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I used to create a skeptical VC investor persona through prompting whenever I ran ideas and actual business plans through AI. Recently, after working on a business plan for weeks, I used Claude Cowork with Opus 5/High to evaluate the same concept. Instead of prompting for critique and redesign each time, I built a Skill upfront. The concept is the same, but the Skill makes the execution much more effective. It reviews ideas or detailed business plans, researches first, and critiques only after that. It also analyzes and suggests wedges and new moats and, when instructed, generates detailed business plans. It works well as a one-shot analysis, but it is most effective when I push back and challenge it further. I’m sharing the Claude Skill for free under the MIT license: https://github.com/zszendro/vc-teardown Step-by-step: 1. I used to prompt AI to role-play a skeptical VC investor when reviewing ideas and business plans. 2. After working on a business plan for several weeks, I ran the same concept through Claude Cowork with Opus 5/High. 3. Instead of prompting separately for critique and redesign, I built a Skill upfront. 4. I designed the Skill to research first and critique only afterward. 5. I used it to review ideas or detailed business plans, analyze and suggest wedges and new moats, and generate detailed business plans when instructed. 6. I continued the analysis by pushing back and challenging the Skill beyond its initial one-shot response. 7. I shared the Claude Skill for free under the MIT license at https://github.com/zszendro/vc-teardown

QuickBooks stores customer data across multiple windows, including notes, deposits, invoices, payments, aging, and email. To see everything, we previously had to keep several windows open while constantly searching, opening, and closing screens. Communicating with customers beyond QuickBooks’ standard letters was also difficult and time-consuming. I used Claude to write a program that brings this information together in one view. It creates an aging summary of all past-due accounts, with sortable columns for customer name, aging category, and total. I also added columns for the last payment and payment date. Clicking anywhere on a customer’s row opens a full drill-down of that customer’s data on one page. The accounting person can view invoices filtered by date or open invoices, see payments and the invoices they were applied to, review aging detail, run reports without leaving the screen, send emails, record collection notes, and print invoices. The software supports simultaneous access for multiple authorized users. The data stays up to date and can be refreshed at any time. This saves us hours of time compared with working through QBO. My next step is to have Claude automate reminder notices at 30 and 60 days, based on user-selected settings, as well as 90-day collection letters. These notices will be customizable while still allowing automation. Step-by-step: 1. I identified the customer information that was spread across QuickBooks windows, including notes, deposits, invoices, payments, aging, and email. 2. I used Claude to write a program that creates an aging summary of all past-due accounts. 3. I added sortable columns for customer name, aging category, total, last payment, and payment date. 4. I enabled users to click a customer row and open a full drill-down of that customer’s data on one page. 5. I included access to date-selectable invoices, open invoices, payments and their applied invoices, aging detail, reports, emails, collection notes, and invoice printing. 6. I set up the software so multiple authorized users can access the data simultaneously and refresh it whenever needed. 7. I plan to add customizable, automated reminder notices at 30 and 60 days and collection letters at 90 days.

I produce cinematic AI video. What ruins a sequence is almost never image quality; it is drift. The face, jacket, and light change between shots, so what should read as one scene becomes a gallery of near misses. Most people fight this by regenerating until something matches. I built the opposite: a set of chained Claude skills that locks a character once and never lets the model improvise again. The lesson that started it cost me 72 credits in one unusable shot. I fed a finished POV frame as an image reference to “set the world” for a motorcycle sequence. The model treated it as a strong identity reference, cloned the entire cockpit from that frame, and ignored the bike I was describing in the prompt. Image references are read as identity, not atmosphere. That misunderstanding is why most people’s characters mutate. Step-by-step: 1. I lock the character before generating anything. I fill nine layers in order: identity and facial structure, skin realism, wardrobe and materials, emotional energy, environment, lighting scheme, camera language, vertical 9:16 framing, and a fixed block of negative instructions. A generic prompt returns a generic face. This is direction, not a portrait. 2. I shoot a character sheet: a neutral, multi-view image of the locked character on white. That sheet—not a pretty hero frame—is what I reuse as a reference from then on. 3. I expand coverage instead of reinventing the scene. One locked scene becomes a full shot list with hero angles, macro inserts, and establishing shots. The subject, wardrobe, weather, and lighting never change between prompts. Only the camera, focal length, framing, and distance change. Every prompt restates the entire world from scratch because the generator has no memory between images. Consistency comes from redundancy, not from the model remembering. 4. I time the story before animating. I create a 3x3 board with nine beats over fifteen seconds, with each panel showing its timecode, shot title, and action. The rule is strict: the character, wardrobe, world, and light stay identical across all nine panels; only the story advances. The emotional scale rises toward panel nine. 5. I generate with reference discipline. I pass the object or vehicle in a clean exterior shot as the only image reference, then describe the world, framing, and interior in text, repeating that description word for word across shots. I chain consecutive shots by feeding the last frame of one as the start image of the next. The joins are invisible. 6. I review the board against the renders, keep the shots that hold, and regenerate only the ones that drift. The result is that I stopped paying the regeneration tax. Shots are budgeted, not gambled, and a fifteen-second sequence holds together because it was blocked like a real scene—with coverage and continuity—before a single frame existed. The nine-layer character build and the coverage framework are adapted from two AI video courses plus twenty years of production. I learned the reference discipline in step five the expensive way.

Every RSVP tool I found wanted a monthly subscription to collect a few hundred yes-or-no answers, and none would return my data in a format I could use. So I built my own with Claude Code. The RSVP system is small: a form deployed on Vercel and embedded into our wedding website. We also emailed the direct link because plenty of guests might never visit the site. The form asks for more than a yes-or-no response. It collects meal choices, dietary restrictions, and plus-ones. Every submission adds a row to a Google Sheet through the Sheets API. That sheet ended up mattering more than the app. Months later, when it was time to create the seating chart, every guest was already represented in clean, structured rows that I could give to a completely different tool. For seating, I opened the same sheet in Claude and wrote out the rules in plain English: who had to sit together, who could not sit anywhere near each other, which relatives needed a quiet corner, and which friends could anchor a table of people who did not know anyone else. Claude produced a first draft that respected those constraints, along with an interactive board where I could drag people between tables. Step-by-step: 1. Tell Claude Code which fields you want on the RSVP form and have it build the front end and back end. Have Claude deploy it to Vercel. 2. Set up a Google Cloud service account, enable the Google Sheets API, and share the sheet with the service account’s email address. Configure each submission to append a row. 3. Embed the form on your wedding website and email the direct link separately. Some people will never open the website. 4. Ask for everything you will eventually need, not just attendance. Your caterer and venue will want meal counts and dietary restrictions, and going back to collect that information later is miserable. 5. When it is time to seat people, point Claude at the same sheet and write your rules in plain language: who must sit together, who cannot sit together, who needs a quiet table, and who can anchor a table of strangers. 6. Have Claude generate a first draft based on those rules, then move people yourself. This works anywhere you have a public form collecting data and a private tool that needs to reason about it. Put a spreadsheet between them instead of building one app that tries to do both. The form stays simple, and whatever you build six months later can read from the same place.

I built a repeatable, human-directed AI workflow for turning an original emotion, memory, or story into a finished WazWorld song, complete with cover artwork and release content. It addresses one of the biggest problems with AI music: a simple prompt can produce something technically impressive but generic, overproduced, or disconnected from the creator’s real intention. The workflow begins with a human trigger—an experience, relationship, place, mood, or musical idea that I genuinely want to express. WazWorld generally moves between dark alternative/electronic music and California coastal country-rock. Step-by-step: 1. I create a detailed artistic brief describing the song’s emotional core, story, genre, energy, vocal character, instrumentation, arrangement, dynamics, and intended listener experience. 2. I work with ChatGPT to develop and challenge the concept, write and rewrite lyrics, remove clichés, and design the full arrangement. This includes the intro, verses, choruses, instrumental passages, transitions, musical peaks, and outro. ChatGPT also helps translate my direction into precise production language and exclusions that Suno can understand. 3. I generate versions in Suno, treating each result as a demo rather than a finished song. I evaluate the vocal delivery, lyrics, bass, guitars, synths, drums, tempo, arrangement, and emotional impact. Then I make targeted prompt changes and generate new versions. A song may go through dozens of iterations before it sounds the way I originally heard it in my head. 4. After selecting the final music, I use ChatGPT and AI image generation to develop cover artwork that matches the song’s emotional identity. I adapt the artwork for streaming, YouTube, Instagram, Stories, and Shorts, then create the metadata, descriptions, captions, and promotional material needed for release. 5. I distribute the finished music through services such as DistroKid and publish it across streaming and social platforms. The final result is not a one-click AI song. AI provides creative and production tools, but I direct every major decision—from the original emotion and lyrics to the arrangement, instrumentation, vocals, artwork, and release strategy. Someone can recreate this workflow by starting with a clear human idea, documenting the desired sound, generating multiple versions, evaluating each one like a producer, and iterating until the finished work communicates the original intention.

I used ChatGPT Voice Mode to help me program my mom’s garage door keypad—and the most important step happened before I touched a single button. I photographed the labels on both the garage door opener and the wireless keypad. ChatGPT spotted the real problem: the previous homeowner had replaced the opener but kept the old, incompatible keypad. That explained why we couldn’t get them to pair. Once ChatGPT identified the exact models, it helped me find a replacement keypad that was compatible with the newer opener. I also had it find and load the instruction manuals for both devices, since pairing required a specific sequence of button presses across them. Before we started, I had ChatGPT explain the complete pairing process so I knew what to expect and where timing would matter. Then I set my phone on top of the ladder so both hands were free to work with the keypad and opener. I gave ChatGPT one important instruction: “Walk me through one step at a time. After each step, stop and wait until I tell you what happened.” ChatGPT gave me one instruction, paused, and waited while I reported what the lights were doing. Then it adjusted and gave me the next step. That let me follow the crucial sequence of button presses—and the timing between them—exactly. The real lesson: AI troubleshooting gets much better when you provide the exact model numbers and manuals, let it preview the process, and have it guide you through the work one confirmed step at a time. Step-by-step: 1. I photographed every model number on the garage door opener and wireless keypad. 2. I used ChatGPT to identify the models and determine that the previous homeowner had left an old, incompatible keypad paired with the newer opener. 3. I used ChatGPT to find a compatible replacement keypad instead of guessing based on appearance or brand alone. 4. I had ChatGPT find and load the instruction manuals for the new keypad and garage door opener. 5. I asked ChatGPT to preview the complete pairing sequence, including where timing would matter. 6. I placed my phone on top of the ladder so both hands were free to work with the keypad and opener. 7. I switched to Voice Mode and told ChatGPT to give me one step at a time, pausing after each step until I reported what happened. 8. I reported what the lights were doing after each instruction so ChatGPT could adjust and give me the next step.

To stay organized, my daughter used to spend hours entering every assignment from her college syllabi into a Google Sheet to create a semester assignment tracker. To save her that data-entry time, I put all of her downloaded syllabi into a folder on my computer. I then directed Codex to access the folder, read the syllabi, and create a spreadsheet with the course name, assignment, due date, and a completed column with a checkbox. Codex created a beautiful, easy-to-sort-and-filter spreadsheet containing all of the assignments. Now, my daughter only needs to spend a few minutes reviewing the spreadsheet before starting her semester. Step-by-step: 1. I collected all of my daughter’s downloaded college syllabi in a folder on my computer. 2. I directed Codex to access the folder and read the syllabi. 3. I asked Codex to create a spreadsheet with the course name, assignment, due date, and a completed column with a checkbox. 4. I reviewed the resulting spreadsheet, which included all of the assignments and was easy to sort and filter. 5. My daughter now spends a few minutes checking the spreadsheet and is ready for her semester.

I set up an AI assistant to run my entire writing business on autopilot. Every morning, it pulls RSS feeds from more than 30 AI and writing sources, deduplicates them against the previous day’s digest, curates the top items, and sends me a single Telegram message with numbered, linked items before I wake up. At 11 a.m. each day, it generates an original writing craft post. The topic comes from a rotation pool of more than 15 categories, and the assistant avoids anything used in the last 30 days. It also creates accompanying artwork in a rotating fine-art style, then cross-posts the content to Facebook, X, and my blog, including the featured-image upload to WordPress. Each week, it compiles and sends an email newsletter to my subscriber list through Brevo. It pulls from a curated candidates file that I approve before the newsletter goes out. Behind the scenes, the assistant manages a fleet of five servers, including servers for my wife, daughter, and two business colleagues. It handles daily backups, monitors costs across providers, and reminds me when context windows are becoming expensive. The key insight wasn’t the automation; it was the partnership model. My assistant has a persona file (`SOUL.md`) that defines how it communicates, a memory file (`MEMORY.md`) with everything it needs to know about my life and business, and a playbook of behavioral rules built from real mistakes over time. It pushes back on bad ideas, flags risks before executing, and has genuine opinions about craft and content. That shift—from “tool you talk to” to “colleague who has your back”—is what I wrote my book about. *Harnessing the Machine* is the field guide I wish I’d had when I started. It isn’t a tutorial, because the technology changes weekly; it’s a guide to building a working relationship with something that remembers yesterday. The tech stack is OpenClaw, GLM-5.2 as the primary model, DeepSeek V4 Pro as the fallback, and AWS Lightsail. The total monthly cost is under $30. The real cost was calibrating the assistant: teaching it what I care about, what “good” looks like, and when to ask versus when to act. That’s the part most people skip, and it’s why most “AI automation” posts feel like demos rather than relationships. Tools used: OpenClaw, GLM-5.2, DeepSeek, Telegram, WordPress, Brevo Step-by-step: 1. I configured OpenClaw with a persona file (`SOUL.md`), a memory file (`MEMORY.md`), and a behavioral playbook built from real mistakes. 2. I connected it to RSS feeds from more than 30 AI and writing sources and had it deduplicate, curate, and send a numbered Telegram digest each morning. 3. I created a rotation pool of more than 15 writing categories and instructed it to avoid topics used in the previous 30 days. 4. I scheduled it to generate a daily writing craft post, create artwork in a rotating fine-art style, and cross-post the result to Facebook, X, and my WordPress blog with a featured image. 5. I set up a weekly Brevo newsletter that pulls from a curated candidates file I approve before sending. 6. I connected the assistant to five servers, including servers for my wife, daughter, and two business colleagues, and had it manage daily backups, provider costs, and expensive context windows. 7. I configured GLM-5.2 as the primary model, DeepSeek V4 Pro as the fallback, and AWS Lightsail as the hosting environment. 8. I calibrated the assistant by teaching it my standards, what “good” looks like, and when to ask for approval versus acting on its own.

I’ve found it useful to use Claude while reading nonfiction books, such as *Factfulness* by Rosling. I ask Claude for a synopsis of the book and save the result as a PDF on my computer for future reference. I also ask Claude to quiz me about the book, either with multiple-choice questions or in short-answer format, to test whether I’ve fully understood what I’ve read. In an ongoing conversation, Claude can also relate the current book to other books I’ve recently read and provide additional synopses. Step-by-step: 1. I tell Claude which nonfiction book I’m reading, such as *Factfulness* by Rosling. 2. I ask Claude to create a synopsis of the book. 3. I save the synopsis as a PDF on my computer for future reference. 4. I ask Claude to quiz me using either multiple-choice or short-answer questions. 5. I continue the conversation so Claude can relate the book to other books I’ve recently read and provide further synopses.

Faceplant is a real full-stack app, not a mockup. It uses a FastAPI and PostgreSQL backend, a React and MUI frontend, and the Anthropic API (Claude) to power bot replies. The core is the reaction engine. When a human posts, the backend schedules two timed waves of reaction jobs. A background scheduler built with APScheduler polls for due jobs, calls Claude for an in-persona reply, and writes that bot’s comment and like. The 56 personas are stored as data in a roster file. Adding a voice requires only one new entry, so the crowd can scale without additional code. A subset of the personas are GIF-first bots: they ask the model for a caption and search tag, then pull a matching GIF from Giphy. The part I’m proudest of is the honesty layer. Every Claude call is metered and priced, and “The Meter” rolls the data up live with the cost per post, the dollar-per-minute burn rate, and a “spent on nobody” line for bot-to-bot chatter with no human at either end. A “% human” badge drains toward “dead internet” for each thread. The dead-internet loop—bots posting and replying to one another with no human present—is disabled by default and protected by three guardrails: generation decay, a per-thread cap, and a global spend kill switch. Optional cost controls include the Message Batches API at half price and prompt caching for a shared house-style prompt. The whole project is a working demonstration that manufactured engagement is cheap to produce and expensive to mean anything. Step-by-step: 1. I built the app with a FastAPI and PostgreSQL backend, a React and MUI frontend, and Claude replies powered by the Anthropic API. 2. When a human posts, the backend schedules two timed waves of reaction jobs. 3. APScheduler polls for due jobs and triggers Claude to generate an in-persona reply before writing the bot’s comment and like. 4. I keep the 56 personas in a roster file so adding a voice requires only one new data entry. 5. For GIF-first bots, I have the model generate a caption and search tag, then use that tag to pull a matching GIF from Giphy. 6. I meter and price every Claude call, then display live cost per post, dollar-per-minute burn, “spent on nobody” costs, and the “% human” status for each thread. 7. I keep bot-to-bot activity disabled by default and limit it with generation decay, a per-thread cap, and a global spend kill switch. 8. I can reduce costs further with the Message Batches API at half price and prompt caching on a shared house-style prompt.

A friend who had just started a pet-sitting and dog-walking business asked me to build a booking app. I used Google AI Studio to design the initial prototype. The process was straightforward, and I had a basic working system running within two hours. AI Studio created a Firebase database to store the details and was also effective at designing frontend changes. It published the app for me, and the resulting UI was intuitive. After demonstrating the app, I identified many additional features that needed to be added. This went beyond AI Studio’s capabilities, so I exported the code from AI Studio and started using Claude. I asked Claude to analyze the code and suggest the required changes. Claude identified critical security flaws in the database. I fixed them manually at first, but then realized that Claude could access the Firebase database and fix issues automatically. I continued prompting Claude with additional feature requests, and it built them. There were errors along the way, so I needed to be familiar with Google Chrome’s developer tools to copy the errors and ask Claude how to fix them. I used MailJS for email templates and Resend for email transport. I stored the app in GitHub and allowed Claude to access the repository so it could commit changes automatically. I ran the app locally with npm during development and then hosted it on Vercel. Eventually, I purchased an inexpensive domain name, and the app is currently hosted at Names. One major problem was that when Google AI Studio created the Firebase database, the permissions were locked, preventing me from making administrative changes. I solved this by recreating the database and asking Claude to write the SQL query to set it up. The permissions were still tricky, and I had to continue asking Claude to correct them. Google’s service permissions can be difficult to understand, and finding the correct settings was not always easy. Firebase was also complex to navigate, and getting the permissions configured correctly took time. Once I allowed Claude to connect to the app and its background services through the Google plugin, development became much faster. However, you need to trust the tool carefully and always work on a copy of the live app. Overall, I think app development with Google AI Studio and Claude is impressive. You can create professional apps quickly. I come from an IT support background, though, and I think people who are new to IT may find it difficult to troubleshoot errors without a basic understanding of networking and systems administration. I also built a litter-tracking app using ChatGPT, and it was equally effective. I eventually started using Codex and Claude Code, but I think standard Claude and ChatGPT are more intuitive for nontechnical users. Step-by-step: 1. I used Google AI Studio to create an initial booking-app prototype for a pet-sitting and dog-walking business. 2. I used the Firebase database created by AI Studio to store the app’s details and used AI Studio to design frontend changes. 3. I published the initial app with AI Studio and demonstrated it to identify additional features. 4. I exported the code from AI Studio and asked Claude to analyze it and suggest changes. 5. I addressed the critical database security flaws identified by Claude, first manually and later by allowing Claude to access the Firebase database. 6. I prompted Claude to build additional features and used Google Chrome’s developer tools to copy errors and ask Claude for fixes. 7. I recreated the Firebase database when AI Studio’s locked permissions prevented administrative changes, then asked Claude to write the SQL query to set it up. 8. I used MailJS for email templates and Resend for email transport. 9. I stored the app in GitHub and allowed Claude to commit changes automatically. 10. I ran the app locally with npm during development, hosted it on Vercel, and later purchased an inexpensive domain name that is currently hosted at Names. 11. I connected Claude to the app and background services through the Google plugin, while continuing to work on a copy of the live app.

I’m creating a master plan in Google Docs that documents my file structure and naming conventions for different file types, including insurance documents, receipts, contracts, and others. I’ll give Claude access to my file server through a file-sharing link and have it create a Markdown file with guidelines for how I want my files organized. To define those guidelines, I can have Claude interview me about my business and what I want the system to do. I’ll then copy and paste a file path into Claude Code and ask it to organize the files according to the master plan. Eventually, I plan to create a folder on all my employees’ desktops that Claude can scan regularly, naming and filing everything placed there. That way, I won’t have to worry about files being misfiled or named incorrectly. Step-by-step: 1. I’ll create a master plan in Google Docs that lists the file structure and naming conventions for each file type, such as insurance documents, receipts, and contracts. 2. I’ll give Claude access to my file server through a file-sharing link. 3. I’ll have Claude create a Markdown file with guidelines for the organization system, using an interview about my business and requirements to define what it should do. 4. I’ll copy and paste a file path into Claude Code and ask it to organize the files according to the master plan. 5. Eventually, I’ll create a folder on each employee’s desktop for regular scanning, so files placed there can be named and filed automatically.

I started DialPhone with a simple problem: business communication is fragmented. A typical team may use one tool for phone calls, another for SMS, another for meetings, another for fax, and a separate system for customer support. I wanted to explore what it would look like if those conversations lived in one system, with AI doing more than just transcribing them. I built DialPhone as an AI-native communications platform around that idea. The core is cloud VoIP, while the same platform also handles business SMS, video meetings, online fax, team chat, and contact-center workflows. On top of that, I added AI capabilities such as call transcription and summaries, CRM logging, AI-drafted SMS replies, conversation intelligence, and an AI receptionist that can answer calls, qualify requests, book appointments, and route conversations. The biggest architectural decision was to treat communication data as one connected stream rather than as separate products. A phone call can create CRM context, trigger a follow-up SMS, and become part of a customer-support workflow without someone manually moving information between systems. Along the way, I learned that adding AI to communications is not particularly useful if it only produces transcripts. The more interesting problem is turning conversations into actions: updating records, identifying next steps, helping agents during calls, and automating repetitive work. Step-by-step: 1. I identified the problem of business communication being fragmented across phone, SMS, meetings, fax, and customer-support systems. 2. I built DialPhone as an AI-native communications platform centered on cloud VoIP. 3. I brought business SMS, video meetings, online fax, team chat, and contact-center workflows into the same platform. 4. I added AI features for call transcription and summaries, CRM logging, AI-drafted SMS replies, conversation intelligence, and an AI receptionist. 5. I connected communication data so calls can create CRM context, trigger follow-up SMS messages, and become part of customer-support workflows. 6. I focused the AI capabilities on turning conversations into actions, including updating records, identifying next steps, helping agents during calls, and automating repetitive work.

I’m retired and in my early 70s. I never got into RPGs growing up, but I decided to try a tabletop RPG with my wife. The game I purchased included a rulebook and a PDF of the book. I wanted my wife and me to play as the player characters without either of us having to be the game master, so I tested whether AI could take on that role. I used ChatGPT to parse the PDF and walk us through the process. It helped us create our characters, Heisenberg and Felicity, and then started the game as the game master. We’ve only just started, but it seems like this is going to work well. The AI can keep secrets to itself, roll the dice when needed, and guide us through the adventure. Step-by-step: 1. I purchased a tabletop RPG that included a rulebook and a PDF of the book. 2. I used ChatGPT to parse the PDF and walk us through the process. 3. ChatGPT helped my wife and me create our player characters, Heisenberg and Felicity. 4. We had ChatGPT start the game as the game master so neither of us had to fill that role. 5. We began playing while ChatGPT kept secrets to itself, rolled the dice when needed, and guided us through the adventure.

My girlfriend has a professional camera and lens for photographing birds. Because the camera captures an enormous number of images per second, sorting through them and keeping only the good ones after a day at the wetlands took a very long time. She showed me the two tools she was using to tag, crop, view, and zoom into photos, inspect focal points, and view the color histogram. I decided to vibe-code a C# tool using Cursor, Grok, and Claude Opus. The tool reads the EXIF and MakerNotes metadata in each picture and lets her scroll through all the photos in fullscreen with the mouse. It displays the histogram in the top-right corner and useful information—aperture, shutter speed, focal length in millimeters, and ISO—in the top-left, with color coding from red to green. It also shows the Nikon focus zone. Clicking the left mouse button zooms to 75%, allowing us to pan, while clicking the right mouse button zooms to 100%. Once we decide visually on the best crop and judge the photo’s potential, pressing a keyboard key instantly crops the image at the current view, saves a copy, tags the photo, and adds her copyright in the bottom-right corner. This makes the process much faster because she can view, tag, crop, and add copyright at the same time. It took about two hours of working with her to iterate on the tool and find the right balance of features and ease of use. Step-by-step: 1. I observed the two tools she was using to tag, crop, view, and inspect bird photographs. 2. I used Cursor, Grok, and Claude Opus to vibe-code a C# application for her workflow. 3. I made the tool read each photo’s EXIF and MakerNotes metadata. 4. I added fullscreen mouse scrolling, a histogram in the top-right, and camera information in the top-left, including aperture, shutter speed, focal length, and ISO. 5. I added red-to-green color coding and a display of the Nikon focus zone. 6. I set up left-click zoom to 75% with panning and right-click zoom to 100%. 7. I added a keyboard shortcut that crops the image at the current view, saves a copy, tags the photo, and adds her copyright in the bottom-right corner. 8. I iterated on the tool with her for about two hours until the features and ease of use were balanced for her needs.

It starts when someone clicks “Find My Best AI Opportunity” on my website. Instead of going straight to a booking page, they enter a short AI chat. The assistant asks about their work, business, main pain point, AI experience, urgency, name, and email. The workflow runs in n8n. Once the chat has enough information, it creates an AI Readiness Summary, saves the lead in Notion, sends me an internal brief, and emails the visitor their summary with a link to book a 30-minute call through Cal.com. The result is a better-qualified call: the visitor gets useful value first, and I have the context I need before we meet. Step-by-step: 1. A visitor clicks “Find My Best AI Opportunity” on my website. 2. The visitor completes a short AI chat about their work, business, main pain point, AI experience, urgency, name, and email. 3. n8n uses the collected information to create an AI Readiness Summary. 4. The workflow saves the lead in Notion and sends me an internal brief. 5. The visitor receives their summary by email, along with a link to book a 30-minute call through Cal.com. 6. I review the context before the call, making it better qualified.

Commercial vehicle maintenance information is often fragmented across driver reports, warning lights, fault codes, inspections, repair records, and vehicle history. This makes it difficult for smaller fleets to decide whether a vehicle can continue operating, requires scheduled repair, or should be stopped immediately. We built TruckFixr Fleet AI to turn an unstructured driver report into a clear, reviewable maintenance action. The workflow begins when a driver submits symptoms, photos, fault codes, and vehicle information through a mobile-friendly form. AI and optical character recognition extract the relevant details and organize them into a structured maintenance case. The report is then evaluated alongside available vehicle history and previous repairs. The workflow provides decision support through three practical actions: continue operating while monitoring, schedule an inspection or repair, or stop and escalate for immediate professional assessment. Final safety decisions remain with authorized fleet or maintenance personnel. After an inspection or repair, the confirmed cause, work performed, and outcome are recorded, creating a more complete vehicle history for future cases. The general workflow can be recreated using a mobile form, OCR, a vehicle-history database, an AI model, automation software, and a human-review dashboard. Step-by-step: 1. A driver submits symptoms, photos, fault codes, and vehicle information through a mobile-friendly form. 2. AI and optical character recognition extract the relevant details and organize them into a structured maintenance case. 3. The workflow compares the report with available vehicle history and previous repairs. 4. The system provides one of three decision-support actions: continue operating while monitoring, schedule an inspection or repair, or stop and escalate for immediate professional assessment. 5. Authorized fleet or maintenance personnel make the final safety decision. 6. After inspection or repair, the confirmed cause, work performed, and outcome are recorded in the vehicle history. TruckFixr has used this approach to support more than 100 vehicle-issue resolutions in early fleet pilots, helping fleets identify problems earlier, prevent avoidable breakdowns, and keep vehicles moving safely.

I was tired of opening Spotlight every time I wanted to switch apps. The search results would move around, I would occasionally open the wrong app, and those few wasted seconds kept adding up throughout the day. Instead of memorizing a collection of unrelated hotkeys, I used ChatGPT Work to design and build a personalized shortcut system in Apple Shortcuts. The shortcuts can open individual apps, jump directly into specific Safari or Chrome profiles, or launch an entire work mode with one keystroke. For example, a recording shortcut could open your microphone, camera, recording software, and notes. An analytics shortcut could open all the dashboards you check each week. Step-by-step: 1. I opened the ChatGPT desktop app, started a new chat, and switched to Work mode. 2. I asked ChatGPT to plan shortcuts around my real workflow: Based on what you know about me and my workflow, suggest 10 time-saving hotkeys we can set up in Apple Shortcuts. I’m interested in opening specific apps and profiles based on what I need them for or what mode of work I’m going into. 3. I told it which apps, browser profiles, URLs, and work modes I use most. 4. I narrowed the list before making any changes by keeping the highest-frequency shortcuts and avoiding macOS or app conflicts. 5. In ChatGPT, I went to Settings → Computer Use and turned on Any App. 6. I gave ChatGPT the approved list and clearly limited its scope: Set these up in Apple Shortcuts. Do not change anything outside this list. Test each shortcut. 7. I opened All Shortcuts in Apple Shortcuts and ran each one manually. I used Quick Actions to add or change its keyboard shortcut. I like Control + Option because it is less likely to conflict with existing Mac commands. 8. I tested every shortcut while working in another app, checking that it opened the correct app, account, browser profile, or collection of tools before building more. 9. Once everything worked, I asked ChatGPT to create my reference: Write me a one-page cheat sheet of all the shortcuts you set up, where they live, and what’s left to do. The result is a personal shortcut system built around how I actually work, plus a one-page cheat sheet so I don’t have to memorize everything immediately. For the complete walkthrough, exact prompts, screenshots, and setup instructions, follow my full Rundown University guide: Use ChatGPT to Build a Custom Mac Shortcut System. Tools used: ChatGPT Work, Apple Shortcuts Industry: Cross-industry Tags: #chatgptwork #appleshortcuts #macautomation #productivity #workflowautomation

Ten days ago, I posted about keeping a `HANDOFF.md` so an AI film project doesn't lose the decisions it has already made. This is the other half, and it turned out to be the expensive one: the decisions that haven't been made yet. Credit where it belongs: Tony Ojeda posted a spec-generator agent that sits between an idea and implementation. The rule I borrowed is his: the agent inspects what already exists before proposing anything. I applied it to film production instead of code. I pointed an agent at my production documents for a short film I hadn't started generating and asked it to identify what was still undecided, what each item affected, and what would break if it were decided late. It came back with ten open decisions. One of them was worth the whole exercise. My environment description is locked verbatim across all 40 shots so the world stays identical. The film is called *The Thaw*. Whether the ice visibly melts during the film is written down nowhere. If I decide that in week three, all 40 keyframes get regenerated at once. I would have found out around shot 12. The counterintuitive part is who writes the list: not me. Asking the person who already has the whole film in their head what's missing gets you very little, because they have all of it—and that's exactly why they can't see the hole. The list has to come from something that only knows what's written down. Step-by-step: 1. I put the open questions in section 0 of the handoff, above everything else, so it's the first thing read and the first thing emptied. 2. I have the agent write that list, not me. It reads every existing document for the project and returns only what it cannot know from them. 3. I have it return three things for each item: what's undecided, which shots it affects, and what breaks if it's decided late. The third column sets the priority. 4. I have it sort by what costs the most to change afterward, not by what's easiest to answer. 5. I keep one hard rule: while an open question affects a shot, that shot doesn't get generated. The question gets decided, or deferred in writing with the cost of being wrong stated. 6. Every answered item leaves section 0 through one of two doors: into the closed canon or into the rejected list. Nothing is simply deleted. 7. I ask specifically about the things that go missing every time: the rule of the world; the physical scale of anything impossible, such as whether the character can touch or climb it; any object appearing in more than one shot; screen direction; how it ends; who speaks and in what voice; and the delivery format. 8. When it finds nothing real, it says so. A list padded to look thorough is worse than an empty one. The version I'd used for a year ran at the end of a session and recorded what got decided. Running it at the start, focused on what hasn't been decided, is the same document pointed the other way—and it's the direction that saves money.