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docs/apps/zen-docs/content/blog/introducing-zen.mdx
zeekay cf191aedc2 content(zen-docs): brand cleanup across blog posts and remove zenlm-models/ui packages
- Update blog/[slug]/page.tsx to use getAllSlugs() instead of getSortedPosts() for static params
- Sweep all blog MDX files to remove upstream model name references (brand policy compliance)
- Add new blog posts: 7680-dim-embeddings, agent-nfts, bitdelta-behavioral-compression, case-for-decentralized-science
- Delete gspo.mdx and qwen2.5-vl-32b.mdx (consolidated/removed content)
- Remove packages/zenlm-models and packages/zenlm-ui entirely
- Update lib/blog.ts, docs/server.ts, docs/browser.ts for blog infrastructure
- Update pnpm-lock.yaml after package removal
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---
title: "Introducing Zen: Open AI for the Open Web"
description: "We're launching Zen, an open research initiative to build AI that serves everyone."
date: "2021-03-15"
author: "Zach Kelling"
tags: ["Announcement", "Vision"]
---
Today we’re announcing Zen, an open AI research initiative from Zoo Labs Foundation.
## Why Zen?
The current trajectory of AI development concentrates power in a handful of organizations. Models are trained on humanity’s collective knowledge, yet the resulting systems remain proprietary. We believe there’s a better path.
Zen represents our commitment to building AI that is:
* **Open** : All research, weights, and training methodologies published freely
* **Decentralized** : No single point of control or failure
* **Aligned** : Built with explicit mechanisms for community governance
* **Efficient** : Optimized for accessibility, not just raw capability
## The Technical Foundation
We’re starting with a focus on three areas:
### 1\. Distributed Training Infrastructure
Training large models requires significant compute. Rather than centralizing this in massive data centers, we’re developing protocols for distributed training across heterogeneous hardware. Our early experiments show promising results with gradient compression and asynchronous updates.
### 2\. Open Data Pipelines
Quality training data is as important as model architecture. We’re building transparent data curation pipelines with clear provenance tracking. Every piece of training data will have documented lineage.
### 3\. Governance Mechanisms
AI systems make decisions that affect people. Those people should have a voice in how these systems evolve. We’re exploring on-chain governance mechanisms that give stakeholders meaningful input.
## What’s Next
Over the coming months, we’ll publish:
* Our initial architecture proposals
* Distributed training benchmarks
* Data curation guidelines
* Governance framework drafts
This is day one. The hard work is ahead. But we believe the destination is worth the journey.
Join us.
* * *
_Zach Kelling is a co-founder of Zoo Labs Foundation._