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I track my recovery daily—mood, energy, focus, activities, medications, and reflections—but typing everything out never lasted more than a few days. So I built a voice-first pipeline: I talk, and it automatically becomes a clean, organized Google Sheet. Step-by-step: 1. I built a free iPhone Shortcut that records my voice when I say, “Hey Siri, Log,” and sends it to the Gemini endpoint. 2. Gemini transcribes the audio and extracts structured details, including wake time; mood, energy, and focus scores; activities with categories and durations; medications; and reflections. 3. Everything is automatically organized in the appropriate place in a Google Sheet: a daily summary, activity log, metrics log, medications log, and clean, human-readable digest for each day. 4. I can say “yesterday” or “on Monday” to log something I forgot, and it files the entry under the correct date. 5. I connected the completed sheet to a separate AI chat scoped specifically to reflection, so I can ask questions such as how my exercise correlates with my mood or how my energy trends over the month. The best part is that the entire workflow runs on free tiers—Google Sheets, Apps Script, iPhone Shortcuts, and Gemini’s free API tier—so there’s zero cost to build or run it. The result is a daily tracker that takes seconds to update from anywhere, requires no typing, and turns my entries into a dataset I can actually learn from—all without writing a traditional app.

I changed my iPhone’s Action Button from mute/unmute to a menu of shortcuts. When I press it, I can select a shortcut I created called “Expense AI.” Expense AI records my voice, appends the transcript to a prompt, and sends it to ChatGPT. ChatGPT returns a JSON object containing the time, amount, expense description, payment method, and category. The data is then saved to a live Google Sheet. Step-by-step: 1. I changed the iPhone Action Button to display a menu of shortcuts instead of controlling mute/unmute. 2. I selected the “Expense AI” shortcut from that menu. 3. Expense AI recorded my voice and appended the transcript to a prompt. 4. It sent the prompt to ChatGPT, which returned JSON with the time, amount, expense description, payment method, and category. 5. The resulting data was saved to my live Google Sheet.

My husband and I run a short-term rental business managing two properties we own in Harpers Ferry, WV. A major part of the job is using PriceLabs, a dynamic pricing and revenue management tool that tracks our properties’ performance against the market, including occupancy, prices, and revenue, and recommends price changes when needed. Previously, we had to log in and review multiple dashboards to make informed decisions. Now, every two days we receive a concise morning briefing that summarizes how the month is going and what needs attention. It’s one of the first things I read in the morning, so I know what to adjust in real time. Step-by-step: 1. I connected the data sources Claude needs through MCP connectors: PriceLabs for reservations, pricing, and market data, and Gmail for drafting the output. 2. I wrote the instruction prompt Claude runs each time. This took the most time to develop. 3. I defined the format and length: a short, numbers-first brief of about 200 words, beginning with “How This Month Is Going” and “What Needs Attention,” with no filler or pep-talk tone. 4. I solved delivery by having the routine draft the briefing as an email with a recognizable subject prefix, such as “Iconic Chalet Briefing — Mon, Aug 10.” Because Claude’s Gmail connector can create drafts but not send emails, a separate time-triggered Google Apps Script watches for drafts with that subject prefix and sends them automatically. 5. I registered the workflow as a scheduled Routine. That combination makes the process feel automatic from end to end.

San Diego County is enormous, and its startup scene is growing rapidly. I wanted a better way to see where companies are located, especially because many of them host networking events and there was no central map or repository. I told Claude what I wanted: an alphabetical directory with an industry selector and a way for people to add their own companies. I also asked it to keep the project as simple as possible, use as few tokens as possible, and wait for my approval before taking any action. Claude helped me design the map, create the Google Form for submissions, set up the Google Sheets workflow for hosting and approving companies, and use Netlify to host the main file. It also guided me through linking the map to a page on my own website: https://sdaimap.michelabood.com/. Claude warned me about the legal issues involved in scraping a list wholesale from other sites, so the map is designed to be populated by people submitting their own companies. I just published it, and 10 companies have already been listed, with more on the way. I have also received great comments on LinkedIn. Now I can see at a glance how far I need to go to find a specific company. I also had Claude create a step-by-step guide, which I am happy to share if people want it. Step-by-step: 1. I described the map I wanted to Claude, including an alphabetical directory, an industry selector, and a way for people to add their own companies. 2. I asked Claude to keep the project as simple as possible, use as few tokens as possible, and wait for my approval before taking any action. 3. I used Claude to design the map and create the Google Form for company submissions. 4. I used Google Sheets to host and approve the submitted companies. 5. I followed Claude's guidance to host the main file on Netlify. 6. I linked the map to a page on my own website: https://sdaimap.michelabood.com/. 7. I avoided scraping a wholesale list from other sites because of the legal issues Claude identified. 8. I published the map and began collecting company listings from people directly.

I kept struggling to stay consistent with social media for my own projects. Writing captions, choosing hashtags, designing graphics, and scheduling content across platforms was taking hours every week—time I would rather spend building. So I built AutoKonnekt. You give it one sentence or a website URL, and it generates a full month of on-brand social posts, including captions, hashtags, and AI-generated images. It then schedules them across Instagram, Facebook, LinkedIn, TikTok, Pinterest, X, and Threads. The hardest part was getting the AI to sound like a specific brand’s voice instead of producing generic marketing copy. That took a lot of iteration on the prompting side. It’s live now with a free plan if anyone wants to try it: https://autokonnekt.com I’d love to hear what the community thinks. For those of you managing social media manually, which part takes the most time? I’m trying to figure out what to build next. Step-by-step: 1. I enter one sentence describing the project or provide a website URL. 2. AutoKonnekt generates a month of on-brand social posts with captions, hashtags, and AI-generated images. 3. I use the generated content across Instagram, Facebook, LinkedIn, TikTok, Pinterest, X, and Threads. 4. AutoKonnekt schedules the posts across those platforms. 5. I iterate on the prompting to make the content sound like the specific brand instead of generic marketing copy.

I built a Driver Management System with ChatGPT and Google. It stores client and driver information, including links, and lets me schedule jobs through a one-click booking wizard. The system also provides reports, helps me search for available drivers by licence type and other criteria, tracks available jobs, and includes a dashboard showing unassigned drivers. This is my first project with AI, and I love what I’ve been able to build. Step-by-step: 1. I used ChatGPT and Google to build a Driver Management System. 2. I added client and driver information, including links. 3. I created a one-click booking wizard for scheduling jobs. 4. I added reports and search tools for finding available drivers by licence type and other criteria. 5. I set up job tracking and a dashboard showing unassigned drivers.