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My father takes a ton of different medications every day for various conditions, and also sees a lot of different doctors. I was concerned about how they were all interacting with each other and if the doctors at different practices were even getting the full picture. I gave Claude a comprehensive list of all of the drugs and his medical history, and asked for a deep research report about potential interactions. Claude provided a thorough list of issues to bring up to his doctors about dangerous interactions and situations that might be getting missed, referencing up-to-date journals, guidelines, and research that would've taken me days. It also laid everything out in an easy-to-read format, and provided specific instructions on who to contact and what specific questions to ask — helping turn a potentially stressful and disjointed doctor conversation into a strong action plan. Step-by-step: 1. I assembled a comprehensive list of medications, medical history, conditions, and the different doctors involved. 2. I asked Claude for a deep research report on potential interactions and risks that might be missed across separate practices. 3. I required the report to reference current journals, guidelines, and research. 4. I had it organize the findings into concrete issues, who to contact, and the exact questions to ask. 5. I used the report as a doctor-ready conversation plan rather than trying to interpret or act on the findings alone.

YouTube is one of the best free education platforms out there, but the problem is that it takes a ton of time to sit through long videos just to find the few ideas that actually matter. That's where Gemini has been really useful for me. Since it's built into Google, I can use it to quickly summarize a specific YouTube video, or even scan the last 5–10 videos from a creator to pull out the key takeaways. Instead of spending hours watching everything, I can quickly understand the main ideas, find the best nuggets, and decide what's worth digging into deeper. It's basically a shortcut for learning from long-form content without consuming every minute of it. Step-by-step: 1. I gave Gemini the URL of a specific YouTube video when I wanted a fast summary of one piece of content. 2. For a broader view, I asked it to scan the latest five to ten videos from the same creator. 3. I had it extract the main ideas and strongest takeaways from each video. 4. I compared the summaries to find recurring themes and the most useful nuggets. 5. I used that overview to decide which videos were worth watching in full.

I used AI to help investigate a family mystery that had remained unresolved for decades: identifying the biological family of my maternal grandfather. The challenge was not a lack of information. It was almost the opposite. I had DNA matches, family trees, names, dates, historical records, old photographs, obituaries, Facebook genealogy groups, and conversations with possible relatives. The difficult part was connecting all these scattered clues without jumping to conclusions. I built a research workflow in which AI acts as an investigation partner, not as the source of truth. Step-by-step: 1. I gathered the information I already had from genealogy platforms, DNA matches, family trees, historical documents, and family records. 2. I used ChatGPT to organize the evidence into people, dates, locations, relationships, DNA connections, documents, and unresolved questions. 3. I separated the information into three categories: confirmed facts, hypotheses, and missing information. 4. Instead of asking AI, “Who was my grandfather's biological father?”, I asked it to analyze possible family connections and identify which hypotheses were compatible with the available evidence. 5. For each hypothesis, I looked for supporting evidence, contradictory evidence, and information that was still needed. 6. I treated AI-generated connections as leads rather than genealogical proof. The goal was to use AI to decide what to investigate next, not to have it find the answer. 7. I used AI to compare family branches, surnames, generations, locations, and possible relationships among DNA matches whose family connections I did not immediately recognize. 8. When a promising connection appeared, I returned to the original genealogy and DNA sources to verify it. This gradually turned a long list of DNA matches into a smaller number of research paths. 9. I used AI to draft respectful, personalized messages to DNA matches and members of genealogy communities, including people in another country and language. 10. In each message, I explained what I was researching, what connection I suspected, what information I already had, and what I hoped the recipient might be able to confirm or rule out. 11. I treated their responses as new evidence and repeated the investigation loop: evidence → AI analysis → hypothesis → verification → human contact → new evidence → updated hypothesis. The final result is not an “AI-generated family tree.” It is a human-led investigation in which AI helps manage complexity, ask better questions, and identify the next useful action. The most important lesson I learned is that AI is particularly useful in genealogy when you do not ask it to give you the answer. Ask it to help you build the investigation.

I built Timelanes so you can type any topic into one text box, such as “The Space Race” or “my grandfather's war years,” and generate a visually engaging, sourced, shareable timeline in seconds. Step-by-step: 1. Type a topic into the text box. 2. Let AI generate the timeline with dated events, short descriptions, and source links. 3. Review citation coverage for each event to see what's verified at a glance. 4. Edit and reorder events, add images and milestones, and choose a theme. 5. Publish the timeline with one click to create a shareable page and embeds that auto-render in Substack and Notion. 6. Export the timeline as a PDF, Markdown file, or CSV. Bonus: Compare mode places two or more timelines on one shared axis so overlaps stand out.

My first media job was as a fact-checker at a major magazine. Those jobs have already mostly vanished before AI arrived, but now I use ChatGPT 5.4 Thinking as a last-pass fact-checker on my newsletters before I hit send. I'll ask it to identify inconsistencies, isolate the assertions that most need verification, and check key numbers, quotes, and factual claims against original or primary sources. In other words, it does not replace reporting, but it is remarkably good at stress-testing a draft before publication — essentially fact-checking my fact-checking. Step-by-step: 1. I pasted the nearly finished newsletter draft into ChatGPT 5.4 Thinking. 2. I asked it to identify inconsistencies and isolate the assertions most in need of verification. 3. I had it check key numbers, quotes, and factual claims against original or primary sources. 4. I reviewed the proposed corrections and followed the source trail rather than accepting changes blindly. 5. I used the result as a final stress test of my own reporting and fact-checking before publishing.

I was initially told by my CA that I might owe close to $10K in taxes on my income. Since this felt higher than expected, I decided to double-check the calculation using Claude. I shared my contract structure, invoices, and payment flow, and asked it to review how my services should be classified under Goods & Services Tax. While going through the latest regulations, it pointed me to a recent government circular that clarified how certain digital services provided to clients can qualify as export of services. I discussed this with my CA, who reviewed and confirmed it applied to my case. This reduced my tax liability by several thousand dollars. Step-by-step: 1. I collected my contract structure, invoices, payment flow, and the original tax calculation. 2. I asked Claude to review how the services should be classified under Goods and Services Tax. 3. I had it search the current regulations for primary guidance that applied to digital services and exports. 4. Claude surfaced a recent government circular that appeared relevant to the way the services were delivered. 5. I took the circular and reasoning to my chartered accountant, who verified that it applied before the tax position was changed.

My sister asked me to check if my niece's name appeared in the preliminary school enrollment results. But the list uploaded online was a PDF with 50+ page photographs. Going through all of it would have been a headache — so I put Manus to work. I provided a link to the results, and Manus searched through all of it, checking each document page by page. After about 15 minutes, it found her name and all the relevant details. This saved me from having to manually search through pages of results across multiple categories. Step-by-step: 1. I gave Manus the link to the school’s preliminary enrollment results. 2. I supplied the exact name I needed to find and the context that the PDF contained photographed pages. 3. I asked Manus to inspect the document page by page rather than relying on normal searchable PDF text. 4. I let it search across more than 50 pages and multiple categories. 5. I reviewed the page and details it returned to confirm that it had found the correct person.

I used Perplexity to find the right earbuds for my iPhone, mainly for light music and calls. It compared AirPods 4, Pro 2, Pro 3, Beats Fit Pro, and Nothing Ear models, breaking down ANC, call quality, Apple ecosystem features, and prices. It helped me see that AirPods Pro 2 gave me everything I needed—strong noise cancellation, seamless pairing, and Spatial Audio—without paying extra for Pro 3 or Beats' upgrades I wouldn't use much. Step-by-step: 1. I described how I would use the earbuds, focusing on light music, calls, and compatibility with my iPhone. 2. I asked Perplexity to compare AirPods 4, AirPods Pro 2 and 3, Beats Fit Pro, and Nothing Ear models. 3. I had it break the comparison down by noise cancellation, call quality, ecosystem features, and price. 4. I separated must-have features from upgrades I was unlikely to use. 5. I chose the model that met the requirements without paying extra for unnecessary improvements.

Granola AI has become my meeting recorder outside of just Zoom meetings. I recently had a 3-hour meeting with legal for business structuring, and I turned on Granola AI on my phone (with other parties' consent), and it picked up the entire transcript nearly word for word (for over 3 hours!), which I later pasted into ChatGPT to go back and forth on things that I didn't fully grasp in the moment. Step-by-step: 1. I obtained consent from the other participants before recording the in-person legal meeting. 2. I turned on Granola on my phone and used it to capture the full three-hour conversation. 3. I took the resulting transcript and pasted it into ChatGPT. 4. I asked follow-up questions about the business-structuring concepts I had not fully understood in the room. 5. I used the transcript as durable context for learning and clarification after the meeting.

I have been looking around at new apartment development projects, looking to potentially buy in the next two years. I used ChatGPT Deep Research to help compare the cost, the quality of the appliances and materials the builders are using, and chart out the overall cost. Step-by-step: 1. I listed the apartment developments I was considering and the roughly two-year purchase horizon. 2. I asked ChatGPT Deep Research to compare the purchase cost of the options. 3. I had it research the quality of the appliances and materials each builder was using. 4. I asked it to chart the overall cost so the differences were easier to see. 5. I used the combined research and visuals to compare projects on more than the advertised price.

My fantasy baseball auction draft is around the corner, and I decided to try Claude Cowork for the planning and research efforts this year. I uploaded my league's settings, the players being kept across the league, and my own rambling of strategies and thoughts on my current players. Claude then performed a deep analysis of my current options and their projected values, provided strong recs on who I should prioritize, and gave me a detailed list of draft targets around the league, perfectly tailored to my needs and league format — also scouring the web for tons of articles that saved me the tedious manual searching. Step-by-step: 1. I uploaded the fantasy league settings and the full list of players being kept by other teams. 2. I added my own current players, draft strategy, and rough thoughts about possible approaches. 3. I asked Claude Cowork to analyze the available options and projected player values. 4. I had it search the web for relevant articles and incorporate that research into the recommendations. 5. I turned the result into a tailored list of priorities and draft targets for the auction.

Last year, my daughter was diagnosed with a rare autoimmune disease. Like any parent in that situation, I wanted to understand everything I could so we could make the best decisions for her care. I used both Gemini and ChatGPT to help me sort through the medical literature, treatment options, possible side effects, and how other countries approach treatment. I also turned to ChatGPT to identify the leading specialists in the country — thankfully, our referred doctor turned out to be one of them, which gave us even more reassurance. More than anything, it helped demystify the disease and make us feel confident that we were doing everything we could. She's doing really well on her medications, and seeing her recover has been an enormous relief. Step-by-step: 1. I gathered the diagnosis, questions, and medical context I needed to understand more clearly. 2. I used Gemini and ChatGPT to work through medical literature, treatment options, possible side effects, and approaches used in other countries. 3. I asked ChatGPT to identify leading specialists and explain why their experience was relevant. 4. I compared the research with the care team we had already been referred to. 5. I used the findings to prepare better questions and support discussions with clinicians, not to replace medical care.

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.