and corrections → build the profile → recommend → update. Listening history records what I played. AI helps me explore why something kept returning. I remain the only person who can finally say: “Yes. That is what this song means to me.”

I built Show Hole to solve my family’s classic streaming problem: we spend too much time deciding what to watch, and the answer changes depending on who is actually on the couch. We each have different tastes, but the more interesting problem is that our tastes overlap differently in different combinations. I watch one kind of thing with my spouse, my spouse watches something different with our kid, and my kid and I have our own lane too. Most recommendation tools flatten that into one account profile, one watch history, or broad genre buckets, so they miss the real context of a household. Show Hole treats people and viewing contexts as first-class citizens. It recommends titles “in the vein of” something we liked, filters recommendations to the streaming services we actually subscribe to, avoids titles that the people present have already seen or vetoed, and explains why a recommendation fits tonight. Instead of relying mainly on genres such as comedy, drama, or sci-fi, Show Hole looks at more human taste signals: pacing, world building, humor, emotional weight, complexity, tone, and similar dimensions. It also learns from what we actually do after a recommendation: what we watch, skip, save for later, love, and drop. The result is a personal household recommender that understands “who is watching tonight?” as part of the question, instead of pretending one streaming profile can represent everyone. I designed the app using Claude design, then used those designs with Claude Code to build it. Step-by-step: 1. I identified the household viewing problem and accounted for the different combinations of people who might be watching together. 2. I designed Show Hole around people and viewing contexts instead of treating the household as one account profile, watch history, or set of broad genre preferences. 3. I had it recommend titles “in the vein of” something we liked, filter them to the streaming services we subscribe to, exclude titles already seen or vetoed by the people present, and explain why each recommendation fits that night. 4. I used taste signals such as pacing, world building, humor, emotional weight, complexity, tone, and similar dimensions instead of relying mainly on genres. 5. I made the recommender learn from what we watch, skip, save for later, love, and drop after receiving a recommendation. 6. I designed the app using Claude design and used those designs with Claude Code to build it.

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.