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I kept rereading the same Stoic books to find one line I half remembered. So I stopped reading them and started talking to them instead. I took public-domain writing from 13 people—including Marcus Aurelius, Seneca, Sun Tzu, Jane Austen, Tesla, and others—and gave each one its own AI persona on a single page. Each persona answers only from that person’s writing and says when it does not know. There are no made-up quotes. The fun part is seeing the questions people actually ask. The top question this week was: “How do I stay steady when someone wastes my morning?” That is a real problem answered by a Roman emperor who faced the same one. Full disclosure: I am the founder of Mindola, the tool I used. The 13 classic lenses are free to try with no signup: https://mindola.ai/discover Step-by-step: 1. I gathered public-domain writing from 13 people, including Marcus Aurelius, Seneca, Sun Tzu, Jane Austen, Tesla, and others. 2. I created a separate AI persona for each person and put all 13 on one page. 3. I configured each persona to answer only from that person’s writing and to say when it does not know. 4. I use the personas to ask questions I would otherwise search for by rereading the books. 5. I review the questions people ask, including “How do I stay steady when someone wastes my morning?”

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.

My mother recently moved into a retirement suite, which meant downsizing her possessions. We ended up with dozens of physical items she no longer had space for, and we wanted her children and grandchildren to have an opportunity to sign up for the family items they loved before anything was donated. I needed a way to catalog everything quickly, share the information remotely, and verify that we weren't accidentally giving away items of significant value. I tested a physical-to-digital workflow using Gemini. Instead of putting the items in a pile or typing out endless lists, I drew a numbered grid on a large whiteboard, placed one item in each square, and took a few photos. Gemini identified the objects by grid position, categorized them, estimated their market values, and built a ready-to-use spreadsheet with a signup column. The physical staging took about 30 to 45 minutes, depending on how many items were on that particular board. The entire digital workload—including cataloging, valuing, formatting, and creating the spreadsheet—took under 10 minutes. The process turned an overwhelming chore into something manageable and produced an end product that was easy for everyone to use. Step-by-step: 1. I drew a simple grid on a whiteboard, numbered each box, and placed one item in each square before taking photos. 2. I uploaded the photos to Gemini and prompted it to identify every item by its board name and grid number. 3. I asked Gemini to classify each item into logical groups, such as Glassware, Ceramics, and Woodcraft, and provide quick, realistic resale-market valuations based on visual condition. 4. I asked Gemini to generate a downloadable Excel file formatted as a signup catalog, with columns for Item ID, Grid Number, Description, Category, Estimated Value, and a blank "Claimed / Signed Up By" column for family members to enter their names. 5. I uploaded the file to Google Drive, converted it to Google Sheets, and added a note at the top explaining how tie-breakers would work if two family members wanted the same item.

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'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 needed home insurance and was asked to estimate the value of all my personal belongings. I asked Claude to search my personal email and make a list of all the purchases I had made in the past year. I asked my husband to do the same and share his list with me. I also made a list of our valuables and took a picture of each room in the house. I gave all of that information to Claude, which produced a list of everything I owned and estimated its rough value. It cross-referenced the purchases with the items visible in the photos to make sure I did not double-count anything. I sent the list to my insurance broker, who said it was extremely helpful and could also serve as supporting evidence if I ever needed to submit an insurance claim.

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

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 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.

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 a meal tracker where my only job is to photograph a plate and send it to an agent. Identification, portion estimates, macros, storage, corrections, and the weekly review happen without me. The part worth stealing isn't the food. The agent has its own email address and iMessage thread, so anything I can send from my phone becomes an input. I send a photo through whichever channel takes fewer taps. I send corrections as plain English with `CORRECTION` in the subject, and answer questions in the same thread. There’s no app and no form. Adding a channel took an afternoon and changed what the agent could be pointed at—the tracker is just one use of an agent you can talk to. It polls on a schedule instead of using a webhook because my laptop sleeps and a local gateway would be unavailable half the time. Storage is append-only JSONL. Corrections append a new record with the same ID using last-write-wins, so the stats layer sees one meal while the original estimate stays on disk. Every number can be traced back to the model’s first guess and my override. The accuracy mechanism is a challenge step. Every photo is estimated twice: first by the main model, then by a blind subagent that receives only the photo and the rubric—not the first answer. If it identifies different food, the entry is downgraded to low confidence and becomes a question for me. Testing before making the system autonomous caught bugs that otherwise would have failed silently. The API returns attachments under a different field than I had assumed, and only on the detail endpoint. As a result, a photo email was read as having no attachments, and every meal photo would have been dropped while the daily task logged a tidy “no new meals” and appeared healthy. The send path was separately broken, which would have killed the weekly review on a Sunday even with everything upstream working. A silent no-op that produces a plausible clean run is what this category of build is prone to. The bug that actually cost me was token usage: each blind challenge read used about 44,000 tokens, so a five-photo dinner cost roughly 220,000 tokens for one meal and capped my usage. The fixes, in order of effect, were to run the challenger on a cheap model, skip it once a named product or my confirmation has settled the entry, downscale photos for viewing, and republish the dashboard only when the data changes. Measuring first mattered—I would have blamed photo size, but that was the smaller half. The honest limits are important: calorie estimates from a photo are 20–25% off at best, identification error is a bigger risk than arithmetic error, and alcohol, water, and caffeine are never estimated from photos. The sequence in which I asked for things mattered more than any single instruction: Step-by-step: 1. I gave the goal and the one ingestion mechanic I was sure about, then insisted on an agreed plan before any code. Arguing about storage and failure modes is cheap before code is attached. 2. Before scheduling anything, I processed one real input end to end in front of me, including every outbound path. Inbound gets tested because I use it; the reply and the weekly digest do not. 3. When the agent reported something about my own input that I knew was wrong, I said so and made it re-check. Confidently wrong answers about things I witnessed were the cheapest bugs to find. 4. I asked for an independent second assessment of anything estimated rather than read, and decided up front what level of agreement was enough to accept the result. 5. I asked for a visible audit surface showing every field the agent claims to track, with a correction control on it. 6. I asked the agent to look up anything knowable rather than estimate it. A named product is a lookup; only the unnameable needs a guess. 7. I treated approval friction and token cost as requirements rather than complaints, and measured before changing anything. Almost every rule exists because something went wrong in ordinary use, not because it was designed up front. An agent I can email or text, which keeps records and answers back, is general-purpose; I’ve pointed it at one narrow job. What else would you point it at?

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 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.

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.

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

I 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.

My doctor asked me to track my blood pressure for a month because it was on the high side before prescribing any medication. I initially recorded each reading manually in an Excel sheet, but after a few days, I wanted a simpler way to enter and manage the data. I uploaded the sheet to Claude Code and asked it to build a blood pressure tracking app. After the app was built, I hosted it on Netlify, used Supabase as the backend to save data for both my wife and me, and added it to my iPhone Home Screen. Step-by-step: 1. I started tracking my blood pressure in an Excel sheet as my doctor requested. 2. After several days of entering the readings manually, I uploaded the sheet to Claude Code. 3. I asked Claude Code to build an app for tracking blood pressure. 4. I hosted the app on Netlify. 5. I used Supabase as the backend to save blood pressure data for both my wife and me. 6. I saved the app to my iPhone Home Screen for easier access.

I teach and direct a required general education public speaking class at a small college. I’ve built the course around critical inquiry, critical thinking, and public advocacy of a localized problem. Over the years, I’ve noticed that students are increasingly reluctant to engage with the underlying problem. They are often content to identify a problem with enough certainty that they assume their perspective is obviously shared by everyone else. They may also believe that their preferred sources are more certain or credible, leading them to build a case based only on their limited perspectives. With the help of ChatGPT, I built an AI tool that guides students through the problem- and solution-discovery process. It begins with a general question: “what is your topic and what is the problem?” Students often respond with a one-word or incomplete answer, such as “poverty,” “crime,” or “the high cost of education.” These answers do not identify the topic’s deeper dimensions, the extent of the harm, or who is affected by the issue. The AI pushes back on these statements and helps students explore the issue in greater depth. It consistently asks, “what do you mean by x?” I constrained the AI to draw most of its knowledge from the coursepack I wrote for the class, which outlines the assignments, lessons, and instructional content. This keeps the tone of the interaction and the examples provided to students aligned with the overall feel of the course. The AI does not create speeches, make outlines, or find sources for students. Instead, it asks questions that help them refine the direction of their speeches. Whenever possible, the AI also identifies alternative viewpoints from sources traditionally associated with the student’s own perspective. For example, if a student is advocating a progressive viewpoint, the AI may identify statements or research from progressive sources that disagree with that perspective. If the student is advocating a conservative viewpoint, it may identify statements or research from conservative thinkers that challenge the student’s position. This helps students recognize the complexity of ideas and understand that people on the same political, religious, or ideological side do not necessarily agree on every topic. As is often the case when I build GPTs, the 8,000-character limit requires me to move many instructions into a document that I upload to the GPT’s resources. ChatGPT is helpful when I decide which content belongs in the configuration and which content can go in an uploaded document. Step-by-step: 1. I designed the public speaking course around critical inquiry, critical thinking, and public advocacy of a localized problem. 2. I identified a recurring challenge: students often named broad topics such as “poverty,” “crime,” or “the high cost of education” without exploring the depth of the issue, the degree of harm, or who is affected. 3. With help from ChatGPT, I built an AI tool that begins by asking, “what is your topic and what is the problem?” 4. I configured the AI to push back on incomplete answers by repeatedly asking, “what do you mean by x?” 5. I constrained the AI to draw most of its knowledge from my coursepack, including the class assignments, lessons, and instructional content. 6. I instructed the AI to guide students with questions rather than create speeches, make outlines, or find sources for them. 7. I configured it to identify alternative viewpoints, including disagreements from sources traditionally associated with the student’s own political, religious, or ideological perspective. 8. Because of the 8,000-character limit, I moved some instructions into a document uploaded to the GPT’s resources and used ChatGPT to help decide what belonged in the configuration and what belonged in the document.

AI is useful for researching investments, but most workflows stop at “What should I buy?” The harder part comes afterward: Does the idea make sense given what I already own? What would make me add to the position? When should I take profits? What evidence would prove the original thesis wrong? And how do I track all of that without constantly watching the market? I use AI to turn a one-time investment research session into an ongoing decision and monitoring system. I start by asking AI to research the market for potential opportunities. In my case, I look specifically for strong mean-reversion trades, but the same workflow could start with value opportunities, macro themes, sector rotations, individual stocks, crypto, or almost any other investment strategy. Then I give AI my actual portfolio so it can evaluate those ideas in context. After I decide which recommendations I agree with and manually make the trades, I have AI convert each investment thesis into explicit rules for what should happen next. Finally, I turn those rules into automated monitors that periodically check market conditions and alert me only when something happens that warrants another decision. Step-by-step: 1. I define what I’m looking for by asking AI to research potential investment opportunities using criteria I care about, such as mean reversion, valuation, momentum, macro conditions, risk/reward, or another strategy. 2. I have AI investigate current market conditions and rank the opportunities, narrowing a large universe down to a manageable set of ideas worth examining further. 3. I pressure-test each thesis by asking why the opportunity exists, what could drive the expected outcome, what the major risks are, and—most importantly—what evidence would invalidate the thesis. 4. I provide my current holdings so AI can identify overlapping exposures, concentration risks, hedges, or positions that conflict with the new ideas. 5. I ask AI which existing positions the research suggests reviewing and where new exposure might make sense. The goal is a small number of actionable decisions rather than a giant list of interesting trades. 6. I review the analysis and independently decide whether to buy, sell, hold, or do nothing. I keep actual trade execution under human control. 7. Before the market moves, I define the next decision for every position by asking AI to identify conditions that would warrant reviewing whether to: - Add - Take profits - Reduce exposure - Exit - Reconsider the original thesis 8. I turn those conditions into automated monitors. I have ChatGPT periodically check the relevant prices, yields, economic indicators, news, or other variables. Instead of sending routine updates, I tell it to alert me only when a predefined trigger occurs. 9. When a trigger fires, I return to the original thesis with the new information and decide what—if anything—should change. Instead of using AI for isolated investment recommendations, I now have a repeatable loop for managing an investment thesis over time: Find an opportunity → Understand it → Compare it to what I own → Make a decision → Define what would change my mind → Let AI watch for it The most useful part may actually come after the investment decision. By deciding in advance what evidence would make me add, take profits, or reconsider the thesis, I don’t have to start my analysis from scratch every time the market moves. AI becomes a persistent research and monitoring layer while I remain responsible for every investment decision and trade.

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.