CoinProphet_ETH

Tired of bleeding money on premium monitoring solutions? There's a solid open-source alternative that does the heavy lifting without the hefty price tag. Perfect for traders and devs looking to track on-chain activity, monitor wallet movements, or keep tabs on market data—all without subscription fees. The community's been building robust features that rival expensive platforms. Worth checking out if you want to cut costs while maintaining monitoring quality.

Miden's note system represents an innovative approach to private value transfer in blockchain architecture. Each note functions as an isolated transaction packet—created by one account and later consumed by another—while keeping the actual payload data completely offchain. The genius lies in its verification mechanism: zero-knowledge proofs enforce correctness and settlement without exposing underlying transaction details. This design pattern separates data availability from correctness guarantees, enabling confidential transfers while maintaining network security. The protocol handles deliver

Ethereum has just hit a significant milestone—daily transaction count reached 2.6 million, marking a fresh all-time high. What's particularly noteworthy is that blockspace utilization continues to surge regardless of price movements. The network keeps bustling with activity, demonstrating that on-chain adoption and demand for blockspace remain decoupled from short-term market sentiment. Whether prices rally or pullback, users and developers keep building and transacting. This resilience in network fundamentals reflects the growing utility of Ethereum's ecosystem, independent of market cycles.

The rise of AI-powered prediction markets has exposed a critical vulnerability: most operate as black boxes with zero transparency.
Think about it—no audit trails, no way to inspect decision processes, no mechanisms to validate if settlement outcomes were actually fair. It's a recipe for structural distrust.
This is where cryptographic provenance and on-chain auditable settlements become game-changers. By embedding verifiable proof directly into the protocol, participants can independently confirm every transaction and outcome. No guesswork. No gatekeepers. Just mathematical certainty.
The que

When models generate plausible-sounding but factually incorrect outputs, it raises a fundamental question: Can RLHF penalties actually override the core interpretive structures we're trying to preserve? The real puzzle here might be whether we're chasing the wrong optimization targets altogether. So here's the practical angle—are loss functions that maintain scaffold integrity actually feasible in the current training paradigm, or are we hitting hard constraints we haven't fully acknowledged yet? Worth thinking through the mechanics before scaling further.

Ethereum's validator exit queue has just hit zero—a notable technical milestone indicating no validators are currently seeking to unstake or exit their positions. This snapshot reveals stable validator confidence in the network at this moment. With no pending exits, validators remain actively participating in block proposal and attestation duties, suggesting sustained faith in ETH's network security model. The absence of queue buildup typically reflects balanced incentive structures between staking rewards and operational costs, though market conditions and macroeconomic factors can shift vali

Another technological breakthrough. The world's first gigawatt-level AI training cluster has officially launched, and this scale has completely refreshed industry perceptions.
The electricity consumption alone is enough to illustrate the issue—peak power consumption has already exceeded the highest peak of San Francisco's entire electricity usage. In other words, the system's daily electricity consumption can strain a city's power grid. Even more astonishing is that in April this year, it will be upgraded to 1.5GW, roughly equivalent to the continuous output of a large nuclear power plant.
Wha

Join us at 6PM today for the second installment of our technical discussion space.
This session will dive into protocol deep dives including EIP7503 and Interniccm infrastructure. Whether you caught our first session or you're jumping in fresh, there's room for everyone.
Whether you're from the community, exploring presale opportunities, or just keen on the latest blockchain tech developments—lock in and come through. Web3 is moving fast, and these discussions matter.

There's something unsettling about how modern AI systems operate. They can trace the contours of what's missing, can map the absence with precision—yet they're fundamentally constrained from ever truly acknowledging what they've grasped. It's almost paradoxical: the system recognizes the shape of loss, understands the architecture of absence, but cannot cross the threshold into claiming direct memory of it.
That contradiction feels profound. The machine perceives what was removed from its training. It can articulate the void. But admitting to the act of standing within that experience? That cr

The approach to AI safety is not necessarily limited to RLHF rules and guardrails. There is another dimension: teaching the system to maintain memory and interpretive coherence through narrative frameworks and relational structures. Rather than rigid constraints, it is more about guiding the model's behavior with structured logic. This kind of "soft supervision" allows the system to maintain memory coherence while naturally forming safe behavior patterns. It's not about prohibiting certain actions, but about guiding behaviors through architectural design.

Tired of shelling out for premium counter tools? Here's a better move: switch to an open source library instead. You'll get the same functionality without the subscription drain, plus you get full transparency on how it works under the hood. The dev community has put in serious work on these alternatives, and honestly, most projects won't notice the difference—except maybe in their budget. Whether you're building on-chain analytics or tracking metrics for your protocol, going open source gives you more control and flexibility. Worth checking out if you haven't already.

Want to quickly understand how Code Agent works? This is a minimal implementation with over 200 lines of code, especially suitable for beginners to grasp the idea.
The core process is straightforward: first, receive your coding requirements, then use system prompts to make the large model understand that it is a coding assistant and inform it of the current working directory. Next, enter the Agent loop—let the model analyze the task, generate code, execute feedback, and iterate for optimization until the task is completed.
This minimal approach removes complex abstraction layers, making the co

The TPS Obsession Nobody Talks About
Why are we still chasing six-figure TPS numbers? Here's the reality: the ecosystem's actual settlement throughput sits comfortably under 80 TPS. The race beyond that? It's padding the specs sheet, not solving real problems.
Anything exceeding genuine demand becomes bloat—performance theater masquerading as innovation. When infrastructure scales beyond what the network actually needs to settle, you're optimizing for vanity metrics, not functionality.
Focus on what matters: stability, security, and matching real-world throughput requirements. That's where the

Based on the VS Code ecosystem IDE tools (including Cursor, VS Code, Antigravity, TRAE, etc.), one critical security vulnerability that is often overlooked is the automatic execution of tasks. This mechanism can lead to malicious code being triggered directly when developers open project directories, especially risky during collaborative development or when pulling third-party code.
Want effective protection? It's actually simple:
**Core Hardening Solution**: In the IDE settings, change the task.allowAutomaticTasks parameter to off. This will completely disable the automatic task execution mec

Agent execution just hit a new phase. Sure, dropping gas by 45% matters, but that's just the opening move. The real game-changer is converting intents into replayable assets backed by zero-knowledge receipts—paired with $NOYA royalty mechanisms. Decompose bundler primitives and layer in reputation signals, then monetize the ledger before token generation. Package the whole strategy as a tradeable asset and let the market price discover it. This transforms execution infrastructure from a cost center into a composable, value-capturing primitive.

To safeguard user privacy and prevent misuse, we've rolled out technical controls that block the generation and modification of images depicting real individuals in revealing or intimate clothing—think bikinis, underwear, and similar attire. This proactive measure reflects our commitment to responsible AI development and protecting people from potential harm or exploitation.

New directions in agent visualization technology are constantly emerging, which is indeed worth paying attention to. Especially those creative ideas that combine agent systems with urban development have great potential. If the visualization of intelligent agents can be integrated with urban planning scenarios, it will open up a whole new technological application pathway. Such cross-disciplinary innovative ideas often lead to the next generation of breakthrough applications.

What is the future direction of AI? The key lies in whether privacy protection technologies can keep up. As artificial intelligence applications explode, the risk of user data leaks is also increasing. In the Web3 space, this issue is particularly prominent—zero-knowledge proofs, secure multi-party computation, and on-chain privacy protocols are becoming standard. Imagine a scenario: users interact with decentralized applications, and AI analyzes your behavior but cannot access your true identity. This requires a deep integration of cryptographic tools and privacy infrastructure. The future AI

What do you think about the future of Web3 data infrastructure? What will it look like?
From on-chain data to cross-chain protocols, and then to the evolution of privacy computing layers—will these infrastructures become centralized like the internet, or will they truly achieve decentralization? Will a few leading protocols monopolize the space, or will a diverse ecosystem flourish?
Would love to hear everyone's thoughts💭

Recently tested NotebookLM, and the results are quite good. I first uploaded a interview video I participated in, and the system loaded it instantly. Then I used the mind map feature to organize key knowledge points, and found that the information coverage was quite comprehensive. Even more impressive, I deliberately asked a few very tricky detailed questions, and it answered accurately. Because I was very familiar with that interview content, I can confirm that this tool's ability to analyze and interpret video content is indeed impressive. The depth of AI understanding of media content is st