Counterview Capital @CounterviewVC
Counterview Capital is a New York-based venture capital firm focused on investments in pre-seed and seed stage technology companies. counterview.vc Joined July 2018-
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āAI is ready. Are your networks?ā By combining Model Context Protocol (MCP) with Shabodi NetAware, AI Applications and AI Agents can tap into real-time network intelligence and trigger network changes ā Intent-to-API Translation That means: ā Faster AI-driven workflows ā ā¦
Take a look at the latest feature in PoliceOne for BLTN. The hits keep coming!
Take a look at the latest feature in PoliceOne for BLTN. The hits keep coming!
We broke 45M transactions and 130,000 users on the network! š Key highlights per product: - BlockAssist: 122,500 sessions - RL Swarm: 26,000 nodes connected - Judge: 17,00 participants
Everyoneās chasing the next shiny model drop ( 5o!), but @gensynai just shipped something far more important for the future of AI. BlockAssist might look like just a Minecraft assistant, but itās actually a leap toward a new paradigm: decentralized active learning. Activeā¦
Everyoneās chasing the next shiny model drop ( 5o!), but @gensynai just shipped something far more important for the future of AI. BlockAssist might look like just a Minecraft assistant, but itās actually a leap toward a new paradigm: decentralized active learning. Activeā¦
š Still relying on SMS for fraud prevention? Itās time for an upgrade. See how banks are using Network APIs like SIM Swap, Device Swap & Location to stop fraud before it happens. Check out the case study š shabodi.com/case-studies/pā¦ #FinTech #FraudPrevention #APIsā¦
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š blocktool.gensyn.ai
what if I told you that personal superintelligence could be sovereign and self-owned on open infrastructure with decentralised tech? gensyn.ai
Gensyn is running the largest-scale decentralized training network in the world, with 12,000 concurrent nodes switched on. AI will run on networks and open protocols similarly to the Internet. We are early, but the data is extremely promising.
Gensyn is running the largest-scale decentralized training network in the world, with 12,000 concurrent nodes switched on. AI will run on networks and open protocols similarly to the Internet. We are early, but the data is extremely promising.
.@gensynai *testnet* does 300K transactions daily. Thatās 20% of daily @world_chain_. Itās 22% of daily @ethereum. And 92% of daily #bitcoin.
.@gensynai *testnet* does 300K transactions daily. Thatās 20% of daily @world_chain_. Itās 22% of daily @ethereum. And 92% of daily #bitcoin.
we've completely overhauled the backend for RL Swarm and released it fully open source you can now specify completely custom games / environments and configure many more parameters launching live in RL Swarm today with a whole new multi-task swarm
we've completely overhauled the backend for RL Swarm and released it fully open source you can now specify completely custom games / environments and configure many more parameters launching live in RL Swarm today with a whole new multi-task swarm
half a million views on Gensynās testnet launch. most visible moment for distributed training in a long time.
Testnet is live! š„š„š„
RL Swarm has a UI Continuing on from our releases, we made a UI for visualising the progress of a Swarm. It runs locally or remotely and can be pointed at any node to pull data from the DHT. Thereās plenty more functionality that could be added so contributions welcome!
š Programmable 5G is here, unlocking new monetization opportunities for MNOs! But success in the network API economy takes more than exposureāit demands seamless integration, customization & monetization. Thatās where Shabodi NetAware stands out: ā Standards-based APIā¦
š Shabodi x Okanii: Revolutionizing Telecom Network Monetization at @MWCHub Barcelona 2025! šš” Weāre unveiling a game-changing pilot program, combining Shabodiās NetAware and Okaniiās tokenization engine to unlock new revenue streams for MNOs: ā Network Slicing &ā¦
Introducing Diverse Expert Ensembles Diverse Expert Ensembles are a model architecture, optimised for decentralised networks. They are trained in an embarrassingly parallel fashion, and use expert diversity to enhance model performance.
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