Wow - over 100 AI assistants trained in BlockAssist on @gensynai already. Purely based on user gameplay.

Most RL pipelines train a brand-new agent for each task, spending huge amounts of data and compute and then throw away those learned weights once you switch environments. Why train AI once when it could learn forever? Gensyn’s BlockAssist proves decentralized continuous RL in a live app, an AI companion in Minecraft, that learns from your gameplay on your device and then improves collectively via a permissionless compute swarm. In contrast, most large models are fine-tuned with human feedback or massive labeled datasets in a centralized pipeline. By contrast, BlockAssist flips the script. Learning happens from each user’s gameplay stream itself, continuously and privately. - For one, it removes the dependence on massive offline training runs for every new capability. The model refines itself on the fly. - It’s also inherently personalized: my BlockAssist might develop different skills than yours because we each train it on our own gameplay. We are essentially crowd-sourcing AI improvement in a way that scales with users, which is a very different paradigm from centralized AI fine-tuning. Also, we are notorious (especially in crypto) talking up infrastructure and its potential, but Gensyn is proving it with a real app. BlockAssist is a major milestone showing what’s possible when RL, continuous learning, and decentralized infrastructure come together.

So many SBC side events and not one of them is at @ViksChaat...

Our portcos kicking off AI day at SBC 💪🏾

AI is the most powerful platform shift of our time, but it’s being shaped by a small group of companies racing to control every layer of the stack: compute, data, models, and distribution. I believe the future of AI shouldn’t be monopolized, and that the most transformative systems will be built on open, decentralized infrastructure. AI agents are becoming active participants in software, and they need environments where they can hold assets, follow rules, and transact. Blockchains, which are now fast, cheap, and expressive enough to support real AI infrastructure, provide that foundation. Decentralized, credibly neutral, and user-owned, they offer exactly what today’s AI systems lack. And for the first time, they’re ready. Just a year ago, decentralized training was dismissed as technically unfeasible, even by leading AI researchers. But breakthrough research like OpenDiLoCo @PrimeIntellect, DeMo @NousResearch, and Protocol Models @PluralisHQ are flipping that assumption. For the first time, high-performance AI training is not only possible on decentralized networks – it’s working. Exceptional teams are focused on core decentralized AI infrastructure. While I believe there will always be opportunities in improving compute, training, and data layers, I see the most practical wedge in agent infrastructure: the frameworks, wallets, and coordination and reputation layers that enable autonomous systems to interact, transact, and evolve. This is where the Crypto x AI stack starts to become real. As primitives for identity, payments, memory, and coordination mature, we’ll move from isolated agents to persistent, interconnected networks of agents cooperating, competing, and evolving onchain. These agent networks will become the foundation for new types of products, experiences, and organizations. Over time, I expect this to unlock fully autonomous agent swarms, governed by cryptographic rules, not corporate APIs. I'm looking to back ambitious, pre-product founders who are building years ahead of the market, starting from day zero. DMs open.

I love this app

Decentralized AI today is where DeFi & stablecoins were circa 2020. Most tech truly transformative AI won’t live inside closed stacks. It is being built on open, transparent, decentralized rails. A breakthrough event will usher in mainstream adoption. Like Stripe acquiring Bridge. And then decentralized AI will just be called AI.

Life must be remembered backward, but lived forward. -Kierkegaard

China's open source and open weights culture around AI is creating a lot of leverage for the open source ecosystem. Companies can openly distill, generate synthetic data, and build on each other’s work. The primarily closed approach in the US is driven by "moat" culture. Unfortunately, this leads to little leverage. It won't end well...

LLMs can’t solve time series. Here’s why: LLMs are powerful, general-purpose tools. But somewhere along the way, we started treating them as the answer to every problem. They’re not. And for time series modeling, they’re the wrong tool entirely. Let’s dig in 👇