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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.

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.

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 used ChatGPT as an emergency care coordinator for my dog, Mopsy. My 10-year-old dog developed a dental abscess that caused severe swelling around her eye. An ophthalmologist told us that the pressure was already damaging her eye and had partially torn her retina, so treating the infected tooth quickly became urgent. I started calling veterinary offices myself, but I repeatedly hit the same obstacles: emergency hospitals did not perform dental surgery, specialty dentists were booked, and most primary-care veterinarians required a consultation, bloodwork, and a separate surgical appointment. After more than a dozen calls and over two hours of research and calling, I was overwhelmed and asked ChatGPT to help. ChatGPT researched veterinary practices within an hour’s drive of my house, filtered for clinics that actually performed dental extractions or oral surgery, found their contact information, and sent urgent, individualized emails from my Gmail. The emails explained Mopsy’s situation and asked each practice to call me if they could fit her in for surgery within 48 hours. We ultimately contacted more than 20 practices. At the same time, ChatGPT created a live outreach tracker containing every clinic we had called or emailed, along with phone numbers, email addresses, availability, responses, status, and next steps. I shared it with my husband so we could stay up to date as ChatGPT worked. When veterinary offices replied to my emails, ChatGPT read the responses in Gmail and updated the shared spreadsheet, allowing both of us to see what was happening without duplicating work. In less than half an hour, one veterinarian responded that they could evaluate Mopsy the next morning, run her pre-anesthetic bloodwork on-site, review the results immediately, and take her straight back for surgery if she was cleared. What started as me frantically calling veterinary offices one at a time became an AI-supported search, outreach, and tracking operation. I still made the medical decisions and handled the phone calls that mattered, but I only had to speak with viable options. AI took over the repetitive research, emailing, organization, and response tracking that I did not have the bandwidth to handle during an emergency. Step-by-step: 1. I began calling veterinary offices to find urgent dental surgery for Mopsy. 2. I documented the barriers I encountered, including emergency hospitals without dental surgery, booked specialty dentists, and primary-care veterinarians requiring multiple appointments. 3. I asked ChatGPT to research practices within an hour’s drive that performed dental extractions or oral surgery. 4. ChatGPT gathered each clinic’s contact information and sent individualized urgent emails from my Gmail explaining Mopsy’s condition and asking about surgery within 48 hours. 5. ChatGPT contacted more than 20 practices and created a shared live tracker with each clinic’s contact details, availability, responses, status, and next steps. 6. As replies arrived in Gmail, ChatGPT read them and updated the shared spreadsheet so my husband and I could coordinate without duplicating work. 7. I made the medical decisions and handled phone calls with viable options. One veterinarian ultimately offered an evaluation, on-site bloodwork, and possible same-day surgery the next morning.

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.

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.

I need to perfect my website that I created with claude.ai but I canceled my subscription due to multiple charges for usage credits and subscription. What ai app is capable of doing tasks like claude.ai Does?

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 predictive maintenance workflow in Awish for one of my manufacturing clients. The client had machine telemetry, production data, and maintenance history spread across different systems. The problem wasn’t collecting the data—it was spotting failure risk early enough to act. I built a custom Awish workflow that continuously checks machine telemetry and production signals in Snowflake alongside asset, meter, and maintenance history from MaintainX. When it detects abnormal performance or increasing failure risk, it identifies the affected equipment, estimates the likely operational impact, and prepares a recommended maintenance action. Nothing is scheduled automatically at that point. The recommendation first goes to the maintenance manager in Microsoft Teams for approval. Once approved, Awish creates and assigns the work order in MaintainX, then keeps tracking and updating its status until the maintenance is completed. The useful part is that the system does not wait for a machine to fail before maintenance starts, but it also does not let AI make the maintenance decision on its own. The analysis is automated, while the actual intervention still requires human approval. Step-by-step: 1. I described the maintenance process I wanted in the Awish chat. 2. Awish planned the workflow and selected Snowflake, MaintainX, and Microsoft Teams for the required steps. 3. I connected the client’s accounts and approved the automation plan. 4. Awish continuously analyzed production and telemetry data in Snowflake together with MaintainX asset, meter, and maintenance history. 5. When it detected abnormal behavior or increasing failure risk, it identified the affected equipment and estimated the likely operational impact. 6. It prepared a recommended maintenance action and sent it to the maintenance manager in Microsoft Teams. 7. Once the manager approved the recommendation, Awish created and assigned the work order in MaintainX. 8. The workflow continued tracking the work order and updating its status until the maintenance was completed. Trigger → Analyze → Approval → Action Machine signals → Failure-risk analysis → Teams approval → MaintainX work order

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 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, with fields for the wine, vintage, region, grapes, 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 what 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 journal is gradually becoming both a wine database and a personal diary. The loop is simple: drink wine, take a photo, send a voice note, let AI structure and save the information, and build a better palate profile for more personal recommendations. Step-by-step: 1. I created a master Google Sheet with fields for the wine, vintage, region, grapes, price, rating, food pairing, where I drank it, who I was with, whether I would buy it again, and my own 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 continue recording the context and memories around each bottle alongside the wine information. 5. I use my accumulated ratings and comments to help ChatGPT understand my palate and make recommendations from restaurant wine lists, bottles in shops, or my own collection based on what I am cooking. 6. As the journal grows, it becomes both a wine database and a personal diary, while the recommendations become more personal.

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

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.

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

Most AI agents are effectively static. You write their instructions, use them repeatedly, notice where they struggle, and manually tweak the prompt when something goes wrong. Valuable feedback from real work is often lost, so the same mistakes can keep happening. I created a self-improvement flywheel that uses actual agent performance data to improve agents over time. The system collects two kinds of evidence: - Task scores showing how well each agent performs across different quality dimensions - Run telemetry and review outcomes revealing recurring failures, coordination problems, and cases where actual behavior differs from expectations A scheduled weekly cycle analyzes that evidence, identifies patterns, creates improvement proposals, evaluates whether those proposals are safe and broadly applicable, updates agent instructions when appropriate, and measures whether those changes actually improve performance. The goal is not to let agents rewrite themselves freely. It is to create a controlled learning loop. Step-by-step: 1. Collect performance data while agents work. Score important outputs across consistent quality dimensions, and record useful execution telemetry such as failures, decisions, reviewer outcomes, and unexpected behavior. 2. Analyze performance trends on a recurring schedule. Calculate per-agent averages, identify weak dimensions, compare agents, and look for improvement or decline over time. 3. Mine run history for recurring patterns across multiple sessions, including agents that repeatedly struggle, low-quality runs, and cases where expected behavior differs from what actually happened. 4. Turn repeated problems into improvement proposals. Before proposing a change, inspect the agent’s current instructions so you do not add a rule that already exists. 5. Evaluate each proposal before applying it. Check whether the lesson is broadly useful, redundant with existing instructions, or in conflict with established behavior. 6. Separate low-risk and high-risk changes. Automatically apply additive or clarifying improvements. Escalate conflicting changes for human review instead of allowing the system to fundamentally change an agent’s behavior on its own. 7. Look for system-level problems. Analyze patterns across agents to identify quality gaps, missing capabilities, or coordination failures that cannot be fixed by changing one agent alone. 8. Apply approved improvements and preserve the history. Update the relevant agent instructions, archive the processed proposals, and version the changes so they remain inspectable and reversible. 9. Measure whether each change actually helped by comparing agent performance before and after the refinement. If quality does not improve, do not automatically assume the change was useful. 10. Repeat the cycle. As agents complete more real work, the system gathers more evidence and gets another opportunity to improve. Instead of treating agent instructions as static prompts, I turned them into a continuously improving system: Work → Evaluate → Find Patterns → Propose Changes → Refine → Measure → Repeat The important part is that the loop is evidence-driven and controlled. Agents improve from real usage, but low-confidence or behavior-changing updates still require judgment rather than being applied automatically.

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 first create my own shortlist of cultural events and collect all the relevant links in a simple text document. Then I give the document to ChatGPT along with my planning criteria. The goal is not to have AI decide what I should attend. I have already chosen the events that interest me. The useful part is turning that scattered shortlist into a realistic yearly plan. ChatGPT checks the event and venue pages, gathers the practical information, and organises everything into a chronological overview divided by month. Under each month, it lists the selected events in date order, including the day, date, location or cultural venue, a short description, and relevant ticket prices. This is especially useful because pricing systems differ between venues. Discounts, subscriptions, social tariffs, and reduced rates may apply in completely different ways. ChatGPT can compare those structures and help me understand what each event would actually cost instead of relying on the headline ticket price. The second planning layer is logistics. For each event, I assess how realistic the trip is by public transport, including the journey there and, importantly, whether getting home after the event is still feasible. I also use separate scores for how strongly I want to attend an event and how easy or difficult the logistics are. This makes it much easier to compare several months at once and decide where my cultural budget and energy are best spent. If an event is too expensive or logistically awkward, ChatGPT can look for practical alternatives, such as another performance date, the same production at a closer venue, a cheaper option, or a similar event that fits the plan better. Step-by-step: 1. I create a personal shortlist of cultural events and collect the relevant links in a simple text document. 2. I give the document to ChatGPT together with my planning criteria. 3. I have ChatGPT check the event and venue pages and organise the selected events into a chronological, month-by-month overview. 4. I include each event’s day, date, location or cultural venue, short description, and relevant ticket prices. 5. I compare discounts, subscriptions, social tariffs, and reduced rates to estimate the actual cost of each event. 6. I assess the public-transport journey to and from each event, including whether getting home afterward is feasible. 7. I score each event separately for how strongly I want to attend it and how easy or difficult the logistics are. 8. I use those comparisons to decide where my cultural budget and energy are best spent. 9. For events that are too expensive or logistically awkward, I ask ChatGPT to identify alternatives such as another performance date, a closer venue, a cheaper option, or a similar event that fits the plan better. The result is a structured yearly planner built from my own cultural shortlist: chronological, budget-aware, and grounded in the realities of public transport.

I built a second workflow that scans my conversations daily for ideas worth developing, especially strange connections, creative concepts, humor, research leads, unfinished experiments, and thoughts that may have seemed minor when they first appeared. Instead of turning everything into tasks, it creates an incubator: a place where interesting material can remain dormant until it connects with something new. The daily scan looks for fragments with creative or exploratory potential. They do not need to be fully formed ideas. A small observation, an odd comparison, a half-finished concept, a recurring image, or a funny side remark can all be worth keeping if they might become meaningful later. Once a week, the incubator revisits the material collected during the previous days. That weekly revisit makes connections across separate conversations more visible. Something that looked isolated on Tuesday may suddenly echo a thought from Friday or fit a project that did not yet exist when the original idea appeared. The important part is that the incubator does not treat every interesting thought as something that must immediately become productive. Some ideas benefit from being left alone for a while. They can sit next to other fragments, reappear in later conversations, or become useful only when a new context gives them meaning. The workflow is less about extracting the “best ideas” and more about preserving creative potential while allowing patterns to emerge gradually. It also surfaces unfinished experiments and research leads that I might otherwise forget, without turning the archive into a traditional task manager. That distinction matters to me: not everything valuable needs an action item. Some things are seeds, references, questions, jokes, aesthetic directions, or possible future worlds. Over time, the incubator becomes a kind of creative compost layer on top of the conversation archive: new material is gathered daily, revisited weekly, and allowed to combine into directions that would be difficult to plan deliberately. In simple terms: daily conversation scan → promising fragments → weekly revisit → emerging connections → incubation → later rediscovery and development The goal is not to force ideas into projects. It is to keep good material alive long enough for the right project to find it. Step-by-step: 1. I scan my conversations each day for strange connections, creative concepts, humor, research leads, unfinished experiments, and other fragments with creative or exploratory potential. 2. I preserve promising material in an incubator instead of immediately turning every item into a task. 3. I keep small observations, odd comparisons, half-finished concepts, recurring images, funny side remarks, seeds, references, questions, aesthetic directions, and possible future worlds—even when they are not fully formed. 4. I revisit the material collected during the previous days once a week. 5. I look for connections across separate conversations, including links between ideas that initially seemed isolated or between fragments and projects that did not yet exist. 6. I allow ideas to remain dormant, sit alongside other fragments, reappear in later conversations, or become useful when a new context gives them meaning. 7. I resurface unfinished experiments and research leads without turning the archive into a traditional task manager. 8. I let the material combine gradually so that new directions can emerge through later rediscovery and development.

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.

I read the news every day, but it had become increasingly depressing and was making me miserable. What bothered me most was that it also felt inaccurate: although bad things are happening, there have been many positive developments over the past five years that rarely receive sustained coverage. Major news sites might publish an article or two about them, but those stories are quickly buried under negativity. I wanted a way to get only positive news stories in my feed each day. Keyword filters did not work: “record” and “breakthrough” also appear in stories about record wildfire seasons, while “war” can appear in “war ends.” Off-the-shelf sentiment analysis was not useful either. A happy press release about layoffs can be classified as positive, while a dry factual story about a disease being eliminated may be classified as neutral. So I trained a basic artificial version of my personality using a series of prompts about what I consider positive in the world. I connected it to Mistral through OpenRouter and gave it access to public RSS feeds from news sites I already trusted. This eventually became Rally News, which I published on Google Play. iOS has been more difficult. The app surfaces positive stories from more than 20 news sites in an endless scroll, giving me an alternative to my uncomfortable TikTok addiction. Because I made it public, I decided not to host article text: publishers keep their traffic and revenue, while the tool remains ethical. The system runs on a GitHub Actions cron job that pushes stories to a PHP and MySQL database. I built the app without coding experience for about $25 per month. Step-by-step: 1. I collected RSS feeds from established publishers I already trusted and stored the list as configuration. I started with about 10 feeds instead of a few hundred so I could realistically read the output. 2. I set up a scheduled GitHub Actions cron job to run a Python script that pulls new items from every feed. 3. I deduplicated incoming articles against the database using the URL and a normalized title. Syndicated stories frequently reappear under slightly different URLs, and I did not want to pay to evaluate the same article twice. 4. I wrote the filter prompt as a long persona document rather than a one-line instruction. It explains what I consider progress, what I consider a puff piece, and which cases should fail—for example, celebrity news is not good news, a company announcing an intention is not the same as taking action, and a local feel-good story without wider significance does not qualify. 5. I sent each new article to an LLM through OpenRouter and required JSON output containing a pass-or-fail decision and a short justification. 6. For the first few weeks, I read the justifications every day. Whenever I disagreed with the model, I added a new rule to the persona document. That review loop required nearly all of the actual work. 7. I wrote passing articles to MySQL with only the headline, source, link, and metadata, leaving the article body with the publisher. 8. I pointed the website and mobile app to the same database. 9. I added a second GitHub Actions job that assembles a daily newsletter from the same data through Brevo, allowing one evaluation pass to feed three surfaces.