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I regularly come across long articles, reports, papers, ebooks, and other documents that I want to understand but realistically don’t have time to read closely. Instead of letting them pile up in a reading queue, I change the format and turn them into audio I can consume during time that would otherwise be less productive. Step-by-step: 1. I choose a long article, report, paper, ebook, or other document that I want to understand but don’t have time to read closely. 2. I upload it to Google Notebook (formerly NotebookLM) and add it as a source so NotebookLM can work directly from the material. 3. I generate an Audio Overview. NotebookLM turns the source into a conversational, podcast-style discussion that summarizes and explains the major ideas. 4. I listen while walking, driving, working out, doing chores, or running errands. 5. I follow up on what matters. After listening, I know the main ideas, what I want to investigate further, whether the document is worth reading in full, and which sections deserve closer attention. The result is essentially a personal podcast generated from whatever I need to learn. What I like about this workflow is that it doesn’t require me to find more time. It lets me use time I already have differently. Because the material is turned into a conversational discussion rather than simply being read aloud, I find it easier to stay engaged with dense material. I don’t treat the podcast as a replacement for reading the source when the details really matter. I use it as a comprehension and triage layer. Even when I eventually go back and read the original, I’m starting with a mental model of what’s in it rather than approaching it cold. The broader lesson is that AI doesn’t always need to save time by doing the work for you. Sometimes it can save time simply by changing the form of the work so it fits into your life.

Today, I wanted to see how far an AI agent could go if I gave it a real business instead of a predefined automation. I entered binayah.com into Awish.ai. Binayah is a real estate company, and instead of telling Awish.ai exactly what workflow to build, I asked it to analyze the business first and find an automation opportunity. It suggested a customer acquisition funnel, which I reviewed and approved. Around 10 minutes later, the automation was ready. Step-by-step: 1. I entered binayah.com into Awish.ai. 2. Awish.ai agents analyzed the website and how the business operates. 3. Awish.ai identified customer acquisition as an area that could be automated. 4. It suggested a funnel designed to capture and qualify potential leads. 5. I reviewed the suggestion and approved it. 6. Awish.ai created the workflow and connected the required steps. 7. The funnel was ready to use in around 10 minutes. What I find most interesting is that I didn’t start by designing a workflow. The system first understood the business, found an opportunity, suggested what should be automated, and built it only after I approved. That feels much closer to having an automation consultant inside the product than using a traditional workflow builder.

I recently bought a house & had an inspection done as a closing condition. I uploaded the inspection report (80 pages) to Claude and asked it to make a list of home maintenance tasks by timeline (closing week, seasonally, etc.). Then, based on that list, I had it create one-off or annually recurring calendar events in my personal Google Calendar, with my husband invited to each event. It was super helpful because the inspection report clearly noted when the maintenance tasks were completed and suggested a frequency for the future. Step-by-step: 1. I uploaded the full 80-page home inspection report to Claude. 2. I asked it to extract every maintenance item and organize the work by closing week, season, and recurring frequency. 3. I had it use the report’s completion dates and recommended intervals to calculate when each task should happen next. 4. I reviewed the list for anything that needed to be corrected or combined. 5. I asked Claude to create one-off and annually recurring Google Calendar events and invite my husband to each one.

My Downloads folder had been filled with random files for the last six months, and I'd been procrastinating on cleaning it up. So I gave Claude Cowork access to the folder, and asked it to "help me organize this folder." In a few minutes, it went through almost 100GB of files, arranged them by file type and folders, and removed duplicate files in just a few minutes, something that could have cost me hours of work. Step-by-step: 1. I gave Claude Cowork access to the Downloads folder that had accumulated files for roughly six months. 2. I asked it to inspect the folder and propose a sensible organization rather than specifying every destination myself. 3. I had it group the files into folders based on type and content. 4. I asked it to identify and remove duplicate files. 5. I let the workflow process the full folder, which was nearly 100GB, in one pass.

I had a file sitting on my desktop that Claude Code needed to see—a spreadsheet, a PDF, or a screenshot someone sent me—but a terminal had nowhere to drop it. Typing the path was the only option. In WSL, that meant rewriting `C:\Users\me\Desktop\report.xlsx` as `/mnt/c/Users/me/Desktop/report.xlsx` by hand every time. I set up two folders at the root of my Claude Code workspace: `_inbox/` for files going to Claude and `_outbox/` for files coming back to me. The names are from the workspace’s point of view, so the direction is never ambiguous. I also added a single line to `CLAUDE.md` so Claude knows where to leave things: "Save deliverables meant for me (reports, exports, drafts) to _outbox/." That one line handles the entire return trip. For the desktop handoff, I installed a small window pointed at the workspace folder. Disclosure: the window is mine—claude-work Desk, open source and free, at github.com/tact0225/claude-work-desk. Setup takes about two minutes and requires no terminal. Anything dropped onto it is copied into `_inbox/`. If you’d rather not install anything, you can drag files into `_inbox/` with Explorer or Finder; every other step still works. Now I drag files onto the window instead of typing a path. The file lands in `_inbox/`, and the window logs it with a timestamp so I can see that it actually arrived. Then I tell Claude, "check _inbox"—no paths and no `/mnt/c` rewriting. When Claude finishes, it saves the results to `_outbox/`. The window watches that folder and marks anything new in the tree until I open it, so finished work announces itself. I no longer interrupt Claude to ask, "Is it done, and where did you put it?" Step-by-step: 1. I created `_inbox/` and `_outbox/` at the root of my Claude Code workspace, using the workspace’s point of view to define the direction of each folder. 2. I added `"Save deliverables meant for me (reports, exports, drafts) to _outbox/."` to `CLAUDE.md`. 3. I installed claude-work Desk and pointed its desktop window at the workspace folder. Alternatively, I can use Explorer or Finder to place files directly in `_inbox/`. 4. I drag files onto the window, which copies them into `_inbox/` and logs their arrival with a timestamp. 5. I tell Claude, "check _inbox". 6. Claude saves finished results to `_outbox/`, where the window marks new files until I open them.

I have Claude connected to all my communication platforms (Slack, Notion, calendars, Granola, etc) and pulls a morning brief for me each day. I've curated so it provides me an interactive page each day with call prep, priority tasks, recommendations of tools to use to solve XYZ, recap of activities overnight, etc. Step-by-step: 1. I connected Claude to the communication platforms I use, including Slack, Notion, calendars, and Granola. 2. I defined the sections I wanted every morning: call prep, priority tasks, overnight activity, and recommended tools. 3. I had Claude gather the relevant updates from each connected source. 4. I asked it to turn the result into an interactive page instead of a long unstructured message. 5. I refined the brief over time so it consistently surfaced the information I actually use.

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 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

I have a Daily Brief agent on Codex with access to my Slack, Gmail, Notion, and Google Drive that checks everything that happened in the last 24 hours on request. It sends me the brief through Slack, and I do maybe 10% of the final manual editing. I also gave it several examples of before/after editing, so now it sends messages in my exact voice and style. Step-by-step: 1. I connected my Daily Brief agent in Codex to Slack, Gmail, Notion, and Google Drive. 2. I told it to review activity from the last 24 hours and pull out the items that actually needed attention. 3. I structured the output as a concise brief and had Codex deliver it through Slack. 4. I manually reviewed the draft and made the final edits before using it. 5. I gave the agent before-and-after examples of those edits so future briefs would sound more like my own voice.

Over the past year, I've relied heavily on LLMs to support my French citizenship application— including drafting required letters and tracking paperwork. Although I'm already fluent, I used ChatGPT to prep for the required language exam to get familiar with the test format for 100% confidence. Step-by-step: 1. I listed the letters, forms, and paperwork required for the French citizenship application. 2. I used ChatGPT to draft the required letters and improve the wording. 3. I created a process for tracking what had been completed and what was still outstanding. 4. I asked ChatGPT to reproduce the format and expectations of the required language exam. 5. I practiced against that format until I felt fully confident despite already being fluent.

As a fast-moving startup, many of our team's best ideas come from random Slack threads, but get lost and never fully hashed out. Instead of spending hours a day manually adding tasks to our databases, we used Notion's new Agents feature (rolling out soon for GA) and built an "AI Project Manager" that monitors Slack messages daily and logs tasks autonomously. Step-by-step: 1. I connected Notion’s Agents feature to the Slack conversations where team ideas usually appear. 2. I defined the kinds of messages that should become projects or follow-up tasks. 3. I scheduled the agent to review Slack messages every day. 4. I had it capture qualifying tasks in the team’s database automatically. 5. I used the database as the durable follow-up layer so promising threads did not disappear in chat history.