Workflow Hub

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.

I used AI to reconstruct more than two decades of professional work scattered across old publications, broken links, archives, indexes, and surviving records. The difficult part was not finding possible matches; it was preventing AI from turning incomplete evidence into false certainty. I built a workflow that treats archive reconstruction as a verification problem rather than a search problem. The process separates verified work from probable matches, resolves same-name conflicts, deduplicates reprints and syndicated copies, preserves source provenance, and treats missing years as research gaps rather than permission to invent records. The result was a source-tracked professional archive containing 250+ recovered records across more than 20 years. The workflow can be adapted to journalism, research, consulting, creative work, speaking histories, or any long-term body of professional output. I use the following research-archivist workflow: - Never invent titles, dates, publications, URLs, or authorship. - Separate records into VERIFIED, PROBABLE, and REJECTED. - Treat same-name individuals as separate identities until evidence proves otherwise. - Deduplicate syndicated copies, reprints, mirrors, indexes, and cached pages. - Preserve provenance for every verified record. - Mark gaps rather than filling them. - Distinguish “not found,” “unverified,” and “confirmed absent.” - State uncertainty explicitly. Step-by-step: 1. Ask for names and bylines, employers, publications, career dates, subject areas, known titles, source files, URLs, and any same-name conflicts. 2. Build an identity fingerprint. 3. Establish verified seed records. 4. Search iteratively across multiple query patterns and sources. 5. Classify every result. 6. Resolve identity conflicts. 7. Deduplicate records. 8. Normalize metadata. 9. Build a chronology. 10. Identify and search gaps. 11. Run a provenance and error audit. The final output includes: - Verified Master Archive - Probable / Needs Verification - Rejected / Other Person - Gap List - Source Notes - Final Audit The archive is never described as complete unless the evidence supports that conclusion.

I built a job-offer net-net calculator to determine what a proposed job is actually worth to the employee’s household after taxes, employment-related expenses, benefit changes, commuting costs, and the economic value of commuting time. Gross salary should not be treated as household value. The calculator should interview me first and ask only for information that materially affects the calculation. If I do not know a number, it should identify it as an estimate and use a clearly labeled reasonable placeholder only with my permission. Step-by-step: 1. Collect the proposed annual gross salary. 2. Collect the employee’s state and locality, tax filing status, and other household taxable income when needed to estimate the incremental federal tax burden accurately. 3. Record the work arrangement: fully remote, hybrid, or on-site. 4. Record the number of commuting days per week and working weeks per year. 5. Collect the home location and job location, or the known one-way mileage. 6. Collect the vehicle make, model, year, drivetrain, and approximate MPG when available, along with current annual vehicle mileage if relevant. 7. Collect the gasoline price or ask permission to obtain a current local price. 8. Record tolls, parking, and the expected change in automobile insurance. If the insurance change is unknown, keep it as a clearly labeled placeholder rather than presenting it as fact. 9. Record employee medical, dental, and vision premiums under the proposed job, along with current household medical, dental, and vision expenses that would disappear if the employee accepts the job. 10. Record the employee’s current or previous annual compensation, or another hourly value to use for valuing commuting time. 11. Record any other recurring costs caused by accepting the job and any recurring household expenses that would disappear because of accepting it. 12. Estimate after-tax pay as follows: - Gross salary - Federal income tax - State and local income tax - Social Security - Medicare - Estimated after-tax salary Use current tax rules when web access is available, and distinguish estimated withholding from actual tax liability when relevant. 13. Calculate annual commute miles: - Round-trip miles × commuting days per week × working weeks per year 14. Estimate the incremental cash cost caused by commuting, including fuel, maintenance, oil and service, tires, repairs, tolls, parking, and the incremental auto-insurance premium when applicable. Do not double-count expenses. 15. Separately estimate additional depreciation and other mileage-related economic costs that were not already included. If using an all-in per-mile vehicle-cost benchmark, do not also add its constituent costs separately. Explicitly state which method was used. 16. Calculate annual commuting hours: - Estimated minutes each direction × 2 × commuting days per week × working weeks per year ÷ 60 17. Calculate the employee’s implied hourly value: - Annual benchmark compensation ÷ 2,080 Multiply annual commuting hours by the hourly value to calculate the economic value of commuting time. Treat this as an economic cost, not a cash expense. 18. Calculate the employee’s annual out-of-pocket premiums for medical, dental, vision, and other mandatory benefits. Separately calculate any current household expense that would disappear because the new employer provides that benefit. 19. Calculate the change from the current household baseline. Do not subtract an existing expense and then add the same expense back in the same scenario. 20. Calculate cash net as: - Estimated after-tax salary - Incremental cash commuting costs - Incremental insurance costs - Employee-paid benefit premiums - Other new cash expenses + Current household expenses eliminated by taking the job This represents the approximate improvement in household cash flow attributable to the job. 21. Calculate economic net-net as: - Cash net - Value of commuting time - Incremental vehicle depreciation or other non-cash economic costs not already counted This represents the broader economic value of the job. 22. Start the final output with only these numbers: - GROSS SALARY: $____ - ESTIMATED AFTER-TAX PAY: $____ - ANNUAL CASH NET: $____ - MONTHLY CASH NET: $____ - ANNUAL ECONOMIC NET-NET: $____ - MONTHLY ECONOMIC NET-NET: $____ 23. Then provide the calculation table. 24. Finally, provide the three largest cost items, the three assumptions with the greatest uncertainty, the break-even gross salary at which accepting the job would produce the target household net value I specify, and any information I should verify before making the decision.

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

I use ChatGPT to turn a physical trading card collection into a structured digital inventory. I start by photographing the collection and uploading the images to Google Drive. I divide the cards into logical groups and store them in separate subfolders, creating a clear structure before identification begins. ChatGPT then works through the folders in manageable batches, using OCR and visual recognition to extract information from the cards. This avoids manually uploading and describing every card one by one. I use consistent processing rules to reduce mistakes: I limit the number of cards per batch, make sure every source image is accounted for, and use one central card per photo when identification could otherwise be ambiguous. For each card, ChatGPT helps identify and organize the information I want to keep: card name, set code, set or series, edition, rarity, language, quantity, approximate year or period, condition notes, confidence level, and the source image. The useful part is not just OCR or card recognition. It is the combination of a structured Google Drive archive, visual identification, consistency checks, and systematic data extraction across a large physical collection. When identification is uncertain, I record that uncertainty rather than silently guessing. Damaged or unusual cards can also receive specific notes, such as foil scratches, edge wear, folds, or other condition issues. Once all folders have been processed, I consolidate the separate batches into one central inventory so I can check duplicates, quantities, and inconsistencies more easily. I first used this workflow for a Yu-Gi-Oh! collection and plan to reuse the same process for a Harry Potter trading card collection. The result is a searchable, structured overview of a physical collection that can later be used for valuation, selling, insurance, cataloguing, or simply knowing what is actually in the boxes. Step-by-step: 1. I photograph the physical trading card collection. 2. I upload the images to Google Drive and divide them into logical groups stored in separate subfolders. 3. I process the folders with ChatGPT in manageable batches using OCR and visual recognition. 4. I limit the number of cards per batch, account for every source image, and use one central card per photo when needed to reduce ambiguity. 5. I extract each card’s name, set code, set or series, edition, rarity, language, quantity, approximate year or period, condition notes, confidence level, and source image. 6. I record uncertainty instead of silently guessing and add specific notes for damaged or unusual cards, such as foil scratches, edge wear, and folds. 7. I consolidate the processed batches into one central inventory to check duplicates, quantities, and inconsistencies. 8. I reuse the process for additional collections, including my planned Harry Potter trading card collection.

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

I built a workflow that prepares late invoice reminder emails for me. Our industry has tight margins, and our back end is rudimentary with limited capabilities, so sending these reminders was annoying, repetitive manual labor. I had Claude Cowork walk me through creating an agent that logs in every Monday night, checks our late AR, and compares the customer names against a Google Sheet list I created. It uses an email template I provided, adds the contact name from the Sheet, and includes each late invoice’s identifying number, amount, and due date. The agent puts the emails in my Drafts folder and emails me a report showing which customers were included, how much each one owes, and the totals. All I have to do is review the drafts, confirm everything looks good, and hit Send. This has saved me about an hour every Monday night of tedious, repetitive manual labor. Step-by-step: 1. I created a Google Sheet with the customer names and contact names. 2. I had Claude Cowork walk me through creating an agent that logs in every Monday night. 3. The agent checks our late AR and compares the customer names against the Google Sheet. 4. It uses my email template and adds the contact name, invoice identifying number, amount, and due date. 5. It places the completed emails in my Drafts folder. 6. It emails me a report listing the included customers, the amount for each, and the totals. 7. I review the drafts, confirm that everything looks good, and send them.

I run Mealie, a self-hosted recipe manager, in Docker on a home server. Mealie is good, but it isn't zero-effort: there's a learning curve and ongoing administration just to keep a recipe library populated. My wife is Spanish, so many of her recipes are in her native language. We also have recipes as handwritten notes, WhatsApp messages, photos, and transcribed cooking videos. Translating the Spanish recipes accurately was beyond my ability, and I had no interest in typing everything into Mealie's editor by hand. I built a custom MCP server that exposes Mealie's API to Claude as a set of tools, along with a project-level skill that encodes our household rules: no mushrooms or celery for me, no ginger or coriander for my wife, a child who is fussy about fish, a pizza oven that only comes out at weekends, and batch cooking that needs to cover school lunches. Now I can give Claude a batch of recipes in whatever form they arrive and in any language. It translates them, restructures them into Mealie's schema, checks for duplicates, and writes them straight into the database. There's no manual entry, and I've never needed Mealie's own foodstuffs database. Ingredients stay as plain text, which is one less thing to maintain. For weekly planning, Claude reads recent meal-plan history from Mealie so that meals do not repeat too soon. It pulls the full ingredients for every candidate recipe rather than trusting the recipe name—a "Veggie Traybake" might contain mushrooms—and drafts the week against our constraints. The plan covers seven family dinners, five weekday lunches for the two of us, and two weekend family lunches. School lunches for the kids are often something we've batch-cooked, so that is built in too. Nothing is written to Mealie until I confirm the draft. After I confirm it, Claude seeds the plan into Mealie, merges ingredients across every meal, rounds them to sensible pack sizes, and groups everything by supermarket aisle. The output feeds through to Home Assistant, so the weekly plan and shopping list appear on our wall-mounted display and mobile phones. Step-by-step: 1. Create a self-hosted instance of Mealie in Docker on a home server. 2. Build a custom MCP server that exposes Mealie's API to Claude. 3. Create a project-level skill that encodes our dietary preferences, cooking setup, and usual meal structure. 4. Batch-feed Claude recipes in any format and language, including photos, WhatsApp messages, video transcripts, and handwritten notes. 5. Skip Mealie's structured foodstuffs database and keep ingredients as plain text while Claude parses them into Mealie's recipe schema. 6. Ask Claude to translate recipes, check for duplicates, and write the structured recipes into Mealie. 7. Ask Claude to read recent meal-plan history, check candidate ingredients against our preferences, and draft a new weekly plan covering our required dinners and lunches. 8. Make changes through chat, and confirm the draft before anything is written to Mealie. 9. Have Claude seed the confirmed plan into Mealie, merge ingredients across every meal, round them to sensible pack sizes, and create a categorised shopping list. 10. Install the Mealie integration in Home Assistant to display the plan, recipes, and shopping list on your devices.

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

I was overwhelmed by the thousands of emails I receive in my Outlook inbox every month. I didn’t have time to analyze them all, generate relevant responses, or track the replies and progress of each case. I used an Outlook feature and the virtual assistant Perplexity to help handle the task. Here’s the process: Step-by-step: 1. In Outlook, open the correct folder and select all the emails. 2. Go to Export/Import and follow all the required steps. 3. Export the emails as a `.csv` file, name the file, and save it in a specific location. 4. In Perplexity, attach the `.csv` file. 5. Use a prompt such as: I am a strategy manager and would like to propose a collaboration to my colleague David. The dashboard should list all topics discussed, proposed actions for each topic, and the remaining work to be done.

I spent twenty years producing documentary and nonfiction films, all of it the expensive way: crews, schedules, financing, and commissioners saying no. I'm proud of much of that work, and I still think about the films that never got made because the money wasn't there. Generative video pulled me in, but not for the reason people assume. It wasn't the speed or the cost. It was the fact that I could finally finish something without asking anyone for permission. No green light from a major platform. Nobody deciding that a story was too small to deserve a crew. A year later, I could produce this way. It turned out to be a different job rather than the same job with new tools. Then I ran into a problem I hadn't considered: how do you show the work? A finished shot doesn't prove much anymore. A client can look at a beautiful frame and have no way to tell whether it took three weeks or three minutes. If I'm honest, neither would I in their position. The thing every portfolio is built to display had stopped being evidence. I'm not a developer. I produce films. The site is bilingual, has seven sections, includes a case study for each film, plays video on hover, and has a comparison slider that works with a thumb as well as a mouse. I built all of it. I'd wanted this exact site for about fifteen years and had never managed to explain it properly to anyone I paid to make it. This time, I stopped explaining and built it myself. The middle of the page has a slider that you drag between the storyboard panel and the final shot. A basic before-and-after would prove nothing; you can fake that by generating twice. The storyboard is the proof because the framing, eyeline, and decision about what stays outside the frame all existed on paper before any model was asked to generate anything. You drag the handle and watch the intention survive. The rest of the site came from the same place. Video only plays on hover and is silent until you click for sound. Most video portfolios are unreadable because fifteen things start moving at once. The sections are organized by register rather than by client: epic, brand, animation, and lifestyle. A wall of logos answers who has hired you, which nobody is actually wondering. What they want to know is whether you can change tone. Nothing is cropped to fit the grid. A 5:33 film sits in 16:9 next to a 2:42 film in scope, and the layout is lopsided because of it. I nearly made them uniform because it looked tidier, then realized I was about to reformat my own films so a webpage would look neat. Anyone who cuts for a living spots that immediately. Concepts are labeled as concepts. Experiments have their own section instead of being scattered among the client work and hoping nobody asks which is which. Step-by-step: 1. I started from what my client couldn't verify: the craft behind a shot. 2. I found the artifact that proves it. It's usually the ugly thing nobody publishes: the storyboard, the reference sheet, or the version before the good version. 3. I put that artifact next to the finished piece inside one interaction, so nobody has to search for the proof. 4. I made playback deliberate. Video plays on hover, and sound requires a click. 5. I sorted the work by the question being asked rather than by the credentials I wanted to lead with. 6. I didn't reformat the work to fit the layout. I let the grid be uneven. 7. I labeled everything honestly: client, concept, or experiment. One word each can pay for the credibility of the whole page. 8. I wrote a brief for each section as a document first. Arguing with a paragraph is free; arguing with a built page is not. 9. I built the site with an AI coding tool. This was the least interesting step, even though it's the one everyone writes about. If your work can be mistaken for a lucky prompt, stop trying to prove it with the finished piece. The site is at www.termopilas.tv if you want to drag the handle yourself.

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.

My partner and I were tired parents trying to help our child sleep better. We had spent hundreds of euros on expert services and courses, but because every child is different, we still hadn't found an approach that worked consistently. For months, we had talked about trying to improve her sleep. She was waking us up multiple times each night, so we used Gemini to develop a plan with stages. In the first stage, we helped her start sleeping in her own room, which she achieved within a week. The second stage focused on getting her to sleep in her bed for the whole night, and we saw great success after the first night. The game changer was having immediate help when things didn't go as planned. At 2 a.m., I described situations such as my child lying on the floor and refusing to move, asking me to stay, wanting to cuddle or be carried, waking up her sibling, or coming back to our room again. Gemini adjusted the plan and told my tired brain what to do next. My child came back from her room 10 times, and each time I described the situation to Gemini and received practical advice. She eventually slept through the night and woke up in the morning feeling proud and happy. To me, that showed that the techniques Gemini suggested had helped create a safe space for sleep. Step-by-step: 1. I described our child's sleep challenges and the approaches we had already tried, including expert services and courses. 2. I asked Gemini to develop a staged plan tailored to her individual needs. 3. I followed the first stage until she was sleeping in her own room, which took one week. 4. I moved on to the second stage: helping her sleep in her bed for the entire night. 5. When unexpected situations came up during the night, I described them to Gemini in real time. 6. I followed the adjusted advice each time she got out of bed, including when she returned 10 times. 7. I continued until she slept through the night and woke up proud and happy.

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.

I built a fantasy hockey assistant with Claude Code for my Yahoo league. It supports two parts of the league: running our in-person draft and managing my team during the season. For the draft, it runs on my laptop as a live draft board. I enter each pick as it is announced, and the assistant re-ranks the remaining players based on what my roster still needs. The rankings combine projection sources with three seasons of NHL statistics, and each player is scored according to how much they would help in each of our ten categories. The board flags players who will likely be selected before my next pick, identifies injury and regression risks, and opens a detail card for any player with projections, historical data, and a short plain-English assessment. A separate display goes on the TV for the room, showing the pick clock, the next player on deck, and recent picks. After the final pick, the view switches to projected standings for every team, color-coded by category. During the season, I paste in my roster, and the assistant pulls the NHL schedule, starting-goalie reports, and injury news. It suggests the best lineup for each day, ranks free agents by value and by games played that week, and projects my weekly head-to-head matchup category by category. It can also ask Claude to propose a lineup and explain its recommendations, which costs about ten cents per run in API credits. Yahoo’s API is read-only. If it supported write access, I would probably have the app manage my team autonomously. Step-by-step: 1. I built the fantasy hockey assistant with Claude Code for my Yahoo league. 2. During the in-person draft, I run the assistant on my laptop as a live draft board and enter each pick as it is announced. 3. The assistant re-ranks the remaining players based on my roster’s needs, using projection sources, three seasons of NHL statistics, and each player’s value across our ten categories. 4. I use the board’s flags and player detail cards to evaluate likely pre-next-pick selections, injury and regression risks, projections, historical data, and plain-English player assessments. 5. I display the pick clock, next player on deck, and recent picks on a separate TV view, then switch that view to color-coded projected standings by category after the final pick. 6. During the season, I paste in my roster so the assistant can pull the NHL schedule, starting-goalie reports, and injury news. 7. I use that information to set daily lineups, rank free agents by value and weekly games played, and project my head-to-head matchup category by category. 8. When needed, I ask Claude to propose a lineup and explain its recommendations, at a cost of about ten cents per run in API credits. 9. Because Yahoo’s API is read-only, I currently use the assistant for recommendations rather than autonomous team management.

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 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’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.

AI agents such as Claude Code, Codex, and Gemini can install skills, plugins, and CLI tools in seconds. That is convenient, but each add-on runs with access to files, passwords, and accounts. A malicious add-on could steal API keys, read private data, or quietly take control of the agent. I use `agent-guard` to make “check first, install after” the default. Before installation, it examines AI agent skills, MCP servers, and CLI tools for malware, prompt injection, and credential theft using professional, open-source security scanners from NVIDIA, Cisco, Datadog, and the Open Source Security Foundation. The tool returns a clear verdict: safe to install or blocked, with the exact reason. It then installs only the verified version, preventing a different version from being swapped in between. A single check can also cover multiple AI agents, installing the approved add-on into every agent on the machine. Free and open source: https://github.com/elliottwaves-20/agent-guard Step-by-step: 1. I select the AI agent skill, MCP server, or CLI tool I want to install. 2. I run `agent-guard` to scan it for malware, prompt injection, and credential theft. 3. I review the verdict and the reason if the add-on is blocked. 4. I install the exact version that was checked rather than an unverified replacement. 5. If I use several AI agents, I install the approved add-on into all of them at once.