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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.

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

Most people use AI as a single general-purpose assistant. The problem is that every new conversation starts from scratch, while one AI constantly switches between roles such as researcher, writer, programmer, strategist, and editor. This often leads to inconsistent results and repeated prompting. Instead, I built a team of specialized AI agents, each with a single responsibility. By giving every agent a clear role, instructions, and context, I created reusable experts that become more consistent over time. Step-by-step: 1. I identified the different roles I needed, including researcher, writer, programmer, strategist, and editor. 2. I assigned each AI agent a single responsibility instead of asking one general-purpose assistant to handle every role. 3. I gave each agent a clear role, instructions, and relevant context. 4. I reused these specialized agents instead of starting every conversation from scratch.

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 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 built a personal daily tracking system called Artifact. It gives me a way to log my days and turn the data into decisions over time. To use it, I copy everything attached—or screenshot the included “green orange red” example—and paste it into my AI assistant, preferably Claude. The assistant briefly interviews me and then builds a personalized daily tracking system.

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

Everyone I know who runs a second brain uses Obsidian. The app is free, but sync is a subscription, and most AI integrations quietly assume you have it. I went another way: Joplin, which is free and open source, with an agent that reads my notebook through Joplin’s REST API, files my INBOX every morning while I sleep, and answers questions strictly from notes I actually wrote. It costs nothing beyond a VPS I already run, and the notebook still opens like a notebook. I use Hermes Agent on the VPS, Dropbox to sync notes between my devices, and Python scripts to connect the notebook and the agent. Every capture goes through `joplin_capture.py` and lands in a single INBOX folder with a source and timestamp attached. Captures can come from a Discord link, a thought from my phone, or a page from the web clipper. The process takes under ten seconds and requires no filing decisions at capture time, because filing at capture time is where second brains die. Joplin already ships with a REST API. I enable it with one setting and one token; the notebook then exposes HTTP on localhost:41184 with token authentication on every call. The same server powers the official web clipper, so this enables infrastructure I use anyway. There is no plugin, cloud service, or subscription. `joplin_filer.py` runs daily at 07:00 and uses a deterministic classifier to score each INBOX note against my existing folders. It uses token coverage rather than Jaccard, which dilutes single-token folders. Confident matches above 0.5 are moved into place: a hosting page goes to the hosting folder, while a security note goes to the security folder. Low-confidence notes stay in INBOX with the `needs-review` tag. Every move is logged to a FILER LOG note in the `__SYSTEM` folder, making the process auditable. The filer never deletes anything. I ran it in dry-run mode for a week before letting it touch a single note, and I recommend doing the same. When I want to know what I have learned, `joplin_ask.py` searches the corpus, reads the top notes in full, and answers with the note titles attached. It answers strictly from retrieved content. If the top hits are irrelevant, I refine the query before concluding there is nothing. It never invents a source, which matters when you write about security for a living. After each working session, `joplin_agent_log.py` prepends a digest to an AGENT LOG note in `__SYSTEM`. The log is newest first, append only, and syncs to my devices like everything else. The agent’s memory records what we did, decided, and deferred in the same place as the notes. The whole build is on GitHub: github.com/ciberjohn/mysecondBrain. It includes five Python scripts and the `joplin-brain` skill, which is the operating manual in a format another agent can load and follow. Step-by-step: 1. I enabled Joplin’s built-in REST API with one setting and one token. It serves HTTP on localhost:41184 with token authentication on every call and also supports the official web clipper. 2. I pointed Hermes Agent on my existing VPS at the Joplin REST API. 3. I routed every capture through `joplin_capture.py` into a single INBOX folder, attaching the source and a timestamp. Captures can come from Discord, my phone, or the web clipper. 4. I configured `joplin_filer.py` to run daily at 07:00 and score INBOX notes against my existing folders using token coverage rather than Jaccard. 5. I moved matches with scores above 0.5 into their folders, while leaving low-confidence notes in INBOX with the `needs-review` tag. 6. I logged every move in a FILER LOG note in the `__SYSTEM` folder and ensured that the filer never deletes anything. 7. I ran the filer in dry-run mode for a week before allowing it to move a note. 8. I used `joplin_ask.py` to search the corpus, read the top notes in full, and answer questions with the source note titles attached. When results were irrelevant, I refined the query. 9. After each working session, I used `joplin_agent_log.py` to prepend a digest to the newest-first, append-only AGENT LOG note in `__SYSTEM`. 10. I used Dropbox to sync notes between my devices and the REST API to move notes between Joplin and the agent—two separate pipes carrying the same notes in different directions.

I took a picture of my wife's spice rack, uploaded it to Grok, and asked it to organize the contents into three categories: Must Have, Keep, and Discard, based on age or lack of common usage. Step-by-step: 1. I took a picture of my wife's spice rack. 2. I uploaded the picture to Grok. 3. I asked Grok to categorize the contents as Must Have, Keep, or Discard based on age or lack of common usage.

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 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 built a weekly interactive training-course generator for my 10-year-old son and eventually landed on a much simpler final version than where I started. I wanted a way to teach him practical safety and life skills, starting with how to swim confidently and what to do if he gets into trouble in the water. I needed something more engaging than simply talking at him, but writing a polished, interactive lesson from scratch every week was not sustainable. First, Claude and I designed a single interactive HTML course as a proof of concept. It was a swim-safety course with a branded look, including a custom color palette, fonts, and a progress tracker styled like pool lanes. The course was divided into modules: a welcome screen, a comfort-and-basics lesson, a step-by-step skills walkthrough, a safety checklist, a “what to do if something goes wrong” module, a quiz, and a certificate at the end. The “what to do if something goes wrong” module was the most important part. Once the course worked, I wanted to reuse the same format for a new topic every week. Claude first built a version that called the Claude API live from inside the page to generate new content on demand. However, it only worked while the page stayed open inside Claude.ai, and occasionally a section failed to generate cleanly. We pivoted to a simpler approach. Instead of using a live tool, Claude documented the entire course format—including the visual identity, module structure, tone, and content rules—as a standing “brief” document. Each week, I open Claude Cowork, paste in the brief and that week’s topic—bike safety, fire safety, or whatever is next—and Cowork returns a finished, fully self-contained HTML file. There is no app to babysit and no live API call; I can simply open the file and hand it to my son. The result is a repeatable, no-maintenance weekly workflow for turning any topic into a polished interactive lesson for my kid. Step-by-step: 1. I identified a need for more engaging lessons on practical safety and life skills, starting with swimming and water safety. 2. I worked with Claude to create a proof-of-concept interactive HTML swim-safety course. 3. I structured the course into a welcome screen, comfort-and-basics lesson, skills walkthrough, safety checklist, emergency-response module, quiz, and certificate. 4. I tested a live-generation version that called the Claude API from inside the page, then noted that it only worked while open inside Claude.ai and that sections sometimes failed to generate cleanly. 5. I had Claude document the course’s visual identity, module structure, tone, and content rules in a reusable brief. 6. Each week, I open Claude Cowork, provide the brief and a new topic, and receive a finished, self-contained HTML course file. 7. I open the file and give the interactive lesson to my son without maintaining an app or making live API calls.

Many organizations have critical process knowledge that exists only in employees’ heads. When someone has a question, they have to track down the right expert, ask questions others may have already asked, and hope they remember every detail. This doesn’t scale and creates knowledge silos. I built a workflow that turns conversations with subject matter experts into structured, searchable organizational knowledge. Instead of asking employees to write documentation, an AI interviewer guides them through the process they know best, converts the conversation into well-structured documentation, and publishes it to an AI-powered knowledge base that anyone can query. Step-by-step: 1. Ask an employee to choose a business process they know well. 2. Have an AI interviewer ask follow-up questions to capture the complete workflow, decisions, exceptions, and best practices through a natural conversation. 3. Save the interview transcript. 4. Use an LLM to convert the transcript into structured documentation with clear sections, steps, decision points, and FAQs. 5. Store the documentation in a searchable knowledge repository and index it in a vector database. 6. Let employees ask questions through an AI assistant that retrieves the most relevant documentation and answers in natural language. 7. Repeat the process over time as processes evolve or additional experts contribute new knowledge. Instead of repeatedly interrupting the same subject matter experts, employees can get consistent answers from an AI assistant backed by documented organizational knowledge. The organization captures valuable expertise before it’s lost, reduces knowledge silos, improves onboarding, and creates documentation simply by having conversations.

I’m a commercial excellence consultant for industrial B2B businesses, and very few of the clients I speak with have a current view of the competition in their market. Nobody has mapped the competitive landscape recently—sometimes they never have. Clients can often name the companies in the market, but they can’t explain what those competitors do differently or why a customer would choose one over them. That gap is expensive. The mid-market manufacturers I work with, typically in the $75 million to $300 million range, usually don’t have a strategy team to close it. The alternatives are consulting rates for research that goes stale as soon as it’s delivered or a generic template that says the same five things about every market. Neither option is necessarily wrong, but neither is very useful. So I built a Claude Skill: an instruction set that runs live research every time instead of pattern-matching to a generic answer. The input is simple: the company name, its brand voice, the research scope, the geography, and a known competitor list if one is available. Before researching a single competitor, the Skill states the decision the report needs to inform—for example, whether to enter a vertical, how to price against a rival, or where to direct sales next quarter. It checks the client’s own website and capabilities next; an early version once recommended something the client already had. It then maps the competitive field across five tiers: direct, adjacent, disruptor, new entrant, and aspirational for PE-backed clients. It checks Asia-Pacific specifically because that’s the blind spot I’ve seen missed most often. Market size carries a confidence flag instead of false precision. Findings become battlecards phrased the way reps actually talk, rather than in analyst language. The report also includes a threat ranking with a timeline attached, then closes with three opportunities, three risks, and four to six moves for the quarter. Every recommendation is filtered through the original decision instead of being included simply because the research was interesting. The output is two files: an interactive HTML report and a matching PDF. Both are built entirely in HTML and CSS rather than with canvas charts, which can break in exactly the ways that matter—blank on load or missing from the PDF. None of what makes this useful is the AI itself. The important work is naming the decision before researching a competitor, checking what the client already has, tagging confidence instead of faking precision, and writing like a rep rather than an analyst. That’s the difference between a report that gets skimmed once and one that gets acted on. This is one piece of a bigger system I run for industrial manufacturers applying AI to their commercial function. I teach the underlying version of this workflow live. Step-by-step: 1. I provide Claude Skill with the company name, brand voice, research scope, geography, and known competitor list, if available. 2. I define the decision the report needs to inform, such as entering a vertical, pricing against a rival, or directing sales next quarter. 3. I have the Skill check the client’s website and capabilities before researching competitors. 4. I map the competitive field across direct, adjacent, disruptor, new entrant, and, for PE-backed clients, aspirational competitors. 5. I check Asia-Pacific specifically to address a commonly missed blind spot. 6. I assign confidence flags to market-size estimates rather than presenting false precision. 7. I turn the findings into sales-rep-friendly battlecards and add a threat ranking with a timeline. 8. I close the report with three opportunities, three risks, and four to six quarterly moves tied back to the original decision. 9. I deliver the result as an interactive HTML report and a matching PDF, using HTML and CSS instead of canvas charts.

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.

Take the AI out of AI-written text. We all know its characteristics: it is sometimes overwritten and repeats itself. /humanise

Teachers give assessments on devices provided to students, but students can use AI to cheat. I built TypeWriter to bring the level of security found in tests like the ACT and SAT to everyday assessments. Teachers can create assessments from existing materials, post them in a locked environment, and grade submissions and provide feedback on the same website. I built TypeWriter as a high school teacher, and it is free for teachers. Step-by-step: 1. Teachers create an assessment using their existing materials in TypeWriter. 2. They post the assessment in TypeWriter’s locked environment. 3. Students complete the assessment on the provided devices. 4. Teachers grade the assessments and provide feedback on the same website.

I built a workflow to track my monthly credit card expenses and keep them within my budget. Tracking my expenses is important to me because I want to reach my financial goals through consistency and healthy habits. However, entering each expense manually into Google Sheets was tiring and took several hours. I used Make to automate the process. The workflow reads the email from my bank every time I use my card, gathers the necessary information, converts it to JSON, and adds it to Google Sheets. It runs every six hours, every day, so I no longer need to spend my weekends reviewing expenses. Now I have more free time to build something else and only need a couple of minutes to review Google Sheets and make sure everything is working properly. Step-by-step: 1. I set up a workflow in Make to monitor emails from my bank whenever I use my credit card. 2. The workflow gathers the necessary expense information from each email. 3. It converts the information into JSON format. 4. It adds the expense data to Google Sheets. 5. The workflow runs every six hours, every day. 6. I spend a couple of minutes reviewing Google Sheets to make sure everything is working properly and that my expenses remain within budget.

I used Claude Code to build a 13-agent workforce for generating micro-courses. Each agent has its own job description and a defined role in the workflow, which begins with document analysis and ends with a course package agent delivering a complete, interactive HTML prototype as a local file. The agentic workforce creates knowledge-check quizzes with corrective feedback. Each micro-course also includes a downloadable handout highlighting the main points. In my case, a human course builder receives the final interactive HTML prototype and uses it to update our organization’s course catalogue. The HTML file is available for everyone to use as well. This reduced the production time from three months to two hours at most. I also included a security gate that redacts information from transcripts used to create course content. I’m happy to share more—we’re talking about folders and files here. Step-by-step: 1. I used Claude Code to build a 13-agent workforce for generating micro-courses. 2. I gave each agent its own job description and defined workflow. 3. I started the workflow with document analysis before the work begins. 4. I used a course package agent to deliver a complete, interactive HTML prototype as a local file. 5. I had the workforce create knowledge-check quizzes with corrective feedback. 6. I included a downloadable handout with highlights at the end of each micro-course. 7. I sent the final interactive HTML prototype to a human course builder, who updates our organization’s course catalogue from it. 8. I added a security gate to redact information from transcripts used for course content. 9. I made the HTML file available for everyone to use.

I used Claude and Nano Banana 2 to create a detailed landscaping plan for my property. The plan included precise recommendations for plant types and locations, soil testing, purchase lists, local suppliers, expected wholesale pricing, and photorealistic images of the mature planting beds. I refined the plan through 20–30 iterations, incorporating all of my requirements. Because I have previous experience working with landscape architects, I was able to use the AI-generated plan effectively and legitimately saved $5,000 in fees. Step-by-step: 1. I used Claude and Nano Banana 2 to develop a landscaping plan for my property. 2. I included requirements for plant types and locations, soil testing, purchase lists, local suppliers, expected wholesale pricing, and photorealistic images of the mature planting beds. 3. I refined the plan through 20–30 iterations until all of my requirements were incorporated. 4. I used my previous experience working with landscape architects to evaluate and apply the plan. 5. I saved $5,000 in landscape architecture fees.