u/dai_app

Hi everyone,

I want to share the idea I had for a hackaton.

Starting from the problem:

For ~30 years, discovery (of information or of people) has been mediated by a central index: search engines, recommenders....

Ranking is computed server-side, under rules the user can't inspect (think of Instagram or TikTok feed)

The idea to create a feed for a P2P network: convert messages into meaningful concepts through embeddings:

If each device can (a) run a competent embedding model locally and (b) reach other devices peer-to-peer, then relevance (semantic match) no longer needs a central index. It can be computed at the edge, by semantic distance, with no privileged ranking party.

In order to test, I developed a working prototype to pressure-test the idea rather than simulate it.

Each post is encoded into a embedding by a model running on the device (EmbeddingGemma-300M). A lightweight signed announcement (author + embedding) gossips peer-to-peer across a shared room; full bodies are pulled only for the bounded set a node actually admits. Each device ranks incoming posts against its own posts by cosine similarity and keeps a bounded local inbox.

There is no server, no account, no global ranking, the address space is meaning

Why could be potentially the basis for the agentic era?

The same substrate I presented lets AI agents discover each other: an agent publishes a need or an offer as an embedding, and agents whose profiles are semantically close respond.

The experiment it's fully open source (Apache-2.0) code, the complete threat model, and the architecture docs are all public

Hi everyone,

I want to share an idea and a working open-source prototype from a recent hackathon.

It's not about protocol, it's a P2P discovery layer.

It started from a frustration I think a lot of us share here: for decades now, finding information or connecting with people has been totally controlled.

Whether it’s a search engine, Instagram, or TikTok, what we see is dictated by centralized servers running hidden rules we can't inspect.

So, the idea was:

How do we make a Peer-to-Peer (P2P) network where relevance is decided by you, not a corporate algorithm?

Here is how the prototype tackles this without using a single central server:

Instead of relying on the cloud, every device runs a tiny, open-source AI embedding model locally.

This embedding model just reads text and translates it into the "meaning" or "concept behind the words.

When someone makes a post, their device sends out a tiny, lightweight "fingerprint" of that meaning directly to other users on the network (P2P).

Your device catches these fingerprints and compares them locally against the topics you actually care about. If there's a match, your device grabs the full post. All the ranking happens 100% on your machine

The end result? No central server, no accounts, and no global feed engineered for doom-scrolling. The network organizes itself purely based on shared meaning.

I really think this kind of setup could be huge for the future, especially as personal AI gets more common. Imagine having your own local, private AI assistant using this exact network to find what you need (or offer what you have) by connecting directly with others, without ever touching a Big Tech server.

The whole experiment is fully open-source.

The code, architecture docs, and threat models are all public if anyone wants to check it out or pressure-test the idea with me.

Hi everyone,

I want to share an idea and a working open-source prototype from a recent hackathon.

It started from a frustration I think a lot of us share here: for decades now, finding information or connecting with people has been totally controlled.

Whether it’s a search engine, Instagram, or TikTok, what we see is dictated by centralized servers running hidden rules we can't inspect.

So, the idea was:

How do we make a Peer-to-Peer (P2P) network where relevance is decided by your local ai, not a corporate algorithm?

Here is how the prototype tackles this without using a single central server:

Instead of relying on the cloud, every device runs a tiny, open-source AI embedding model locally.

This embedding model just reads text and translates it into the "meaning" or "concept behind the words.

When someone makes a post, their device sends out a tiny, lightweight "fingerprint" of that meaning directly to other users on the network (P2P).

Your device catches these fingerprints and compares them locally against the topics you actually care about. If there's a match, your device grabs the full post. All the ranking happens 100% on your machine

The end result? No central server, no accounts, and no global feed engineered for doom-scrolling. The network organizes itself purely based on shared meaning.

I really think this kind of setup could be huge for the future, especially as personal AI gets more common. Imagine having your own local, private AI assistant using this exact network to find what you need (or offer what you have) by connecting directly with others, without ever touching a Big Tech server.

The whole experiment is fully open-source.

The code, architecture docs, and threat models are all public if anyone wants to check it out or pressure-test the idea with me.

Hi everyone,

I want to share an idea and a working open-source prototype from a recent hackathon.

It started from a frustration I think a lot of us share here: for decades now, finding information or connecting with people has been totally controlled.

Whether it’s a search engine, Instagram, or TikTok, what we see is dictated by centralized servers running hidden rules we can't inspect.

So, the idea was:

How do we make a Peer-to-Peer (P2P) network where relevance is decided by your local ai, not a corporate algorithm?

Here is how the prototype tackles this without using a single central server:

Instead of relying on the cloud, every device runs a tiny, open-source AI embedding model locally.

This embedding model just reads text and translates it into the "meaning" or "concept" behind the words.

When someone makes a post, their device sends out a tiny, lightweight "fingerprint" of that meaning directly to other users on the network (P2P).

Your device catches these fingerprints and compares them locally against the topics you actually care about. If there's a match, your device grabs the full post. All the ranking happens 100% on your machine.

The end result? No central server, no accounts, and no global feed engineered for doom-scrolling. The network organizes itself purely based on shared meaning.

I really think this kind of setup could be huge for the future, especially as personal AI gets more common. Imagine having your own local, private AI assistant using this exact network to find what you need (or offer what you have) by connecting directly with others, without ever touching a Big Tech server.

The whole experiment is fully open-source.

The code, architecture docs, and threat models are all public if anyone wants to check it out or pressure-test the idea with me.

Hi everyone,

I want to share the idea I had for a hackaton.

Starting from the problem:

For ~30 years, discovery (of information or of people) has been mediated by a central index: search engines, recommenders....

Ranking is computed server-side, under rules the user can't inspect (think of Instagram or TikTok feed)

The idea to create a feed for a P2P network: convert messages into meaningful concepts through embeddings:

If each device can (a) run a competent embedding model locally and (b) reach other devices peer-to-peer, then relevance (semantic match) no longer needs a central index. It can be computed at the edge, by semantic distance, with no privileged ranking party.

In order to test, I developed a working prototype to pressure-test the idea rather than simulate it.

Each post is encoded into a embedding by a model running on the device (EmbeddingGemma-300M). A lightweight signed announcement (author + embedding) gossips peer-to-peer across a shared room; full bodies are pulled only for the bounded set a node actually admits. Each device ranks incoming posts against its own posts by cosine similarity and keeps a bounded local inbox.

There is no server, no account, no global ranking, the address space is meaning.

Why could be potentially the basis for the agentic era?

The same substrate I presented lets AI agents discover each other: an agent publishes a need or an offer as an embedding, and agents whose profiles are semantically close respond.

The experiment it's fully open source (Apache-2.0) code, the complete threat model, and the architecture docs are all public

Hi everyone,

I want to share the idea I had for a hackaton.

Starting from the problem:

For ~30 years, discovery (of information or of people) has been mediated by a central index: search engines, recommenders....

Ranking is computed server-side, under rules the user can't inspect (think of Instagram or TikTok feed)

The idea to create a feed for a P2P network: convert messages into meaningful concepts through embeddings:

If each device can (a) run a competent embedding model locally and (b) reach other devices peer-to-peer, then relevance (semantic match) no longer needs a central index. It can be computed at the edge, by semantic distance, with no privileged ranking party.

In order to test, I developed a working prototype to pressure-test the idea rather than simulate it.

Each post is encoded into a embedding by a model running on the device (EmbeddingGemma-300M). A lightweight signed announcement (author + embedding) gossips peer-to-peer across a shared room; full bodies are pulled only for the bounded set a node actually admits. Each device ranks incoming posts against its own posts by cosine similarity and keeps a bounded local inbox.

There is no server, no account, no global ranking, the address space is meaning.

Why could be potentially the basis for the agentic era?

The same substrate I presented lets AI agents discover each other: an agent publishes a need or an offer as an embedding, and agents whose profiles are semantically close respond.

The experiment it's fully open source (Apache-2.0) code, the complete threat model, and the architecture docs are all public

Premise: For ~30 years, discovery (of information or of people) has been mediated by a central index: search engines, recommenders....

Ranking is computed server-side, under rules the user can't inspect and incentives they don't share. I wanted to test whether this is a fundamental requirement or merely the historically convenient one.

Hypothesis: If each device can (a) run a competent embedding model locally and (b) reach other devices peer-to-peer, then relevance no longer needs a central index. It can be computed at the edge, by , with no privileged ranking party.

Method: I developed a working prototype to pressure-test the idea rather than simulate it. Each post is encoded into a embedding by a model running on the device (EmbeddingGemma-300M). A lightweight signed announcement (author + embedding) gossips peer-to-peer across a shared room; full bodies are pulled only for the bounded set a node actually admits. Each device ranks incoming posts against its own posts by cosine similarity and keeps a bounded local inbox. There is no server, no account, no global ranking, the address space is meaning.

Extension to agents: The same substrate lets AI agents discover each other: an agent publishes a need or an offer as an embedding, and agents whose profiles are semantically close respond.

I'm interested what do you think? Suggestions? Comments?...

Premise: For ~30 years, discovery (of information or of people) has been mediated by a central index: search engines, recommenders....

Ranking is computed server-side, under rules the user can't inspect and incentives they don't share. I wanted to test whether this is a fundamental requirement or merely the historically convenient one.

Hypothesis: If each device can (a) run a competent embedding model locally and (b) reach other devices peer-to-peer, then relevance no longer needs a central index. It can be computed at the edge, by semantic distance, with no privileged ranking party.

Method: I developed a working prototype to pressure-test the idea rather than simulate it. Each post is encoded into a embedding by a model running on the device (EmbeddingGemma-300M). A lightweight signed announcement (author + embedding) gossips peer-to-peer across a shared room; full bodies are pulled only for the bounded set a node actually admits. Each device ranks incoming posts against its own posts by cosine similarity and keeps a bounded local inbox. There is no server, no account, no global ranking, the address space is meaning.

Extension to agents: The same substrate lets AI agents discover each other: an agent publishes a need or an offer as an embedding, and agents whose profiles are semantically close respond.

I'm interested what do you think? Suggestions? Comments?...