Decasonic

@decasonic

Decasonic is the venture and digital assets fund building blockchain, Web3, AI and metaverse innovation.

Chicago, IL

linktr.ee/decasonic

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Decasonic
@decasonic
Sep 24
We’re watching the application layer of agentic finance form in real time. Arc already has meaningful concentration across agentic banking, trading, and payments. As agents gain the ability to transact, allocate, and coordinate capital, the financial rails underneath them become
Abdul
@abdulalali
Sep 24
actively working on mapping out the @arc AI ecosystem of projects. we break down the 107 projects identified within the ecosystem across: - interfaces - applications - model - data given the benefits of building on Arc and the relationship with @circle it is not unsurprising to
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Decasonic
@decasonic
Sep 24
As connector ecosystems expand, the value of an assistant will increasingly come from how well it can understand context, move between services, and complete workflows with the right permissions.
Muse
@Muse
Sep 24
Muse now connects to even more of the apps you already use, with more ways to help get things done.
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Decasonic
@decasonic
Sep 22
Personal AI is finding consumers quickly. Muse reached 2.8M installs in its first 12 days. On comparable U.S. and Canada iOS data, it reached 1.8M downloads versus 1.3M for ChatGPT at the same point after launch. Early downloads are only the first test. The more important
Decasonic
@decasonic
Sep 21
Safety is becoming part of the intelligence stack. As robots leave fixed cells, they need real-time perception and control around people. Digit 5’s close-proximity safety architecture is a good example of why capability and safe deployment have to improve together.
Agility
@agilityrobotics
Sep 15
Introducing Digit 5. Agility’s next-generation humanoid, engineered for cooperatively safe work at scale, allowing it to work in close proximity to people without the physical safety barriers required by traditional automation. Explore Digit 5: agilityrobotics.com/solutions/digit Watch the
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Decasonic
@decasonic
Sep 20
Physical AI is moving from programmed behavior toward learned behavior. As systems need fewer task-specific instructions and can generalize from demonstrations, the economics of training and deployment begin to change. The value increasingly moves into the models, data and
Skild AI
@SkildAI
Aug 25
Introducing S1, our new foundation model that learns from one example. It can be taught 10-minute long tasks that it has never seen before, from one video prompt without any fine-tuning. Watch S1 operate in real-time via in-context learning:
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