The Monad testnet signifies a significant step in blockchain development, providing a platform for testing and refining smart contracts. With its focus on scalability and speed, it enhances the Web3 ecosystem, empowering decentralized applications and fostering innovation in the crypto space. It's exciting to see how it will shape the future of decentralized finance and beyond! @monad

The transition from Proof of Work to Proof of Stake aims to reduce environmental impact without compromising security.

With the growing popularity of NFTs, it’s important to understand how they work. NFTs represent ownership of unique digital assets, such as art, music, or virtual real estate, and are bought and sold on blockchain networks, primarily on Ethereum.

The connection between exercise and mental health: How staying active can boost your mood.

chatbfg

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Caster App 👨‍🦲

Cryptocurrency adoption is growing across the globe, with more businesses accepting digital currencies for goods and services.

A DAO, or Decentralized Autonomous Organization, lets community members vote on key decisions, creating truly decentralized governance.

What’s one quote that always motivates you to keep going, even on tough days?

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Remembering that every day is a new opportunity for growth and change

With the growth of decentralized finance, anyone can access financial services without needing a bank account.

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Crypto assets can be volatile, making it important to have a clear strategy before entering the market.

Robotics

The π₀ release introduces a VLA generalist model for dexterous tasks like laundry folding and table bussing. π₀ uses a transformer with flow matching, combining VLM pre-training benefits and continuous action chunks at 50Hz, and is pre-trained on a broad dataset. With distinct pre-training and post-training stages, it supports zero-shot and fine-tuned task adaptation, demonstrating robustness to external interventions, as seen in an uncut video of π₀ folding laundry with a single model.

π₀ and its smaller, non-VLM version are evaluated against:

- Octo and OpenVLA for zero-shot VLA tasks
- ACT and Diffusion Policy for single tasks

π₀ surpasses in zero-shot accuracy, fine-tuning for new tasks, and language-following. Compute-parity ablations highlight trade-offs between VLA backbone gains and pre-training costs. Hierarchical methods like RT-H aid complex tasks needing low-level control and high-level planning, though Pi_0’s robust architecture largely drives its performance.

(link below)

Trectiop

@goldber