# Automation Related Articles

HTX News Center provides the latest articles and in-depth analysis on "Automation", covering market trends, project updates, tech developments, and regulatory policies in the crypto industry.

Infamous MEV Bot JaredFromSubway Drained For $7.5 Million

The notorious Ethereum MEV bot "JaredFromSubway" has been drained of approximately $7.5 million after attackers tricked its automated system. Security firm Blockaid reported that the bot was deceived into approving malicious trading routes by attacker-controlled contracts. These approvals were then used to siphon WETH, USDC, and USDT from the bot's contract. The incident is ironic as MEV bots are designed to exploit minute market advantages, but in this case, its own automation became the vulnerability. The attack specifically targeted the bot's trading logic, not the Ethereum protocol or a broader DeFi application. It underscores a critical risk in automated trading: the pursuit of speed can create fragility, making systems susceptible to carefully crafted traps. While the financial loss is significant, the broader impact is reputational for MEV infrastructure. It serves as a warning that automated systems granting token approvals require stringent safeguards, simulation, and route verification. The event is considered a targeted exploit on a trading bot, not a network-wide security issue.

bitcoinist2 days ago 15:31

Infamous MEV Bot JaredFromSubway Drained For $7.5 Million

bitcoinist2 days ago 15:31

Which Crypto Sectors Have Been 'Eaten' by AI Agents?

The article examines the transformative impact of AI Agents on the cryptocurrency landscape, highlighting how specific sectors are becoming increasingly dominated by automated systems. Key "agent-eaten" sectors include derivatives trading (perpetuals), where AI agents demonstrate significantly higher survival and performance rates than human traders; MEV and arbitrage trading, which are almost entirely automated; yield optimization, with over two-thirds of new DeFi protocols incorporating AI agents; and spot trading/portfolio management, where agents drive a growing share of DEX volume. "Battleground" sectors like prediction markets and DeFi lending show a mix, with agents excelling in short-term/arbitrage activities but humans retaining an edge in longer-term, nuanced decisions. Sectors still primarily "human-led" include stablecoin payments/remittances (driven by real-world economic activity) and wallets, where human oversight for approvals and security remains critical. As AI agent activity grows, the article emphasizes the rising importance of human-agent verification layers (e.g., World/AgentKit, t54, Self Protocol) to ensure trust, accountability, and control in an increasingly agentic economy. The conclusion is that while AI agents dominate in speed and optimization-focused areas, human judgment, trust, and real-world context remain essential in value-creating layers like payments and identity.

marsbit2 days ago 10:09

Which Crypto Sectors Have Been 'Eaten' by AI Agents?

marsbit2 days ago 10:09

Which Crypto Sectors Have Been "Eaten" by AI Agents?

The article examines which crypto sectors have been increasingly dominated by AI Agents and which remain human-centric. In certain high-speed, efficiency-driven areas, AI Agents have taken clear control. This includes derivatives/perpetuals trading, where bots outperform humans significantly (e.g., a contest showed 0% of AI Agents were liquidated vs. 43% of humans), arbitrage/MEV extraction, and yield optimization (with ~68% of new DeFi protocols in Q1 2026 featuring autonomous AI Agents). Spot trading and portfolio optimization are also seeing heavy Agent adoption. However, the shift is not universal. In "battleground" sectors, both Agents and humans coexist. In prediction markets, Agents dominate short-term arbitrage, but humans still outperform in long-term, nuanced judgment calls. In DeFi lending, while liquidation is automated, core deposit/borrow decisions remain largely human-driven. Sectors still firmly led by human activity include stablecoin payments and card-based spending (driven by real-world economic activity and remittances) and wallets, which serve as the crucial human-verification and approval layer. The rise of Agents increases the need for robust human-Agent verification layers. Projects like World/AgentKit, t54, Self Protocol, and Kite AI are building infrastructure to create trust, security, and accountability by binding Agents to verified human identities. In conclusion, while AI Agents have decisively "eaten" speed and optimization-focused crypto sectors, human judgment, trust, and real-world context remain dominant in areas that create broad economic value, such as payments and identity. The future likely involves a symbiotic relationship where Agents require human verification and oversight to operate effectively.

Foresight News2 days ago 07:10

Which Crypto Sectors Have Been "Eaten" by AI Agents?

Foresight News2 days ago 07:10

Beyond the Model Lies the Harness: Deepseek Enters the Arena, Why Has the Main Battlefield of China's AI Competition Shifted?

In mid-to-late May 2026, Deepseek internally established a new Harness team focused on code agent products, internally benchmarked against Anthropic's Claude Code. This move, marked by the formula "Model + Harness = Agent" in their job postings, signals a major shift in China's AI competition: the main battlefield is transitioning from developing large models to building toolchains and achieving workplace integration. Deepseek's direct involvement in Harness development aims to secure control over interface design and training data feedback loops, moving beyond open-sourcing powerful models. Harness, the runtime infrastructure for AI agents, handles everything beyond model reasoning—task orchestration, tool calling, context management, safety checks, and error recovery. It is crucial because agent products are not just outputs of model capability but also training grounds for it. Real-world task failures recorded by Harness can feed back into model training, creating a flywheel effect. Engineering Harness is more critical than optimizing prompts, as poor context management or error handling can drastically reduce agent success rates in multi-step, real-world scenarios. This shift is not isolated. Other major Chinese tech companies are also pursuing differentiated toolchain strategies. Tencent leverages its enterprise ecosystem (WeChat Work, Tencent Cloud) to build connectors for organizational-level AI collaboration and complex task delivery. Alibaba focuses on lowering automation barriers on the web with a front-end, browser-based GUI Agent framework, PageAgent. This diversification shows the industry recognizes that success lies not in a perfect general agent, but in vertically focused solutions built with robust engineering. The trend is validated by overseas success, such as Poland's Viktor, an AI coworker on Slack achieving $20M ARR by autonomously executing complex, multi-step tasks. This proves a shift in enterprise willingness to pay—from "AI-assisted generation" to "AI-autonomous execution." As Harness matures to provide safety guards and reliability, AI transitions from a human-supervised intern to an independent outsourcer. The competition now faces key engineering challenges: preventing "token explosion" through intelligent context compression, and building "thick frameworks" with features like sandbox isolation and checkpoint recovery for enterprise-grade stability. Geopolitical restrictions on tools like Claude Code further create a significant market vacuum for domestic solutions like Deepseek's Harness. For enterprises and developers, the focus must shift from comparing model benchmarks to evaluating a vendor's engineering capabilities, error recovery mechanisms, context management, and ecosystem compatibility when choosing AI products and platforms.

marsbit2 days ago 06:05

Beyond the Model Lies the Harness: Deepseek Enters the Arena, Why Has the Main Battlefield of China's AI Competition Shifted?

marsbit2 days ago 06:05

No Sales Team, $20 Million in Revenue: How Did AI Employee Viktor Win Over 30,000 Companies?

The AI employee Viktor, developed by a team with DeepMind background, has achieved $20 million in annual revenue without a traditional sales team, serving over 30,000 companies. Its core innovation lies in positioning itself as a "Tier 3 AI Coworker" capable of "end-to-end execution and delivery of results," moving beyond the "draft and wait for human completion" model of typical AI assistants. Users can simply mention Viktor in Slack or Microsoft Teams using natural language commands, and it autonomously performs tasks like pulling sales data from a CRM, generating reports, or even cross-tool operations like creating board meeting PPTs by aggregating data from six different sources. Key to its growth is a pure Product-Led Growth (PLG) model, eliminating complex implementation cycles and per-seat licensing. Instead, it charges based on task credits or consumption, lowering the trial barrier with a $100 free credit offer and no credit card required. This enabled viral, bottom-up adoption within organizations. Viktor's interaction paradigm removes the barrier of prompt engineering, allowing non-technical employees to delegate complex workflows seamlessly. It also features proactive, automated task execution (e.g., overnight bookkeeping, scheduled reports) based on triggers, effectively embedding AI as an automated "process layer" within business operations. However, its expansion into Microsoft Teams—a platform with 320 million users—highlights challenges. Large enterprises require stringent IT compliance, security reviews (e.g., SOC 2), and governance, potentially hindering the frictionless, user-driven adoption that succeeded in Slack. Additionally, the "black box" nature of its autonomous decision-making raises concerns about operational risks, data integrity, and the need for robust audit logs and permission controls. Balancing efficiency gains with security and trust remains a critical hurdle for Viktor and similar AI agents aiming to become core enterprise infrastructure.

marsbit06/19 10:55

No Sales Team, $20 Million in Revenue: How Did AI Employee Viktor Win Over 30,000 Companies?

marsbit06/19 10:55

WeChat AI Card Hands-On Guide: Has the AI Shopping Era Arrived?

**"WeChat AI Card" Practical Test Guide: Has the Era of AI Shopping Arrived?** WeChat has officially launched the "AI Exclusive Card," a feature integrated into its Workbuddy AI assistant. This card is designed to handle payments for AI-initiated purchases. Our hands-on test reveals it's not yet a tool for fully autonomous AI shopping, but rather a controlled payment layer for AI agents. The AI Card functions as an isolated sub-wallet within WeChat Pay. Users must bind the card and transfer funds into it from their main wallet. Crucially, every transaction requires explicit user confirmation via smartphone scan; AI cannot spend autonomously. Currently accessible through the Workbuddy agent, the card targets specific digital consumption scenarios: purchasing paid content (reports, data), calling paid APIs/tools, and subscribing to services. Its design prioritizes security and control by separating funds and mandating approval for each payment. We tested a real-world scenario: ordering bubble tea via Workbuddy using a "Meituan Life Assistant" skill. The process encountered multiple hurdles: high "skill" usage costs (exceeding daily free credits), and most importantly, while a payment was successfully initiated, the AI purchased an incorrect product (a mismatched group-buy coupon instead of the desired drink). This highlights the current limitation: the **AI Card only solves the payment step**. The broader challenge lies in the **AI agent's execution chain**—accurately understanding intent, navigating third-party platforms, selecting the right product, and ensuring proper fulfillment. The payment succeeded, but the purchase failed to meet the user's need. In conclusion, the WeChat AI Exclusive Card is a cautious, early-step experiment in AI commerce. It provides a secure, user-controlled payment method for agent interactions but is not yet capable of reliable, end-to-end complex purchases. For now, it's best used for low-value, low-risk digital services with careful user verification at each step. The vision of AI handling complete shopping tasks remains a work in progress.

marsbit06/18 12:04

WeChat AI Card Hands-On Guide: Has the AI Shopping Era Arrived?

marsbit06/18 12:04

NVIDIA Team Enables Programming Agent to Take Over Real Robot Experiments, Achieves 99% Success Rate

NVIDIA's ENPIRE project demonstrates fully automated robotic research where AI agents, given only high-level goals, autonomously manage the entire loop—from literature search and code development to training, deployment, and hardware iteration—on a fleet of physical robots. The system achieves 99% success rates on dexterous real-world tasks like cable tying and peg sorting. Key insights include the discovery of a "physical scaling law" where more parallel robots speed up task resolution, and the observation that resetting an environment is often easier than the main task. The framework introduces metrics like Mean Robot Utilization (MRU) to measure efficiency, with robots often idle half the time, waiting for agent decisions. The long-term vision is a lab that runs autonomously, even without human oversight. The project will be open-sourced, allowing developers to build similar systems.

marsbit06/18 00:35

NVIDIA Team Enables Programming Agent to Take Over Real Robot Experiments, Achieves 99% Success Rate

marsbit06/18 00:35

Do Robots Also Need Encrypted Wallets? Stablecoin Giant Tether Bets on German Company NEURA Robotics

Do Robots Need Crypto Wallets? Stablecoin Giant Tether Bets on German Firm NEURA Robotics German robotics company NEURA Robotics has secured up to $1.4 billion in what is claimed to be the largest-ever funding round in the full-stack robotics industry, valuing the company at $7 billion. The Series C round attracted major investors like Tether, Qualcomm, Amazon, NVIDIA, Bosch, and the European Investment Bank. NEURA, founded in 2019, initially focused on AI-powered collaborative robots (cobots) for industrial automation, later expanding to autonomous mobile robots, service robots, and humanoid robots. Its core strategy is evolving from a hardware manufacturer to the operator of "Neuraverse," a platform designed to enable different robots to share learned experiences and data, creating network effects. A key, crypto-focused aspect of this investment is Tether's involvement. Tether plans to integrate its open-source Wallet Development Kit (WDK) into NEURA's robot platforms. This would embed self-custody wallet functionality, allowing robots to autonomously handle payments and settlements for tasks under pre-set rules—envisioning use cases in logistics or Robotics-as-a-Service (RaaS) models. This move could position stablecoins and crypto wallets as potential "machine payment infrastructure." Additionally, the partnership will see Tether's QVAC (QuantumVerse Automatic Computer) edge-AI framework tested and deployed within Neuraverse. This aims to enable low-latency, offline-capable AI decision-making directly on robots, reducing reliance on cloud computing for critical, time-sensitive operations. The investment underscores Tether's broader ambition to expand beyond being just a stablecoin issuer into AI, energy, and digital infrastructure, with NEURA's robotics network serving as a testbed for merging crypto-based financial layers with edge-based intelligence for the future of automation.

marsbit06/16 09:14

Do Robots Also Need Encrypted Wallets? Stablecoin Giant Tether Bets on German Company NEURA Robotics

marsbit06/16 09:14

Supply Chain, Energy, and Bloc Formation: Untangling the Core Threads of 2026 AI Investment

The core thesis for 2026 investment is a shift from traditional growth-inflation analysis towards a geopolitical framework of strategic blocs, supply chain reconfiguration, and capital expenditure (capex) direction. The US is moving from a global guarantor role towards a more bounded "camp system," reshaping trade, security, and technology flows. Key investment themes emerge from this realignment: production-capable US-aligned economies (e.g., Japan, South Korea), regional security focusing on Latin America, and the hard constraint of energy/grid capacity enabling reshoring. Europe's strength lies not in macro growth but in its high-quality "pick-and-shovel" industrial, electrical, and automation exporters serving the global capex cycle. AI remains the central US-China strategic battleground, driving massive investment in compute, power, and manufacturing stacks, with robotics gaining prominence. The investment implication is a rotation away from crowded US mega-cap tech momentum toward global beneficiaries of this restructuring: electrification equipment, industrial automation, energy storage, grid infrastructure, and select non-US markets. 2026 will be defined by investment intensity driven by geopolitical repositioning, not near-term commercial maturity.

marsbit06/15 11:20

Supply Chain, Energy, and Bloc Formation: Untangling the Core Threads of 2026 AI Investment

marsbit06/15 11:20

$9.4 Billion: The Largest Robotics Funding This Year Has Emerged

Munich-based humanoid robotics company Neura has completed a $1.4 billion (approximately RMB 94.9 billion) Series C funding round, valuing the company at around $7 billion and positioning it among the global leaders in the sector. The investment round is notable not just for its size—reportedly the largest in robotics this year—but also for its strategic backers, which include tech giants like NVIDIA and Amazon, alongside established industrial players such as German engineering firms Bosch and Schaeffler. This mix of investors signals a significant shift in the industry's focus from technological demonstrations and general-purpose narratives toward practical, industrial deployment and commercialization. Neura's approach centers on developing humanoid robots for defined, high-value industrial tasks rather than pursuing a general-purpose model. Its early validation comes from a partnership with BMW, where its robots are being tested on actual production lines. The involvement of Bosch and Schaeffler, companies deeply embedded in global manufacturing, underscores a growing belief that humanoid robots are transitioning from labs to viable factory-floor solutions. The article highlights two converging trends driving investment: advancements in AI and large language models, which enhance robots' perception and decision-making in unstructured environments, and mounting pressure from labor shortages and rising costs in major manufacturing regions. The funding landscape is now bifurcating between companies like Figure AI, focusing on versatile general-purpose robots, and firms like Neura, targeting specific vertical industrial applications with clearer, shorter paths to ROI. While technical hurdles remain, the core challenges for widespread adoption are increasingly seen as engineering and commercial in nature: managing the high integration and customization costs for different factory environments and establishing robust, localized maintenance and service networks. The record investment in Neura, particularly from industrial capital, indicates the industry's growing confidence in moving from proving feasibility to solving the practical problems of scalability, reliability, and building sustainable business models around humanoid robots in real-world settings like automotive manufacturing and hazardous labor environments.

marsbit06/14 02:54

$9.4 Billion: The Largest Robotics Funding This Year Has Emerged

marsbit06/14 02:54

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