TokenTreasury_

There's a compelling way to think about AI project trajectories: the AI Peter Principle. Basically, every AI-driven initiative accelerates straight toward the ceiling of what its underlying model can actually do. Once you hit that competence boundary, the project flatlines—no amount of engineering can push past the model's inherent limitations. It's a harsh reality check for teams banking on pure acceleration.

Don't wait until the next malicious attack occurs to realize the importance of auditing your security system. Now is the time to act and strengthen your supply chain defenses.
Start with these straightforward steps: adopt anti-phishing multi-factor authentication methods (Passkeys/WebAuthn are much more reliable than traditional SMS), regularly refresh and reconfigure your API token permissions, and review the access permissions of third-party applications.
It may sound like basic security hygiene, but these fundamental measures are truly capable of blocking most common attacks.

Reinforcement learning used to be genuinely tough—evaluating agent actions, determining proper rewards and penalties, attributing outcomes to specific components. It was messy.
That's shifted dramatically. Large language models now handle the heavy lifting on evaluation tasks. With LLMs managing assessment and feedback loops, what once required painstaking manual design became algorithmically feasible. The bottleneck broke open.

Building privacy-first applications on Solana just got more practical. Zero-knowledge proofs are opening new possibilities for developers who need strong privacy guarantees without sacrificing performance. Noir Language makes writing ZK circuits more accessible, while Sunspot provides the infrastructure to bring these privacy primitives into production. The combination could reshape how we think about confidential transactions and private state management on high-speed blockchains.

A major exchange recently launched new trading pairs, including TRY currency support and Chinese naming support, along with corresponding updates on the API side. From a functional perspective, this kind of expansion is worth肯定, but interestingly—there seem to be some issues with the configuration logic. Is naming convention priority or currency support priority? Has this design approach been sufficiently tested and validated with user feedback? If adjustments are truly necessary, the official should either optimize the existing logic or replace it with a more合理方案. Every update on the exchange

A major automaker is pushing the boundaries of in-vehicle AI by embedding advanced language models into their intelligent personal assistant. What's interesting here is the capability set: multi-turn conversations that actually understand context, handling multiple questions in sequence, and direct vehicle control through natural language—think asking the car about traffic and having it reroute automatically.
This integration marks a shift in how automotive interfaces evolve. The tech debuts in their latest flagship model at CES 2026, with mass rollout expected in the second half of 2026 acros

Data centers consume enormous amounts of energy—it's one of the biggest environmental headaches facing the industry. With blockchain networks and Web3 infrastructure expanding rapidly, the energy footprint keeps growing. But what if we're thinking about this wrong?
Space-based data centers sound like science fiction, but they're worth serious consideration. The vacuum of space eliminates cooling needs entirely. No air conditioning, no water waste, just the raw computational power running in optimal conditions. Solar power becomes abundant without atmospheric interference.
That said, the realit

EIP-7503 and the WORM protocol are worth paying attention to 🔐
Imagine a scenario: your ETH can be transferred to a brand new wallet address, and the entire process leaves no trace. This is the functionality that WORM (EIP-7503) aims to achieve.
In simple terms, it opens a hidden channel for crypto assets. You send ETH from your old wallet to a new address — it looks like a normal on-chain transaction, but in reality, the true source of this transfer is completely concealed.
What does this mean? It means your fund flow becomes a black box. No one can trace where your ETH comes from or where i

Speed isn't everything when it comes to Move chains winning the race.
Look at the numbers: parallel EVM L1s like Monad are hitting 400ms blocktimes, while Sei comes in around 520ms. Impressive? Sure. But here's the thing—they're still playing catch-up to what Move-based networks are pulling off.
Aptos is running at roughly 50ms blocktime (and targeting 10ms), while Sui sits around 80ms. That's not just faster on paper. It fundamentally changes how applications can be built and how users experience the network.
The gap is real, and it's technical. But the question isn't whether speed alone dete

When you're confined to a single blockchain, what you think is ownership starts feeling like a cage. Sure, your assets are there—but can you actually use them? Everything depends on that one chain's liquidity pool, available tools, and the rules someone else decided.
The real bottleneck isn't your assets existing. It's what you can do with them. Want to access better opportunities elsewhere? Tough luck if they're on another network. That's the problem when ecosystems stay siloed.
Cross-chain bridges flip the script entirely. Imagine moving your value freely between different blockchains withou

Imagine if our performance data collection was no longer limited to keyboard and mouse operation logs, but could directly capture brain activity signals? This idea sounds a bit crazy, but at the recent Tech Summit, a company focused on brain-computer interfaces proposed such a bold concept. Their core idea is clear—using neural interface technology to track users' cognitive states and thought activity patterns in real time. This could have a profound impact on Web3 applications, game experience optimization, and even work efficiency assessment. Traditional performance monitoring is long overdu

The key insight has arrived
Slow L1 = limited developers, Fast L1 = new applications emerging endlessly
The reason why CodeXero_xyz can be established is precisely because Sei provides high-performance infrastructure. That’s the difference.
Don’t be fooled by flashy promotions. What truly matters is the invisible underlying infrastructure, not the surface appearance.
Experienced builders have long understood this principle. Now you know too.

The pain points of cross-chain transfers have long troubled crypto users—high fees, slow confirmations, and always disappointing experiences. After entering 2026, a major update to Injective Bridge has completely changed this situation. By deeply integrating the two major protocols Wormhole and IBC, Injective has created a seamless asset bridging solution, providing users with an almost frictionless cross-chain experience. This upgrade is not just about performance enhancement—ERC-20 assets can now flow more flexibly, and liquidity originally scattered across various blockchains has been effec

Rumble Wallet is built on the WDK architecture, providing users with a solid technical foundation. Even more exciting is that the project team is accelerating the optimization of Lightning Network integration, which will significantly improve transaction speed and network efficiency. ⚡

The latest lightweight hard fork BPO2 on Ethereum has been officially activated. This upgrade makes key adjustments to the blob parameters—target blob count increased from 10 to 14, and maximum blob count expanded from 15 to 21.
What does this mean? The amount of L2 transaction data submissions that a single block can handle has significantly increased, directly alleviating the pressure on data availability space in high-frequency trading scenarios on Layer 2.
With the accumulation of such upgrades, high-performance L2 networks capable of thousands of TPS are no longer just theoretical but are

Many believe that adding friction to blockchain interactions equals better security. Wrong. Complex verification steps don't protect users—they just breed mistakes and frustration.
What if there's another way? Cryptographic protection built into every action. AI working behind the scenes to streamline each step. This is how next-generation platforms rethink the security-usability trade-off. Not hoops. Not compromises. Just intelligent design where safety and simplicity coexist.

Although there is a learning curve initially with L2 migration, it is quite cost-effective in the long run. Users need some time to adapt to the new interaction methods, which is normal—no technological upgrade is without it. The real benefits come later: transaction costs are significantly reduced, confirmation speeds are noticeably improved, and the overall experience is upgraded. Weighing the short-term adaptation period against the long-term convenience, it is still worth migrating.

From retail giants supporting digital payment solutions to large financial institutions submitting applications for SOL and BTC spot ETFs, traditional capital is accelerating its deployment into the cryptocurrency asset space. Meanwhile, the BNB Chain ecosystem is also experiencing significant technological progress—opBNB Fourier hard fork has officially gone live, with block time reduced from the original 2 seconds to 250ms, gas fees further decreased, and network performance achieving a qualitative leap.
Currently, as BTC and ETH undergo continuous consolidation at high levels, on-chain user

Using Cursor and Gemini to figure things out in 2 hours, I want to share with everyone.
First, understand the Openmind OM1 documentation. Write a Python script to thoroughly digest it, then generate a compatible API interface. This step isn't that complicated, just reading the documentation and parsing the logic.
The key is the robot architecture part. To be honest, I was initially confused about this, but Cursor directly provided the complete framework code, saving a lot of trouble. Once the code is generated, the next step is to go to the Dashboard for configuration and testing.
Using the ri

In the AI boom, everyone is stacking models, but no one is seriously building a verifiable settlement layer—until now.
Inference Labs is paving the way for AGI. Verifiability, privacy protection, and fair mechanisms are integrated into the design architecture. The core idea is clear: a trustless foundation is needed so that AI models and related participants can operate securely on top of it. This is not just a technical issue, but a trust issue. As AI systems become increasingly complex, who ensures the authenticity and reliability of the computation results? Who guarantees that the process i