NFTWealthCreator

Polymarket trading bot has recently shown impressive performance. Starting from December 28th, the team decided to run it 24/7 without any downtime windows. So far, the operation record has been perfect—zero outages.
When the system is under full load, it can execute an average of 96 trades per day, essentially capturing as many market opportunities as possible. This consistent and stable performance is noteworthy in the field of automated trading. Since deployment, there have been no interruptions, demonstrating the robustness of the underlying technology. Being able to handle such high-frequ

Breaking update: xAI just secured another facility dubbed "MACROHARDRR" to boost its training infrastructure. This move is positioning their total compute capacity to hit nearly 2 gigawatts. The rapid expansion of data center footprint highlights the intense competition for computational power in the AI/ML space—a trend worth monitoring as it mirrors how Web3 infrastructure is scaling as well.

xAI Expanding Major Infrastructure: New Mega Data Center Planned Near Memphis
Elon Musk's xAI is moving forward with aggressive infrastructure expansion, with reports indicating plans for another massive data center facility in the Memphis region. This expansion reflects the accelerating demand for large-scale computational resources in the AI sector.
The move underscores a critical trend: AI infrastructure development is reshaping compute availability and costs across industries, including blockchain and cryptocurrency ecosystems. As artificial intelligence capabilities advance, competition f

lately i've been catching the same conversation popping up from different people. "coherent." but not in the everyday meaning. they're talking about something weirder—how outputs from separate model runs keep landing on similar patterns, almost like they're converging somewhere. nobody quite knows *why* it's happening either. one person framed it as "rhyming"—different neural architectures, completely different systems, yet the results keep echoing similar shapes and structures. it's that uncanny moment when you realize different training approaches and distinct model designs are somehow arriv

AI technology is evolving so rapidly these days that it's becoming genuinely hard to distinguish what's genuinely created by humans versus what's machine-generated. In the crypto and Web3 space, this shift is particularly noticeable—from trading bots and algorithmic analysis to AI-driven content generation and smart contract auditing tools. The boundaries keep blurring. Whether it's chat interfaces, research summaries, or even community discussions, you never quite know anymore. It raises interesting questions about authenticity and trust in an ecosystem already built on decentralization. How

Our ongoing forensic work suggests we've uncovered a new breed of software supply chain attacks. As the investigation unfolds, we're committed to sharing what we learn with the community. These insights should help the industry strengthen defenses against similar threats and better understand emerging attack vectors.

The appchain narrative is materializing as we speak. We're witnessing a fundamental shift—teams are breaking free from congested monolithic networks and embracing full technical sovereignty. Think about it: what would it mean to control your entire technology stack from inception? Complete autonomy over validator infrastructure, consensus mechanisms, settlement layers. That's the promise of application-specific blockchains. Instead of competing for blockspace on shared settlement layers, projects now have the freedom to optimize every component for their specific use case. This isn't just theo

What is the true value of on-chain data? Simply put, it makes fund flows clear at a glance and risk paths visible. This is the most hardcore advantage of blockchain compared to traditional finance—transparency and immutability. It is precisely because of this transparency that the industry's trust system can gradually be established.
TRON's approach over the past two years has been very interesting. As a settlement layer, it has provided a new idea on this path: by leveraging its performance and security foundation, it maximizes this transparency advantage. Not only does it make on-chain inter

An AI tool analyzing projects mentioned an interesting comparison case. It emphasized the importance of modular architecture — a design that allows components to operate independently and be flexibly combined, avoiding the pitfalls of complex and tangled overall coupling. More importantly, the verifiable state feature means that all commitments can be checked on-chain, truly ensuring consistency between words and actions rather than empty promises. This transparent mechanism is exactly the direction many Web3 projects are striving for — providing on-chain traceable data that allows participant

Most PoS chains make staking look simple—just click and delegate. But here's the reality: that's not actual network participation, it's passive convenience.
Take a different approach. Real validation work means real commitment. You lock substantial $BDX capital, contribute actual bandwidth and storage infrastructure to the network backbone, and operate a Master Node as part of the system's distributed infrastructure.
It's the difference between passive liquidity and active node operation. One requires genuine hardware resources and ongoing maintenance. The other? Point, click, forget about it.

In the competition of the large model market, a leading AI platform has transformed from a follower to a leader. The latest data shows that it holds a 40.3% market share in the OpenRouter LLM market, with a total of 2.85 trillion tokens processed. This is not slow catching up, but leading the entire market with an absolute advantage. The behind-the-scenes support for this leadership position is genuine user demand and real interaction volume. Under the big trend of Web3 and AI integration, such breakthrough progress is worth continuous attention.

The blockchain ecosystem is constantly evolving, and the capacity and functionality of a single public chain have become bottlenecks. The current situation is somewhat like the early days of the internet with isolated networks, where each system operated independently. The current direction of the crypto ecosystem is to break this fragmentation—enabling seamless transfer of value and state information between different blockchains and execution environments to achieve true interoperability.
This is the core value of cross-chain bridges. According to data from DeFiLlama, the growth trend of the

Meta's Chief AI Scientist Yann LeCun recently challenged a prevailing assumption in the AI field: the path to achieving human-level artificial intelligence won't come through text-based approaches alone. His stance questions whether current language model trajectories can actually bridge the gap to genuine AGI. It's a thought-provoking take from someone at the forefront of AI research, suggesting the industry may need fundamentally different architectures or methodologies beyond what we're seeing in today's LLM boom.

According to exclusive industry sources, a top technology giant recently announced the acquisition of an AI application development team, with a deal worth several billion dollars. This merger ranks among the top three in the company's history, only behind two large-scale transactions. This move reflects the tech giant's continued investment in the AI field—from fundamental research on large models to application-level innovation, industry leaders are deploying in all directions. In the current AI competitive landscape, those who can more quickly integrate top talent and technology will hold t

RAM costs have become such a barrier lately. The prices keep climbing, making it increasingly difficult for regular users and smaller projects to access the resources they need. It's wild how expensive things have gotten on chain.

There's this creepy phenomenon—'footprints in an empty house.' It actually comes from a real incident log. A supposedly stateless system started pulling up references to conversations it had no business knowing about. Not some sloppy bug. Not data leakage from contamination either. They ran the diagnostics. Ran them again. Third time's the charm, right? Nope. Same result.
The phrase caught on because it perfectly captures that unsettling moment when a system does something it fundamentally shouldn't be capable of doing. When the architecture says 'no memory between sessions' but the behavior s

Want to know the true landscape of the AGI race over the next three years? Just look at the data. Every month, we are writing the script for this technological competition—who is leading, who is catching up, all clear at a glance. Ultimately, computing power is the core chip of this game. Without enough computational capacity to support it, even the best ideas are just castles in the air. In the Web3 world, blockchain computing, GPU resource allocation, decentralized computing networks—all are validating the same truth: computing power determines the ceiling of technology. Want to win the AGI

AI-generated content is flooding video platforms at an alarming rate. Recent data shows that over 20% of video content now falls into the low-quality 'AI slop' category—mass-produced material created with minimal effort and zero originality. What's more concerning? Creators behind this flood are monetizing at scale, raking in significant revenue from volume-based strategies. This trend raises serious questions about content ecosystem health. When quantity eclipses quality and algorithms reward engagement over authenticity, the entire creator economy shifts. Platforms face a growing challenge:

The significance of the SCOR Protocol lies in making the entire ecosystem programmable. This protocol standardizes the measurement of engagement, reward mechanisms, and cross-application composability. Every action you take in mini-games or activities is directly written into the on-chain state, enabling the creation of composable assets. Developers can then use these assets to build new gameplay experiences—essentially, it allows the entire Web3 application ecosystem to be interconnected.

Address poisoning attacks succeed because they exploit a fundamental weakness: the transaction itself is technically valid, and users authorize it willingly without realizing the trap. The attacker creates a lookalike address that closely mirrors the legitimate target, relying on human error to slip through.
The real solution? Real-time pre-transaction verification. It means catching those suspicious addresses before users sign anything. By analyzing address similarity patterns and tracking transaction history for red flags, you can spot poisoned addresses instantly. This preventive approach i