Today we're launching B3OS in public beta.
Onchain workflows any agent or human can run.
Live today:
𓏠 Traders copy-trading on Polymarket or trading X headlines
𓏠 Developers powering onchain apps with scheduled sends, recurring swaps, and more
𓏠 Teams paying vendors in USDC from email or Slack
Build in our UI or through our MCP server.
This is the future of autonomous onchain finance.
In @PanteraCapital's The Convergence of AI and Blockchain, @cosmo_jiang makes the case that AI and blockchain push each other forward.
Blockchain gives AI open systems to pull from. In return, AI abstracts away the complexity that has held crypto back.
Abstracting it away is the hard part.
The rails exist and the models can reason, but the layer that turns intent into reliable execution onchain is still early.
Introducing Claude Fable 5: a Mythos-class model that we’ve made safe for general use.
Its capabilities exceed those of any model we’ve ever made generally available.
Where does crypto fit once AI is in everything?
@darylX24's take on @latenightonbase: the models keep getting better, but someone has to turn that power into tools regular people can actually use.
That's where B3OS comes into the picture.
Meet B3 (@b3dotfun) the operating system for onchain automation built by the former minds behind Coinbase Wallet, Base, and USDC. 🛠️
They just dropped B3OS (https://t.co/KuuyWfwZHp), a modular execution layer that changes how we interact with crypto. Here is why it’s game changer:
🔹 No Code Workflows: Build complex trading systems & automations using simple Lego blocks.
🔹 AI Agent Ready: High reliability with low latency perfect for autonomous onchain tasks.
🔹 Zero Friction: Solves gas spikes RPC failures and chain reorgs out of the box.
Backed by an ecosystem that includes B3 Labs & Strategic Holdings to drive long term value The future of Base is automated. ⚡️ #Base #B3OS #Web3 #AIAgents cc: @jessepollak@base@BuildOnBase@brian_armstrong@seangeng@ItsGioLogist@TimmyGruber@Gabedotninja@b3labs_
Great variety of guests heading to the stream this week
• Former Base employees discussing the fallout from the recent layoffs
• Coinbase-backed teams building the next generation of products
• Founders operating in the AI agent space
• Crypto researchers and industry personalities
Each brings a unique perspective, story, and set of lessons worth sharing.
Big fan of agents interfaces being in native messaging channels like iMessage, telegram, WhatsApp
My team is working on similar surfaces and it’s a super interesting engineering / product challenge to make the agents conversational / feel “natural”
iMessage is especially a challenge cause you don’t have rich input elements like slack does with blocks or telegram / WhatsApp can do, so you need to come up with hacky (but still low latency) ways to present information to the user.
For teams who use agents, you want them to behave like teammates and the problem is different shape in multi-tenant systems like slack or discord where you need to juggle user context, org context, and permissions.
Two very different problems, same goal: make the agent feel like a person, not software you're operating.
Gonna write up a blog post about it below
When you pay for inference today, you're renting it.
ChatGPT, Claude, whatever you run, it lives on someone else's machine, metered behind a monthly fee.
Our CTO @seangeng sees that shifting.
Open models are catching up to closed ones, demand for compute already outruns supply, and inference is moving local, onto machines people own and networks they control.
Local AI is starting to feel more practical
love their tagline, LM Studio’s new app lets you run local models, in your pocket
when Android and who else is building surfaces like this? I want to try all of them
Set-and-forget only works if you trust what the agent is reading before it moves.
That's the part @DatalineAI handles, and it sits right in front of where B3OS takes over to execute onchain.
Glad to be a Dataline launch partner.
We are welcoming @b3dotfun to the Dataline Launch Partner cohort.
B3OS is the execution plane for crypto AI agents: workflows, nodes, and connectors that run set-and-forget. Dataline is the data layer those agents query before they execute.
Been looking into token optimization and model routing, I think super obvious optimization to tackle both cost + demand on inference
Here’s a small post about different techniques and methods
https://t.co/I4Ob7WC5rJ