THIS / THAT model 1.1 is already here. Nearly 2x improvement on complex-decision test, ahead of Kimi, GLM & DeepSeek. Same 1.88B parameters. Same ~31ms. Build. Ship. Repeat. trained by FLock

My Model Token, Made in FOMO She said she wouldn’t FOMO, she said she wouldn’t FOMO. She lied, she lied, she lied. Her model token? Made in FOMO. Her model token? Made in FOMO.

The AI race is moving from compute to data. Much of the world’s most valuable data sits inside institutions, private companies, and governments, where it can’t simply be handed over to centralised AI providers. In a written interview with Korea IT Times, our founder and CEO Jiahao Sun (@0x7sun) explains how federated learning can unlock this data for AI training while keeping raw data local and how blockchain can add accountability and fairness to decentralised training. From healthcare in the UK to sovereign AI in Sarawak and public-sector innovation with UNDP in the Dominican Republic, this decentralized, privacy-preserving approach to model training is already being put to work. Many thanks to Chief Editor at Korea IT Times, Monica Younsoo Chung, for inviting us to contribute to the series. Read the full interview ↓

Your team knows how your business works. How do you build AI that does, too? At the builders' breakfast today with @UseCorgi, @fotor_com, and BuildHer Labs, our Head of EMEA, @TiffanyWang98, shared how businesses can build private, custom AI around their own knowledge and workflows. Your documents and policies give the model context. Fine-tuning helps it follow your processes and communicate in your brand’s voice. But these materials often contain sensitive information that needs to stay within your organisation. FLock provides the infrastructure for businesses to train and fine-tune domain-specific models through federated learning without exposing their raw data, and deploy them on infrastructure they control. A router connects each request to the appropriate model and applies rules on information access and responses. Smaller, custom models can handle routine work, while larger ones support more complex reasoning. The end goal is to build AI capabilities your organisation owns, shaped around real workflows and the people who use them. It was an insightful meetup where we discussed with fellow builders what enterprise AI solutions look like in practice. Thanks to @claudiaamaggi, Nora Chen, and Shreya Choudhuri for bringing us together, and to everyone who joined us at Corgi Cafe!

Governments worldwide are exploring how to adopt frontier technologies responsibly and securely. Today, under an executed framework agreement, we’re strengthening our relationship with @UNDP to advance public sector digital innovation in the Dominican Republic, leveraging distributed systems, privacy-preserving technologies, and AI applications. More details👇

It's never easy for a startup to sign an official framework agreement with a UN agency - the diligence, the alignment, the patience it takes to get there. But we've done it, and this marks our first step into sovereign AI and frontier tech collaboration in the LATAM region. We'll keep expanding on this, and we'll keep building. 🇺🇳🇩🇴

Governments worldwide are exploring how to adopt frontier technologies responsibly and securely. Today, under an executed framework agreement, we’re strengthening our relationship with @UNDP to advance public sector digital innovation in the Dominican Republic, leveraging distributed systems, privacy-preserving technologies, and AI applications. More details👇

Last week marked the close of Cohort 2 of @UNDP_AltFinLab #SDGBlockchainAccelerator’s# post-acceleration programme, with Cohort 3 on the horizon. Two featured projects came from FLock’s track, showing how blockchain and AI can improve public infrastructure and create real-world impacts. More details 🧵

What does it mean for everyone? For builders: If you build tools or agents using FLock API Platform, you are no longer just paying for compute. Stake MT of the model you use to enjoy a discount, and earn $FLOCK + MT incentives. Inference costs become even lower. For MT holders: You now gain exposure to the explosive demand for models like Kimi, DeepSeek, GPT, Gemini, and more in the era of agentic AI. For $FLOCK holders: Emissions become harder to dilute when only active users earn, not farmers. A more sustainable tokenomics is built on demand and value accrual.

Why FOMO is going to change the AI model economy. As API demand for agents like OpenClaw, Hermes Agent, and multiple third-party models grows over time, the competition for $FLOCK rewards becomes increasingly usage-driven. Usage = rewards.

gmFLOCK introduces ecosystem-wide amplification. By locking $FLOCK for gmFLOCK, you will earn more reward exposure across multiple MTs on FOMO, not just one. This aligns long-term holders with the overall ecosystem performance.

$FLOCK continues the base incentive asset across the economy built around AI models. What's different now is that $FLOCK is driven by AI inference usage from FLock API Platform, triggering organic $FLOCK demand ties to modern AI developments.

How is it possible? Every FOMO MT represents a tokenised version of the AI model hosted on FLock API Platform. When users call a specific model, they generate demand for that MT. Staking MT of the AI model you actually use increases your weight in the reward system while having certain discounts for your model usage.

The mechanism is simple: FLock API Platform usage → AI inference spend → FOMO's Model Token (MT) demand → stake MT of the model you use → increase reward weight and enjoy inference discounts at the same time → earn more $FLOCK emissions + MT incentives. Rewards are now tied to real model usage, not just staking.