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Vietnamese police busted a group suspected of trying to establish a large-scale online scam center in the country, authorities said, as criminal networks spread their operations across Southeast Asia

很多人第一次看到 HootArk，会以为只是一个 Web3 浏览器。 但更准确地说，HootArk 是一款 Agentic Web3 Browser & Wallet 🦉 HootArk把移动浏览器、内置多链钱包、dApp 入口和 AI Copilot 放在同一个 App 里，让你可以从“看到信息”直接走到“完成链上操作”。 你可以像平时上网一样打开网页、看资讯、查项目，也可以直接进入 Web3 场景：访问 DeFi、NFT marketplaces、DAO tools 等 dApps，连接钱包，查看资产，发起转账或 Swap。不需要额外插件，也不用在浏览器和钱包之间来回切换。 HootArk内置了钱包模块。在创建或导入钱包后，可以直接无缝接入Ethereum、BSC、Polygon 等主流网络。对刚进入 Crypto 的用户来说是一个更简单的入口；对 DeFi 用户、NFT 玩家和 Web3 老用户来说，它也能让移动端操作更顺手 ⚡ AI 也是 HootArk 很重要的一部分，但它不是一个单独摆在旁边的聊天框。 你可以边浏览边提问，让 AI 帮你理解项目、网页和链上信息； 看到新闻、公告或长文章，也可以直接让它总结重点； 在交易或交互前，帮你核查事实信息； 它更像一个随时跟着你的 Web3 copilot，帮你少查几次资料，少切几个页面 🤖 安全和隐私方面，HootArk 始终坚持安全第一：私钥保存在本地设备上，平台不收集数据，不追踪用户。你的钱包、你的身份、你的数据，都应该由你自己控制 🔐 【重点来啦】 我们即将发布 HootArk Lite 版 🚀 期待更轻、更快、更智能的Web3 AI 移动浏览器🎉 欢迎持续关注HootArk A lot of people see HootArk for the first time and think it’s just a Web3 browser. But more accurately, HootArk is an Agentic Web3 Browser & Wallet 🦉 HootArk brings a mobile browser, a built-in multi-chain wallet, a dApp gateway, and an AI Copilot into one app, so you can move naturally from “finding information” to “taking on-chain action.” You can use it like a regular browser to open websites, read news, and check projects. But you can also jump straight into Web3: access DeFi, NFT marketplaces, DAO tools, and other dApps, connect your wallet, view your assets, and make transfers or swaps. No extra plugins needed. No constant switching between your browser and wallet. HootArk also comes with a built-in wallet module. After creating or importing a wallet, you can seamlessly connect to major networks like Ethereum, BSC, Polygon, and more. For people new to crypto, it’s a simpler entry point. For DeFi users, NFT collectors, and Web3 natives, it makes mobile on-chain actions much smoother ⚡ AI is also an important part of HootArk, but it’s not just a chatbot sitting on the side. You can ask questions while browsing and let AI help you understand projects, web pages, and on-chain information. When you see news, announcements, or long articles, AI can help summarize the key points. Before a transaction or interaction, it can also help you check relevant facts and context. It feels more like a Web3 copilot that stays with you, helping you search less, switch tabs less, and understand things faster 🤖 When it comes to security and privacy, HootArk always puts security first: private keys stay on your local device, the platform does not collect data, and users are not tracked. Your wallet, your identity, and your data should always be controlled by you 🔐 And here’s the big update: We’ll soon be releasing HootArk Lite 🚀 Stay turned! Browser + Wallet + dApp Gateway + AI Copilot. This is HootArk 🦉✨ #HootArk# #Web3# #Web3Browser# #Wallet# #dApps# #AI#

Elon is spot on. Tunnels + Robotaxis = the real fix. No more 2D gridlock. Turn cities into 3D networks that move people faster, cheaper, and safer than subways or buses ever could. The future is underground autonomy.

Litecoin should not still be here. It launched in 2011, watched thousands of flashier coins promise to replace it, and quietly buried almost all of them. The survivor everyone wrote off is about to do the one thing nobody expected. In this conversation, Roc and Aztec of LitVM walk through a major expansion that finally brings smart contracts to one of crypto's oldest names. Their argument is that the recent downturns, which they attribute to predatory shorting, are masking a quieter trend, with capital steadily rotating back into proven legacy assets that have already weathered more than a decade of cycles. The project itself is a purpose-built layer-two (L2) modular stack that brings Ethereum Virtual Machine compatibility natively to Litecoin. They point out that while the major chains support dozens of layer twos, LitVM is the only one building on Litecoin, using zero-knowledge cryptography to let users launch smart contracts and real-world assets trustlessly. Here is the part that should make you pay attention. With no airdrop incentives dangled in front of anyone, the testnet has already crossed 40 million transactions and 3 million unique active wallets, which means people are showing up for the technology rather than a payout. Roc and Aztec explain what that signals, and why they believe the oldest survivor in crypto might be the one writing its most important chapter yet... @LitecoinVM Disclaimer: This content was produced in collaboration with the other party and is intended for informational purposes only. It does not constitute financial or investment advice. Always conduct your own research before making any decisions. 00:00 Mario Nawfal, Roc, and Aztec break down the current market cycle and the reasons capital is returning to legacy chains. 04:40 The founders address crypto's narrative crisis and why sovereign networks are essential against financial weaponization. 13:51 The discussion shifts to LitVM and how it delivers smart contract and EVM functionality directly to the Litecoin network. 23:14 The team elaborates on LitVM's position as Litecoin's exclusive layer two and their community-driven distribution strategy. 35:48 The founders reveal strong organic testnet numbers before Mario closes out the session.

What users can do with BridgeKitty: BridgeKitty makes cross-chain crypto swaps simple. Users can swap $BTC assets and stablecoins across 11+ networks including Base, Ethereum, BNB Chain, Monad, BOB and more all from one interface. With BridgeKitty, users can: 
• Swap assets across chains in seconds 
• Compare multiple bridge providers instantly 
• Get the best available routes with lower fees 
• Bridge BTC assets & stablecoins seamlessly 
• Manually choose preferred providers and fee options 
• Use supported wallets directly from the browser 
• Track confirmations easily after every swap. Users will face no complicated bridging process, no stress. Just connect your wallet, choose your chains, approve the transaction, and BridgeKitty handles the routing for you. Perfect for traders, DeFi users, BTCFi explorers, and cross-chain users looking for speed and simplicity. If you haven’t tried it yet, now’s the time to explore seamless cross-chain swaps with BridgeKitty. built by @PersistenceOne.

Elon Musk is building something no platform in history has actually attempted. A system that judges ideas without knowing who wrote them. Musk: “It should be possible for somebody to post content as a new user with no followers, and if that content is excellent, it gets seen by a lot of people.” Every platform before this ran on a single hidden variable. Identity. Not quality. Not originality. Not depth. Identity. Who you were determined what got seen. The architecture didn’t surface the best thinking. It surfaced the most established thinker. It chose pedigree over precision. Every single time. Musk is the first person with the infrastructure, the capital, and the sheer indifference to consensus required to strip that variable out. Grok reads everything. Every post from every account. Zero followers or ten million. No weighting for legacy. No deference to tenure. It measures one thing. Intrinsic excellence. The printing press created publishers. Radio created networks. Television created anchors. Social media created influencers. Every technology of liberation produced a new gatekeeper within one generation. Musk is betting AI is the first tool that can’t be captured. An algorithm with no concept of identity has no incumbency to protect. It just reads. And it surfaces what’s best. If that works, it doesn’t just change a platform. It exposes something about every system that came before it. Every trending page, every algorithm, every feed that claimed to surface quality was never measuring quality. It was measuring proximity to power and calling it merit. We never had meritocracy. We had hierarchy with better marketing. Musk is building the first real one. And the question it forces isn’t whether you can compete. It’s whether your work was ever actually good, or you were just early. Everyone wants meritocracy. Almost nobody has ever lived in one.

What happens when Cuban regime networks, Palestinian militant organizations, and U.S.-based activist groups intersect? My latest @WashTimes piece examines the PFLP's presence in Cuba, the role of Havana in regional influence operations, and links to a California nonprofit.

One of the new, buzzy jobs in Silicon Valley is the AI Forward Deployed Engineer (FDE), an engineer who is embedded within a client organization to help customize solutions, such as building and tuning agentic workflows that suit the client’s particular needs. I’ve heard from people who are wondering anew about the FDE career path since OpenAI and Anthropic started building new teams to place FDEs within client organizations. The rise of FDEs for AI workloads is one way AI is creating new jobs (and why the jobpolcalypse narrative of upcoming job market collapse is false -- there will be many AI and non-AI jobs). However, I believe there will be far more AI Engineer jobs than FDEs, as I explain below. The FDE role was pioneered about two decades ago by Palantir, which sent engineers to government locations to work on secure, air-gapped networks. In addition to having good technical skills, FDEs need communication skills and sometimes business skills. For example, they may need to speak with clients to understand their needs, formulate a strategy to prioritize projects, explain complex technology, and respectfully push back if a client asks for something unrealistic. They’re enjoying a resurgence because of the amount of work involved in taking an off-the-shelf LLM and building it into a custom agentic workflow that fits particular business needs. However, I believe the number of AI Engineer jobs will be far larger. A company might accept a few FDEs to be embedded within its organization. But most companies will want far more of their own employees working on their projects. While my organizations do hire FDEs, we hire far more AI Engineers! Also, a common client concern is that it is hard to find vendor-neutral FDEs — they are, after all, there to deeply integrate a particular vendor’s product into a company. In this moment when it’s hard to predict which AI service will be the best one in a year’s time, optionality (the ability to pick whatever vendor turns out to fit best in the future) is very valuable. In contrast, letting FDEs tightly bind a company’s processes significantly reduces optionality. Right now, I see surging demand for AI Engineers who can build software applications using AI software components (like LLM prompting, agentic frameworks, evals, etc.) and effectively use AI coding agents (like Claude Code, Codex, Antigravity CLI, and OpenCode). As the AI Engineer role matures, I expect it to fragment into more specialized roles, like the generic Software Engineer role from decades ago fragmented into frontend, backend, mobile, data engineering, devops, and so on. What will be the future, specialized AI engineering roles? I don’t know. Perhaps there will be AI FDEs, LLMOps Engineers, Evals Engineers, AI Data Engineers, Harness Engineers, and other roles we don’t have names for yet. But for now, I see a lot of AI engineers who are generalists create a lot of value. Skilled AI Engineers are in very high demand! As our field continues to mature over the coming decade, I look forward to new specializations within AI Engineering that create even more job opportunities. [Original text: The Batch newsletter]

The field of artificial intelligence was officially born at the 1956 Dartmouth workshop, where John McCarthy coined the term “artificial intelligence.” Key founders include McCarthy, Marvin Minsky, Allen Newell, and Herbert Simon, who presented the first AI program, the Logic Theorist. Alan Turing laid the theoretical groundwork earlier with his 1950 paper and the Turing Test, asking if machines could think. So it’s more a group effort than one inventor. The original founders didn’t complete it at all. They set up the field and built early programs that solved math problems or played checkers, but the tech hit big limits. There were two “AI winters” where funding dried up because results didn’t match the hype. What we use today, like ChatGPT, comes from deep learning and neural networks that really took off around 2012 with AlexNet. That work was led by Geoffrey Hinton, Yann LeCun, and Yoshua Bengio, decades later. The Dartmouth group laid the vision, but modern AI is a completely different approach built on massive data and computing power they couldn’t dream of.