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Coordinate Urgent Veterinary Dental Surgery With ChatGPT

I used ChatGPT as an emergency care coordinator for my dog, Mopsy. My 10-year-old dog developed a dental abscess that caused severe swelling around her eye. An ophthalmologist told us that the pressure was already damaging her eye and had partially torn her retina, so treating the infected tooth quickly became urgent. I started calling veterinary offices myself, but I repeatedly hit the same obstacles: emergency hospitals did not perform dental surgery, specialty dentists were booked, and most primary-care veterinarians required a consultation, bloodwork, and a separate surgical appointment. After more than a dozen calls and over two hours of research and calling, I was overwhelmed and asked ChatGPT to help. ChatGPT researched veterinary practices within an hour’s drive of my house, filtered for clinics that actually performed dental extractions or oral surgery, found their contact information, and sent urgent, individualized emails from my Gmail. The emails explained Mopsy’s situation and asked each practice to call me if they could fit her in for surgery within 48 hours. We ultimately contacted more than 20 practices. At the same time, ChatGPT created a live outreach tracker containing every clinic we had called or emailed, along with phone numbers, email addresses, availability, responses, status, and next steps. I shared it with my husband so we could stay up to date as ChatGPT worked. When veterinary offices replied to my emails, ChatGPT read the responses in Gmail and updated the shared spreadsheet, allowing both of us to see what was happening without duplicating work. In less than half an hour, one veterinarian responded that they could evaluate Mopsy the next morning, run her pre-anesthetic bloodwork on-site, review the results immediately, and take her straight back for surgery if she was cleared. What started as me frantically calling veterinary offices one at a time became an AI-supported search, outreach, and tracking operation. I still made the medical decisions and handled the phone calls that mattered, but I only had to speak with viable options. AI took over the repetitive research, emailing, organization, and response tracking that I did not have the bandwidth to handle during an emergency. Step-by-step: 1. I began calling veterinary offices to find urgent dental surgery for Mopsy. 2. I documented the barriers I encountered, including emergency hospitals without dental surgery, booked specialty dentists, and primary-care veterinarians requiring multiple appointments. 3. I asked ChatGPT to research practices within an hour’s drive that performed dental extractions or oral surgery. 4. ChatGPT gathered each clinic’s contact information and sent individualized urgent emails from my Gmail explaining Mopsy’s condition and asking about surgery within 48 hours. 5. ChatGPT contacted more than 20 practices and created a shared live tracker with each clinic’s contact details, availability, responses, status, and next steps. 6. As replies arrived in Gmail, ChatGPT read them and updated the shared spreadsheet so my husband and I could coordinate without duplicating work. 7. I made the medical decisions and handled phone calls with viable options. One veterinarian ultimately offered an evaluation, on-site bloodwork, and possible same-day surgery the next morning.

Anon Stingray
  • ChatGPT
3

Turned my company’s business continuity (BCEP) policy into an app that tracks storms, sends SMS updates, and triggers policy actions

I turned my company’s business continuity (BCEP) policy into an app that tracks storms, sends SMS updates, and automatically triggers policy actions. When a storm, tornado, or other emergency is reported within range of one of our office locations, the app sends SMS alerts to affected employees. It also triggers policy actions to prepare for office closures and safety check-ins, while maintaining an audit trail and compliance reporting. Step-by-step: 1. The app monitors reports of storms, tornadoes, and other emergencies within range of our office locations. 2. It identifies the affected employees and sends them SMS alerts. 3. It automatically triggers policy actions to prepare for office closures and safety check-ins. 4. It maintains an audit trail and generates reporting for compliance.

John Duval
  • Claude Code
  • Claude
  • ElevenLabs
3

Build a self-improving ad scriptwriting system

I connected Claude Code to Apify's API to scrape high-performing content from IG/TikTok for ad creative inspiration, and to ElevenLabs' API to automatically transcribe videos — so I can analyze not just visuals, but the exact hooks, pacing, and language top creators are using. From there, I developed a scriptwriting system that takes rough thoughts on a hook and angle and turns them into ad scripts, drawing inspiration from winners and applying proven copywriting principles. After generating, it also grades itself against a 12-point rubric. If it doesn't score at least 90%, it rewrites until it does. I then feed it back in and say, "This is the final version." It analyzes the changes I made and updates its understanding of my style. Step-by-step: 1. I connected Claude Code to Apify’s API for collecting high-performing Instagram and TikTok content. 2. I connected ElevenLabs’ API so the workflow could automatically transcribe each video. 3. I analyzed the winning examples for their hooks, pacing, visual patterns, and exact language. 4. I fed the system a rough hook and angle and had it draft ad scripts using those proven patterns. 5. I created a 12-point grading rubric and required a rewrite whenever a script scored below 90%. 6. After making my own final edits, I fed the approved version back into the system so it could learn my style.

Rishi Lalwani
  • Claude Code
  • Apify
  • ElevenLabs
1

Build an AI Writing Business Automation System with OpenClaw

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.

Steve Alcorn
  • ElevenLabs
  • Gmail
  • Nano Banana Pro
  • OpenClaw
2

Get Fired Up! How to BBQ Safely

I had a project brief to build an e-learning experience that would help people barbecue safely. I used Claude to generate the storyboard, ElevenLabs to create the voice-overs, and Claude Design to bring everything together into a multiscreen e-learning experience with interactive screens. Step-by-step: 1. I used Claude to generate a storyboard for the e-learning experience. 2. I used ElevenLabs to create the voice-overs. 3. I used Claude Design to bring the storyboard and voice-overs together into a multiscreen e-learning experience with interactive screens.

Carolyn Brown
  • Claude
  • ElevenLabs
1