# X thread draft

## Chain abstraction

> Note: This post assumes a basic level of understanding of how chains operate, and interlayer/cross-chain message passing.

**Post 1:**
Siloed situation: Poor UX is the top barrier holding potential users back from trying out #web3. This cycle, advancements in chain #abstraction could change that. Here’s why this may be one of the most impactful developments yet 🧵👇

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**Post 2:**
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Problem: Users expect seamless UX in a dApp, no matter how many chains or assets are involved in an operation. But chains in today’s siloed web3 ecosystem function like isolated islands, each with its own gas token, account system, and often exclusive dApps.

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**Post 3:**
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This leads to fragmented liquidity, and a flow where users need to send and approve a transaction for each chain interaction, even for simple operations like bridging. :confused:

<img src="https://hackmd.io/_uploads/S1oB-xfzyx.png" width=65% />

*Source: X (ex-Twitter)*

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**Post 4:**
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Solution: Chain abstraction simplifies asset and wallet management, enabling an integrated UX where users can complete complex operations with a single action. Stay with me to learn more about what's going on behind the scenes. :information_source:

<img src="https://hackmd.io/_uploads/HJ6w21Gzkg.png" width=90% />

*Source: [Particle Network](https://developers.particle.network/landing/realized-vision)*

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**Post 5:**
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How it works: Chain abstraction operates at both the account and dApp levels. When combined, it enables a **universal account** that automates transaction routing, multi-token gas fees, cross-chain contract calls, and more—all through a single interface. :dart:

<img src="https://hackmd.io/_uploads/ry-UHeMMyl.png" width=90% />

*Source: [Particle Network](https://blog.particle.network/chain-abstraction-levels-user-experience/)*

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**Post 6:**
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Potential: Think dApps executing multichain logic, hiding all underlying complexity. Buy, swap, borrow, or bridge tokens without needing to know which chain is in use. This grants immense freedom for developers with access to cross-chain liquidity and vastly improves UX. :100:

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**Post 7:**
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Conclusion: dApps need to operate like smartphones—multiple modules functioning under the hood while users remain unaware and don’t need to understand or interact with them directly. This level of seamlessness is the game changer web3 needs. :heavy_check_mark:

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[End of Thread]

## Compute in decentralized AI

> Note: This post assumes a basic level of understanding of AI models, distributed computing, and blockchain networks.

**Post 1:**
Decentralized AI (#DeAI) is transforming the #AI landscape by distributing models and training data across a decentralized network. This boosts security, accessibility, and privacy within the AI ecosystem. Let’s dive into how compute plays a key role. 🧵👇

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**Post 2:**
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The rapid rise of LLMs like OpenAI's #ChatGPT and Google's #Gemini seems unstoppable, but there's a hidden threat: the compute crunch. GPT-3 was trained using 1024 GPUs, while GPT-4 needed 25,000! Will there be enough GPUs to keep up with the demand? #Bottleneck

<img src="https://hackmd.io/_uploads/By8b_Xzzkl.png" width=85% />

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**Post 3:**
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Generally speaking, scaling centralized data centers is costly and very limiting. #DeAI on the other hand taps into underutilized computing resources, offering a more cost-effective and efficient scaling solution. :chart_with_upwards_trend:

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**Post 4:**
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DeAI in action: Projects like io.net (Solana) leverage unused GPUs from data centers and crypto mining rigs to create an on-demand network for DeAI compute. HyperCycle (SingularityNet) links AI models across 350k+ nodes, accelerating learning by providing faster access to compute. :desktop_computer:

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**Post 5:**
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Decentralized compute reduces reliance on single-point failures by distributing tasks across global nodes, making the system more resilient, more reliable for real-time applications, and less vulnerable to events that can cause outages. :warning:

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**Post 6:**
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DeAI also enables the avenue of 'Compute-as-a-Service' in AI. Nodes can automatically bid on compute tasks, optimizing resource usage and offering GPU, CPU, and storage resources to AI systems on demand. :robot_face:

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**Post 7:**
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Beyond scalability and cost savings, decentralized compute keeps AI development independent. Rather than concentrating control with a few entities, DeAI democratizes compute, preventing monopolies and fostering diverse, parallel AI development. :heavy_check_mark:

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**Post 8:**
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The future of AI compute lies in decentralized networks where shared resources lower barriers for entry, enabling innovation. DeAI has the potential to accelerate development and reshape the global AI infrastructure. :hammer_and_wrench:

[End of Thread]

## Data in decentralized AI

> Note: This post assumes a basic level of understanding of AI models, model training, and blockchain networks.

**Post 1:**
Data is the backbone of Decentralized #AI (DeAI). But how do we ensure data privacy, security, and efficient utilization in a decentralized setting? Let's explore the role data plays in #DeAI systems! 🧵👇

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**Post 2:**
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Scaling AI systems is heavily dependent on efficient data handling. DeAI systems use innovative techniques like #sharding, replication, and elaborate consensus mechanisms to manage access to #data that is distributed across multiple nodes. Let's take a closer look.

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**Post 3:**
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**#Sharding** enables DeAI systems to handle large volumes of data by splitting it across nodes, which allows models to scale horizontally. Nodes can train on 'shards' :diamond_shape_with_a_dot_inside: of data independently, speeding up training while reducing the load on any single node.

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**Post 4:**
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**Data #replication** adds redundancy across nodes, ensuring data availability and network resilience even if some nodes fail. This is essential for DeAI since consistent data access and fault tolerance are key to allow for model training at scale.

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**Post 5:**
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#Privacy preservation techniques such as **federated learning** allow models to scale without compromising user data. The data either remains local to a node, or has very little movement, while updates drive global model improvement.

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**Post 6:**
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Consensus algorithms like **Proof of Learning (PoL)** help verify that nodes contribute valid model updates in decentralized networks. By verifying data integrity across nodes, PoL supports trust in the network, which is vital for scaling DeAI without central oversight.

**Post 7:**
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**Data quality** is crucial for scaling AI. Dario Amodei, CEO of @AnthropicAI (creators of Claude AI), discusses the data limits in scaling #LLMs on Lex Fridman's podcast. Watch here for insights: https://youtu.be/ugvHCXCOmm4?t=939

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**Post 8:**
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Dario Amodei notes that while the internet holds trillions of words, much of it is low-quality or SEO-driven. Future systems may use synthetic data to overcome these limits. DeAI can play a role by enforcing strict validation, ensuring accuracy in generating high-quality data.

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**Post 9:**
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As DeAI grows in size, efficient data management and the network's ability to handle distributed data will be key to enable access to data-driven insights for scalable, secure, and diverse AI development. #FutureofAI

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[End of Thread]