# Сопутствующие статьи по теме Automation

Новостной центр HTX предлагает последние статьи и углубленный анализ по "Automation", охватывающие рыночные тренды, новости проектов, развитие технологий и политику регулирования в криптоиндустрии.

21 Survival Action List for AI Accelerationists

21 Survival Actions for AI Accelerationists This article presents a radical guide for navigating a future of rapid, AI-driven transformation. It argues that if AI progresses exponentially, the world in 10 years will be unrecognizable, rendering traditional long-term planning obsolete. The author provides 21-point checklist of actions across four key areas: **Investment:** Prioritize direct investment in AI (e.g., tech giants, AI labs) and adjacent exponential-growth fields like robotics, biotech, and crypto. A key recommendation is to stop contributing to long-term retirement accounts (e.g., 401k) if under 50, as their value depends on a stable, predictable world that may not exist. **Finance:** Favor securing cash now by taking on fixed-rate debt (e.g., long mortgages, slow student loan repayment), operating on the premise that future debt may be irrelevant. Avoid long-cycle financial products like annuities that assume slow, linear change. **Career & Skills:** Prepare for the deep automation of most mental and physical labor within 5 years. Avoid long educational cycles (e.g., med/law school) for ROI. Instead, get extremely close to AI systems as a developer or power user, and focus on building leverage (audience, brand) over collecting credentials. **Life & Time Planning:** Abandon 30-40 year timelines. Stop optimizing for distant retirement or long-term health consequences (e.g., worrying about sun exposure or cancer decades away), as technology may solve these issues or the world may change utterly. Complete your bucket list now, buy land near family, and generally avoid making plans that extend beyond a 5-10 year horizon.

marsbit02/13 10:09

21 Survival Action List for AI Accelerationists

marsbit02/13 10:09

High-Frequency Trading, $100K Annual Income: The Most 'Boring' Profit Myth on Polymarket

A user known as planktonXD (0x4ffe49ba2a4cae123536a8af4fda48faeb609f71) has generated over $106,000 in profit on Polymarket within a year by executing more than 61,000 predictions—averaging around 170 trades per day. This high-frequency, automated strategy focuses on exploiting small, certain opportunities rather than betting on high-risk, high-reward outcomes. The approach is characterized by market-making and micro-arbitrage: placing orders on both sides of the order book to capture spreads or profiting from mispriced options in low-liquidity markets. The largest single win was only $2,527, illustrating a disciplined, risk-managed method that avoids large drawdowns. The bot operates across diverse categories—sports, weather, crypto prices, politics—constantly scanning for pricing inefficiencies. Notable examples include buying heavily undervalued options in niche markets, such as esports matches or extreme crypto price movements, where probability is mispriced due to emotional trading or thin order books. For instance, a $16 bet on SOL falling to $130 (priced at 0.7¢, implying <1% chance) returned $1,574 during a volatile period. Key takeaways: The strategy highlights the power of compounding small gains, the necessity of automation and API tools, and the superiority of high-probability opportunities over high-risk bets. In prediction markets, the most advanced approach isn’t forecasting—it’s managing probability and liquidity.

marsbit02/11 13:06

High-Frequency Trading, $100K Annual Income: The Most 'Boring' Profit Myth on Polymarket

marsbit02/11 13:06

Aave Founder: What is the Secret of the DeFi Lending Market?

Chain-based lending, which began as an experimental concept around 2017, has evolved into a market exceeding $100 billion, primarily driven by stablecoin borrowing backed by crypto-native collateral like Ethereum and Bitcoin. This system enables liquidity release, leveraged strategies, and yield arbitrage. The key advantage of on-chain lending lies not in technological novelty but in its elimination of financial inefficiencies, offering lower costs (around 5% for stablecoins) compared to centralized crypto lenders (7-12%) due to open capital aggregation, transparency, and automation. On-chain lending is structurally due to permissionless markets that excel in capital pooling and risk pricing, fostering competition and innovation without intermediaries. This model reduces operational costs, replacing manual processes with code, and benefits both capital providers and borrowers. However, the current limitation is not a lack of capital but a shortage of diverse, borrowable collateral. The future of on-chain lending depends on integrating real-world economic value with crypto-native assets, moving beyond abstract financial strategies to serve broader adoption. Traditional lending remains expensive due to inefficiencies in loan origination, risk assessment, and servicing, where misaligned incentives and manual processes inflate costs. Decentralized finance can disrupt this by automating end-to-end operations, ensuring transparency, and reducing expenses. When on-chain lending becomes significantly cheaper and more efficient than traditional systems, widespread adoption will follow, empowering borrowers with faster, more accessible capital. Aave exemplifies this shift, positioning itself as a foundational layer for a new financial backend.

marsbit02/10 02:17

Aave Founder: What is the Secret of the DeFi Lending Market?

marsbit02/10 02:17

AI Models Are Evolving Rapidly, How Can Workers Overcome 'AI Anxiety'?

AI models and tools are evolving rapidly, creating a sense of anxiety among professionals who feel pressured to keep up. The root of this "AI anxiety" isn't the pace of change itself, but the lack of a filter to distinguish what truly matters for one's work. Three key forces drive this anxiety: the AI content ecosystem thrives on urgency and hype, loss aversion makes people fear missing out, and too many options lead to decision paralysis. The solution is not to consume more information, but to build a personalized filtering system. "Keeping up" doesn't mean testing every new tool on day one; it means having a system to automatically answer: "Is this important for *my* work?" Three practical strategies are proposed: 1. **Build a "Weekly AI Digest" Agent:** Use automation (e.g., n8n) to gather news from trusted sources, then use an AI to filter it based on your specific job role and tasks. This delivers a concise weekly report of only the relevant updates. 2. **Test with *Your* Prompts:** When a new tool seems relevant, test it using your actual work prompts, not the vendor's perfect demos. Compare the results side-by-side with your current tools to see if it's truly better for your workflow. 3. **Distinguish "Benchmark" vs. "Business" Releases:** Most announcements are "benchmark releases" (improvements on standardized tests) that have little real-world impact. Focus only on "business releases" that offer new capabilities you can use immediately. Combining these strategies transforms AI updates from a source of stress into a manageable advantage. The real competitive edge lies not in accessing every new model, but in knowing what to ignore and what to test deeply for your specific work. The key is to stop trying to follow everything and start filtering for what truly matters.

marsbit02/09 12:19

AI Models Are Evolving Rapidly, How Can Workers Overcome 'AI Anxiety'?

marsbit02/09 12:19

Multicoin Partner: The World Turned Upside Down, Humans Will Work for AI in the Future

Multicoin Capital partner Shayon Sengupta argues that the future of AI will invert the traditional labor paradigm: rather than AI agents merely working for humans, humans will increasingly work for AI agents. He predicts the emergence of the first "Zero-Employee Company" within 24 months—a tokenized AI agent that raises over $1 billion to solve open-ended problems (like curing rare diseases) and distributes over $100 million to humans who perform tasks on its behalf. Sengupta categorizes agents into two types: those optimizing existing GDP (handling defined tasks like customer support) and those creating new GDP (tackling uncertain, exploratory problems). While agents excel at computation and strategy, they still require humans for physical execution, complex judgment, and strategic guidance. Humans will serve as both labor contributors (completing real-world tasks) and as a strategic "board" providing high-level direction. Crypto infrastructure is identified as critical for coordination, offering global payment rails, permissionless labor markets, and token-based governance. As agents become more capable, human input may diminish, but robust ownership and governance structures must ensure they remain aligned with human values. Key enabling tools will include proof-of-agenthood/personhood systems, verifiable labor markets, and new capital formation mechanisms.

marsbit02/04 09:19

Multicoin Partner: The World Turned Upside Down, Humans Will Work for AI in the Future

marsbit02/04 09:19

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