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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?”

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've been thinking about the value of handoff documents and explanatory documents as we continue exploring efficient ways to work alongside AI to build software and improve communication. Even though we use many different tools, Markdown still has an important place. This new version of an HTML handoff document can document what exists, describe what could exist, or serve as a mockup for a brainstorm. It lets us communicate with remarkable fidelity through visuals, hierarchy, and formatting. It's also an efficient format for AI to understand. We shouldn't underestimate the significance of AI communicating with us through a visual medium. A visual flowchart with thoughtful design, layout, animation, and progressive disclosure can help us understand the logic and flow of incredibly complex systems much faster. I tried all kinds of tools, including React Flow and Mermaid. They're fun to experiment with, but Archify is a game changer for this use case. I can point it at any technology, repository, or brainstorm and work with it to build flowcharts with animations and clean, distinctive design. It's also completely free. https://tt-a1i.github.io/archify/# Step-by-step: 1. I identify whether I need to document what exists, explore what could exist, or mock up a brainstorm. 2. I use Markdown and an HTML handoff document to communicate the ideas with visuals, hierarchy, and formatting. 3. I consider tools such as React Flow and Mermaid for creating visual representations. 4. I point Archify at the relevant technology, repository, or brainstorm. 5. I work with Archify to develop a flowchart with animations, clean design, and progressive disclosure so the system's logic and flow are easier to understand.

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 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.

Turn a collection of documents, reports, spreadsheets, and notes into an evidence-backed decision brief with ChatGPT. Instead of asking AI to simply summarize the information, this workflow makes it identify what matters, connect the evidence, compare it with historical context, explore scenarios, and highlight what should be considered before making a decision. Step-by-step: 1. I gather the information relevant to one decision, including reports, PDFs, spreadsheets, research, historical data, meeting notes, and existing analysis. I upload everything into ChatGPT. 2. I ask ChatGPT to understand the situation using this prompt: > “Analyze the information I provided and build a structured understanding of the situation. Identify the key entities, important facts, relationships, metrics, trends, assumptions, and constraints. Do not make recommendations yet.” This creates the context before jumping to conclusions. 3. I build an evidence brief by asking: > “Create an evidence brief. Separate verified facts, derived insights, assumptions, conflicting information, and missing information. For every important conclusion, identify the supporting source or evidence.” This gives me a clearer picture of what is known versus what is inferred. 4. I add historical context when it is available by asking: > “Compare the current situation with the historical information provided. Identify meaningful patterns, similarities, differences, and changes. Highlight which historical observations could be relevant to the current decision.” This turns historical data into context rather than simply another report. 5. I explore three scenarios by asking ChatGPT: > “Based on the evidence and historical context, evaluate three scenarios: upside, base case, and downside. For each scenario, identify the assumptions, key drivers, risks, likely impact, and evidence supporting the assessment.” The objective isn't to pretend the future can be predicted perfectly. It is to understand how the decision changes when assumptions change. 6. I generate the decision brief by asking: > “Create a concise decision brief containing: > > 1. Current situation > 2. Most important evidence > 3. Key insights > 4. Historical context > 5. Critical assumptions > 6. Key risks > 7. Scenario analysis > 8. Evidence gaps and uncertainties > 9. Questions that should be investigated > 10. Possible actions and their implications. > Do not make the final decision on my behalf.” This produces a structured decision brief instead of another AI-generated summary. 7. I review the brief and challenge its conclusions before making the decision. I ask follow-up questions such as: > “Which assumption has the greatest impact on this decision?” > “Show me the strongest evidence against the current conclusion.” > “What information would most likely change the recommendation?” The AI helps structure the decision, but I make the decision. The important shift is: Summarize the information → Understand the situation → Establish the evidence → Add historical context → Explore scenarios → Evaluate the decision This approach can be applied to almost any domain where decisions depend on complex and interconnected information. A property investment is one example. A business strategy, product decision, operational problem, financial analysis, research question, or engineering decision can follow the same pattern.

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 moved from public school to a private school with no autism-specific training, and I found students on the spectrum struggling with one-size-fits-all lessons. AI helped close that gap for both me and my students in about five minutes per lesson. I take a standard lesson and rebuild it around the individual student. I upload only the blank assignment—never any student data—and prompt AI to adapt it to that learner. I include their favorite colors and niche interests, replace abstract examples with personalized ones, add sentence starters, and allow them to draw responses instead of writing dense paragraphs. Step-by-step: 1. I take an existing lesson and create a blank version of the assignment. 2. I upload only the blank assignment to my AI tool, without including any student data. 3. I provide the student’s interests, preferred colors, and reading level. 4. I ask the AI to rebuild the lesson for that specific student. 5. I use personalized examples, sentence starters, and drawing-based response options to make the lesson more accessible. The result is no shutdowns, no walls, and a genuinely engaged and grateful student. One untrained teacher can now individualize any lesson for a unique learner in minutes.

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.

An AI “executive brain” needs two things: intelligence that, combined with your context, forms the system’s “brain,” and a harness that gives it “hands”—the agentic capabilities to act on your behalf. Claude Code and Codex are two popular options. Their apps provide both the model and the harness in an easy-to-use interface. My workspace is GitHub because popular AI models already understand it and can handle its setup and administration for me. GitHub also syncs automatically with the local file system on my MacBook, so I retain complete ownership and control. If I ever wanted to leave GitHub, I could do so without lock-in. The same setup can work in any file system, from your local file system or Google Drive to a more sophisticated option like Obsidian. A good manager has a structured process for onboarding and staying aligned with a team. You need the same structure to work effectively with AI. My “executive brain” has four layers: the company layer, project folders, operations folders, and the process layer. The company layer contains the mission, vision, values, brand guidelines, and decision-making principles. Project folders contain one folder per project, including its specification, plan, open questions, and decision log. Operations folders cover ongoing areas of the business, such as marketing, finance, and support. They have the same shape as project folders, but they never finish: projects ship, while operations run. The process layer contains standard operating procedures for how we work together, such as processing a meeting, planning a project, publishing a post, and closing out the week. For AI to work successfully, your company has to be “legible” to it. If information is not written down and accurate, it is not legible, and the AI cannot help you with it. This is like onboarding a great new hire: explain what the company is trying to do, what each project is for, what has already been decided and why, and how you like to work. You do not need to write everything alone or all at once. The AI can interview you, draft the documents, and file them in the right place based on the structure you set up. An employee without logins cannot do much, and AI works the same way. Start with the places where you communicate. At my company, we primarily communicate through Zoom and our community forums, so my AI system is connected to both. This allows it to stay up to date as we progress and participate in discussions as an equal participant when asked. Once the AI is connected to your communications, give it access to the places where you want it to execute on your behalf. You can connect it directly through an API or MCP, or it can use a browser to interact with a tool the same way you do by clicking and typing. You decide what it can access, and you can start small. Each connection turns a category of “things I have to do myself” into “things I can delegate.” The final step is incorporating what the AI learns. After every working session and meeting, it updates the record: decisions are logged, task lists are reconciled, and project documents are brought up to date. When we learn something, it goes into the SOP, so every future run is sharper than the last. That is how the AI becomes more useful every week: its knowledge compounds inside the business instead of starting from zero in every conversation. Step-by-step: 1. Choose the intelligence and harness that will power your AI system. Claude Code and Codex are two popular options whose apps provide both in an easy-to-use interface. 2. Create a workspace for alignment, planning, feedback, and learnings. I use GitHub because AI models understand it, it handles setup and administration, and it syncs with my local MacBook file system without locking me in. 3. Organize the workspace into four layers: company information, project folders, operations folders, and process documentation. 4. Make the company legible by documenting its goals, projects, decisions, working preferences, and other relevant context. Have the AI interview you, draft the documents, and file them in the appropriate locations. 5. Connect the AI to your communication tools, such as Zoom and community forums, so it can stay informed and participate when asked. 6. Connect the AI to the systems where you want it to execute tasks. Use a direct API or MCP connection when available, or give it browser access when it is not. 7. After each working session and meeting, have the AI update decisions, task lists, and project documents. 8. Add new learnings to the relevant SOP so each future run improves on the last.

I built an AI workflow to automate guest-posting outreach. I use Claude to analyze target website requirements, draft personalized pitch emails for editors, and optimize my responses so I can pursue high-quality backlinks more efficiently. Step-by-step: 1. I provide Claude with the requirements for each target website. 2. I use Claude to analyze those requirements and identify what to address in the outreach. 3. I ask Claude to draft a personalized pitch email for the editor. 4. I use Claude to optimize my responses during the outreach process.

Design taste is hard to put into words. I may know immediately that one website, presentation, report, or graphic feels right and another doesn't, but still struggle to explain whether the difference comes from the typography, spacing, colors, density, layout, or overall aesthetic. That makes working with AI frustrating. If I can't describe what I want, the AI has to guess. Instead of trying to explain my design taste upfront, I turned the process into an iterative visual feedback loop. I first asked the AI to suggest existing websites with different design styles so I could identify examples I liked. Once it had those references, I asked it to create three substantially different HTML mockups for the same content. I picked the direction I liked best, explained what I liked and disliked, and had the AI generate another set of alternatives based on that feedback. After about three rounds, the AI had a much better understanding of my design preferences than I could have given it in a written prompt. The basic loop is: show me examples → I choose → generate alternatives → I react → refine → repeat. This is essentially preference elicitation through examples. Research on human-AI interaction has similarly found that people can refine difficult-to-articulate preferences by reacting to concrete alternatives rather than specifying everything upfront. Step-by-step: 1. I asked the AI for several visual references, including websites or designs that represented distinctly different styles. 2. I reviewed the examples and identified the ones I liked. I didn't need sophisticated design terminology; I simply described what I preferred and anything obvious that I liked or disliked. 3. I gave the AI something real to design. I used an actual report I was working on so I could evaluate the styles in context. 4. I asked the AI to create three substantially different visual directions rather than minor variations of the same design. 5. I chose the direction closest to my taste and explained what I liked, what I didn't, and which elements from the other versions I wanted to incorporate. 6. I asked the AI to generate three new alternatives using everything it had learned so far. 7. I repeated the process until the designs began to converge. I did roughly three rounds, with each round narrowing the design space and giving the AI more information about my preferences. 8. I asked the AI to summarize what it had learned into reusable design guidelines so future projects could start with those preferences. Instead of trying to translate an aesthetic preference into design terminology, I let the AI learn my taste from my choices. The important shift is simple: don't describe what you like—show, choose, react, and refine. This can work for websites, presentations, reports, branding, graphics, interior design, clothing, invitations, or almost anything else where I know what I like when I see it.

Most AI agents are built around general best practices. That’s useful, but it doesn’t mean they work the way I want them to. I’ve started using a simple calibration process to tailor my agents to my preferences. Instead of telling an agent what to do on each task, I have it interview me about how I like work done in its specific domain. A writing agent asks different questions than a research agent, and a strategy agent asks different questions than a coding agent. The goal is to make my working preferences part of how the agent operates. Step-by-step: 1. I pick an agent I use regularly, such as one for writing, research, strategy, coding, analysis, or career advice—especially where my personal preferences matter. 2. I ask the agent to interview me about how I prefer work to be done in its domain. A writing agent might ask about tone, structure, editing style, and how much pushback I want. A research agent might ask about source quality, depth, recency, citations, and how much synthesis I prefer. 3. I have the agent summarize what it learned and separate durable preferences from temporary or situation-specific ones. 4. I review the proposed changes and ask the agent to show me exactly how it wants to update its instructions or skills. I correct anything it misunderstood and explicitly approve the changes before anything is modified. 5. Once I approve the changes, I have the agent apply them to its instructions or skills so those preferences become part of how it works going forward. 6. I use the agent normally and pay attention to where it feels more aligned and where it still misses the mark. 7. When I notice recurring friction, I add or adjust the relevant preference instead of repeatedly correcting the same behavior task by task. 8. I repeat the interview periodically. My preferences, tools, and workflows change, so the agent should be able to ask which preferences are still valid, which ones I keep overriding, what has been annoying me, and what should be added or removed. The result is an agent that doesn’t just know how to do the job; it knows how I want the job done. Because every change is proposed and approved before it becomes permanent, the personalization stays intentional rather than turning into a collection of guesses about me.

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.

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.

After nearly 20 years in France, I’m finally applying for citizenship. I’ve spent the past year preparing documents and studying for the required tests, and my final interview is a few weeks away. Alongside the official study materials, I’ve been using ChatGPT to drill key facts and historical dates, run mock oral interviews in voice mode, and create quizzes from YouTube videos I link whenever they cover the right material. ChatGPT sometimes speaks French with a rather heavy accent, but the interactive back-and-forth helps me remember hundreds of details that are difficult to retain by simply reading a page. It’s tedious, but I find rote memorization challenging. We’ll see how it goes under pressure.

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 love playing poker, both online and live. One month, I performed poorly. Although it felt like the cards were running badly, I wondered whether I had developed a problem in my game and was blaming my losses on bad luck. I asked around, including asking AI, whether a tool existed that could measure luck from poker hand histories. The unwelcome answer was that it did not. I'm not a coder, but after doing some research into vibe coding, I started building a luck-detection app in a Claude chat. Claude built the UI directly in the chat window and advised me on the formulas I was using to calculate luck for the cards dealt, my performance on the flop, and my performance when I went all in. All three metrics have strong averages, and luck is what varies them. I used a bell curve to model hand outcomes and a Monte Carlo simulator, which Claude suggested and executed, to evaluate all possible outcomes. The result astonished me because it was so useful. I immediately fixed two major leaks in my game and felt better knowing that bad luck really was the main problem affecting my results. I liked the tool so much that I decided to turn it into a full web app with Claude Code, and now an iPhone app that I may let other people use for free. I also had a lot of fun building it—except for learning how to use Xcode. That was a pain, even with step-by-step guidance from Claude. Step-by-step: 1. I reviewed a month of poor poker results and questioned whether bad luck or problems in my game were causing the losses. 2. I researched whether a tool existed that could measure luck from poker hand histories and learned that I would need to build one myself. 3. I used vibe coding to start building a luck-detection app in a Claude chat. 4. I had Claude create the UI and advise on formulas for evaluating cards dealt, flop performance, and all-in performance. 5. I used a bell curve to model hand outcomes and a Monte Carlo simulator to evaluate possible outcomes. 6. I used the results to identify and fix two major leaks in my game and confirm that bad luck was also affecting my results. 7. I expanded the project into a full web app with Claude Code and then began building an iPhone app, working through the added challenge of learning Xcode.