# Сопутствующие статьи по теме AI

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

When AI Takes Over Productivity, Which Web3 Jobs Begin to Disappear?

In the evolving landscape of Web3, the integration of AI and automation is reshaping the job market, leading to the decline of certain roles while creating new opportunities. Jobs that involve repetitive or standardized tasks are increasingly being automated. These include: - Junior Solidity developers, as AI can generate standard smart contract code. - Web3 researchers/analysts, with AI handling data analysis and report generation. - Community managers and customer support roles, replaced by AI-driven communication systems. - Crypto traders, outperformed by AI in speed, data processing, and execution. - NFT content creators and low-barrier NFT creators, as generative AI produces art quickly, reducing demand for basic creative work. Simultaneously, new roles are emerging that require interdisciplinary skills: - AI × Web3 architects, designing integrated AI-blockchain systems. - AI Agent training coordinators, managing multi-agent behaviors in DeFi and DAOs. - Web3 prompt engineers, crafting prompts for code generation and AI interactions. - AI on-chain data analysts, extracting insights from blockchain data using AI models. - AI-powered smart contract auditors, enhancing security with automated tools. - Web3 automation strategy designers, developing algorithmic systems for DeFi. Overall, Web3 teams are becoming smaller but more efficient, with a growing emphasis on advanced, cross-disciplinary expertise in architecture, security, and innovation. AI is not diminishing Web3’s potential but is driving it into a new phase of growth, where creativity and technical depth are paramount.

比推03/05 06:00

When AI Takes Over Productivity, Which Web3 Jobs Begin to Disappear?

比推03/05 06:00

In a World of Dramatic Change, How Should Humanities Workers Better Use AI?

In a rapidly changing landscape, humanities professionals are increasingly turning to AI not as a magic solution, but as a practical tool integrated into their research, writing workflows. This guide outlines key principles for effectively using AI, moving beyond simple "prompts" to a systematic, controllable methodology. The approach is built on three core tenets: processes must be traceable, verifiable, and supervised; the user must remain in control; and the final output must be something the creator is willing to sign their name to. Key principles include: * **Treat AI as a workbench, not a wish-granter:** Clearly define tasks, audiences, and standards instead of making vague requests. * **You are the responsible agent:** Provide clear context, constraints, and executable steps. Dissatisfaction often stems from unclear instructions, not AI failure. * **Compare multiple models:** Different AIs have different strengths (writing, reasoning, coding); use them like a team. * **Manage expectations:** Assume AI has the knowledge level of a top undergraduate; provide examples and standards for specialized tasks. * **Break tasks into steps:** A white-box process of small, reliable steps is better than a single, error-prone black-box request. * **Industrialize first, then automate:** Define and structure your workflow into reproducible steps before assigning sub-tasks to AI. * **Anticipate AI's laziness:** Remove format barriers (e.g., clean text from PDFs/websites) to focus its effort on comprehension. * **Prioritize compression over expansion:** It's more reliable to condense large amounts of provided material than to ask AI to generate content from little context. * **Iterate on the pipeline, not the output:** Aim for a system that consistently produces good-enough drafts (e.g., 75/100) rather than manually perfecting each result. * **Generate quantity to find quality:** Request multiple versions (e.g., 5 summaries, 50 headlines) to combat mediocrity and discover excellent samples. * **Act as a head chef:** Provide clear feedback for revisions instead of rewriting the output yourself. The ultimate quality of work depends on **materials × taste**. AI enhances interaction with materials, but genuine research, unique sources, and cultivated judgment remain irreplaceable. The goal is to replace anxiety with practical skill by engineering tasks, making processes transparent, and integrating AI as a verb within a credible,署名-worthy creative process.

marsbit03/05 05:20

In a World of Dramatic Change, How Should Humanities Workers Better Use AI?

marsbit03/05 05:20

Interview with Sui Founder: Leaving Meta at 50 to Start a Business, How to Rebuild the 'Foundation' for the Internet

Evan Cheng, co-founder and CEO of Mysten Labs (the core developer behind the Sui blockchain), shares his journey from working at Apple and Meta to starting his own venture in his 50s. He left Meta’s Libra (Diem) project due to creative constraints and a desire to build foundational internet infrastructure tailored for automation and AI agents. Cheng believes current web architecture is ill-suited for automation and aims to create a unified, efficient, and secure blockchain layer to support future automated interactions between humans, machines, and agents. He addresses key industry challenges, including misconceptions about blockchain (e.g., the "blockchain trilemma"), technical immaturity, and fragmented privacy and security models. Sui tackles these with its object-centric architecture, integrated privacy features, and a full-stack approach that offers iOS-like developer convenience. Sui has attracted major partners like CCP Games, who are building persistent, automated game economies on the network. Cheng also highlights DeepBook, Sui's native central limit order book, which acts as a shared liquidity hub to improve capital efficiency across DeFi applications. Despite market’s volatility, Cheng remains focused on long-term goals, emphasizing real-world adoption and the need for robust, scalable infrastructure beyond short-term speculation.

marsbit03/05 02:59

Interview with Sui Founder: Leaving Meta at 50 to Start a Business, How to Rebuild the 'Foundation' for the Internet

marsbit03/05 02:59

YZi Labs Portfolio Data Analysis: 229 Investments, 95 Listed on Binance

YZi Labs Portfolio Data Insight: 229 Investments, 95 Listed on Binance In March 2026, YZi Labs investment partner Dana Hou departed after four years, witnessing the crypto VC industry's cycle from frenzy to contraction. RootData recorded 229 YZi Labs investments, involving 218 unique projects. Among them, 154 have issued tokens: - 150 are listed on at least one exchange - 95 are listed on Binance - 22 have a market cap below $500K - 20 have ceased operations Approximately 45% of the listed projects (69) are considered relatively healthy, including successes like Ethena ($920M market cap), Aster ($1.73B), Sui ($3.54B), and Aptos ($790M). These primarily focus on DeFi infrastructure and L1/L2 solutions. Half of the tradable projects (76) have a market cap below 50% of their fully diluted valuation (FDV), with 26 projects below 20%. Notable pivots include STEPN (to lifestyle platform), MyShell (AI Agent infrastructure), and Open Campus (education solutions ecosystem). YZi Labs maintained investment pace during bear markets, with 49 deals in 2022 versus 44 in the 2021 bull market. However, lead investment rates dropped from 36% in bull markets to 16-27% in bear markets, indicating a more cautious approach. DeFi has become YZi Labs' preferred sector, while game investments declined significantly. A notable characteristic is the non-disclosure of investment amounts in 45.6% of deals (104), particularly during bear markets and strategic rounds. The firm is strategically expanding into AI and stablecoins, aiming to rebuild its portfolio and upgrade its research capabilities for cross-cycle performance.

marsbit03/04 13:09

YZi Labs Portfolio Data Analysis: 229 Investments, 95 Listed on Binance

marsbit03/04 13:09

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