Share your best AI workflow. We could show it to 2M+ people.

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.

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'm planning a housewarming party for about 70 people: 60 adults and 10 children. I knew I wanted to serve a large grazing board, along with a couple of big buckets of ice filled with canned beer, coolers, sparkling water, and other drinks. I told Claude about the party and asked it to create a shopping list based on the number of attendees. Claude produced a cohesive list with six cheeses and approximate quantities for each one. It did the same for the cured meats, crackers, bread, fruit, nuts, jams, and pickled items. It also suggested beverages, including a ratio of beer to coolers and nonalcoholic options. The list included juices and non-caffeinated sodas for the kids, along with a template showing how to lay out the grazing table. This was not a complex use case, but it was very helpful. Step-by-step: 1. I estimated the guest list at about 70 people: 60 adults and 10 children. 2. I described my plan to serve a large grazing board and drinks kept cold in a couple of big buckets of ice. 3. I asked Claude to create a shopping list based on the number of attendees. 4. I used Claude's recommendations for six cheeses, cured meats, crackers, bread, fruit, nuts, jams, and pickled items, including approximate quantities. 5. I reviewed its beverage suggestions, including the beer-to-cooler ratio, sparkling water, juices, and non-caffeinated sodas for the children. 6. I used the layout template to plan how to arrange the grazing table.

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.

Organic social content is one of the best sources of ideas for ads, so I built a workflow that finds short-form videos performing well in my niches, analyzes why they work, and rewrites the strongest hooks for my brand. Apify scrapes Instagram Reels by hashtag and TikTok videos by keyword across three niches: AI, productivity, and career growth. The workflow filters for English-language videos posted within the last 15 days that are 15–90 seconds long and have more than 50,000 plays. It then ranks them by engagement rate rather than views alone. A simple log file tracks every video I’ve already analyzed, so the workflow doesn’t process the same video twice. ElevenLabs transcribes each video’s audio. Claude then extracts the exact opening line, identifies the hook formula and video structure, and writes a version of the hook for my brand. Everything is compiled into one dated document with the top three discoveries, the strongest hook from the run, and any new patterns I haven’t tried yet. With one command—"Run research"—I get about 20 proven hooks and video structures to test in ads for less than $1 per run. Step-by-step: 1. I use Apify to scrape Instagram Reels by hashtag and TikTok videos by keyword across the AI, productivity, and career growth niches. 2. I filter the results for English-language videos posted within the last 15 days, 15–90 seconds long, with more than 50,000 plays. 3. I rank the remaining videos by engagement rate instead of views alone. 4. I check a log file to skip videos I’ve already analyzed. 5. I use ElevenLabs to transcribe each video’s audio. 6. I ask Claude to extract each video’s exact opening line, hook formula, and structure, then rewrite the hook for my brand. 7. I compile the results into one dated document with the top three discoveries, the strongest hook from the run, and new patterns to test. 8. I run the workflow with the command "Run research" to generate about 20 hooks and video structures for ad testing at a cost of less than $1 per run.

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 built a backend operations system for my husband’s general contracting business. It manages new leads, bids and estimates, in-progress jobs, receipts, expense tracking, job photos, invoicing, and other functions he needs—all in one app built with ChatGPT and Base44. I rolled it out in phases. Phase one focused on tracking jobs and their status, phase two added financials, and phase three introduced executive functions. Phase four will cover marketing, although he does not need that right now because he is solidly booked for months. Step-by-step: 1. I built an all-in-one backend operations app using ChatGPT and Base44. 2. In phase one, I added tracking for jobs and their current status. 3. In phase two, I implemented financial functions, including receipts and expense tracking. 4. In phase three, I added executive functions along with other operational features such as leads, bids and estimates, job photos, and invoicing. 5. I planned marketing features for phase four, but postponed them because the business is solidly booked for months.

Midlifecurious is a newsletter for women navigating midlife—honest, funny, and allergic to being talked down to. Its Sunday issue, the Midlife Missive, is a roundup covering health, wellness, money, beauty, and family. My sister, Claire, edits it; I build the machine behind it. That machine is Missive, a five-part publishing intelligence system that runs the newsletter as one closed loop: scan → triage → publish → measure → remember. It monitors Reddit, search trends, and RSS to identify what midlife women are paying attention to before those topics reach our feeds. Discovery pulls in those sources, ranks every feed using a click-rate-based quality score, and lets Claire triage articles into the week’s issue. Curation composes Sunday’s newsletter and drafts the introduction in her voice. Performance reads the Mailchimp results back into the system and feeds them into the rankings, so strong sources rise and weak ones fall over time. Underneath all four stages is Memory: a vector-searchable corpus of every article, save, rejection, and the reasoning behind each decision. Memory is the real spine of the system. It lets Missive ask editorial questions such as “Have we covered this before?” and “Is this source still earning its slot?” instead of requiring one person to hold everything in her head. We’re a two-person operation: I build with Claude Code, and Claire edits. The system runs on one database for under $25 a month. I built it because the alternative was Claire drowning in a Feedly-and-spreadsheet routine that discarded everything as soon as an issue shipped. We had no record of what we had run and no feedback on what actually landed. My bet is that the corpus is the moat. Claire’s editorial taste—every save, rejection, and “cornerstone” stamp, with the reasoning stored alongside the decision—is a training set no one else has. A system that remembers turns her job from synthesizer into judge. Missive is deliberately internal-only: no SaaS and no customers, ever. That frees me to build for our exact workflow instead of a hypothetical buyer, and to build for 2028 instead of this quarter. The near-term payoff is a calmer Sunday. The long-term goal is a proprietary editorial-intelligence layer we could never buy off the shelf—the foundation for the research and audience products that come next. Step-by-step: 1. I monitor Reddit, search trends, and RSS for topics that midlife women are paying attention to. 2. I pull those sources into Missive and rank each feed using a click-rate-based quality score. 3. Claire triages the ranked articles into the week’s Midlife Missive. 4. Missive composes Sunday’s newsletter and drafts the introduction in Claire’s voice. 5. I import the Mailchimp results so the system can update source rankings based on performance. 6. Missive stores every article, save, rejection, “cornerstone” stamp, and the reasoning behind each decision in a vector-searchable corpus. 7. We use that memory to check whether a topic has already been covered and whether a source is still earning its place. 8. I build and maintain the internal system with Claude Code, while Claire handles editing, using one database that costs under $25 a month.

Several months ago, my dad was in and out of the hospital. My two brothers and I were trying to coordinate his doctor appointments, manage his medications and potential interactions, and keep track of all the other details involved in his care. At one point, up to seven different doctors were seeing him in the hospital on any given day. It became important to track every medication he was taking, what each one was for, and information such as his weight and other vital statistics. When he returned home, we also had to make sure someone checked on him and his wife every day, helped him stay on schedule with his medications, and recorded his diet, mood, and weight. We initially used Apple Notes, a shared iCalendar, multiple text threads, and a weekly call between the three of us. The mental load was huge. If we needed to find information from the previous week, we had to scroll through pages of Apple Notes to locate it. We also struggled to keep the rest of the family updated. Before my dad passed away, I started using Claude Code and Codex to build a simple tool that would keep everything organized and searchable. It also displayed trends in areas such as his mood, appetite, and vital signs, and included a calendar showing who was covering which days and times. The tool was still fairly basic when he passed away. Afterward, we encountered the administrative headaches involved in closing out his estate. It was far more complicated than we expected. We thought having a will, power of attorney, and other documents meant we were prepared, but we were wrong. I began integrating those lessons—and the things we learned not to do—into the final product, Eldercare Log: eldercarelog.com. I built the final tool with Claude helping draft a PRD, which I then handed to Codex for the coding work. It took a few weeks of refining the product with Codex. The tool is hosted on Vercel, with Supabase and Stripe on the backend, and includes the security features I built into it. It is the tool I wish had existed when my brothers and I were going through this journey before my dad’s passing. Step-by-step: 1. I coordinated my dad’s doctor appointments, medications, vital statistics, and other care details with my two brothers while he was in and out of the hospital. 2. We tracked his medications, their purposes, his weight, diet, mood, appetite, and other vital signs while he was at home. 3. We coordinated daily visits and coverage using Apple Notes, a shared iCalendar, text threads, and a weekly call. 4. I used Claude Code and Codex to start building a searchable tool that organized his care information and showed simple trends. 5. I added a calendar to track which family member was covering each day and time. 6. After my dad passed away, I incorporated what we learned from handling his estate, including the things we wished we had known earlier. 7. I used Claude to draft a PRD, then gave it to Codex to handle the coding work and refined the product with Codex over several weeks. 8. I built the final tool with Vercel, Supabase, and Stripe on the backend.

I built a custom AI knowledge-transfer GPT designed to prevent institutional knowledge from walking out the door when an experienced employee leaves. The trigger was an experienced operations manager preparing to leave the organization. I realized that while we had procedures, files, emails and account documentation, a huge amount of operational knowledge existed only in that manager’s head: customer preferences, recurring staffing problems, site-specific quirks, key relationships, historical issues, workarounds, lessons learned and the small details that can take a replacement months to discover. My goal was to capture that knowledge and turn it into an interactive resource for the incoming operations manager. First, I conducted and recorded an in-depth interview with the departing manager. Instead of only asking standard turnover questions, I had them walk through the operation as if they were personally training their replacement. We discussed customers, employees, locations, scheduling, recurring problems, escalation procedures, communication preferences, historical decisions and things they believed a new manager might not realize immediately. I then transcribed the conversation and used AI to analyze the interview for knowledge gaps. I asked the AI to approach the transcript from the perspective of someone taking over the job and identify important questions that had not yet been answered. Using those gaps, I had AI create customized knowledge-transfer questionnaires specifically for the departing manager. These asked more targeted questions such as: What problems happen repeatedly? What customer preferences are not documented? What exceptions exist to normal procedures? What mistakes is a new manager likely to make? What information exists only in your memory? What would you make sure your replacement understood during their first 30 days? The departing manager completed those documents, giving me another layer of institutional knowledge that would normally be lost. Next, I organized the interview transcript, completed questionnaires, operational procedures, account information, contacts, historical notes and other relevant documents into a knowledge base. I uploaded that information into a custom GPT and instructed it to function as an operational knowledge-transfer assistant. The GPT was told to base answers on the captured information, not invent answers when information was missing, and clearly tell the user when something needed to be verified. The incoming operations manager can now interact with that knowledge conversationally. Instead of searching through folders or wondering who to ask, they can say things like, “I’m meeting with this customer tomorrow. What should I know?”, “Has this location had staffing problems before?”, “Why do we handle this account differently?”, or “What should I watch for during my first month?” The GPT is now being used by the incoming manager as an ongoing reference tool. The result is essentially a searchable, interactive version of the institutional knowledge that previously would have disappeared with the departing employee. It reduces the “I don’t know what I don’t know” period of starting a new position, shortens the learning curve, improves operational continuity and helps prevent the next person from having to relearn years of lessons through trial and error. The same workflow could be recreated for retiring executives, salespeople, plant managers, administrators, project managers, maintenance supervisors or anyone whose experience contains valuable institutional knowledge. The employee can leave. Their knowledge doesn’t have to.

I built a job application workflow that goes beyond finding vacancies and generating generic CVs. First, GPT helps me scan for vacancies that match my practical requirements, interests, skills, and preferred types of work. The more useful part starts when a vacancy looks genuinely promising. I connected GPT to Canva and created several “base CVs” for the sectors and roles I was interested in. Instead of using one universal template, I designed each CV to fit the visual and cultural tone of a particular type of work. For example, a front-desk role, a back-office administrative position, and an assistant role in a creative company may involve overlapping skills, but they communicate very different expectations. Each base CV therefore uses different layout choices, colour use, visual tone, and photo selection. For every version, I review several possible photos and choose the one that best matches the role and the impression I want to convey. When I apply for a specific vacancy, GPT helps turn the relevant base CV into a more personalized version. Instead of rewriting my entire work history, we emphasize the experience, tasks, strengths, and values that are genuinely most relevant to that role. The same applies to motivation letters. Rather than generating a generic corporate letter, GPT uses a tone of voice shaped through months of conversation with me. The goal is for the application to sound recognizably like me while still matching the language, priorities, and culture of the vacancy. Before building this workflow, I did everything manually. For a vacancy that felt worth applying to, I typically spent around two hours refining the CV and motivation letter alone, not including the time spent searching for the vacancy. Even a small typo in the final application email could undermine hours of careful work. Now, once I decide a vacancy is a good fit, the full personalization process takes around twenty minutes on average. That includes selecting the right base CV, adapting the emphasis, refining the letter, checking the tone, and preparing the final application. The workflow works across several layers: vacancy discovery, role and sector matching, base CV selection, adapting experience and values, adjusting visual tone, personalizing the letter, final review, and application. What I like about this approach is that personalization is not limited to inserting keywords from a vacancy. It includes content, visual identity, emphasis, tone, and context. The result is a small family of CVs rather than one document being stretched awkwardly across every possible job. Each version keeps the same underlying career history while presenting the parts that matter most for a particular type of role. The biggest improvement is not only speed but consistency. The workflow reduces repetitive manual rewriting while keeping each application specific, personal, and carefully matched to the role. It turns roughly two hours of manual polishing per strong vacancy into about twenty minutes of collaborative refinement, with fewer opportunities for small final-stage errors to spoil an otherwise strong application. Step-by-step: 1. I use GPT to scan for vacancies that match my practical requirements, interests, skills, and preferred types of work. 2. When a vacancy looks promising, I identify the relevant sector, role, and type of impression I want to convey. 3. I use Canva and GPT to create and maintain several base CVs, each with its own layout, colour use, visual tone, and photo selection. 4. For each base CV, I review several possible photos and choose the one that best fits the role and the impression I want to convey. 5. I select the base CV that best matches the vacancy. 6. I adapt the CV by emphasizing the experience, tasks, strengths, and values that are genuinely most relevant instead of rewriting my entire work history. 7. I use GPT to personalize the motivation letter in a tone shaped through months of conversation with me, while matching the vacancy’s language, priorities, and culture. 8. I check the tone, review the application for small errors such as typos, and prepare the final application. 9. I complete the personalization process in around twenty minutes on average instead of spending roughly two hours on manual polishing for a strong vacancy.

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 have a lot of calls, and follow-ups were the first thing to get lost once my day filled up. Granola notes helped, but I still had to remember to go back and read them. I connected Granola, Claude, and Notion, then set up a scheduled task in Claude. Every day at 6 p.m., it reviews that day’s Granola meeting notes, finds any to-do items or commitments I made, and creates them as tasks in my Notion to-do list. Step-by-step: 1. I connected Granola and Notion to Claude through Settings > Connectors. 2. I created a scheduled task with a prompt like: "Read today's Granola meeting notes. For each action item assigned to me, create a task in my Notion to-do database with the meeting name and date." 3. I set the task to run daily at the end of the day.

AI is good at generating polished answers, but polished answers are not always your answers. When people ask AI for help with writing, strategy, or difficult decisions, it can jump too quickly to a conclusion and fill in beliefs the user has not fully examined. I created a thought-partner agent that interviews me before producing recommendations or drafts. Its job is to ask probing questions, challenge weak assumptions, surface contradictions, and separate my actual views from ideas suggested by the AI. Instead of writing for me immediately, it helps me clarify my position first. Step-by-step: 1. I give the agent the topic, decision, or idea I want to explore. 2. I tell it not to draft the final output yet. Its first job is to interview me. 3. I have it ask one focused question at a time about my reasoning, evidence, assumptions, audience, and uncertainty. 4. I require it to challenge vague claims and point out contradictions or overlap with my previous thinking. 5. I ask it to clearly separate: - conclusions I stated - ideas the AI proposed - issues that remain unresolved 6. I continue until the central belief, argument, or decision becomes clear. 7. I have the agent create a structured synthesis containing the core thesis, supporting reasoning, counterarguments, open questions, and useful language from the discussion. 8. I pass that synthesis to a writing, planning, or execution agent. I end up with a position that reflects my actual thinking rather than a plausible answer generated by AI. The final writing or strategy is more original, more consistent, and easier for downstream agents to execute.

When I’m continuing a book series or a show season and have forgotten key plot points—especially in fictional stories with large casts—I use NotebookLM to research a spoiler-free refresher before starting the new season or latest book. I used this approach with the new *Murderbot* series and the latest science-fiction book by James S. A. Corey, who wrote *The Expanse* and the *Leviathan Wakes* series. After loading the sources, I ask NotebookLM to create a slide deck with illustrations of the key characters, their relationships, and a quick dossier for each one. This gives me a visual anchor for the characters and earlier plot lines without revealing future events. I also want a hero image designed as a slide, cover, or thumbnail to promote this workflow. It should use and represent the NotebookLM logo and the logos of any other relevant tools, track down their current branding where possible, and turn these .dcs into HTML slide decks.

“Honey, do you remember where Ryan’s practice is? Do you remember our Wi-Fi password? Do you know my TSA PRE number? Remember who that painter was that we used last year?” If you have a family with children, you may hear questions like these all the time. In a large family, there always seem to be questions about the house, the kids, or technology that could be answered more easily. The information is usually stored in different places: password files, a Rolodex of business cards, or sheets of paper in junk drawers. I set out to build an app where anyone in my family could use voice to save something for the future or retrieve something they had already stored. I integrated AI to understand the meaning of each voice note. It might create a calendar event, store a password in a vault, or simply remember a phone number. Then anyone else in the family could access the information. The app uses multi-factor authentication for anything particularly secret. It is not intended to be a dedicated password protector; I would build in much more security if that were the goal. Instead, it is an everyday-life app for remembering the little things: How long is the warranty on this appliance? Which exact light bulbs did we use here last time? What was our daughter’s email address that we needed to set up on her phone? Being able to use voice to enter information or retrieve it was the key for me. I think that will save people time. As I started adding entries and validating the idea, my wife came up with the name. After a few weeks, I knew she was completely right, because I now use it about 10 times a day—and I hear that exact phrase every time. Step-by-step: 1. I identified recurring family questions about locations, passwords, identification numbers, contacts, warranties, and household items. 2. I built an app that lets family members use voice to save or retrieve information. 3. I integrated AI to interpret the meaning of each voice note and determine whether to create a calendar event, store a password in a vault, or remember a phone number. 4. I made the stored information accessible to other family members. 5. I added multi-factor authentication for information that is particularly secret, while keeping the app focused on everyday memory rather than dedicated password protection. 6. I added entries and validated the idea through regular use, eventually using the app about 10 times a day.

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

I use a Claude skill to reduce the manual work involved in my job search. It searches for the latest roles, assists with my applications, and tracks my application journey. I can fully automate the process with Claude scheduling. I download the SKILL file from my GitHub repository (https://github.com/petehawtree/job-search-tracker-skill) and launch it in Claude Desktop. Alternatively, I can download the entire repository and recreate it in my Claude skills directory. I schedule it to run each day or trigger it manually. Each run summarizes the latest activity and tracks it against previous runs. The workflow includes a dashboard for monitoring roles and applications, along with a daily digest that guides me through the new roles it finds. Step-by-step: 1. I download the SKILL file from https://github.com/petehawtree/job-search-tracker-skill. 2. I launch it in Claude Desktop, or download the entire repository and recreate it in my Claude skills directory. 3. I put the workflow on a schedule or trigger it manually each day. 4. I review the daily summary, which is tracked against previous runs. 5. I use the dashboard to monitor roles and applications. 6. I review the daily digest for guidance on the new roles the workflow finds.