Share your best AI workflow. We could show it to 2M+ people.

I wanted to learn how to build websites and sell them, but I didn’t know where to start. I used AI—specifically DeepSeek—to help me plan a roadmap. Because I already had experience prompting large language models to get the results I wanted, I asked DeepSeek which areas of knowledge I would need for this path. I also asked it to prioritize each area using statistics and facts. I reviewed the areas I didn’t know and prioritized them, then told the AI that I needed free resources only. I asked it to rank the topics based on what I didn’t know or understood the least. Finally, I asked it to create a Markdown file with all the resources formatted as checklists and imported the file into Notion. Now I have a plan I’m following instead of a “someday I’ll do this, hopefully” idea. Step-by-step: 1. I explained to DeepSeek that I wanted to learn how to build and sell websites but didn’t know where to begin. 2. I asked it to identify the areas of knowledge I would need for that path. 3. I asked it to prioritize those areas using statistics and facts. 4. I reviewed the topics I knew the least about and used that information to prioritize them. 5. I specified that I wanted free learning resources only. 6. I asked DeepSeek to create a Markdown file listing the resources as checklists. 7. I imported the Markdown file into Notion and started following the resulting plan.

I first create my own shortlist of cultural events and collect all the relevant links in a simple text document. Then I give the document to ChatGPT along with my planning criteria. The goal is not to have AI decide what I should attend. I have already chosen the events that interest me. The useful part is turning that scattered shortlist into a realistic yearly plan. ChatGPT checks the event and venue pages, gathers the practical information, and organises everything into a chronological overview divided by month. Under each month, it lists the selected events in date order, including the day, date, location or cultural venue, a short description, and relevant ticket prices. This is especially useful because pricing systems differ between venues. Discounts, subscriptions, social tariffs, and reduced rates may apply in completely different ways. ChatGPT can compare those structures and help me understand what each event would actually cost instead of relying on the headline ticket price. The second planning layer is logistics. For each event, I assess how realistic the trip is by public transport, including the journey there and, importantly, whether getting home after the event is still feasible. I also use separate scores for how strongly I want to attend an event and how easy or difficult the logistics are. This makes it much easier to compare several months at once and decide where my cultural budget and energy are best spent. If an event is too expensive or logistically awkward, ChatGPT can look for practical alternatives, such as another performance date, the same production at a closer venue, a cheaper option, or a similar event that fits the plan better. Step-by-step: 1. I create a personal shortlist of cultural events and collect the relevant links in a simple text document. 2. I give the document to ChatGPT together with my planning criteria. 3. I have ChatGPT check the event and venue pages and organise the selected events into a chronological, month-by-month overview. 4. I include each event’s day, date, location or cultural venue, short description, and relevant ticket prices. 5. I compare discounts, subscriptions, social tariffs, and reduced rates to estimate the actual cost of each event. 6. I assess the public-transport journey to and from each event, including whether getting home afterward is feasible. 7. I score each event separately for how strongly I want to attend it and how easy or difficult the logistics are. 8. I use those comparisons to decide where my cultural budget and energy are best spent. 9. For events that are too expensive or logistically awkward, I ask ChatGPT to identify alternatives such as another performance date, a closer venue, a cheaper option, or a similar event that fits the plan better. The result is a structured yearly planner built from my own cultural shortlist: chronological, budget-aware, and grounded in the realities of public transport.

It's finally gardening season in the Midwest, and I wanted to do a big overhaul of my space with a new large in-ground bed and a more organized layout of crops. I first took some images of the space and had ChatGPT / Codex generate some visualizations of what it could look like based on my rambling list of requirements and measurements. After deciding on the vision, I asked for help creating a materials list, a step-by-step guide to building the bed (with instructions on sawing, drilling, etc.). I also provided a list of what we wanted to grow, and was able to get a well-planned map of where to place each plant, optimizing for aspects like shade vs. sun, companion plants, vining, etc. Several trips to Home Depot and a couple of days of work later, we have an awesome, refreshed space and a thoughtful, organized garden for the season! Step-by-step: 1. I photographed the existing garden space and wrote down the measurements, requirements, and rough ideas I had in mind. 2. I gave the images and requirements to ChatGPT and Codex and asked for several visualizations of the finished space. 3. Once I chose a direction, I asked for a materials list and step-by-step instructions for building the in-ground bed. 4. I supplied the full list of plants and asked for a placement map based on sun, shade, companion planting, and vining behavior. 5. I used the plan to buy materials, build the bed, and install a more organized garden layout.

I adopted three kittens last week when they were just four weeks old. At first, they were overwhelmed by the new environment and refused to eat, which really worried me. I turned to ChatGPT for help and got clear, practical guidance on what to feed them, how to keep them hydrated, and how to set up a comfortable space so they could adjust more easily. I followed a simple feeding plan and kept track of their behavior daily. Gradually, they started eating, became more active, and settled in. Now they're happy, playful, and fully adapted to their new home. Step-by-step: 1. I described the kittens’ age, the move into a new environment, and the fact that they were refusing to eat. 2. I asked ChatGPT for practical guidance on feeding, hydration, and making the space feel safe and comfortable. 3. I turned the advice into a simple feeding and care plan I could follow consistently. 4. I tracked their eating, hydration, activity, and behavior each day. 5. I used those daily observations to confirm that they were gradually eating more, becoming active, and adapting.

Ten days ago, I posted about keeping a `HANDOFF.md` so an AI film project doesn't lose the decisions it has already made. This is the other half, and it turned out to be the expensive one: the decisions that haven't been made yet. Credit where it belongs: Tony Ojeda posted a spec-generator agent that sits between an idea and implementation. The rule I borrowed is his: the agent inspects what already exists before proposing anything. I applied it to film production instead of code. I pointed an agent at my production documents for a short film I hadn't started generating and asked it to identify what was still undecided, what each item affected, and what would break if it were decided late. It came back with ten open decisions. One of them was worth the whole exercise. My environment description is locked verbatim across all 40 shots so the world stays identical. The film is called *The Thaw*. Whether the ice visibly melts during the film is written down nowhere. If I decide that in week three, all 40 keyframes get regenerated at once. I would have found out around shot 12. The counterintuitive part is who writes the list: not me. Asking the person who already has the whole film in their head what's missing gets you very little, because they have all of it—and that's exactly why they can't see the hole. The list has to come from something that only knows what's written down. Step-by-step: 1. I put the open questions in section 0 of the handoff, above everything else, so it's the first thing read and the first thing emptied. 2. I have the agent write that list, not me. It reads every existing document for the project and returns only what it cannot know from them. 3. I have it return three things for each item: what's undecided, which shots it affects, and what breaks if it's decided late. The third column sets the priority. 4. I have it sort by what costs the most to change afterward, not by what's easiest to answer. 5. I keep one hard rule: while an open question affects a shot, that shot doesn't get generated. The question gets decided, or deferred in writing with the cost of being wrong stated. 6. Every answered item leaves section 0 through one of two doors: into the closed canon or into the rejected list. Nothing is simply deleted. 7. I ask specifically about the things that go missing every time: the rule of the world; the physical scale of anything impossible, such as whether the character can touch or climb it; any object appearing in more than one shot; screen direction; how it ends; who speaks and in what voice; and the delivery format. 8. When it finds nothing real, it says so. A list padded to look thorough is worse than an empty one. The version I'd used for a year ran at the end of a session and recorded what got decided. Running it at the start, focused on what hasn't been decided, is the same document pointed the other way—and it's the direction that saves money.

I recently had an Evolt body scan. I've uploaded my results to Claude, shared my daily habits (sleep, diet, schedules, etc.), and asked it to create a full workout and diet plan based on the areas I want to improve. I was also able to include my recent Oura ring report to see if there's anything wrong with me. Will I follow it? We'll see.... Step-by-step: 1. I uploaded my Evolt body-scan results to Claude. 2. I added context about my sleep, diet, schedule, daily habits, goals, and the areas I wanted to improve. 3. I included my recent Oura report so the analysis could consider recovery and other wearable data alongside the scan. 4. I asked for a complete workout and diet plan tailored to the combined information. 5. I reviewed the plan as one organized starting point instead of trying to reconcile each source manually.

I've never been to Greece, so for my upcoming trip, I went all in and handed the whole itinerary over to Claude. Flights booked, transit times dialed, restaurant lists curated city by city. I'm now showing up with a plan tighter than most travel agents could put together! Step-by-step: 1. I gave Claude the trip context and handed over the responsibility for organizing the full Greece itinerary. 2. I used it to arrange the flights and make sure the travel sequence made sense. 3. I had it work through transit timing between each stop. 4. I asked for curated restaurant lists organized city by city. 5. I reviewed the finished plan as a single itinerary instead of managing each booking and recommendation separately.

I'm knitting a sweater for my baby nephew, and accidentally knitted extra rows, but didn't notice until I was 30 rows past it. I dropped the pattern I was using into ChatGPT and asked it to revise it so the stripes on the front & back aligned. Saved me from undoing my work and made sure the sweater stayed cute! Step-by-step: 1. I gave ChatGPT the original knitting pattern and explained where I had accidentally added the extra rows. 2. I told it how far I had continued knitting before noticing the mistake. 3. I asked it to recalculate the remaining pattern instead of telling me to undo 30 rows of work. 4. I checked that the revised stripe counts would align across the front and back. 5. I continued knitting from the corrected instructions while preserving the completed work.