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

I'm 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.

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.

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 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’m building an automated content engine for a client using n8n and the Claude API. Every week it produces blog articles and social posts for eight brands. The client already has a blog with [number] articles, and we want the output to match that voice. What I’ve tried: [e.g. a tone-of-voice description in the system prompt / a few example articles in the prompt]. What I get is text that’s fine but sounds AI-written and slightly translated. It doesn’t read like a native Swedish copywriter wrote it. What I’m weighing: Is it better to extract a tone-of-voice guide from the blog (rules, word choices, sentence length, do’s and don’ts) or to pass example texts as few-shot? How many examples do I need, and should they be full articles or excerpts? Is a separate editor step that only reviews and adjusts tone worth it? How do you handle one shared base voice across eight brands that each have their own variant?

Sometimes I remember something perfectly badly: not its title, creator, or where I found it, but a sound, a color, a vague cover, a sentence, or a period in my life. I know it exists because I loved it, but I cannot name it. I use AI and my digital traces to recover songs, books, videos, articles, and quotes. Memory is not a catalog; it stores traces. AI can turn those traces into searchable hypotheses. Step-by-step: 1. I dump every surviving clue without waiting for a proper search query. The clues can be as rough as “The title sounded like nu nau tau,” “It was ambient or humming,” “The cover was maybe a green desert?” or “I knew it around 2019.” Another example is: “A book about makers and Disney. White 3D-printed figures, tiny people, and bears on a blue cover.” This is raw memory evidence. 2. I separate certainty from guessing by labeling each clue as certain, fairly sure, possible, or probably distorted. I keep uncertain clues, but do not treat them as facts. A wrong attribution or outdated cover does not invalidate everything else. 3. I reconstruct the original encounter. I ask where I was and whether I Shazamed it, saved it, sent it to someone, screenshotted it, or bought it at a book fair. In one case, I remembered hearing a song in someone’s car and Shazaming it because I could not parse the pronunciation. Remembering that capture action pointed to a retrieval source. 4. I search my own collections first: playlists, liked songs, Shazam history, saved videos, old posts, messages, bookmarks, notes, photos, receipts, and reading lists. The object may already be there under metadata I no longer remember. I start with the smallest reliable fragment. 5. I search for “nu” inside my playlists after remembering “nu nau tau.” That recovered Man O To — Nu. The artist and title had blended into one phrase, and the song was sitting in several playlists, including my Shazam collection. A clue does not need to be complete; it only needs to be distinctive enough inside the right collection. 6. I allow memory to scramble metadata. An artist and title may merge, album art may become song art, and a quotation may survive with the wrong poet attached. I split remembered phrases, swap artist and title, try different word boundaries, consider older releases, and search phonetically using similar syllables, vowel sounds, homophones, alternate spellings, or other languages. I also ask what the cover looked like when I knew it, because artwork changes. 7. I use context and cross-reference traces. A year, trip, relationship, or platform can narrow the search more than a half-correct title. Spotify might preserve the song, Shazam the discovery moment, and an old message the recommendation. Old posts are accidental archives, and dead links may still leave names, dates, or descriptions. If memory fails completely, I inspect behavior from the relevant period, such as repeated listening, watch history, purchases, or library loans. 8. I let AI propose candidates and compare them against several independent clues, including sound, subject, period, cover, and personal context. One matching detail is not confirmation. I show titles, covers, creator names, and short descriptions because recognition may succeed where recall fails. Rejections also help: “No, but the cover was more minimal,” “The title was shorter,” or “It was fiction, not nonfiction.” Each correction sharpens the search. 9. I pay attention to the strangest clue. The blue cover with white figures, bears, and a makers-and-Disney theme led to *Makers* by Cory Doctorow. 10. Once I confirm the object, I save the title, creator, link, why it mattered, and how I recovered it. For example: - Clue: “nu nau tau” - Encounter: heard in someone’s car and Shazamed - Search: Spotify/Shazam collection → “nu” - Result: Man O To — Nu - Distortion: artist and title blended together This becomes a retrieval playbook: which clues survive, where traces live, and how my memory distorts them. The overall workflow is: collect fragments → mark uncertainty → reconstruct the encounter → search personal traces → try phonetic, visual, and contextual clues → compare candidates → recognize and confirm → save the result and retrieval path. I do not need to remember the database fields. I need enough fragments to rebuild the trail. Sometimes the best clue is what I did when I first encountered something. Sometimes it has been sitting in five of my own playlists while I spend years wondering what it was called. 😑😭🎧

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.

I built a custom web app that uses chance to combine random images and texts into new, unplanned pairings. It recreates a Dadaist technique I use often when making photobooks, which I previously did manually with physical prints. I used Replit to create a digital version of the game. It was easy to connect the app to Project Gutenberg for random texts and the Library of Congress photography archive for images. More importantly, I made it possible for users to work with their own image and text archives. So far, it has produced many new and interesting results, and other artists have used it too. Step-by-step: 1. I identified the manual process I use when making photobooks: combining printed images and texts by chance. 2. I used Replit to build a digital version of this process as a web app. 3. I connected the app to Project Gutenberg to pull random texts. 4. I connected it to the Library of Congress photography archive to pull images. 5. I allowed users to work with their own image and text archives. 6. I used the app to create new pairings and shared it with other artists, who used it as well.

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 use Claude Desktop on my Mac, connected to Canva and Metricool, to build and schedule social media posts. I used ChatGPT to help me set everything up because it has more history and access to my company knowledge documents. ChatGPT created instruction files for Claude that provide our company context and guidelines. I pointed Claude to our website, which contains many articles, and connected our Canva and Metricool accounts. We discuss suitable topics to post about. Claude reviews the website for relevant articles or landing pages to link to, then suggests a series of Facebook and Instagram posts. It checks the designs in our Canva account, adjusts them as needed, or asks me for photos and other artwork. Claude then builds the posts in Metricool and saves them as drafts for me to review, following our working rules. I review the drafts and move them to pending. Claude checks that they will publish automatically on the correct date and at the correct time, and it creates links with meaningful UTM parameters. After publishing, I can ask Claude to analyze the results, identify what is working and resonating with my market, and help us test different approaches, content, and topics. Metricool captures the data, and Claude uses it to inform the next round of posts. This saves me hours every week, gives me time to work on other things while Claude handles the process, and produces results that are improving each week. Step-by-step: 1. I used ChatGPT to help configure Claude Desktop and create instruction files containing our company context and guidelines. 2. I connected Claude to our website, Canva account, and Metricool account. 3. I discuss potential topics with Claude, and it finds relevant website articles or landing pages to link to. 4. Claude suggests a series of posts for Facebook and Instagram. 5. Claude reviews our Canva designs, adjusts them when necessary, or asks me for photos and other artwork. 6. Claude builds the posts in Metricool and saves them as drafts for me to review according to our working rules. 7. I review the drafts and move them to pending. 8. Claude checks the publishing date and time and creates links with meaningful UTM parameters. 9. After publication, I ask Claude to analyze the results and identify what is resonating with my market. 10. I use the Metricool data and Claude’s analysis to test different approaches, content, and topics in the next round of posts.

MinecraftEdu first launched nearly 15 years ago, but the classroom world we built with it is still growing. Years of students have contributed to the school and town-building project, creating a shared record of their creativity and learning. Getting it running on a modern Mac meant preserving that history and giving the next group of students a way to add their own chapter. Finding the right version of Java—the software MinecraftEdu needs to run—was central to the process. We used OpenAI’s Codex desktop app, powered by GPT-6, to examine the old setup, research compatibility issues, and test what would work. An older Java version launched the client, but resizing its window caused it to crash. ChatGPT read the crash reports and tested a newer Java 8 version, which gave us a setup that could launch the game and handle window resizing successfully. ChatGPT also repaired the server launcher and created standalone apps, using Apple’s Rosetta compatibility layer to run the older software on Apple Silicon. The result is a usable MinecraftEdu setup on modern hardware with the existing worlds preserved. We can now continue teaching and building in a place that successive classes of students have helped create. Step-by-step: 1. We examined the existing MinecraftEdu setup and researched its compatibility requirements on a modern Mac. 2. We used OpenAI’s Codex desktop app, powered by GPT-6, to investigate the old setup and test possible solutions. 3. We tested an older Java version, which launched the client but caused crashes when the window was resized. 4. We had ChatGPT read the crash reports and test a newer Java 8 version. 5. We used the working Java setup to launch MinecraftEdu and handle window resizing successfully. 6. We had ChatGPT repair the server launcher and create standalone apps. 7. We used Apple’s Rosetta compatibility layer to run the older software on Apple Silicon. 8. We preserved the existing worlds so future classes can continue building and learning in them.

I built a workflow in which AI agents build a product under the kind of governance I’d expect from a senior engineering team. After 20 years in software engineering, including 10 years running my own businesses, I learned that writing code isn’t the hard part. Keeping control of it is. So I set up a build loop on GitHub. One thing I’ve kept for myself is decision-making: approving plans, answering product questions, and signing off on what the system has learned. The product it’s building is Cordexa, a tool for document-heavy expert work, such as quantity take-offs from engineering drawings or assessing a tender against a company’s own CVs and case studies. It’s designed to run inside the customer’s own cloud, so their documents never leave it. Every figure is cited to the document it came from or flagged for a person. It never guesses. The problem it solves is complex, but the build loop lets the system work in the background and notify my phone when it needs a decision. That allows me to stay focused on my day job. Step-by-step: 1. I start every module with a plain-English plan and approve it before any code exists. 2. One agent writes the tests from the plan. A different agent writes the code, and it cannot modify the tests. 3. An independent reviewer agent checks every change against its plan. Safe changes merge automatically; anything that changes the product waits for my approval. 4. A watchdog checks every 15 minutes for anything that is stuck, fixes it when possible, or tells me why it cannot. 5. Real test jobs run after every merge and are scored against known answers, so progress is measured rather than guessed. 6. I handle the decisions, answer product questions, and sign off on what the system has learned.

I use Instinct, an AI assistant I text or send voice notes to over iMessage, to manage personal to-dos that used to live in my head or in a pile of iPhone alarms. Many tasks come up when I’m away from my desk, so I used to forget them. Alarms didn’t help much either: they went off once, I swiped them away, and the task was gone. Now, when something comes up, I send Instinct a quick voice note. I don’t need to open another app or type. For example: “Remind me tomorrow at 10 AM to respond to book my next dental appointment, and follow up until I say it's done.” Instinct turns the voice note into a clean reminder with a time attached. If I don’t reply “done,” it checks back in and keeps following up until I close out the task. I use the same thread for recurring tasks, partner follow-ups, ad hoc tasks, and advertising to-dos, so everything I owe people lives in one place. Capturing a task takes a few seconds, which means I actually do it. The built-in follow-up keeps things from slipping just because I was busy when the reminder went off, and the workflow lives in iMessage, where I already am throughout the day. Try sending one voice note today with a real task and end it with “follow up until I say it's done.” Step-by-step: 1. When a task comes up, send Instinct a voice note in iMessage. 2. Include the task, when to be reminded, and whether Instinct should follow up until you confirm it is done. 3. Let Instinct turn the voice note into a scheduled reminder. 4. Reply “done” when you complete the task. 5. If you do not reply “done,” let Instinct check back in and continue following up until you close it out. 6. Keep recurring tasks, partner follow-ups, ad hoc tasks, and advertising to-dos in the same iMessage thread.

I used Claude to review the book *Why Machines Learn*. Then I went through each chapter while Claude questioned me at relevant points. To make the concepts more concrete, I used interactive graphs with x and y axes. The first graph shows the relationship between x and a function, along with the slope of the curve. It uses f(x) = x³ with a tangent line illustrating the derivative. I can drag a slider to move the point and watch the slope change, then switch to 3x to compare it with a straight line. The graph demonstrates several ideas: - Sliding x to 0 on x³ makes the tangent flat and the slope 0, so a small nudge barely changes the output. This is similar to a flat spot at the ends of a sigmoid, where learning stalls. - Sliding to 2 and then to −2 shows a slope of 12 at both points. Because the derivative is 3x², it is positive on both sides and the curve is always climbing. - Switching to 3x makes the tangent lie directly on top of the line, with a constant slope of 3 everywhere. - The nudge-check box tracks the derivative closely, showing that the derivative is essentially “change in output ÷ small change in input.” The second graph shows the input and output layers of a small pattern detector. It represents a 2 × 2 image flattened into four inputs, a hidden layer of two neurons, and two outputs. The weights are set by hand so the network can distinguish a vertical line from a horizontal one. I can click the pixels to change the image and watch the numbers flow through the network. The setup uses weight +1, weight −1, ReLU activation defined as max(0, z), softmax output, and all biases set to 0. For example: - Flatten: x = [1, 0, 1, 0] - h1 = ReLU((1)(1) + (-1)(0) + (1)(1) + (-1)(0)) = ReLU(2) = 2 — left column versus right column - h2 = ReLU((1)(1) + (1)(0) + (-1)(1) + (-1)(0)) = ReLU(0) = 0 — top row versus bottom row - Output: scores (2, 0) → softmax → Vertical 88%, Horizontal 12% The graph includes examples for a left vertical line, top horizontal line, right vertical line, and diagonal. Seeing the numbers change as the factors change really helps bring these concepts to life for me. Step-by-step: 1. I used Claude to review *Why Machines Learn* and question me as I worked through each chapter. 2. I used an interactive x-and-y graph to explore how a function changes and how its slope relates to the derivative. 3. I moved the slider across f(x) = x³, including 0, 2, and −2, to observe how the tangent and slope changed. 4. I switched the graph to 3x and used the nudge-check box to compare a constant derivative with the changing derivative of x³. 5. I explored a second interactive diagram representing a 2 × 2 image, four inputs, two hidden neurons, and two outputs. 6. I clicked different pixels and watched the ReLU activations, output scores, and softmax predictions change for vertical, horizontal, and diagonal patterns.

During a period when my medical symptoms suddenly became much worse, the hardest part was not only the symptoms themselves. Pain, dizziness, visual disturbances, brain fog, and acute stress reduced my ability to explain what was happening, make decisions, and keep track of multiple healthcare contacts. That created an awkward problem: the moment I most needed to communicate clearly was often the moment I had the least cognitive capacity to do so. I used ChatGPT as a temporary coordination and communication layer. I kept a symptom diary so I did not have to reconstruct everything from memory. When my symptoms changed, I could compare the new episode with previous days and prepare a concise description. I also used ChatGPT to think through where to seek care. Depending on the severity and type of symptoms, the relevant question might be whether something belonged with my regular GP, an out-of-hours GP service, emergency care, or one of the specialists already involved. Before speaking with a clinician, I asked ChatGPT to help reduce a messy description to a few medically useful points: what changed, when it started, how severe it was, what made it better or worse, which associated symptoms were present, and what had already been investigated. One of the most useful workarounds was extremely simple: if speaking became difficult, I prepared a short note on my phone and showed it to the clinician. At the top, I explained why I was using written communication—for example, that acute stress, autism, pain, dizziness, or brain fog were making speech harder. Underneath, I included my symptoms, timeline, medications, relevant history, and the questions I wanted answered. That mattered because someone arriving alone, visibly anxious, or struggling to speak can easily have difficulty presenting a coherent medical history. The same is true when symptoms reduce concentration, working memory, or language ability. AI did not diagnose the problem. It helped preserve my ability to participate in my own care when some of the abilities normally required to do that were temporarily impaired. During that period, clinicians investigated several possible causes, treated the problems they found, provided medication when needed, and ruled out more serious possibilities. The valuable part of the workflow was not AI replacing a doctor. It was AI helping me reach doctors with a clearer history, better questions, and a communication method I could still use when my normal one was failing. Step-by-step: 1. I kept a symptom diary so I could compare new episodes with previous days instead of relying on memory. 2. I used ChatGPT to help me think through whether to contact my regular GP, an out-of-hours GP service, emergency care, or a specialist already involved, based on the severity and type of symptoms. 3. Before speaking with a clinician, I used ChatGPT to organize what had changed, when it started, how severe it was, what affected it, which associated symptoms were present, and what had already been investigated. 4. When speaking became difficult, I prepared a short note on my phone and showed it to the clinician. 5. I put the reason for using written communication at the top of the note, followed by my symptoms, timeline, medications, relevant history, and questions. 6. I used the clearer history, better questions, and written communication method to remain involved in my care while clinicians investigated possible causes, treated problems they found, provided medication when needed, and ruled out more serious possibilities.

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 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 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.