# 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.

OpenClaw and Cursor Just Invaded Phones! Agents Are Now in Your Pocket

AI Agents have officially arrived on mobile. In a landmark move, both OpenClaw and Cursor launched native mobile apps on the same day, fundamentally shifting how AI assistants are accessed and controlled. OpenClaw has released full-featured native apps for iOS and Android. Its "local-first" architecture, developed by the OpenClaw Foundation, keeps user data private by running the agent on a user's private Gateway. The mobile app now allows seamless remote control and approval of the agent's actions directly from a smartphone, with access to device capabilities like the camera, GPS, and contacts. Simultaneously, Cursor, the AI-powered coding tool, launched a public beta of its native iOS app. It enables developers to start and manage cloud-based AI coding agents from their phones. These agents can work asynchronously for extended periods—debugging, writing code, and creating pull requests—while developers are away from their computers. The app sends notifications for key decisions, allowing users to review and merge PRs from anywhere. Together, these releases signal a major shift: AI agents are no longer confined to desktop browsers or terminals. They are becoming persistent, autonomous assistants that work independently in the cloud, with humans transitioning from constant operators to mobile supervisors who approve key steps. The era of pocket-sized, on-demand AI is now here.

marsbit06/30 02:30

OpenClaw and Cursor Just Invaded Phones! Agents Are Now in Your Pocket

marsbit06/30 02:30

Lao Huang: Prompt is Dead, the Entire AI Community is Frenziedly Chasing Loops

The article "Prompt is Dead: The AI Industry is Obsessively Chasing Loops" discusses a major shift in AI development, where "Loop Engineering" is replacing traditional prompt engineering. Industry leaders like NVIDIA's Jensen Huang, Andrew Ng, and engineers from Anthropic and OpenAI argue that manually crafting prompts is becoming obsolete. Instead, the new focus is on designing autonomous, self-improving AI systems (loops) that can operate 24/7. A loop system typically involves five key phases: Discovery (finding tasks), Handoff (assigning to agents), Validation (critical independent review), Persistence (saving progress), and Scheduling (automated operation). The core idea is to move humans from being the operational "engine" to being the system "architects" who design the loop, define goals, and set up verification mechanisms. A major challenge and necessity is implementing robust, independent validation to prevent AI from uncritically approving its own work. The trend is seen as part of a move towards "inference-time compute," where allocating computational budget effectively becomes a key engineering skill. While loops can produce higher-quality outputs, they are more expensive and time-consuming than simple prompting. The article warns of risks like "verification debt," "comprehension corrosion," and "cognitive surrender," where engineers might stop understanding the code their systems generate. Ultimately, the article concludes that in an era of automated loops, human judgment and oversight remain the most critical and scarce resources.

marsbit06/29 08:37

Lao Huang: Prompt is Dead, the Entire AI Community is Frenziedly Chasing Loops

marsbit06/29 08:37

A Trillion-Dollar Entry Point for Pension Funds? Franklin's Bitcoin Dividend Reinvestment ETFs Come with a Built-In Selling Pressure Ceiling

Franklin Templeton filed for two ETFs on June 18 that embed a "default option" logic into Bitcoin investing. These funds—the Franklin US Equity Bitcoin Dividend Reinvestment Index ETF and the Franklin US Innovative Equity Bitcoin Dividend Reinvestment Index ETF—aim to automatically allocate a portion of investor dividends to Bitcoin, initially with a 95% stock and 5% Bitcoin allocation. The mechanism is designed for financial advisors, not retail investors. By packaging Bitcoin exposure within a standard equity fund label, advisors can bypass internal compliance restrictions against direct cryptocurrency allocation for their clients. Dividends from the stock holdings are automatically used to buy Bitcoin via spot ETFs, futures, or options. However, the structure imposes strict rebalancing rules: if Bitcoin's allocation exceeds 5%, it is trimmed back to 4.5% quarterly, with a hard cap of 20%. This means the fund becomes a systematic seller during Bitcoin price rallies. Realistically, the potential buying pressure is minimal. Based on dividend yields (approximately 1.05% for broad market, 0.52% for innovative equity), the annual inflow into Bitcoin would be a tiny fraction of the fund's assets. For comparison, Franklin's existing Bitcoin ETF ($359 million AUM) would generate only about $3.6 million in annual Bitcoin purchases—negligible against Bitcoin's daily trading volume. The innovative equity fund, heavily weighted in low-dividend stocks like Nvidia, would have even weaker buying power. The product utilizes an offshore Cayman subsidiary to hold Bitcoin, a common compliance tactic for commodity exposure in mutual funds. A key drawback for investors is the tax liability: they must pay taxes on dividends that are automatically converted into Bitcoin, requiring out-of-pocket cash for a gain they never directly receive. For the strategy to scale significantly, such funds would need to become a default or near-default option in retirement plans like 401(k)s. Recent regulatory moves, including a Trump executive order and a Department of Labor proposal offering fiduciary safe harbors for including crypto assets, could pave the way. However, widespread employer adoption likely awaits further legal clarity. Ultimately, the fund's model leverages investor inertia and automated systems, rather than convincing anyone to actively choose Bitcoin. While it creates a new, albeit small, structural buyer, its rebalancing rules also establish a built-in "selling ceiling" that could dampen price upside if similar products proliferate.

Foresight News06/26 02:48

A Trillion-Dollar Entry Point for Pension Funds? Franklin's Bitcoin Dividend Reinvestment ETFs Come with a Built-In Selling Pressure Ceiling

Foresight News06/26 02:48

From Signal Monitoring to Strategy Copy Trading: How PPP Lowers the Barrier to Trading on Polymarket?

From Signal Monitoring to Strategy Copy Trading: How PPP Lowers the Barrier to Polymarket Trading The surge in trading demand on prediction markets like Polymarket, especially during events like the World Cup, exposes a common challenge for novice users: emotional and impulsive trading due to a lack of stable strategies and reliable signals. Prediction Position Platform (PPP) addresses this by serving as a Telegram-based tool for strategy discovery and automated copy-trading on Polymarket. PPP offers a suite of features through a subscription model. Key functionalities include 24/7 market signal monitoring (tracking smart money movements and rapid probability shifts), an "AI Address Analysis" tool to evaluate trader performance metrics, and specialized sections like a "World Cup Zone" for quick access to related markets. Its core value lies in two curated lists: the "Strategy Square," which identifies addresses suitable for long-term tracking based on comprehensive metrics like returns, win rate, and drawdowns, and the "Trading Leaderboard," highlighting recently outperforming addresses for short-term opportunities. Users can manually analyze any address or set up automated copy-trading with customizable parameters like investment amount and stop-loss. After initiating copy-trades, users can manage all positions from a unified dashboard, adjusting parameters or stopping follows as needed, and review historical performance data. Crucially, PPP employs a non-custodial wallet model, meaning user funds remain in their own self-custodied wallets, enhancing security and trust. In summary, PPP aims to reduce the learning curve and trial-and-error cost for Polymarket users by aggregating signals, curating and analyzing profitable traders, and facilitating automated, yet manageable, copy-trading execution.

Odaily星球日报06/26 02:01

From Signal Monitoring to Strategy Copy Trading: How PPP Lowers the Barrier to Trading on Polymarket?

Odaily星球日报06/26 02:01

Jensen Huang: Prompts are Becoming Obsolete, Loops are the New Paradigm

Jensen Huang, alongside AI leaders like Peter Norvig, Boris Cherny, and Andrew Ng, is advocating for a shift from "prompt engineering" to "loop engineering" as the new paradigm for AI development. Instead of manually crafting individual prompts, the focus is now on designing autonomous loops—systems where AI agents execute tasks, self-validate results, and iterate until completion without constant human oversight. A loop is a management framework that enables agents to operate independently. Key implementations are seen in Claude Code (with features like /loop, /goal, and /schedule) and OpenAI Codex, which employ multiple agents working in parallel within isolated environments. A core principle is the separation of roles: one agent (or model) performs the task, while an independent agent (or a smaller, separate model) validates the output to ensure objectivity. The article outlines a practical roadmap for implementing loops, starting with a "four-condition test" to assess suitability, building a minimal viable loop, and emphasizing critical pitfalls to avoid, such as lacking hard stop conditions or allowing loops to handle tasks requiring human judgment. This evolution is framed as the fourth major shift in AI interaction: from Prompt Engineering (crafting instructions) to Context Engineering (providing background information), then to Harness Engineering (building tool-enabled environments), and finally to Loop Engineering (creating self-sustaining systems). This progression reflects a consistent trend of increasing abstraction, moving human involvement from direct instruction to system design and rule-setting. The concept has academic roots in frameworks like ReAct, which formalized the "reason-act-observe" cycle. While loop engineering promises greater automation, experts caution about managing token costs and warn against outsourcing understanding—AI can assist, but deep problem comprehension remains essential.

marsbit06/25 14:26

Jensen Huang: Prompts are Becoming Obsolete, Loops are the New Paradigm

marsbit06/25 14:26

Former SpaceX Engineer Reconstructs Financial Execution System Using First Principles

Former SpaceX engineer Lex Li applies "First Principles Thinking" to financial infrastructure with Plan Execution Lab, recently raising angel funding at a $50M post-money valuation. The team argues that the core function of finance is capital allocation, and the critical gap is not in trading but in execution, which remains highly manual and fragmented. While assets, liquidity, and settlement have migrated on-chain, execution workflows (monitoring, risk management, liquidity coordination) are still human-native. In an era of accelerating AI agents, strategy decay is rapid, shifting the competitive edge from having the best strategy to having the most robust execution network. Plan Execution Lab introduces two core components: 1. **PlanX**: A Financial Execution Protocol designed as infrastructure for the migration from CEX to DEX, providing on-chain execution capabilities, liquidity access, risk management, and capital orchestration. 2. **Xgent**: An Autonomous Financial Runtime. Users define investment intents, risk preferences, and constraints; Xgent automatically constructs an execution graph, verifies it, and handles ongoing execution and optimization—streamlining the process from Intent to Autonomous Execution. The long-term vision is to create the "Bloomberg Terminal for Autonomous Finance"—a shared operating environment and execution network built collectively by participants like execution nodes, liquidity providers, and autonomous agents. The future of finance, they contend, belongs not to isolated algorithms but to open, collaborative execution networks.

marsbit06/25 09:06

Former SpaceX Engineer Reconstructs Financial Execution System Using First Principles

marsbit06/25 09:06

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.

bitcoinist06/22 15:31

Infamous MEV Bot JaredFromSubway Drained For $7.5 Million

bitcoinist06/22 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.

marsbit06/22 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 News06/22 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.

marsbit06/22 06:05

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