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The fragmentation problem at the centre of #tokenisedfinance is why #interoperability infrastructure is the enabling condition for everything else. Central banks, commercial banks, and public blockchains are building toward a shared three-layer architecture for #digitalmoney. Each layer performs a distinct function. All three must work together. And they each...

13,495 просмотров • 1 месяц назад •via X (Twitter)

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🌍 Big Names. Bigger Vision. The Kima Story. From central banks to global tech giants, Kima’s journey is powered by some of the most influential names in finance and technology, all united by one mission: making money move freely, securely, and compliantly across ecosystems. Here’s who’s shaping the future with us 👇 🏦 European Central Bank (ECB): Recognized by the ECB as a Pioneer in the Digital Euro Innovation Platform, where Kima showcased programmable public finance and conditional payments for real-world impact. 💰Commercial Bank International (CBI), Dubai: Partnering with Kima to power next-gen cross-border remittances from the UAE to major corridors like India, Pakistan, and Bangladesh, fast, compliant, and cost-efficient. 💶 Bank of Israel: Selected participant in the Digital Shekel Challenge, building innovative use cases for national digital currency integration. ☁️ Alibaba Cloud: Providing enterprise-grade infrastructure to run global Kima nodes, ensuring speed, reliability, and compliance at scale. 💳 Mastercard: Collaborating through sandbox pilots to enable real-time stablecoin top-ups, bridging traditional banking and blockchain rails. 🔗 Kima Network is the universal settlement layer making all this possible: connecting banks, blockchains, and digital assets in one interoperable system. Kima is the settlement backbone connecting banks, blockchains, and digital assets, where TradFi meets DeFi, for real. 🚀

Kima Network

13,380 просмотров • 10 месяцев назад

LangGraph. CrewAI. Agno. Which one to pick? The good news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.

Avi Chawla

30,762 просмотров • 9 месяцев назад

Full Fine-tuning vs. Freezing Layers. Interact 👉 and == Full Fine-tuning == A real network has many — three layers in this example, billions of parameters in a production model. What does fine-tuning look like when you update all of them? That’s full fine-tuning: continue training every weight in the pretrained network on your new task. Every layer’s W gets its own ΔW. Nothing is frozen — every parameter is in play. Think of an MLP as a chain of prerequisites leading to an advanced course. Layer 1 might be Linear Algebra, layer 2 Probability, layer 3 Advanced Machine Learning — each one building on what came before. Fine-tuning is what happens during graduate study: the foundations are already there from undergrad, so you’re not re-learning. Full fine-tuning is reviewing every prerequisite to see what new topics have appeared and what discoveries the field has made since the last time you sat through them. Effective — but exhausting. This diagram shows the same three-layer MLP twice, side by side. On the left, the pretrained network runs on input X: three weight matrices W₁, W₂, W₃, each followed by a ReLU activation. Full fine-tuning gives the model the most freedom to specialize. Every parameter can move — and every parameter that can move must be stored. But not every prerequisite needs revisiting. The further you go back in the chain, the less the material has changed since pretraining — the linear-algebra basics under your computer-vision course are largely the same as they ever were. The next page does exactly that: freeze the prerequisites that haven’t moved, and only refresh the advanced one closest to your specialization. == Freezing Layers == Full fine-tuning reviewed every prerequisite — Linear Algebra, Probability, Advanced ML — to refresh each subject with the latest topics. Effective, but exhausting. Then you realize something. The prerequisites haven’t actually changed that much. Linear Algebra is still Linear Algebra; the matrix decompositions you learned still hold. Probability is still Probability; the distributions and Bayes’ rule haven’t moved. Almost all the new material — the new ideas, the recent discoveries — lives in the advanced layer at the top. That’s freezing layers: keep the prerequisite layers fixed at their pretrained state, and only update the advanced one. In the diagram below, W1​ and W2​ — the foundational prerequisites — stay frozen. Only W3​ — the layer closest to your task-specific output — gets a ΔW.

Tom Yeh

27,587 просмотров • 3 месяцев назад

A traveler taps her card at a market thousands of miles from home. The payment feels instant, simple, straightforward. It’s not. Behind that tap, money may need to move across currencies, banks, payment providers, and borders. Traditional systems often require companies to park funds in local accounts before they are needed, leaving capital stagnant for long periods of time where it’s not able to earn a return or be put to work. Members of the Avalanche Payments Collective are building a more efficient global payments model, one that keeps capital productive up until the moment it needs to move. Here are five members working across different parts of cross-border payments and treasury infrastructure: Axiym provides payment and treasury infrastructure that helps institutions route liquidity and settle transactions without pre-funding every market. Its technology supported Hyundai Motor America’s stablecoin payment to its Mexico affiliate, completed on Avalanche in minutes instead of days. Nonco helps institutions exchange currencies and stablecoins by requesting prices directly from a network of professional liquidity providers. That gives them access to more competitive rates while allowing both sides of the transaction to settle together onchain. SMBC, Japan’s second-largest bank, is exploring stablecoin infrastructure for wholesale institutional payments. It is also working alongside MUFG and Mizuho on a potential yen-pegged stablecoin initiative built on Avalanche. StraitsX is working with KBank through Project BLOOM to develop payment infrastructure that can improve settlement across Southeast Asian corridors where moving money remains particularly slow and expensive. AeraTech helps multinational companies manage cash across subsidiaries, offset internal obligations, and settle inter-company payments without unnecessarily routing every transaction through external banks. These different companies all represent or engage with different parts of the payment and treasury stack. But they all have a shared goal: keep capital working longer, move it when it is needed, and make cross-border payments faster and more efficient. That is what the Avalanche Payments Collective is bringing together. Frictionless, borderless payments that really are instant, simple, and straightforward.

Avalanche🔺

45,390 просмотров • 9 дней назад