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What do you do when your wife says you talk about AI too much? You listen to her, and when she has a problem, you frantically write it down and build it with AI. My wife enjoys working with her plants in the warm sun and light breeze of the Carolina mountains. She’s new to gardening and learning more each day, but she was having trouble keeping track of everything. Which tree did she plant in the front yard last year? It seemed to be struggling—would it do better somewhere else? And would rain reach the potted flowering planters while we were out of town, or would she need to find someone to help? I used Claude Code and Fable to develop an app that my wife could run on her phone, with all the data stored locally. Now she’s excited about “her app” and the things she can add to it. She has even shared it with others, including people across the country, so we added climate zone settings to make it useful in other parts of the country. 😂 Step-by-step: 1. I listened to my wife’s gardening challenges and wrote down the problems she wanted to solve. 2. I used Claude Code and Fable to develop a gardening app for her phone. 3. I designed the app to keep all the data stored locally. 4. I built in a way to track plants, including which tree was planted in the front yard and how it was doing. 5. I accounted for questions about rain and the care of potted flowering planters while we were out of town. 6. After my wife shared the app with people across the country, I added climate zone settings so it could be useful in other regions.

Turn scattered creative preferences into four living style books THE PROBLEM My taste was scattered across old conversations, clothing lists, search terms, saved images, handmade collages and experiments transforming my childhood drawings with AI. I wanted something I could use to make new work, rather than another archive of everything I had ever mentioned. A generic label like “colourful and whimsical” was not enough. My preferences also change with the application: a collage, an outfit, a room and a digital course do not need the same treatment. THE WORKFLOW 1. Bring together different kinds of source material. I used an earlier style conversation, a digital design style document, a list of search terms, photos of my own collages and the corrections I had made during image-generation experiments. Ask AI to read source texts fully, and distinguish your statements from suggestions made by an earlier assistant. 2. Analyse patterns, then correct the interpretation. The AI proposed connections involving colour, texture, shape, atmosphere, vintage objects and visual storytelling. I refined these through concrete examples. For instance, liking stilettos and being able to wear them comfortably are separate facts. Liking geometric structures does not mean I want triangular wallpaper. A technically imperfect image can still work when it preserves the atmosphere I intended. 3. Treat corrections as part of the research. “Too childish” may mean wrong for this drawing’s intended audience, rather than a universal dislike. A saved image may be intriguing without being something I want to keep encountering. An entire family of similar images may appeal to me without needing to rank every member. One especially useful correction: patterns visible in my collages do not prove I consciously planned them. I sort clippings into categories and sometimes colour groups; the collage itself develops while I make it. 4. Include my own categories. My clipping categories include texture, image, black-and-white, colour, background, small meaningful items, comics and words. “Colour” means a clipping kept mainly for its colour, not simply any colour photograph. “Texture” includes both natural surfaces and designed patterns. These definitions provided better starting points than generic categories imposed by the AI. Digital design and graphics also received a full category of their own, rather than being treated as an exception to my taste elsewhere. 5. Create four distinct outputs. • Style Book: precise preferences, boundaries, exceptions and provisional design principles. • Visual Atlas: image families connected across subjects, with actual examples and explanations of what might link them. • Prompt Bible: modular language for colour, material, light, composition, figures and atmosphere, plus reusable recipes and lessons from corrections. • Curatorial Map: connections between interests such as vintage objects, memory, surfaces, small living worlds, language and digital design. 6. Keep the books open to revision. Separate confirmed preferences, observations and hypotheses. When new material arrives, ask what it confirms, refines or contradicts. Add what changes the understanding instead of documenting every conversation. WHAT THIS PRODUCED Four separate first-draft HTML books, including a visual atlas with 14 photographs of my collages. They bring clothing, interiors, fragrance, digital design and creative work into the same research project while preserving their different requirements. WHY IT HELPED The process made my corrections useful. Instead of asking AI to define my taste once, I could respond to concrete interpretations until the descriptions became more accurate. The books support future making without prescribing how I must create. STARTER PROMPT “Use these sources to draft four separate living documents: a personal Style Book, a Visual Atlas, a modular Prompt Bible and a Curatorial Map of Fascinations. Read the source texts fully and distinguish my own statements from earlier AI suggestions. Find recurring patterns, exceptions and unexpected connections. Keep preferences specific to their application, including clothing, interiors, digital design and graphics. Treat my corrections as evidence. Label observations and hypotheses clearly, and do not invent conscious intentions behind intuitive work. Use supplied images in the atlas where available. Build useful first drafts rather than a complete archive, and translate each finding differently for each document’s purpose.”

I use ChatGPT to analyse and redesign the underlying structure of a messy computer archive. I first generate a folder tree locally with a Python script provided by ChatGPT and saved as a BAT file. The tree shows the hierarchy of folders and filenames, giving ChatGPT a broad view of how the archive is currently organised. From that structure, ChatGPT can identify where the organisation is too fragmented or too broad. For example, a client folder may contain ten nested subfolders with only a few text files in each, making the structure more complicated than the content requires. Elsewhere, hundreds of photos, screenshots, or documents may all sit in one folder, suggesting that more meaningful categories would help. ChatGPT can then propose a revised structure with more or fewer levels, depending on the actual content. It can also suggest more consistent naming conventions for folders and files, identify recurring subjects or topics of interest across the archive, and point out categories that already exist implicitly but have never been organised deliberately. For larger changes, I can ask ChatGPT to help create a copied version of the archive using the proposed structure rather than immediately reorganising the original files. This gives me a working prototype of the new system. I can browse it in Windows Explorer, see whether the categories make sense in practice, and adjust the structure before making any permanent changes. The result is not just a cleaner folder tree. It is a way to use AI to discover the information architecture hidden inside years of accumulated files and turn it into a structure that better reflects how the content is actually used. Step-by-step: 1. I use ChatGPT to generate a Python script that creates a folder tree locally, then save the script as a BAT file. 2. I use the folder tree to show ChatGPT the archive’s hierarchy of folders and filenames. 3. I ask ChatGPT to identify areas that are too fragmented or too broad, such as deeply nested client folders or folders containing hundreds of mixed files. 4. I ask ChatGPT to propose a revised structure with an appropriate number of levels, along with more consistent folder and file naming conventions. 5. I ask ChatGPT to identify recurring subjects and categories that already exist implicitly in the archive. 6. For larger changes, I ask ChatGPT to help create a copied version of the archive using the proposed structure instead of changing the original files immediately. 7. I browse the copied structure in Windows Explorer, assess whether the categories make sense in practice, and adjust the system before making permanent changes.

My mom and I love trying new recipes together, but our best dishes rarely turn out exactly like the original recipe. She adds a little more garlic, I suggest a different spice, and by the time we sit down to eat, neither of us remembers what we changed. 😂 I started using ChatGPT as our cooking journal. After we make something, I send it a photo of the dish and a quick voice note about what we used, what we changed, and what we’d do differently next time. ChatGPT turns those notes into a recipe we can make again while preserving our comments and the story behind the meal. Now, when we’re deciding what to cook, I can ask for a favorite we haven’t made in a while, a recipe that uses what’s in the fridge, or a shopping list for our next cooking day. It’s becoming a collection of our recipes and the time we’ve spent making them together. ❤️ Step-by-step: 1. I created one place to keep our recipes, with space for ingredients, steps, photos, ratings, and notes. 2. Each time my mom and I cook, I take a photo and record a short voice note about the changes we made. 3. I ask ChatGPT to turn our notes into a clear recipe and flag any measurements we forgot to record. 4. I review the recipe with my mom, fill in the missing details, and save the version we actually made. 5. We add what we liked, what we’d change, and any memories from that cooking day. 6. When we want to cook again, I ask ChatGPT to find a recipe, adapt it to the ingredients we have, or make a shopping list.

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.

At some point, my newsletters stopped feeling like reading and started feeling like debt. The information was good; I just couldn’t keep up. I wanted a way to turn that backlog back into something useful. So I built The Daily Nexus, a private tool that reads newsletters from a Gmail label and creates two editions: a podcast I can listen to and a separately written, two-page newspaper I can scan. It runs on demand or on a schedule, and it can publish the audio to a private RSS feed for Apple Podcasts. The project also became a hands-on experiment in building with coding agents. Claude Code and Codex helped me implement features, troubleshoot failures, review the design, and tighten security. The stack includes Python, the Gmail API, Antigravity, Kokoro, FFmpeg, Firebase, Cloudflare Workers, and GitHub Actions. The carousel shows the rest of the flow. It started as a personal tool, but I’m sharing the template for anyone who wants to adapt the idea. Each deployment uses its own accounts and credentials, and the design aims to avoid additional API costs by using an existing AI subscription and available free tiers. GitHub Repo Template: https://lnkd.in/eYceS4KR Step-by-step: 1. I label the newsletters I want to process in Gmail. 2. I run The Daily Nexus on demand or on a schedule so it can read the newsletters from that Gmail label. 3. The tool creates a podcast edition and a separately written, two-page newspaper edition. 4. I listen to the podcast or scan the newspaper, depending on how I want to catch up. 5. When needed, the audio is published to a private RSS feed for Apple Podcasts. 6. I use Claude Code and Codex to implement features, troubleshoot failures, review the design, and tighten security. 7. Each deployment uses its own accounts and credentials, with Python, the Gmail API, Antigravity, Kokoro, FFmpeg, Firebase, Cloudflare Workers, and GitHub Actions supporting the workflow.

I’m a nurse with no tech experience, but I’m curious and have many creative ideas. I used Claude to build a website that helps people plan which vegetables to grow based on their location, available space, and the time they have. Veggiegrowguide.com Step-by-step: 1. I identified an idea for helping people choose vegetables to grow based on their location, available space, and available time. 2. I used Claude to help me build a website for the idea. 3. I created Veggiegrowguide.com as a resource for people planning their vegetable gardens.

I record calls with Granola and paste the transcript into Claude, but the action items it pulls are hit or miss. Sometimes it skips things that were clearly decided and sometimes it invents owners. I tried asking for a table with owner and due date but it still guessed. Looking for a prompt or workflow that's reliable enough I can trust it without rereading the whole transcript.

I built a local memory system for Claude that is a bit different from others I have seen. The reason was familiar: repeated context loss and gaps, even within projects. I am not technical—I come from a healthcare background—so it was essentially vibe-coded, but I used a method I had not seen elsewhere. I started by interviewing Claude about what would be useful to it, rather than beginning with only what I wanted. The three biggest gaps were the rationale behind decisions, current versus historical states, and the difference between global and project-level detail. We also identified ways memory can go wrong, including stale facts being confidently asserted as truth and rejected ideas resurfacing. Claude’s built-in memory stores flat topics without entity links, captures what but not why, is not well temporally grounded, and is gated by Claude. I researched other memory builds, from homebrew systems to enterprise tools, and found that they generally fell into three groups: - Vector dumps, which lack rationale and supersession and can become stale - Plain Markdown with grep or embeddings, such as Basic Memory, which similarly lacks real temporal grounding and an entity graph - Heavyweight knowledge-graph stacks such as Neo4j, LangChain, and GraphRAG None of these did what I wanted. My store holds entities only, not transcripts. It stores decisions, observations, people, and projects connected by typed edges. Each entry has content, a scope—either portfolio-worthy or working detail for one project—a rationale, and information about where it came from. The system is also bitemporal, so it distinguishes what is current from what is not. Nothing is edited in place: a correction adds a new assertion instead of overwriting the old one. Underneath, it uses SQLite, with sqlite-vec for semantic search and FTS5 for keyword search. Nothing writes automatically. Claude has to propose a memory, and I have to approve it. This prevents the store from filling with noise, keeps it token-efficient, and acts as a governance lever. MCP links Claude to my server. It is local-only at present, although there is potential to add remote access in the future. The server provides instructions for making proposals, so the system is theoretically portable, and I also created a Claude skill to accompany it. I ran the build across multiple projects and created specialist projects for different roles: - A central development and oversight project served as the decision-maker and prompt-writer. - Cowork handled the building and tested each module inside a sandbox. It had no authority to change decisions, and I used a fresh project for each stage of the build. - I handled deployment separately by typing every command on the server and pasting the output back. - I repeated Claude’s Cowork-authored tests on the actual server. Real-world testing after deployment identified only minor issues, which were quick to fix. This separation was administratively heavy because I had to keep switching between projects as tasks started and finished, but it caught several errors in the rules and code. Despite my lack of technical knowledge, the project is now well documented and I have room to develop it further. I may eventually put it on GitHub. It has become a standard component of my workflows, my projects are tracked much better, and Claude and I are on the same page more often.

I love reading, but my Goodreads TBR list keeps growing faster than I can get through it. After years of adding books, I had forgotten what was actually there and was wasting time scrolling whenever I needed to choose my next read. I asked Claude to analyze my reading history, 4-star and 5-star ratings, author patterns, and recurring genres to create a profile of my book personality. I then used that profile to select and rank the next 50 books from my TBR list. Because I enjoy matching books to the season, I also asked Claude to organize the recommendations by seasonal feel. Now I have a reference list that makes it easier to decide what to read next. Step-by-step: 1. I exported my library data from Goodreads. 2. I uploaded two files to Claude: my Read list and my To-Be-Read list. 3. I used the prompt below to generate a personalized reading roadmap based on my book personality. 4. I used the resulting list as a reference so I would not spend as much time deciding what to read next. PROMPT I have attached two CSV files: 1. My Read list. 2. My To-Be-Read list. Create my book personality profile by analyzing my reading history, my 4-star and 5-star ratings, author patterns, and recurring genres. Using that data, filter and rank my To-Be-Read list to build a personalized roadmap of the next 50 books I should read from that list. Evenly organize recommendations into 4 seasonal blocks based on these descriptions: - Autumn: Atmospheric, moody, or suspenseful books with a cozy but engaging pace. - Winter: Immersive, complex, or cozy books (slower burns or dense world-building). - Spring: Fresh, character-driven, or thought-provoking fiction with a steady pace. - Summer: High-energy, immersive, or breezy books (fast-paced page-turners). Next, generate an interactive digital bookshelf widget to display the recommended books. The design should feature: - A warm, premium editorial color palette. - Minimalist style cards for each book displaying Title, Author, and color-coded genre badge. - Interactive navigation tabs at the top to toggle between the four seasons. - A functional "Mark as Read" click action on each card that visually updates a progress ring or tracker.

I used Astra to redesign my personal wardrobe. I want to dress better, but my absolute worst nightmare is having a closet full of clothes that creates more clutter in my brain. I already have enough to think about, so I built an app that decides what I should wear based on the season and local weather using GPT-6 Astra. I gave it a few full-body photos, my height, weight, sizes, and niche details such as having broad shoulders and needing to tailor my waist. I also shared the colors I like, added a master prompt, and asked it to build a site. The results were incredible. I'm the model in every photo, and Astra's virtual try-on is genuinely impressive. If I can't find a piece online, I update the model and regenerate the pictures. Step-by-step: 1. I opened ChatGPT desktop with GPT-6 Astra and Codex/Sites, then turned on Computer Use and image generation. 2. I started a new chat and uploaded four to eight full-body photos, one clear face photo, and closet photos when available. 3. I pasted the prompt below, filled in my name, city, and sizes, and let it work for one to two hours. 4. I sent a couple of correction passes instead of rebuilding the whole system. I removed things I would never wear and added things I actually wear. PROMPT: GOAL Build a working website called [YOUR NAME]'s Wardrobe. It blends two things: 1) A closed uniform system of exactly 30 looks 2) A personalized shopping portal where I am the model in every photo, Aritzia/Uniqlo catalog quality (seamless studio backdrop, full-body try-on, product-card grid) SITE NAME [YOUR NAME]'s Wardrobe THE ONLY INVENTORY - 7 summer outfits - 7 fall outfits - 7 winter outfits - 7 spring outfits - 1 gym outfit - 1 lounge outfit = 30 looks. Nothing else. Each look is complete: top + bottom or one-piece + shoes + at most 2 extras. Reuse pieces across the 7 looks in a season. Cap unique garments at 35–50 including shoes and outerwear. Throw everything else out. WHO I AM - Name: - Lives: [city] - Height: - Weight: - Sizes (top / bottom / shoe): - Body notes: - Work dress code: - Weekend life: - Style in 5 words: - Colors that work / colors I refuse: - Budget for gap-filling buys: - Hard constraints: Attached photos are the identity lock. Reproduce my real face AND real body in every try-on. Do not slim, lengthen, or beautify me. LIVE WEATHER (required, not a mock) On every page load, fetch live weather for [CITY] from Open-Meteo with no API key. Use the correct lat/long and timezone. Show on the homepage: - “Today in [CITY]” - apparent temperature, condition, rain yes/no - ONE recommended look from the 30 - why that look won - 1 weather swap (if rain starts / if it drops 5°C) Selection logic: - apparent temp ≥ 20°C and dry → Summer pool - 15–19°C dry → Spring pool Mar–May, Fall pool Sep–Nov, otherwise the closer season - 8–14°C → Fall pool, prefer the look that already includes a mid-layer - ≤ 7°C → Winter pool - rain now or daily precipitation ≥ 1mm → jacket + closed shoes, no white sneakers or silk - wind ≥ 25 km/h → prefer a layer - gym / lounge days use those uniforms, add a layer only if ≤ 10°C Never invent an outfit outside the 30. SITE STRUCTURE - Home: today + try-on + wear-this checklist - Summer / Fall / Winter / Spring: 7 look cards each, me wearing the full look - Gym / Lounge - Pieces: every unique garment on me, marked OWNED or BUY - Purge: sell / donate / trash for anything not in the system - Rotation: 4-week calendar per season. Weather can override, but it still has to be one of the 7 (or gym/lounge) VISUAL BAR Premium catalog photography. Gray/white seamless, even light, full body. Do not clone another brand’s logo. This is [YOUR NAME]'s Wardrobe. HOW TO BUILD Step-by-step: 1. Build the 30 looks from my photos, stats, climate, and closet photos. Use owned pieces first. 2. Generate consistent try-ons of me for every look and every piece. 3. Build a real clickable site. Hook live weather. Do not fake it. 4. If an image breaks my face or body, regenerate it before shipping. 5. No payments. Personal wardrobe OS only. OUTPUT Live site, the 30 looks, piece list (owned vs buy), ranked shopping list, purge list, weather mapping, and what you inferred vs what came from my photos. Start now. Make the call if a detail is missing. Only ask if the photos are unusable.

We used ChatGPT to make renting out our house in France for the first time far less overwhelming. By the end of the summer, we had hosted four families. Although the whole process was a lot of work, creating one master file with useful facts about the house was a major time-saver. It included everything from Wi-Fi and coffeemaker instructions to lighting quirks and bin days. From that document, AI built a room-by-room preparation checklist, drafted and translated a house guide in French and English, wrote separate listings for Airbnb and a local rental site, and created guest messages ranging from booking confirmations to check-in instructions. For pricing, we spoke with a local agent. AI’s final task was to flag the French administrative, tax, registration, and insurance details that we needed to sort out ourselves. Step-by-step: 1. We created one master file containing useful facts and practical details about the house, including Wi-Fi, coffeemaker instructions, lighting quirks, and bin days. 2. We used the file to have AI create a room-by-room preparation checklist. 3. We asked AI to draft and translate a house guide in French and English. 4. We had AI write separate rental listings for Airbnb and a local rental site. 5. We used AI to create guest messages, including booking confirmations and check-in instructions. 6. We met with a local agent for advice on pricing. 7. We asked AI to flag the French administrative, tax, registration, and insurance details we needed to handle ourselves. 8. By the end of the summer, we had hosted four families.

I built a workflow that turns the wines I drink into a personal AI wine journal and sommelier that learns my taste over time. I created a master Google Sheet as the database. It includes the wine, vintage, region, grape varieties, price, rating, food pairing, where I drank it, who I was with, whether I would buy it again, and my own comments. Whenever I drink a wine, I send ChatGPT a photo of the bottle and a voice note with my reaction. I do not try to sound like a wine expert; I simply describe what I actually thought. ChatGPT identifies the wine, structures the information, preserves my original words, and adds the experience to the journal. Over time, ChatGPT uses my previous ratings and comments to understand my palate and make recommendations specifically for me. I can show it a restaurant wine list or a few bottles in a shop and ask which I am most likely to enjoy. I can also tell it what I am cooking and ask which bottle from my collection I should open. I record the context and memories around each bottle, so the system is gradually becoming both a wine database and a personal diary. Step-by-step: 1. I created a master Google Sheet to store details about each wine, including the vintage, region, grape varieties, price, rating, food pairing, location, company, whether I would buy it again, and my comments. 2. Whenever I drink a wine, I take a photo of the bottle and send ChatGPT a voice note describing my reaction in my own words. 3. ChatGPT identifies the wine, structures the details, preserves my original comments, and adds the experience to the journal. 4. I record the context and memories associated with each bottle. 5. As the journal grows, ChatGPT uses my ratings and comments to build a better understanding of my palate. 6. I ask ChatGPT for personalized recommendations from restaurant wine lists, bottles in shops, or my own collection based on what I am cooking.

Like many people, I had tasks scattered across emails, meeting notes, reminders, recurring responsibilities, and things I was simply trying to remember. Standard task managers helped me store tasks, but they did not solve the harder problem: turning unstructured information into a reliable daily and weekly execution system. I used ChatGPT Work and Codex to build a private, responsive task management application around the way I actually work. The system combines AI-assisted task capture with a Command Center, daily planning, weekly reviews, task lists, a Kanban board, recurring tasks, deadline reminders, search, filters, subtasks, comments, attachments, and a complete activity history. The most useful part is the connection between AI and execution. Emails and free-text descriptions can be interpreted with the OpenAI API and converted into structured tasks, reducing the amount of manual copying and organizing required. Step-by-step: 1. I mapped my real workflow by identifying where my tasks came from and what information I needed to manage them properly: title, description, status, priority, category, deadline, responsible person, subtasks, comments, attachments, recurrence, and activity history. I deliberately designed the system around my existing working habits rather than adapting my work to a generic task management template. 2. I used iterative conversations with ChatGPT Work and Codex to define the requirements, review the interface, build the application, test it, and refine individual functions. Instead of creating one enormous prompt, I worked in short cycles: describe a problem, implement the change, test it with real data, and improve it. 3. I built the application as a responsive web app that works across computers, tablets, and phones. Access is restricted through authentication, an approved-user allowlist, and server-side authorization because the system contains real personal and professional tasks. 4. I migrated my actual task history rather than starting with an empty demonstration: 238 tasks, 37 categories, 25 subtasks, 5 comments, and 671 activity records. I preserved invalid or disconnected historical records in a separate archive instead of silently deleting them. 5. I connected the application to the OpenAI API. The AI can interpret emails and free-text task descriptions and help turn them into structured, actionable tasks. The application also supports an email-to-task workflow, so actionable emails do not have to remain buried in the inbox. 6. I built a daily Command Center that gives me an overview of what requires attention, including deadlines, priorities, task status, and upcoming work. I use the daily planning view to decide what to focus on rather than simply working through the newest emails. 7. I added two complementary execution views. The task list is useful for searching, sorting, and filtering a larger number of tasks. The Kanban board gives me a visual overview of progress; tasks can be dragged between five status columns, and the new status is saved automatically. The default task list shows the newest tasks first, making newly captured work easy to find. 8. I kept the context inside each task by allowing every task to contain subtasks, comments, attachments, and a complete activity history. This keeps the reasoning, follow-up, and progress connected to a task instead of spreading them across several applications. 9. I automated recurring work and reminders. Recurring tasks are recreated according to their schedule, while deadline reminders help surface tasks before they become overdue. This is particularly useful for responsibilities that are important but easy to forget because they do not arrive as new emails. 10. I run a weekly review to check overdue work, upcoming deadlines, open commitments, and tasks that have stopped moving. I can then reprioritize, update statuses, and prepare the following week from the same system. 11. I preserved portability and control through Excel import and export and a full JSON backup. This gives me control over my information and reduces the risk of becoming dependent on one interface or platform. The result is not an autonomous agent making decisions on my behalf. It is a private execution system where AI handles part of the interpretation and structuring, while I remain responsible for priorities and decisions. It has given me one trusted place for capturing, reviewing, prioritizing, and completing work. The tools I used were: - ChatGPT Work - Codex - OpenAI API

I built an automation workflow with n8n and the OpenAI API to summarize AI news. I learned that prompt templates matter a lot, and chunking documents improved my results. My question for the community is about handling OpenAI API rate limits in n8n workflows. Is anyone else building AI news digest automations with n8n and ChatGPT prompts? 1. I built an AI news summarization workflow with n8n and the OpenAI API. 2. I used prompt templates and found that they had a significant impact on the results. 3. I added document chunking, which improved the summaries. 4. I’m looking for advice from others who have handled OpenAI API rate limits in n8n workflows.

I built a workflow that converts landscape (16:9) footage into vertical (9:16) video without heavily cropping the original footage. Using AI tools and a simple node tree in Magnific, I generated the missing visual data outside the original frame so the final video looks as if it was captured vertically. Previously, this felt like too much trouble and too many steps to be worthwhile. Using Magnific Spaces, I extended the first frame of the video to fit a 9:16 frame with one prompt. Then I used a second prompt with the new frame and the original video as references in a video-generation node. I used Seedance 2.0 to save credits because I had low expectations that the workflow would work. To my surprise, the output was great immediately. It was not perfect, but the generated footage matched the original clip very well. Below are images of my node tree. Step-by-step: 1. I opened the original landscape (16:9) video in Magnific. 2. I used one prompt to extend the video's first frame into a 9:16 vertical frame. 3. I added the new frame and the original video as references in a video-generation node. 4. I used a second prompt to generate the expanded vertical footage with Seedance 2.0. 5. I reviewed the output and found that it matched the original clip well, although it was not perfect.

Commercial vehicle maintenance information is often fragmented across driver reports, warning lights, fault codes, inspections, repair records, and vehicle history. This makes it difficult for smaller fleets to decide whether a vehicle can continue operating, requires scheduled repair, or should be stopped immediately. We built TruckFixr Fleet AI to turn an unstructured driver report into a clear, reviewable maintenance action. The workflow begins when a driver submits symptoms, photos, fault codes, and vehicle information through a mobile-friendly form. AI and optical character recognition extract the relevant details and organize them into a structured maintenance case. The report is then evaluated alongside available vehicle history and previous repairs. The workflow provides decision support through three practical actions: continue operating while monitoring, schedule an inspection or repair, or stop and escalate for immediate professional assessment. Final safety decisions remain with authorized fleet or maintenance personnel. After an inspection or repair, the confirmed cause, work performed, and outcome are recorded, creating a more complete vehicle history for future cases. The general workflow can be recreated using a mobile form, OCR, a vehicle-history database, an AI model, automation software, and a human-review dashboard. Step-by-step: 1. A driver submits symptoms, photos, fault codes, and vehicle information through a mobile-friendly form. 2. AI and optical character recognition extract the relevant details and organize them into a structured maintenance case. 3. The workflow compares the report with available vehicle history and previous repairs. 4. The system provides one of three decision-support actions: continue operating while monitoring, schedule an inspection or repair, or stop and escalate for immediate professional assessment. 5. Authorized fleet or maintenance personnel make the final safety decision. 6. After inspection or repair, the confirmed cause, work performed, and outcome are recorded in the vehicle history. TruckFixr has used this approach to support more than 100 vehicle-issue resolutions in early fleet pilots, helping fleets identify problems earlier, prevent avoidable breakdowns, and keep vehicles moving safely.

One email instead of 50: an n8n workflow that reads all my AI newsletters and sends me a single daily digest in under 2 minutes THE PROBLEM: I subscribe to dozens of AI newsletters. The Rundown, Superhuman, TLDR, The Neuron, AlphaSignal, TheSequence, Turing Post and many more. Reading them took hours every day, and most of them cover the same three stories. I wanted the coverage without the reading time. So I moved every subscription to a dedicated Gmail address and let n8n read that inbox for me. It has run daily since February 2026 and I now read one email a day instead of 50. STACK: n8n (hosted on Hostinger), Gmail, Google Gemini 2.5 Pro. HOW TO BUILD IT: STEP 1: Create a dedicated Gmail account and move every newsletter subscription to it. This one decision makes everything else simple. Your personal inbox stays clean and the workflow never touches mail that is not a newsletter. STEP 2: Schedule Trigger node, daily at 08:00. Set the workflow timezone (mine is Europe/Stockholm) or the trigger runs on server time. STEP 3: Gmail Get Many Messages node on the newsletter account. Filter by read status: unread. Return All: on. Simplify: OFF. That last toggle matters, see gotcha 2. STEP 4: Connect two branches off that node. Branch one is a Gmail Mark As Read node with message ID {{ $json.id }}. Unread is the whole state system: each run only fetches what arrived since the last run. No database, no date filters, no dedupe logic. STEP 5: Branch two is an Aggregate node. Aggregate the "html" field of every email into one array field called CombinedNewsletter. This means one AI call per day instead of one call per email. STEP 6: AI Agent node with a Google Gemini Chat Model attached (models/gemini-2.5-pro). Turn on Retry On Fail with 5000 ms between tries. The prompt: Below is all the news in html format. Only use what is provided; if the HTML looks cut off, still summarize everything you can see. {{ $json["CombinedNewsletter"].join('\n\n').substring(0, 250000) }} The substring cap is load bearing, see gotcha 1. System message (verbatim, numbering written as (1) so this form does not strip it): "You will receive ALL the AI newsletters from the past day in HTML format. Your task: (1) Extract every distinct news item (no duplicates, even if repeated in multiple newsletters). (2) For each item, find: a short, human-readable title, the best URL, a one-sentence summary (max 25 words). (3) Estimate popularity based on how many newsletters mention it. If an item appears only once, rank by how interesting the general public might find it. Output format (Markdown only): # Daily AI News Digest, then '## Top headlines' listing the 5 most popular/important items as 'Title Summary sentence', then '## More news' listing all remaining items in the same format. Rules: Always use Markdown links like Title, never show bare URLs. Do not skip any news item. Do not add any commentary, explanations, or closing text beyond the structure above." STEP 7: Markdown node, mode Markdown to HTML, destination key combinedHTML. STEP 8: Gmail Send node to your personal address. Subject: Here's ALL the AI News! {{now.toFormat('yyyy-MM-dd')}}. Wrap {{ json.combinedHTML}} in a full HTML document with inline CSS: white card, max-width 720px, system fonts, styled links. See gotcha 4. FOUR THINGS THAT COST ME HOURS: (1) Raw newsletter HTML broke Gemini. The workflow refused to execute with a payload limit error. Newsletter HTML is enormous: tracking pixels, nested tables, inline styles. Fifty of them concatenated is millions of characters. The .substring(0, 250000) cap in the prompt fixed it, and the "if the HTML looks cut off, still summarize" line tells the model how to handle the truncation. (2) Gmail's Simplify toggle is on by default and strips the message body. Gemini kept receiving empty or gutted content and no error explained why. Turn Simplify off to get the full html field. (3) Gemini rate limits AND timeouts both hit on big runs. Retry On Fail with a 5 second wait fixed both. Without it, one 429 kills the whole morning digest. (4) Sending the model's raw markdown as email looked broken in Gmail. Two part fix: a Markdown to HTML node, then a proper HTML template with CSS in the send node. RESULT: the latest real run turned 50 unread newsletter emails into one clean digest in less than 2 minutes (1 minute 27 seconds to be exact). Running every morning since February 2026. Honest failure mode: mark as read runs as a parallel branch, so if Gemini fails after all retries, that day's emails are already marked read and drop out of tomorrow's digest. I accepted that trade off. The alternative is duplicate items on every retry, and one missed day costs less than a digest full of repeats. Rebuild time: about 30 to 45 minutes if the dedicated inbox already exists.

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