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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].
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]
不用AI就被淘汰?国外工程师:“10倍生产力”太荒谬了
Hu Xiu· 2025-08-26 04:04
Group 1 - The article questions the validity of the claim that AI can lead to a tenfold increase in programming efficiency, suggesting that such assertions may be exaggerated [1][10][24] - It highlights the author's personal experience of anxiety regarding the rapid advancement of AI and its implications for software engineers [1][3][30] - The author critiques the performance of AI programming tools, stating that while they can generate template code, they often struggle with understanding larger codebases and can produce insecure code [4][5][15] Group 2 - The article argues that the notion of a "10x engineer" is often misunderstood, emphasizing that true productivity gains come from preventing unnecessary work rather than simply writing code faster [19][20][23] - It discusses the limitations of AI in software development, noting that while AI can assist in certain tasks, it does not fundamentally change the human processes involved in software engineering [12][18][24] - The author warns against the pressure to adopt AI tools hastily, advocating for a balanced approach that prioritizes quality and enjoyment in coding over mere speed [31][32][33]
GPT-5变蠢背后:抑制AI的幻觉,反而让模型没用了?
Hu Xiu· 2025-08-22 23:56
Core Viewpoint - The release of GPT-5 has led to significant criticism, with users claiming it has become less creative and more rigid in its responses compared to previous versions [1][2][3]. Group 1: Model Characteristics and User Feedback - GPT-5 has a significantly reduced hallucination rate, which has made its outputs appear more rigid and less dynamic, particularly affecting its performance in creative writing tasks [3][5][10]. - Users have expressed dissatisfaction with GPT-5's responses, describing them as dull and lacking emotional depth, despite improvements in areas like mathematics and science [9][10]. - The model's requirement for detailed prompts to generate satisfactory outputs has been seen as a regression for users accustomed to more intuitive interactions with earlier versions [3][9]. Group 2: Hallucination and Its Implications - Hallucination in AI models refers to the generation of content that does not align with human experience, and it is categorized into five types, including language generation errors and logical reasoning mistakes [14][17]. - The industry has recognized that completely eliminating hallucinations is impossible, and there is a need to view the impact of hallucinations in a nuanced manner [10][11][12]. - The perception of hallucinations has shifted from being viewed solely as a negative issue to a more balanced understanding of their potential utility in certain contexts [131]. Group 3: Mitigation Strategies - Current strategies to mitigate hallucinations include using appropriate models, In-Context Learning, and fine-tuning techniques, with varying degrees of effectiveness [30][31][32]. - The use of Retrieval-Augmented Generation (RAG) is prevalent in high-precision industries like healthcare and finance, although it can significantly increase computational costs [35][46]. - In-Context Learning has shown promise in reducing hallucination rates but faces challenges related to the quality and structure of the context provided [70][72]. Group 4: Industry Trends and Perspectives - The industry has moved towards a more rational understanding of hallucinations, recognizing that some scenarios may tolerate them while others cannot [131]. - There is a growing acknowledgment that traditional machine learning methods still hold advantages in complex reasoning tasks compared to large language models [107][108]. - The trend indicates a shift towards integrating traditional machine learning techniques with large language models to enhance their capabilities and mitigate hallucination issues [108][109].
“AI让你变成10x工程师?其实是一个骗局......”
3 6 Ke· 2025-08-12 09:57
Core Viewpoint - The discussion around AI's potential to increase engineer productivity by 10x or even 100x is largely exaggerated, driven by commercial interests and management pressures, rather than reflecting the real experiences of developers [1][2][3]. Group 1: AI Tools and Developer Experience - Many developers feel anxious about their skills in the face of AI advancements, fearing they may become obsolete if they do not adapt quickly [2][3]. - AI tools like Claude Code and Cursor are seen as useful for repetitive tasks but often struggle with understanding specific codebases and can introduce errors [5][6]. - The actual productivity gains from using AI tools are often overstated, with many developers finding that AI can assist but not replace the need for human oversight and expertise [9][12]. Group 2: Misconceptions about Productivity Gains - The claim of achieving 10x efficiency is misleading, as it implies that all aspects of software development, including communication and testing, would also need to improve by the same factor, which is unrealistic [8][9]. - Even if coding speed were to increase, the majority of a developer's time is spent on reading, thinking, and debugging, which AI cannot significantly accelerate [9][11]. - The notion of a "10x engineer" exists, but it is often due to their ability to avoid unnecessary work rather than a direct result of AI usage [12][14]. Group 3: The Role of Management and Industry Perception - There is a tendency for management to promote the idea of AI-driven productivity to maintain pressure on engineers, which can lead to a toxic work environment [16][21]. - Many claims about AI's capabilities come from those distanced from actual coding work, such as entrepreneurs and investors, rather than from engineers who use these tools daily [18][22]. - The narrative around AI's transformative power can create unnecessary anxiety among engineers, leading them to doubt their skills and contributions [17][22]. Group 4: Emphasis on Enjoyment and Work Satisfaction - The focus should be on finding joy in coding rather than solely on efficiency; enjoying the work can lead to better outcomes in the long run [19][20]. - Engineers are encouraged to choose methods that make them happy, as this can enhance their productivity and creativity [20][22]. - The industry should recognize that fostering a supportive environment is crucial for long-term success, rather than pushing unrealistic productivity expectations [21][22].
别焦虑!不会用AI也不会被淘汰,工程师老哥实测各类工具:10倍生产力神话太夸张了
量子位· 2025-08-10 04:11
Core Viewpoint - The article discusses the limitations of AI in software engineering, emphasizing that while AI can enhance productivity in specific tasks, it cannot replace the critical thinking and judgment required by engineers in complex projects [6][10][25]. Group 1: AI's Role in Software Engineering - AI tools can assist in writing boilerplate code and scripts quickly, but struggle with understanding the context of large codebases, leading to inefficiencies [8][9]. - Engineers must guide AI by breaking down complex tasks into smaller units to avoid logical confusion during processing [11][13]. - The myth of "10x productivity" with AI is challenged, as achieving such efficiency would require a complete overhaul of workflows, which is impractical [15][18]. Group 2: Challenges and Limitations of AI - AI-generated content often has defects and may not meet codebase standards, especially as the size of the codebase increases [19][25]. - Engineers face diminishing returns when relying on AI, as the complexity of projects grows, leading to potential productivity bottlenecks [26][22]. - The article suggests that the promotion of AI's capabilities may stem from inexperienced users or those with vested interests in AI products [28][30]. Group 3: Engineer's Perspective and Advice - Engineers should not feel pressured to adopt AI if it does not align with their preferred working style, and they should focus on their strengths [32][33]. - Leadership should avoid creating anxiety among engineers regarding AI's potential to replace them, as this can lead to decreased morale and quality of work [34]. - Trusting engineers' expertise and allowing them to use AI as a tool rather than a crutch is essential for maintaining quality in software development [34].