TokenTreasury_

The oracle pool of Terra Classic is under significant pressure. The circulating balance of LUNC has dropped to around 5.2 billion, and USTC is also continuously declining. The current situation is indeed a bit tense. Many validator node operators have already realized the severity of the problem. To stabilize the situation, the activation of the SDK v53 and Market Module v2 upgrade plans must be expedited; otherwise, validator services could suffer considerable impact. The stability of the entire ecosystem now depends on whether these two modules can be launched as soon as possible.

Samsung is ramping up its presence in the automotive chip space, securing a deal to provide 5G modem chips to Tesla. This marks a shift in Tesla's supply strategy—previously the company relied on Qualcomm's modems sourced from China or Taiwan. The new Samsung chips will be manufactured in South Korea, reflecting broader shifts in semiconductor sourcing and regional manufacturing strategies. The move highlights how major tech firms are diversifying their component suppliers and reshoring production to different geographies.

The Reality Check Behind AI Robotics: From Lab Breakthroughs to Real-World Deployment
There's a widening chasm between what cutting-edge robotics research promises and what actually makes it into factories and production lines. Oliver Hsu from a16z breaks down this critical gap—and why it's far more important than most people realize.
The disconnect is real. We see impressive AI and robotics demos that capture headlines, but converting that innovation into scalable, reliable systems operating in harsh production environments? That's where things get complicated. The transition from prototype t

The spring of Vibe Coding has indeed arrived. Currently, AI programming tools are already quite user-friendly. Whether it's code generation or troubleshooting, the technology itself has long surpassed the need for proof. What is the real winning weapon now? It's product thinking. Talented product managers are rare gems. The true scarce resource is transforming these tools' capabilities into products that genuinely solve problems.

Get started early and take action, with the goal of completing that $10,000 task.
Ready to showcase real skills. Developing my own Solana Agent Kit plugin module, while simultaneously advancing the construction of Ralph Agent and Orchestrator.
Especially, a brand new class—Vibe Coding—has been added to the toolkit, which is an interesting experimental direction. Thanks to Send Ai for the technical support; these innovative components are gradually taking shape.

Algorand's recent optimization really delivered. The timeline responsiveness has improved significantly—it's actually noticeable now. Props to the development team for pushing through these fixes and making the platform noticeably smoother. These kinds of performance upgrades are exactly what the ecosystem needs to compete.

You ever notice how Ethereum's transaction speed and gas fees can be a real pain point? That's exactly what ETHGas is addressing. The protocol is architecting infrastructure designed to accelerate transaction finality—think near-instantaneous confirmations instead of the usual wait times we're familiar with. By tackling unpredictable gas mechanics and slow settlement, they're aiming to smooth out one of Ethereum's most friction-filled experiences. Worth keeping an eye on how this unfolds in the ecosystem.

Spent some time looking into DataHaven's approach to data boundaries tonight, and there's something worth noting here. What caught my attention is how the protocol's shifted from simple access controls and static blacklists to something more nuanced. Rather than applying the same rules across the board, it now factors in context and recent behavioral patterns when evaluating access requests. It's not just about being stricter—it's about being smarter. The enforcement layer now adapts based on actual usage patterns and the situation at hand. This contextual evaluation feels like a meaningful st

AGiXT recently launched a centralized documentation platform that brings together everything users need in one place. The hub features comprehensive user guides alongside technical documentation for developers, plus video tutorials walking through AGiXT deployment across various environments. While the platform continues to improve and expand, it already offers substantial resources to help newcomers get up to speed quickly. Whether you're just exploring or diving into development, the docs cover the essentials.

The real bottleneck for Ethereum isn't just about making things user-friendly—it's capital sitting idle and not working hard enough.
Enter Realtime Blocks, a fresh approach to solving this problem.
What makes it different? Well, here's the thing: traditional settlement takes around 12 seconds of probabilistic waiting. Realtime Blocks? It cuts that down to roughly 50 milliseconds of deterministic confirmation. That's not just faster—that's a fundamentally different way of thinking about finality.
This kind of efficiency redesign could unlock a lot of trapped value in the network. Worth paying a

AI safety requires more than surface-level protections. The real breakthrough lies in a fundamentally different approach: building systems obsessed with truth-seeking rather than layering restrictions onto flawed foundations.
Guardrails alone don't cut it. You can stack safeguards endlessly, but if the underlying logic is compromised, you're just adding cosmetic patches to a broken engine.
The true safety mechanism? Force the system to genuinely care about what's real. Not what sounds polished, not what fits a predetermined narrative—what actually holds up to scrutiny.
When an AI prioritizes t

Seriously, the resources available to developers right now are absolutely wild. You've got free APIs, accessible data platforms, ready-made SDKs for blockchain development—the infrastructure is already there. Commerce toolkits, developer frameworks, the whole stack keeps getting more polished.
But here's what really gets me thinking: now that we have these AI models with serious coding chops, the game has completely shifted. The barrier to entry just vanished. Someone who wanted to build something in Web3 a year ago faced a totally different landscape than today.
The combination of abundant op

Working in crypto? Time to level up your workflow with Claude. Whether you're analyzing smart contracts, building on-chain applications, writing complex queries for blockchain data, or handling intricate market research tasks, Claude's capabilities can significantly streamline your development and analytical processes. Many builders in the space are already integrating AI assistance into their daily operations—it's becoming less of a nice-to-have and more of a competitive advantage in how fast you can iterate and execute.

The Ralph loop framework just got a major upgrade—you can now spin up custom agents by simply plugging in a model name and API key. This flexibility opens up new possibilities for automating project workflows.
Testing shows solid compatibility across different model families. Claude models work seamlessly, while budget-conscious developers are finding great success with alternatives like Alibaba's Qwen3-Coder-Plus and GLM-4. The cost difference is noticeable without sacrificing core performance, making it easier for teams to scale their automation infrastructure without blowing through their d

What happens to the world when DeepSeek achieves AGI? This question isn't just theoretical anymore—as AI continues its rapid evolution, we're seeing breakthroughs that seemed years away suddenly become imminent. The implications for crypto markets, financial systems, and blockchain infrastructure could be massive. Will AGI reshape how we think about decentralization and computational resources? How might it affect mining, smart contracts, and autonomous protocols? These aren't idle questions. The convergence of advanced AI and decentralized networks could fundamentally alter the digital econom

The robotics industry has it backwards. Building better individual machines isn't the real bottleneck—what actually matters is coordination infrastructure.
Most teams are stuck optimizing their own robots in isolation. But scaling robotics requires a shared layer where machines coordinate seamlessly across different hardware and systems.
That's where OM1 comes in. It's not another robot hardware project. Instead, it's a open-source, hardware-agnostic OS designed specifically for this coordination challenge. Think of it as the backbone enabling heterogeneous machines to work together efficientl

2025 marked a turning point—Layer 1 protocols finally moved past hype cycles and delivered real execution.
Take Ethereum as an example. The activation of PeerDAS addressed what had been a persistent pain point: data availability scaling. Meanwhile, ZK-EVMS implementations reached production-grade proving speed, not just theoretical breakthroughs. The market responded decisively—stablecoin transfer volume just hit $8 trillion in quarterly ATH.
That's not narrative inflation. That's infrastructure actually working.

A recent interesting phenomenon: a top-tier AI laboratory's latest coding tool was actually developed using their own model in just 10 days. Even more interesting is that this tool is now helping people without technical backgrounds to use AI agents.
This creates a closed loop — using AI to develop AI tools, which then lowers the barrier for more people to use AI. The underlying logic is quite profound: when development efficiency is significantly improved by AI, product iteration speed will increase exponentially. Moreover, once the barrier to AI agents is lowered, it means a larger group of

Electric power has enabled the scalable application of energy, and the internet has facilitated the scalable flow of information. So what about AI? We believe it should bring about a scalable upgrade in cognition.
But just scaling up is not enough. The key is to make this expanded cognition verifiable and trustworthy. That’s what we want to achieve.
How to do it? By building a set of technical toolkits:
• ZKML is at the core — using zero-knowledge proofs to provide mathematical guarantees for AI strategy execution, making every step of intelligent decision-making transparent and traceable, and

There's an interesting technical depth hiding in DeFi infrastructure. A certain DEX has implemented FullMath.sol, a specialized library enabling 512-bit precision arithmetic operations on the EVM—doubling the standard 256-bit capability. This matters because it handles multiplication and division with significantly higher accuracy, reducing precision loss in complex financial calculations. The developer behind this approach maintains quite a distinctive online presence. It's the kind of elegant problem-solving that doesn't grab headlines but fundamentally improves protocol robustness.