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账单不会说谎:9月OpenRouter Top10盘点,哪些AI应用才是真实好用?
Founder Park· 2025-09-18 09:59
Core Insights - The article discusses the transformative impact of AI across various industries, focusing on the real-world applications and usage of AI products, particularly through the lens of OpenRouter's backend data [3][4]. Group 1: AI Product Rankings - OpenRouter's top 10 AI applications by call volume as of September 2025 include Kilo Code, Cline, BLACKBOX.AI, Roo Code, liteLLM, SillyTavern, ChubAI, HammerAI, Sophia's Lorebary, and Codebuff [5][6]. - Notably, well-known applications like Cursor and GitHub Copilot are absent from this list, as they typically utilize self-built services or directly integrate with Azure and OpenAI, rather than relying on third-party routing [6]. Group 2: Developer Preferences - Over 13,000 developers participated in the "AI Product Marketplace" community, indicating a strong interest in discovering valuable AI applications [7]. - The ranking reflects a clear preference for coding agents, which occupy six of the top ten spots, highlighting the essential demand for developer tools [10]. Group 3: Kilo Code - Kilo Code, developed by a remote-first team, aims to automate repetitive programming tasks such as dependency management and bug fixing, allowing developers to focus on architecture and innovation [12][14][16]. - It integrates over 400 models, enabling users to call them directly without complex API configurations, and offers a zero-commission pricing model with a $20 free credit [21][24][25]. Group 4: Cline - Cline, another prominent coding agent, emphasizes a "self-sufficient yet controllable" approach, allowing developers to confirm each step of the coding process [29][31][33]. - It has raised approximately $32 million in seed and Series A funding, with over 500,000 stars on GitHub and more than 2 million installations on VS Code [30][38]. Group 5: BLACKBOXAI - BLACKBOXAI positions itself as a comprehensive commercial AI coding agent, offering both a VS Code extension and a web app for various user interactions [40][41]. - It has surpassed 10 million users and 4 million VS Code installations, with a subscription model ranging from $9.99 to $99.99 per month [50][51][52]. Group 6: Roo Code - Roo Code is an open-source VS Code plugin that allows local AI agent usage for reading, writing, and debugging code, emphasizing user control and privacy [53][54][57]. - It has completed $6.4 million in seed funding and is designed to run in offline environments for enhanced security [64]. Group 7: liteLLM - liteLLM is an open-source library that simplifies the integration of over 100 language models, providing unified access and cost tracking features [67][69][73]. - It was founded by Krrish Dholakia and Ishaan Jaffer, raising approximately $1.6 million in seed funding [73]. Group 8: SillyTavern - SillyTavern is a local front-end tool designed for advanced users, allowing seamless interaction with various AI models while providing extensive customization options [75][78]. - It is a community-driven project with over 200 contributors and has not yet sought external VC funding [79]. Group 9: ChubAI - ChubAI is a GenAI platform aimed at content creators and role-playing enthusiasts, offering high customization and immersive experiences [82][86]. - It operates on a subscription model, relying on user engagement and product development for growth [88]. Group 10: HammerAI - HammerAI focuses on privacy and creative expression, allowing users to engage in interactive storytelling and character dialogue without cloud dependency [90][92]. - It supports offline usage and does not require user registration, appealing to privacy-conscious individuals [95]. Group 11: Sophia's Lorebary - Sophia's Lorebary enhances existing role-playing tools by providing lorebook, scenario, and plugin management capabilities, enriching user experiences [101][102]. - It is an open-source project led by community volunteers, currently without public funding records [106].
蚂蚁开源2025外滩大会发布大模型全景图,AI开发现三大趋势:工具、路线与生态分化
Sou Hu Cai Jing· 2025-09-14 15:25
Core Insights - The report released by Ant Group and Inclusion AI highlights the rapid evolution of the global AI open-source ecosystem, showcasing China's active role in this field [1][3] Group 1: Open Source Ecosystem - 62% of open-source projects emerged after the "GPT moment" in October 2022, with an average age of only 30 months [3] - Approximately 36,000 global developers contributed to the projects, with the US accounting for 24% and China for 18% of the contributions [3] - Chinese companies prefer open-weight models, while leading US firms tend to adopt closed-source strategies, indicating a clear divergence in open-source approaches [3][6] Group 2: AI Programming Tools - The explosive growth of AI programming tools is a notable trend, significantly enhancing programmer efficiency [4] - New coding tools launched in 2025 average over 30,000 developer stars, with Gemini CLI achieving over 60,000 stars in just three months [4] - The development of these tools is reshaping the software development industry, with a shift towards AI handling repetitive tasks, allowing developers to focus on creative design and complex problem-solving [4][6] Group 3: Key Trends in Model Development - The report identifies several key directions in model development, including a clear division between open-source and closed-source routes in China and the US [6] - Model parameters are trending towards scalability under the MoE architecture, and reinforcement learning is becoming a crucial method for enhancing model inference capabilities [6] - Multi-modal models are accelerating to become mainstream, and model evaluation methods are diversifying into subjective voting and objective assessment [6]
蚂蚁开源2025全球大模型全景图出炉,AI开发中美路线分化、工具热潮等趋势浮现
Sou Hu Cai Jing· 2025-09-14 14:39
Core Insights - The report released by Ant Group and Inclusion AI highlights the rapid development and trends in the AI open-source ecosystem, particularly focusing on large models and their implications for the industry [1] Group 1: Open-source Ecosystem Overview - The 2.0 version of the report includes 114 notable open-source projects across 22 technical fields, categorized into AI Agent and AI Infra [1] - 62% of the open-source projects in the large model ecosystem were created after the "GPT moment" in October 2022, with an average age of only 30 months, indicating a fast-paced evolution in the AI open-source landscape [1] - Approximately 360,000 global developers contributed to the projects, with 24% from the US, 18% from China, and smaller contributions from India, Germany, and the UK [1] Group 2: Development Trends - A significant trend identified is the explosive growth of AI programming tools, which automate code generation and modification, greatly enhancing programmer efficiency [1][2] - These tools are categorized into command-line tools and integrated development environment (IDE) plugins, with the former being favored for their flexibility and the latter for their integration into development processes [1] - The report notes that the average new coding tool in 2025 has garnered over 30,000 developer stars, with Gemini CLI achieving over 60,000 stars in just three months, marking it as one of the fastest-growing projects [1] Group 3: Competitive Landscape - The report outlines a timeline of major large model releases from leading companies, detailing both open and closed models, along with key parameters and modalities [4] - Key directions in large model development include a clear divergence between open-source and closed-source strategies in China and the US, a trend towards scaling model parameters under MoE architecture, and the rise of multi-modal models [4] - The evaluation methods for models are evolving, incorporating both subjective voting and objective assessments, reflecting the technological advancements in the large model domain [4]
蚂蚁开源发布2025全球大模型开源生态全景图,揭示AI开发三大趋势
Sou Hu Cai Jing· 2025-09-14 11:36
Core Insights - The report titled "Global Large Model Open Source Development Ecosystem Panorama and Trends" was released by Ant Group and Inclusion AI, revealing the current state and future trends of the AI open-source field [1][3] - The report highlights China's significant position in the AI open-source ecosystem, with a data-driven approach to present the real status of global AI open-source development [3] Development Trends - The report includes 114 notable open-source projects across 22 technical fields, categorized into AI Agent and AI Infra [3] - 62% of the open-source projects in the large model ecosystem were created after the "GPT moment" in October 2022, indicating a rapid iteration characteristic of the AI open-source ecosystem [3][4] Developer Participation - Among approximately 360,000 global developers involved in the projects, 24% are from the United States, 18% from China, followed by India (8%), Germany (6%), and the UK (5%), with the US and China contributing over 40% of the core development force [4] Open Source Strategies - Chinese companies tend to favor open-weight models, while leading US firms often adopt closed-source strategies, reflecting a divergence in approaches to large model open-source development [4][8] AI Coding Tools Growth - There is a significant surge in AI programming tools that automate code generation and modification, enhancing developer efficiency and becoming a hot topic in the open-source community [5] - Tools are categorized into command-line tools (e.g., Gemini CLI) and integrated development environment plugins, each catering to different developer needs [5] Future of Software Development - The demand for AI assistants among global developers is rising, with a trend towards delegating repetitive tasks to AI tools, allowing programmers to focus on creative design and complex problem-solving [5] Timeline of Large Model Development - A timeline of large model releases from major domestic and international companies was published, detailing both open and closed models along with key parameters and modalities [6][8] - Key directions for large model development include a clear divergence between open-source and closed-source strategies in China and the US, a trend towards scaling model parameters under MoE architecture, and the rise of multi-modal models [8]
X @xAI
xAI· 2025-08-28 18:12
Model Introduction - Grok Code Fast 1 is a fast and economical reasoning model for agentic coding [1] - The model excels at agentic coding [1] Availability - Grok Code Fast 1 is available for free on multiple platforms including GitHub Copilot, Cursor, Cline, Kilo Code, Roo Code, opencode, and Windsurf [1]
一年成爆款,狂斩 49.1k Star、200 万下载:Cline 不是开源 Cursor,却更胜一筹?!
AI前线· 2025-08-20 09:34
Core Viewpoint - The AI coding assistant market is facing significant challenges, with many popular tools operating at a loss due to unsustainable business models that rely on venture capital subsidies [2][3]. Group 1: Market Dynamics - The AI market is forming a three-tier competitive structure: model layer focusing on technical strength, infrastructure layer competing on price, and coding tools layer emphasizing functionality and user experience [2]. - Companies like Cursor are attempting to bundle these layers together, but this approach is proving unsustainable as the costs of AI inference far exceed the subscription fees charged to users [2][3]. Group 2: Cline's Approach - Cline adopts an open-source model, believing that software should be free, and generates revenue through enterprise services such as team management and technical support [5][6]. - Cline has rapidly grown to a community of 2.7 million developers within a year, showcasing its popularity and effectiveness [7][10]. Group 3: Product Features and User Interaction - Cline introduces a "plan + action" paradigm, allowing users to create a plan before executing tasks, which enhances user experience and reduces the learning curve [12][13]. - The system allows users to switch between planning and action modes, facilitating a more intuitive interaction with the AI [13][14]. Group 4: Economic Value and Market Position - Programming is identified as the most cost-effective application of large language models, with a growing focus from model vendors on this area [21][22]. - Cline's integration with various services and its ability to streamline interactions through natural language is seen as a significant advantage in the evolving market landscape [22][23]. Group 5: MCP Ecosystem - The MCP (Model Control Protocol) ecosystem is developing, with Cline facilitating user understanding and implementation of MCP servers, which connect various tools and services [24][25]. - Cline has launched over 150 MCP servers, indicating a robust market presence and user engagement [26]. Group 6: Future Directions - The future of programming tools is expected to shift towards more natural language interactions, reducing reliance on traditional coding practices [20][22]. - As AI models improve, the need for user intervention is anticipated to decrease, allowing for more automated processes in software development [36][39].
BERNSTEIN:GenAI在资产管理中 - 面向投资者的顶级 AI 工具(第二部分)_亚洲量化策略
2025-07-07 15:44
Summary of Key Points from the Conference Call Industry Overview - The report focuses on the integration of Generative AI (GenAI) tools in asset management, specifically highlighting various AI tools for trading, technical analysis, risk management, and quant investing [1][5]. Core Insights and Arguments AI Tools for Trading and Risk Management - **TradeWeb** has introduced AiSNAP and AiEX, which enhance dealer selection and liquidity discovery for IRS trading, providing automated trade execution capabilities [2][8]. - **BlackRock's Aladdin** is a comprehensive platform for portfolio management, risk analytics, and order execution, utilizing historical data and Monte Carlo simulations for scenario analysis [2][11]. - **TrendSpider** offers technical analysis tools and trading strategy development, catering to a wide range of financial professionals with subscription costs ranging from $50 to $280 per month [2][7]. - **Permutable Trading Co-Pilot** provides real-time market intelligence and sentiment analysis, particularly useful for macro funds and FX traders [2][9]. - **Riskfuel** specializes in fast computational models for real-time valuations and risk sensitivities, aiding in scenario analysis for trading portfolios [2][9]. AI Tools for Quantamental Investors - **Quant Insight** offers macro analytics and portfolio construction support, focusing on macro sensitivities and asset valuations [3][28]. - **Trendlyne** is popular in India for its stock screening and quant scoring capabilities, also covering US equities [3][28]. - **MDOTM** provides AI-driven investment insights for portfolio optimization and asset allocation [3][28]. - **Koyfin** offers a user-friendly analytics platform with extensive market data coverage, suitable for ETF and mutual fund comparisons [3][28]. - **Kavout** focuses on US equities, providing factor scores and rankings through machine learning algorithms [3][28]. AI Tools for Quant Investors - **Cline** and **Cursor** are AI coding assistants favored by quant investors for generating code and managing data [4][61]. - **KX** is a high-performance time series database used for quantitative research and backtesting by major financial institutions [4][62]. - **QuantConnect** allows users to develop and test trading strategies with extensive financial data support [4][62]. - **QuantRocket** is a Python-based platform for multi-asset research and trading, offering various backtesting tools [4][62]. Additional Important Insights - The report emphasizes the growing interest among fundamental investors in incorporating quant signals into their investment processes, facilitated by GenAI tools [27]. - The tools mentioned cater to a diverse range of investors, from institutional to retail, highlighting the democratization of advanced investment strategies through technology [1][5][27]. - The report also notes the importance of real-time data and analytics in navigating complex market conditions, particularly in the context of geopolitical and economic factors [2][9]. This summary encapsulates the key points from the conference call, focusing on the advancements in AI tools within the asset management industry and their implications for various types of investors.
5 万行代码 Vibe Coding 实践复盘:最佳实践、关键技术,Bitter Lesson
海外独角兽· 2025-06-05 11:00
Core Viewpoint - The article discusses the transformative potential of AI coding agents, highlighting their ability to generate code and automate programming tasks, thus enabling even those without extensive coding experience to become proficient developers [3][6]. Group 1: My Vibe Coding Journey - Vibe Coding refers to the practice of using coding agents to generate nearly 100% of the code, with tools like Cursor, Cline, and GitHub Copilot being popular choices [7]. - The author completed approximately 50,000 lines of code over three months, successfully developing three different products, demonstrating the effectiveness of AI in coding [8][9]. - The experience revealed that a lack of prior knowledge in certain programming languages can be advantageous when relying on AI, as it necessitates full dependence on the coding agent [8]. Group 2: Key Technologies of Coding Agents - Key coding agents include Cursor, Cline, GitHub Copilot, and Windsurf, with a strong emphasis on using the agent mode for optimal performance [13][14]. - The effectiveness of coding agents relies on three critical components: a powerful AI model, sufficient context, and an efficient toolchain [15][18]. - The article emphasizes the importance of providing clear and comprehensive context to the AI for successful task execution [11][12]. Group 3: Comparison of Coding Agents - Cursor is highlighted as the current leader in the coding agent space, particularly when using the Claude 3.7 Max model, capable of generating 100% of the code for large projects [44]. - Cline is noted for its open-source nature and superior support for the Model Context Protocol (MCP), but it lacks semantic search capabilities, which limits its effectiveness in handling large codebases [45]. - GitHub Copilot is seen as lagging behind in context management and MCP support, but it has the potential to catch up due to Microsoft's strong development capabilities [46]. Group 4: The Bitter Lesson in Agent Development - The article references "The Bitter Lesson," which suggests that embedding too much human experience into AI systems can limit their potential, advocating for a design that allows AI capabilities to dominate [47][48]. - The author’s experience indicates that reducing human input in favor of AI-driven processes can significantly enhance product performance, achieving a test coverage rate of over 99% [48].
AI辅助编码将如何改变软件工程:更需要经验丰富的工程师
AI前线· 2025-05-12 04:28
Core Viewpoint - Generative AI is set to continue transforming software development, with significant advancements expected by 2025, despite current tools not fully democratizing coding for non-engineers [1][35][67]. Group 1: Impact of Generative AI on Software Engineering - The introduction of large language models (LLMs) like ChatGPT has led to a significant increase in AI tool usage among developers, with approximately 75% utilizing some form of AI for software engineering tasks [1]. - The media has sensationalized the potential impact of AI on software engineering jobs, often lacking insights from actual software engineers [1][2]. - AI tools are reshaping software engineering but are unlikely to cause dramatic changes as previously suggested [2]. Group 2: Practical Observations and Challenges - Addy Osmani's article highlights the dual modes of AI tool usage among developers: "Accelerators" for rapid prototyping and "Iterators" for daily development tasks [3][7][10][11]. - Despite increased efficiency reported by developers using AI, the overall quality of software has not significantly improved, indicating underlying issues in software development practices [5][26]. - The "70% problem" illustrates that while AI can help complete a majority of tasks quickly, the remaining complexities often lead to frustration, especially for non-engineers [14][15][20]. Group 3: Effective AI Utilization Strategies - Successful AI integration involves methods such as "AI Drafting," "Continuous Dialogue," and "Trust and Verify" to enhance productivity [27][28][32]. - Developers are encouraged to start small, maintain modularity, and trust their own experience when using AI tools [33][32]. Group 4: Future of Software Engineering with AI - The rise of software engineering agents is anticipated, which will operate more autonomously and collaboratively with human developers [35][38][42]. - The demand for experienced software engineers is expected to increase as they are better equipped to leverage AI tools effectively and manage the complexities that arise from AI-generated code [67]. - The evolution of AI tools may lead to a resurgence in personal software development, focusing on user-centric design and quality [53][54].