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Recommendations from people, not black boxes

Recommendations are better when you know where they came from. A book from a friend, a place from someone in your circle, or a list from a person whose taste you understand can be more useful than a ranking engine guessing what will keep you scrolling.

TouchGrass supports recommendations from people, not black boxes. The goal is discovery with context: books, shows, music, places, lists, and other recommendations connected to profiles, friends, and circles.

Why we chose this

Recommendation systems often pretend to be neutral.

In practice, many are optimized for attention. They learn what makes people click, watch, react, stay, and return. That can be useful in small doses, but it also changes discovery into a contest for engagement. The system may not care whether something is meaningful, trustworthy, generous, local, personal, or worth keeping. It cares whether it moves the metric.

Human recommendations work differently.

A friend’s bookshelf is not just a ranked list. A place someone recommends carries context: why they liked it, who it might be good for, and what kind of day it fits. A music recommendation from someone you know is not only a content item. It is part of a relationship.

TouchGrass wants discovery to feel more like that.

This does not mean every algorithm is bad or every human recommendation is perfect. It means social software should not replace people with opaque ranking systems as the main way to discover culture, places, ideas, and memories.

Recommendations should be connected to chosen people and social context. They should be easy to save, find, share, and revisit without turning discovery into an attention machine.

How this works on TouchGrass

Today, TouchGrass supports recommendations as part of the social product direction. recommendations may include books, shows, music, places, lists, or related formats.

The important design choice is that recommendations belong with people. They can appear on profiles, connect to friends and circles, and sit alongside posts, photos, albums, and long-form writing. This gives recommendations context instead of treating them as anonymous feed units.

TouchGrass also supports visibility choices. That matters because recommending a neighborhood place to close friends is different from publishing a public list.

TouchGrass avoids building discovery around engagement traps. It does not center videos, infinite scroll, public like-count races, or algorithmic feed ranking as the main product experience.

The open-web direction also matters here. Portability and federation can make recommendations less trapped inside one platform. Work around ATProto/Atmosphere, Solid, ActivityPods, private sharing, and related protocol directions should be stated carefully.

Worth knowing

A recommendation from a person is not automatically correct, safe, or unbiased. Friends can be wrong. Tastes differ. Lists can become outdated. A personal recommendation can still need judgment.

Private or circle-based recommendations also have privacy limits. If you share a recommendation with someone, they may screenshot it, copy it, repeat it, or misuse it. TouchGrass cannot promise protection from recipient behavior, remote-server behavior, or every future integration.

Finally, open does not mean public. Open-web support is about portability, interoperability, and exits. Visibility controls are a separate part of the product.

FAQ
Why not just use algorithmic recommendations?

Algorithmic recommendations can be useful, but many are optimized to maximize attention. TouchGrass is focused on recommendations with social context, where the source is a person, profile, friend, or circle you understand.

Can recommendations be private?

TouchGrass is designed around public, friends, and circles visibility. Also, private sharing cannot protect against screenshots, recipient misuse, or all remote-server behavior.

Does TouchGrass reject all ranking?

The point is not to reject every form of ordering. The point is to avoid making opaque engagement ranking the center of discovery. Recommendations should keep human context.

Learn more about recommendations