Personal Life
How to Keep a Running List of Recommendations (Books, Shows, Restaurants)
Never forget a book, restaurant, or show recommendation again. AI notes capture, organize, and recall every suggestion people give you.
Someone at dinner mentions a book that sounds incredible. You think, "I'll definitely remember that." You don't. Two weeks later, you're standing in a bookstore trying to recall the title and all you can summon is "something about decision-making, I think? A woman wrote it?"
This happens constantly. Recommendations flow in from conversations, podcasts, articles, social media, group chats. Books you should read. Shows you need to watch. Restaurants you have to try. Products people swear by. Podcasts that will "change your life." Each one feels memorable in the moment and becomes completely irretrievable within days.
The problem isn't that you don't have a list app. You probably have three. The problem is that recommendations arrive at unpredictable times, in unpredictable formats, from unpredictable sources -- and no system survives that unless capturing is effortless.
The Capture-First Approach
The only system that works for recommendations is one where capture takes less than five seconds and requires zero decision-making about where something goes. That's the entire design philosophy.
In Mem, the moment someone mentions something worth remembering, you capture it:
In conversation -- pull out your phone and hit Voice Mode. "Book recommendation from dinner with friends: 'Thinking in Bets' by Annie Duke, about making better decisions under uncertainty." Seven seconds. Done.
From a podcast -- same thing. "Podcast rec: Patrick O'Shaughnessy's interview with Morgan Housel about the psychology of money. Referenced by a colleague."
From a text or group chat -- forward it to Mem via email or clip it with the Web Clipper. The context gets saved alongside the recommendation.
From an article -- clip the page or just capture a quick note. "Restaurant mentioned in the Times food section: small pasta spot, Italian, neighborhood place, supposed to have incredible cacio e pepe."
The key is: don't try to organize it in the moment. Don't open a specific list or navigate to a folder. Just capture it and move on. Your only job is getting the information into the system. Organization is not your problem.
The Retrieval Moment
The payoff comes later, when you actually need a recommendation. Standing outside a restaurant trying to decide where to eat. Browsing a bookstore. Scrolling Netflix on a Friday night. Packing for vacation and wanting a good audiobook.
Open Mem Chat and ask:
"What restaurants have been recommended to me?"
"Any book recommendations I haven't read yet?"
"What shows have people told me to watch?"
Mem searches across every note where you've captured a recommendation -- regardless of when, how, or from whom -- and gives you a compiled list. With context. "Your colleague recommended 'Thinking in Bets' at dinner in March. Your sister mentioned 'The Bear' as a must-watch. The food section article mentioned that pasta place on the Lower East Side."
The context matters because it helps you decide. A recommendation from someone whose taste you trust carries more weight than one from a random podcast. And knowing who recommended something lets you circle back: "Hey, I finally read that book you mentioned -- you were right."
Beyond Simple Lists
A flat list of recommendations is useful but limited. AI-powered notes unlock something better: contextual recommendations based on mood, situation, or preference.
"What's a good book for a long flight? Something lighter than what I usually read."
"My parents are visiting -- any restaurant recommendations that would work for a group of six?"
"I'm in the mood for a show that's like Succession but funnier."
Because your notes capture context -- who recommended it, what they said about it, why it came up -- the AI can match recommendations to moments. This is impossible with a flat list in Apple Notes or a Goodreads "Want to Read" shelf.
The Compound Value of Recommendations
Here's what most people don't realize: recommendations are social currency. When someone gives you a recommendation and you actually follow through, it deepens the relationship. "I watched that show you mentioned -- the third episode was wild" is a better conversation starter than any small talk.
And when someone asks you for a recommendation, being able to pull up a curated, context-rich list makes you the person everyone goes to. "Actually, three people have told me about this one restaurant -- here's what they each said about it." That's a recommendation with provenance.
Over time, your recommendation archive becomes a map of your taste and your network's collective intelligence. Mem users who maintain running captures describe it as having "a sommelier for every domain" -- books, food, travel, shows, products -- all drawn from the people they actually trust. This same approach works beautifully with the broader capture habit we describe in our guide on building a note-taking routine.
The Recommendation Collection (Optional)
Some Mem users create a single "Recommendations" collection and tag notes as they capture them. Others skip this entirely and rely on AI search. Both approaches work.
If you prefer structure, create collections by type: Books, Restaurants, Shows, Travel. But this adds friction to capture -- you have to decide which collection something belongs to. The beauty of Mem is that you don't need to decide. Capture everything in one undifferentiated stream and let Chat sort it out when you need something.
The best system is the one you actually use. If adding a collection tag takes you from "I'll capture it" to "I'll do it later" (which means never), skip the tag.
Getting Started
For the next week, capture every recommendation that comes your way -- voice, text, clip, whatever
Don't organize anything -- just get it into Mem
On Friday, ask Mem Chat: "What recommendations have I captured this week?"
Pick one and act on it -- read the book, try the restaurant, watch the show
Tell the person who recommended it that you followed through
The system works because it mirrors how recommendations actually flow in your life -- messy, unpredictable, scattered across conversations and media. Instead of fighting that reality with a rigid list, you work with it. Capture everything, organize nothing, retrieve on demand.
Never lose a recommendation again.
