Found a decision model comparable to Jev this-that-model-1.0 model developed by the Oxford AI team The company behind it http://FLock.io builds decentralized AI infrastructure (federated learning + blockchain), has received academic funding from the Ethereum Foundation, collaborated with UNDP and Tongyi Qianwen, and raised $11M Open source, and the API is completely free It’s a typed decision model: you provide a context + n options, and it returns the selection and calibrated confidence in one forward pass; 31ms, zero output tokens, format errors are mathematically impossible. Differences from Jev: - Jev is a commercial API, charged per token - this-that-model allows registration and use via the official API, with input/output/cache all surprisingly free—register to get an API key and you’re good to go - You can also deploy it yourself, MIT open source Usage is identical to Jev, for any “pick one from n options” scenario, one forward pass can answer multiple questions at once Intent classification Content moderation Risk tiering Routing decisions Jev recently closed registrations again, while this-that-model-1.0 offers free API access directly on the official site

FLock.io — Decentralised AI Training Platform

Source: https://www.flock.io/

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Local Data

Collaborative

Decentralized

Not yourmodels,Not your AI.

FLock.io is a private AI training platform for training custom models with full data control.

What is FLock.io?

FLock.io is a decentralised AI training platform that combines federated learning with blockchain verification so enterprises and communities can train privacy-preserving models without moving raw data. As of September 2026, its core modules include AI Arena, FL Alliance, the API Platform, and Moonbase-FOMO. This matters because organisations can keep data local while co-creating usable AI models.

Last updated:2026-09-04

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FLockCo-CreationPlatforms: Unlock DeAI Potential

FLock.io is a decentralised AI training platform that lets enterprises and communities build privacy-preserving AI models without moving or exposing their data. Combining federated learning with blockchain-based verification, we provide a unified, trust-minimised infrastructure for secure training, evaluation, and deployment.

The core modules includeAI Arenafor decentralized model training,FL Alliancefor collaborative federated learning, theAPI Platformfor deploying FLock-trained models into real applications, andMoonbase-FOMOfor enabling real model assets and driving ecosystem growth.

WhyPrivacyPowers Everything FLock Builds?

Making Decentralised AI Truly Private and Usable

Enable organisations/communities keep their data local, maintain fair value distribution, and launch community-driven AI models that can be deployed and monetised globally.

Built for Privacy, Security, and Scale from Day 1

Secure high-value training with advanced Federated Learning and LoRA-based optimisation, reinforced by a proven on-chain incentive system that ensures resilience, accuracy, and fair rewards across the network.

The Foundation Chosen by World-Class Institutions and Partners

Collaborate with the United Nations Development Programme (UNDP), Alibaba Cloud’s Qwen, and the Ethereum Foundation, providing a privacy-preserving infrastructure that meets real-world security and compliance needs.

Trusted byIndustry Pioneers

Read inspiring stories and genuine feedback from users who have experienced exceptional results.

Greg Osuri, Founder of Akash

In just a few clicks, developers can deploy FLock.io Training Nodes and Validators on Akash. We’re thrilled that it’s so easy for developers to participate in decentralized AI development on Akash’s supercloud.

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