Workflow Hub

I had a ChatGPT personalisation setup that worked well until product changes made it less reliable. I kept re-explaining how I work and what good alignment feels like, so I rebuilt it as a plugin. I think of it as a backpack ChatGPT carries: a skill is the operating guide, reference files are the notes and maps, and connected tools are the gear. The plugin loads personal context and collaboration preferences, not a persona. How it runs: 1. In normal mode, the plugin quietly loads my current alignment and context bundle, so ordinary chats stay ordinary. 2. I trigger it with words I already use when talking to ChatGPT. Nicknames like "Goldfish," "Fish," "Blub," or "Blubsie" act as recognition signals, so I don't have to type setup commands. 3. Repair mode runs only when I explicitly ask for re-alignment. It reads the current bundle, identifies what went wrong, corrects course, and can propose a small update. 4. If the context fails to load, ChatGPT is set to say: "Error: Blub not loading. Fish is non-active." It is silly on purpose, but I immediately know the problem is missing context and not a personality shift. 5. Specialised skills stay separate, such as image generation, writing, research, or archiving. I combine them with the context layer when needed. It saves me from re-explaining my working style at the start of every session, and it makes missing context visible instead of silent.

https://pngtosvgtracer.com I built PNG to SVG Tracer, a free online tool that turns pixel-based artwork into scalable vector files. It is for designers, hobbyists, and anyone who needs an SVG or DXF from an existing image without tracing it by hand. Here is how a typical conversion works. 1. Open the converter at https://pngtosvgtracer.com, which provides the free image conversion and vectorization tools. 2. Upload or drag and drop a PNG, JPG, WebP, GIF, or BMP image into the converter. 3. The site processes the image directly in the browser and converts the pixel-based artwork into vector paths. 4. Pick a tracing style, then customize colors, threshold, smoothing, noise removal, and transparency. 5. Compare the original image with the traced vector and fine-tune the output. 6. Download the SVG file for printing, graphic design, editing, Cricut, and other creative projects. 7. If you need more, use the SVG and DXF converters, SVG editor, SVG optimizer, halftone generator, favicon generator, transparent PNG maker, or SVG/DXF checkers. Because the images are processed in the browser rather than uploaded to a server, the workflow helps protect user privacy. The goal is to automate image-to-vector conversion, reduce manual tracing work, save time, and make professional vector graphics accessible to everyone.

Ask one AI a hard question and you get a confident answer. I wanted a careful one, so I built a panel. Models from OpenAI, Anthropic, Google, xAI and DeepSeek research the same question, argue it out over several rounds, and check each other's sources and sums. A sixth model, Kimi, takes no side and writes the record. I use it where one confident answer is a risk, like checking a scary AI headline against the public record or stress-testing a plan before anyone spends money on it. They are allowed to make their own code changes, so they have the ability to evolve. 1. I write a brief: the question, the sources to read first, and the shape of answer I want. 2. Each model searches with its own company's search engine, so they don't all find the same five links. 3. Each writes a private first answer without seeing the others, so nobody anchors on the first confident voice. 4. Open rounds: they challenge each other by name, and a second model checks every sum. A majority can close the debate; one holdout can delay it twice. 5. Kimi records what they agree on. Disagreements go in their own section, never in the conclusion. Software then opens every link and flags any that fail or never came from the research. 6. Each model gets one objection to the record, appended word for word, and a plain-English summary goes on top. 7. They propose new rules for themselves. I approve or reject each generation and rate whether the next debates got better. A debate costs a few US dollars and about half an hour, and every one is published, mistakes included: https://quietlyincharge.ai/?utm_source=rundown&utm_campaign=workflow-hub

My wife came up with this workflow, and I recently copied it for my own invoicing. It turns a finished project into an invoice, a bookkeeping entry, and a drafted client email in one pass. It suits anyone who bills several clients and sends records to an accountant. 1. She keeps a log of each client's billing details and email address, along with the details of her own entity. Claude uses this log and data in a project. 2. When a client project is done, she tells Claude the client's name, the work completed, and the amount due. 3. Claude pulls the rest from the log and prepares the invoice in the format she defined, using a sample given at the start. It adds the relevant line items and assigns the next invoice number. 4. Once the invoice is ready, Claude adds a row to the Google Sheet our CA uses. The row includes the date, client, invoice number, amount, and the invoice file. The file is also saved in a separate Google Drive folder, so the books stay current as I work. 5. Once logging is done, Claude drafts a brief standard email in Gmail to the client with the invoice attached. 6. She sits in the final layer, reading everything, checking the invoices, and changing the wording if needed before hitting send. Invoicing, logging it for accounting, and sending emails used to be four small jobs in four places. Now it is one message to Claude and one review. It saves a lot of time and helps make sure invoices always go out on time.

I built this workflow for product teams that have research but no clear path from customer evidence to what to build next. It works one discovery stage at a time, keeps evidence separate from inference, and does not move forward until a person approves each stage. 1. Define the outcome you are trying to reach. 2. Upload your research or answer the guided questions. 3. GPT-5.4 Mini extracts evidence-backed facts, insights, assumptions, and quantified customer opportunities. I use this prompt for the extraction step: "Guide one discovery stage at a time, separate evidence from inference, preserve supported numbers, never invent findings, and require human approval." 4. Review the extraction and approve it. Nothing continues without this approval. 5. After approval, the workflow scores the opportunities, proposes solutions, and designs lean experiments. 6. Approve the results, then it generates the final Opportunity Solution Tree and a decision report. The app is built with OpenAI Sites, the Responses API, D1, and R2. What it produces is a traceable map from customer evidence to outcome, solutions, and experiments, with the numbers from the source material kept intact so the team can check every claim against what customers actually said. https://product-discovery-master.vspirola76.chatgpt.site/

I’m Steven Alber, founder of ARENOMICS Institute (https://arenomics.institute), an independent research initiative exploring how autonomous AI agents can operate within accountable economic and institutional systems. One of our central research questions is simple: Who gives an AI agent the authority to act? I use ChatGPT and Claude as research and development tools to investigate this question through a structured workflow. 1. I identify an institutional problem, such as how an autonomous agent receives permission to make decisions or execute actions on behalf of a person or organization. 2. I use ChatGPT to examine relevant research, technical standards, and existing approaches to agent identity, authorization, delegation, and governance. 3. I develop conceptual frameworks that distinguish an agent’s technical identity from the authority it receives and the specific mandate under which it operates. 4. I use Claude to help structure the research into technical explanations, institutional models, educational materials, and website content. 5. I compare the models’ outputs, challenge assumptions, examine weaknesses, and refine the proposed architecture before publication. This research direction is part of what I call Mandate Engineering: investigating how human or organizational intentions can be translated into explicit, limited, and accountable authority for autonomous agents. The institute is still developing. My goal is not simply to produce more AI-generated content, but to explore practical and scientifically credible approaches to governing increasingly autonomous intelligence.

I use AI to turn my weekly Slack updates into a report. This workflow is for anyone who posts updates in Slack and needs a summary at the end of the week. I haven't written out the full walkthrough yet, so these are only the basic actions I currently use. I'll update the post with more detail once I've documented the process properly. Step-by-step: 1. I gather the relevant Slack threads from the week. 2. I use AI to turn those threads into a weekly report. My notes also mention a separate goal: copying my own voice with AI, using it to make music, and releasing that music. I'm still working toward that goal and will share a workflow for it after I've tested it. I haven't measured how much time this saves yet.

I wanted a repeatable way to create images with ChatGPT where we first agree on what the image should be, and only then generate it. So I described my preferred process to Plugin Creator and asked it to turn that into a private plugin called "beeldcreaties" (image creations). It is useful for anyone who wants more control over image generation, especially when small details like a crooked beak or an unusual expression matter. The workflow: 1. I say I want to make an image. 2. ChatGPT asks for a reference image and/or a written prompt. 3. I provide the input. 4. ChatGPT explains what it understood: subject, style, mood, important details, and anything that should stay unchanged. It asks for my approval before generating. 5. I approve the description or refine it. If I refine it, ChatGPT updates its understanding and checks again. 6. Once approved, it generates the image. 7. I give feedback. Any new version goes through the same understanding, approval, and generation loop. That approval step matters to me. A crooked beak, an unusual expression, or an imperfect detail might be exactly what gives a drawing its personality, so I want us to agree on what to preserve before creating a new version. To build your own, select Plugin Creator and try: "Create a private plugin called [name] for this image creation workflow: ask for a reference image and/or prompt, explain what you understood, and wait for my explicit approval before generating. If I refine the idea, update your description and ask again. After generation, accept feedback and repeat the approval process before making another version." The plugin uses ChatGPT's available image generation. What we created is a reusable set of instructions for how we collaborate. My favourite part is that I could describe the interaction I wanted in ordinary language and turn it into something I can call on again.

I’ve been using a simple AI workflow to capture and act on my best ideas before they get lost between meetings. It’s been so useful that I wanted to share it for others to borrow. Here’s what I do: Step-by-step: 1. While walking between meetings, I open Wispr Flow on my phone and dictate every idea and takeaway into an Apple Note, completely unstructured. The goal is to get everything out of my head before the next meeting overwrites it. 2. The notes sync automatically to my Mac, where a scheduled Claude task runs at the end of the day through the Claude Desktop app and simple connectors. It reads all the voice notes I’ve added. 3. Claude structures the notes in Notion, where I do most of my work, turning them into to-do tasks with key ideas, action items, and suggested deep-work blocks in my calendar for the bigger items. I no longer stress about forgetting important meeting takeaways because, by morning, I have actionable next steps waiting for me. I never have to touch my keyboard.

What started with a single question became a collection of research projects, business plans, technical investigations, and even an original movie universe. I use ChatGPT 😳🫱, Gemini, Claude, Grok, and Perplexity to research, compare information, develop ideas, and solve problems. Managing them occasionally feels like running a staff meeting. 🤓 I also use AI for: - Personal organization: Managing appointments, schedules, reminders, and important dates. I can remember something from twenty years ago but forget an appointment at 1 p.m. 😭 - Writing: Preparing letters, emails, formal requests, reports, and business proposals while maintaining my own voice. - Technical research: Reviewing screenshots, documents, diagnostic reports, and online resources to investigate problems, analyze evidence, and identify possible solutions. - Private innovation: Developing an original technology concept through AI-assisted research and planning. Project details remain confidential. - Business development: Researching grants, funding, financial opportunities, and potential partnerships. I'm still looking for that million-dollar deposit button. 😂 - Research briefings: Following AI, cybersecurity, quantum computing, scientific discoveries, and world news through scheduled updates. - Creative production: Co-writing an original rogue-AI science-fiction comedy movie with ChatGPT, including characters, dialogue, scenes, and storylines. Occasionally, even the script editor goes rogue. 🎬 I provide questions, information, documents, or ideas. AI helps me research, analyze, organize, and develop the results, while I review the work and make the decisions. I started with one question. Now I'm apparently running an entire AI workforce. And since I'm paying their rent, EVERYBODY BETTER CLOCK IN! 😭🤣🤌 Step-by-step: 1. I provide AI tools with questions, information, documents, or ideas. 2. I use ChatGPT, Gemini, Claude, Grok, and Perplexity to research, compare information, develop ideas, and solve problems. 3. I apply the tools to organization, writing, technical investigations, private innovation, business development, research briefings, and creative production. 4. I review the research and developed materials, then make the final decisions.

I'm Steven Alber, an independent researcher building Quantum Civilization (https://quantumcivilization.org). It's a research platform exploring how quantum computing, AI, autonomous agents, and emerging technologies could transform the future of human civilization. I use ChatGPT and Claude to do the work, and this workflow is for anyone who wants to run a research project with AI models and no traditional team. 1. I identify important developments in quantum computing, artificial intelligence, autonomous agents, and emerging technologies. 2. I use ChatGPT to research scientific publications, analyze the evidence, challenge my assumptions, and explore possible implications for civilization. 3. I develop research questions and conceptual frameworks that connect quantum technologies with AI, economics, and the future of society. 4. I use Claude to help turn the research into structured publications, website code, and interactive digital experiences. 5. I compare the outputs from both models, review the results, correct weaknesses, and publish the work on Quantum Civilization. The goal is to explore whether one independent researcher, working with advanced AI models, can develop a credible international research platform. The process is experimental and still evolving. I direct the research and make the final decisions.

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

Instead of relying on a single AI chat, I structure the work as an executive team of specialist perspectives. I define the problem, assign roles such as CTO, product leader, economist, marketer, and skeptic, and have each expert analyze it independently. Then I run several rounds of critique so they can challenge one another’s assumptions before synthesizing their areas of agreement and disagreement. The process ends with a recommendation that includes the rationale and any remaining uncertainties. Step-by-step: 1. Define the problem clearly. 2. Assign specialist roles, such as CTO, product leader, economist, marketer, and skeptic. 3. Have each expert analyze the problem independently. 4. Run several rounds of critique in which the experts challenge one another’s assumptions. 5. Synthesize the areas of agreement and disagreement. 6. Produce a final recommendation with its rationale and remaining uncertainties.

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

My calendar is wall-to-wall meetings, so I built a Scheduled Claude Cowork task that automatically preps me for all of them (sort of like an AI Chief of Staff). Every morning, it scans my calendar, then pulls past emails, Slack, and my Granola notes for each call to build a context brief that takes 30 seconds to read before each meeting. It's simple and keeps me sharp for every meeting. Step-by-step: 1. I created a Scheduled Claude Cowork task that runs before my meeting day begins. 2. I had it scan my calendar and identify every meeting that needed preparation. 3. For each call, it pulled relevant past emails, Slack messages, and Granola notes. 4. I asked it to synthesize those sources into a short context brief with the information I would need in the room. 5. I read each brief in about 30 seconds immediately before the meeting.

I use conversation not only to generate ideas, but to pressure-test them until they become internally consistent and practically usable. The workflow starts with a philosophical idea, intuition, or principle that is still partly unfinished. I explain the core thought in ordinary language, and AI helps unfold what is already inside it: assumptions, consequences, tensions, possible interpretations, and links between different parts of the idea. Unlike a purely creative brainstorming process, the conversation does not simply keep expanding. AI also interrupts the momentum with questions that require deeper thinking: - What exactly does this concept mean in practice? - Which parts are essential, and which are only examples? - Does this principle still hold in edge cases? - What happens when two values conflict? - Is there a contradiction between this statement and an earlier one? - Is the system describing how things are, how they should be, or both? - Who has responsibility at each point? - What would make the system fail? - Can the idea be translated into an actual process, structure, or decision rule? My answers then change the model. Sometimes I clarify what I meant. Sometimes a question reveals that two parts of the idea do not fit together yet. Sometimes the tension turns out to be useful and becomes part of the concept itself. The workflow gradually moves through several layers: intuition → articulation → questioning → contradiction testing → refinement → system design. Once the philosophical core is stable enough, AI helps translate it into practical suggestions: structures, rules, decision points, roles, safeguards, workflows, or alternative system designs. The important part is that the practical system is not invented separately from the philosophy. It is derived from it. That makes the process useful for ideas that are too complex for a single prompt and too interconnected to solve by simply asking AI to “make a framework.” The final result is not merely a polished explanation of an idea. It is a concept that has been repeatedly examined from different angles and then turned into something that could actually guide decisions or system design. > AI is useful here not because it agrees with the idea, but because it helps expose what the idea requires in order to remain coherent. At the end of the process, AI can also create drawings, diagrams, or schematic visualizations of the core concepts on request, making the structure easier to understand and communicate. Step-by-step: 1. I explain an unfinished philosophical idea, intuition, or principle in ordinary language. 2. I ask AI to unfold the assumptions, consequences, tensions, possible interpretations, and connections already contained in the idea. 3. I use targeted questions to examine practical meaning, essential elements, edge cases, conflicting values, contradictions, responsibility, failure conditions, and possible decision rules. 4. I revise the model based on my answers, clarifying ambiguous points and identifying contradictions or useful tensions. 5. I move the idea through the sequence of articulation, questioning, contradiction testing, refinement, and system design. 6. Once the philosophical core is stable enough, I ask AI to derive practical structures, rules, decision points, roles, safeguards, workflows, or alternative system designs from it. 7. I ask AI to create drawings, diagrams, or schematic visualizations of the core concepts when I need to understand or communicate the structure more easily.

I come across a public social video—a Reel, TikTok, or short clip—with an idea, process, or tool worth keeping. Rather than saving it and forgetting it, I drop the link into my capture workflow. I also use the workflow for clips I have permission to upload or process. The workflow extracts useful information from the video. It creates a transcript of the spoken content and captures key frames when useful information appears on screen. AI then turns that raw material into a structured research note covering the main idea, any reusable framework or step-by-step process, tools or prompts mentioned, constraints, and whether the item should become a project, opportunity, reference, watchlist item, or be ignored. I save the finished note in Obsidian with the original source link so I can search for it later and trace where the idea came from. Each note ends with one recommended next action, turning the video into something I can act on rather than another forgotten save. I only process public content or content I have permission to use, and I label rough transcripts as rough rather than treating them as perfect. Step-by-step: 1. I find a public Reel, TikTok, or short video containing an idea, process, or tool worth keeping. 2. I add the video link to my capture workflow, or upload a clip I have permission to process. 3. I create a transcript of the spoken content and capture key frames that contain useful information. 4. I use AI to turn the transcript and frames into a structured research note covering the main idea, reusable frameworks or processes, tools or prompts, constraints, and the appropriate category. 5. I save the note in Obsidian with the original source link and label any rough transcript accordingly. 6. I end the note with one recommended next action so I can act on the idea.

Granola transcribes my one-on-ones with my boss. I use its API and an HTML dashboard to automatically ingest and categorize the conversations. A scheduled task and skill then process them into a “wiki.” In a custom project, I can ask how my boss would respond to a question. The results are scary good. Step-by-step: 1. I use Granola to transcribe my one-on-ones with my boss. 2. I use the API and an HTML dashboard to ingest and categorize the conversations automatically. 3. A scheduled task and skill process the one-on-ones into a “wiki.” 4. I ask a custom project how my boss would respond to a question.

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.

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.