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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

Reduced weeks of complex tax research time down to 2–3 hours of HITL

The ETHOS™ Framework (Evaluation Through Hierarchical Oversight of Sources) is a multistage forensic audit system designed to transform AI-generated research into “audit-ready” ground truth. Its five-stage lifecycle is designed to ensure technical precision: Step-by-step: 1. EXTRACT (Stage 1): Using the Ground Truth Manifesto and the six-tier Authority Ladder, ETHOS extracts structured tax reports from multiple LLM archetypes: Technical Specialists, Strategic Advisors, and Operational Drafters. This forces their initial findings to follow a strict legal hierarchy. 2. TEST (Stage 2): The Tax Citation Auditor subjects the reports to a three-pass forensic review, testing every citation for existence, pinpoint accuracy, and application fit. Any citation that cannot be verified in a primary repository is immediately downgraded or flagged. 3. HEAL (Stage 3): The Post-Audit Correction Protocol (PACP) repairs the evidence chain by requiring the LLM to resolve flagged citations, replace fabrications with 3–15-word verbatim micro-quotes, and revalidate all “knock-on” effects across downstream computations and thresholds. 4. ORGANIZE (Stage 4): The Human-in-the-Loop (HITL) controller organizes the pre-audit and post-audit artifacts in a consolidated AI sandbox, such as Google Notebook, Claude Cowork, or Perplexity Spaces. This manages the collectively exhaustive data, elevates mutually exclusive advisory angles, and maintains institutional version control. 5. SYNTHESIZE (Stage 5): ETHOS applies MECE principles (Mutually Exclusive, Collectively Exhaustive) and the Weighted Authority Confidence Index (WACI) to adjudicate model conflicts and synthesize a single ground-truth memo. Every load-bearing conclusion is certified for release.

Travis Call
  • Claude Code
  • Notion
  • Claude Cowork
  • Perplexity Pro
  • NotebookLM
1

Prepare for the French Hunting License Exam with Perplexity

I moved to France, where my German hunting license is not accepted. As a resident, I have to pass the French hunting exam to obtain a French license. I downloaded a guide for the exam from various internet sources, along with publicly available literature on the relevant topics. I gave these materials to Perplexity and instructed it to develop a preparation course with logically structured learning units covering French hunting vocabulary, exam topics broken down into detailed subunits, game, hunting law, and more. For each unit, I requested a test with 12 questions combining multiple-choice and free-text formats. I also asked Perplexity to include every mistake in the next unit and continue reviewing those errors until I know the material 100%. Now, every second day when I open my computer, Perplexity presents a new unit aligned with my progress and the errors from previous units. The preparation schedule is optimized for the exam this November, with October reserved for reviewing all topics. Step-by-step: 1. I downloaded an exam guide and publicly available literature on the relevant French hunting topics. 2. I provided these materials to Perplexity. 3. I instructed Perplexity to create a logically structured preparation course covering French hunting vocabulary, detailed exam topics, game, hunting law, and related subjects. 4. I required each learning unit to include a 12-question test with a mix of multiple-choice and free-text questions. 5. I asked Perplexity to carry every mistake into the next unit and continue reviewing it until I know the material 100%. 6. Every second day, I open my computer and complete the new unit generated according to my progress and previous errors. 7. I use the schedule leading up to the exam in November, reserving October for a full review of all topics.

Udo H.
  • Perplexity Pro
  • Perplexity Computer
1

Run citation-first robotics research across two AI research tools

I put Gemini Deep Research and Perplexity Pro to work as my personal research assistants. For robotics research, I tell them to stick to top-tier journals and primary sources only, and demand citations and dates for everything. Then I have them distill the highlights into a ranked list so I know exactly where to dig deeper. Step-by-step: 1. I defined the robotics research question and the boundaries of the search. 2. I instructed Gemini Deep Research and Perplexity Pro to use top-tier journals and primary sources only. 3. I required citations and dates for every important claim. 4. I asked each tool to distill the results into a ranked list of the most important findings. 5. I used that ranked, cited overview to decide which original papers deserved a deeper read.

Jennifer Mossalgue
  • Gemini Deep Research
  • Perplexity Pro
0