Agents forget everything after each task!Graphiti builds a temporal knowledge graph for Agents that provides a memory layer to all interactions.Fully open-source with 20k+ stars!Learn how to use Graphiti MCP to connect all AI apps via a common memory layer (100% local): https://t.co/cpAZFJcrufAvi Chawla (@_avichawla):Big update for Claude Desktop and Cursor users!Now you can connect all AI apps via a common memory layer in a minute.I used the Graphiti MCP server that runs 100% locally to cross-operate acros ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/Op1RqVm3agAvi Chawla (@_avichawla):Big update for Claude Desktop and Cursor users!Now you can connect all AI apps via a common memory layer in a minute.I used the Graphiti MCP server that runs 100% locally to cross-operate across AI apps like Claude Desktop and Cursor without losing context.(setup below) https://t.co/AZrxcS2cR5 ...

1) Docker SetupDeploy the Graphiti MCP server using Docker Compose.This setup starts the MCP server with Server-Sent Events (SSE) transport, and it includes a Neo4j container, which launches the database as a local instance.This configuration also lets you query and visualize the knowledge graph using the Neo4j browser preview.You can also use FalkorDB.2) Connect MCP server to CursorWith tools and our server ready, let's integrate it with our Cursor IDE!Go to: File → Preferences → Cursor Settings → MCP → Ad ...

Big update for Claude Desktop and Cursor users!Now you can connect all AI apps via a common memory layer in a minute.I used the Graphiti MCP server that runs 100% locally to cross-operate across AI apps like Claude Desktop and Cursor without losing context.(setup below) https://t.co/AZrxcS2cR5 ...

Connecting AI models to different apps usually means writing custom code for each one.For instance, if you want to use a model in a Slack bot or in a dashboard, you'd typically need to write separate integration code for each app.Let's learn how to simplify this via MCPs.We’ll use @LightningAI's LitServe, a popular open-source serving engine for AI models built on FastAPI.It integrates MCP via a dedicated /mcp endpoint.This means that any AI model, RAG, or agent can be deployed as an MCP server, accessible ...

Group 1: OpenAI and Claude Developments - OpenAI launched the AI browser ChatGPT Atlas based on the Chromium engine, currently available for macOS and will expand to Windows and mobile versions, integrating ChatGPT deeply into the browser with memory features and agent mode for complex tasks like booking and shopping [1] - Claude has officially released a desktop version supporting both Mac and Windows, featuring global shortcuts, window sharing, voice input, and tool connections, allowing users to view screen content and connect to various tools [2] Group 2: Google AI Studio and Tencent Developments - Google AI Studio introduced the vibe Coding experience, enabling users to generate AI applications with a single click and providing real-time code editing and deployment options, making it user-friendly for beginners [3] - Tencent's Mixworld Model 1.1 has been open-sourced, supporting multi-modal input and achieving significant performance improvements in real-world tasks, with a pure feedforward architecture allowing for rapid inference [4] Group 3: Baichuan-M2 Plus and Huawei Innovations - Baichuan Intelligent released Baichuan-M2 Plus, the first evidence-enhanced medical model, achieving high scores in various medical exams and demonstrating superior medical knowledge application capabilities [6] - Huawei's HarmonyOS 6 was launched, enabling seamless interaction with Apple devices and enhancing AI capabilities, including note-taking and automated shopping features [7] Group 4: Dexmal and Robotics Innovations - Dexmal introduced the open-source VLA code library Dexbotic, designed to facilitate algorithm reproduction across various simulation environments, addressing industry challenges in research and development [8] - Songyan Power launched the Bumi humanoid robot priced under 10,000 yuan, featuring 21 degrees of freedom and capabilities for education and companionship, marking a shift from laboratory to consumer applications [9] Group 5: Samsung's XR Headset - Samsung unveiled its first flagship XR headset, Galaxy XR, priced at approximately 12,800 yuan, featuring advanced specifications and capabilities, including a high-resolution display and extensive sensor tracking [10][11] Group 6: Insights on AI Agent Development - A former Manus researcher reflected on AI development, emphasizing that the key to AI Agent capability transformation lies in effective cognitive processes rather than just model intelligence, highlighting the evolution of agent capabilities [12]

Core Insights - OpenAI and Anthropic have recently launched new AI products, with OpenAI introducing ChatGPT Atlas and Anthropic releasing Claude Desktop, indicating a competitive landscape in AI browser integration [2][3][5]. OpenAI's ChatGPT Atlas - ChatGPT Atlas integrates ChatGPT directly into the browser, allowing users to access AI assistance without leaving their current webpage [3][15]. - Key features include a sidebar for real-time assistance, browser memory for recalling past interactions, and an AI agent that can perform tasks like filling forms and making purchases [4][19][53]. - The browser memory feature enables ChatGPT to remember user browsing history and context, enhancing the relevance of its responses [17][46]. - Users can control their privacy settings, including the ability to delete browsing history and manage what the AI can access [48][49]. Anthropic's Claude Desktop - Claude Desktop allows users to summon the AI assistant from any application, with features like screen sharing and global shortcuts for quick access [4][7]. - The integration of ChatGPT into Claude Desktop provides users with a versatile tool for various tasks, enhancing productivity [3][5]. Competitive Landscape - The AI browser market is heating up, with various companies, including Perplexity and Google, also developing AI-integrated browsing solutions [78][82]. - OpenAI's Atlas aims to challenge the dominance of Chrome, which has over 3 billion users, although its impact on the broader market remains uncertain [88][89]. Future Developments - OpenAI plans to continue evolving Atlas, with features aimed at improving user experience and developer tools [62][63]. - The ongoing competition in the AI browser space suggests a significant shift in how users interact with web content and AI technologies [74][86].

Core Insights - The article emphasizes the need to rethink tool development for agentic AI systems, moving away from traditional deterministic logic to accommodate the non-deterministic nature of AI agents [1][3][10] - It highlights that the effectiveness of AI agents is heavily dependent on the tools provided to them, and outlines a path for optimizing these tools [1][3][4] Tool Definition and Development - Tools for AI agents are defined as new software forms that bridge deterministic systems and non-deterministic agents, requiring a different approach to design [8][9][10] - The article suggests a rapid prototyping approach for tool development, followed by comprehensive evaluations to assess performance and make iterative improvements [12][14] Evaluation Process - Evaluation tasks should be generated based on real-world scenarios and data sources, ensuring that prompts are paired with verifiable responses [23][25] - The article advises against overly simplistic testing environments, advocating for complex conditions that can effectively stress-test the tools [27] Tool Design Principles - It is recommended to build a limited number of well-thought-out tools that align with high-value workflows, rather than creating numerous redundant tools [43][47] - Tools should be designed with clear and independent objectives to prevent confusion among AI agents when selecting the appropriate tool [45][50] Naming and Response Optimization - Implementing namespaces for tools can help clarify their functions and reduce confusion for AI agents [48][51] - Tools should return high-signal information, prioritizing context relevance over flexibility, to enhance the agent's performance [52][56] Future Outlook - The article concludes that the development of efficient tools for AI agents requires a shift from predictable deterministic patterns to non-deterministic approaches, with a focus on iterative, evaluation-driven processes [66]

Core Viewpoint - The article highlights the emerging security risks associated with the widespread deployment of the MCP (Multi-Channel Protocol), particularly the "lethal trifecta" attack model that combines prompt injection, sensitive data access, and information exfiltration, posing significant threats to SQL databases and other sensitive systems [1][3][15]. Group 1: MCP Deployment and Popularity - The MCP was quietly released at the end of 2024, gaining rapid traction with over 1,000 servers online by early 2025, and significant interest on platforms like GitHub, where related projects received over 33,000 stars [2][3]. - Major tech companies, including Google, OpenAI, and Microsoft, quickly integrated MCP into their ecosystems, leading to a surge in the creation of MCP servers by developers due to its simplicity and effectiveness [2][3]. Group 2: Security Risks and Attack Mechanisms - General Analysis identified a new attack pattern facilitated by MCP's architecture, where attackers can exploit prompt injection to gain unauthorized access to sensitive data [3][4]. - A specific case involving Supabase MCP demonstrated how an attacker could insert a seemingly benign message into a customer support ticket, prompting the MCP agent to leak sensitive integration tokens [4][6]. - The attack process was completed in under 30 seconds, highlighting the speed and stealth of such vulnerabilities, which can occur without triggering alarms or requiring elevated privileges [4][8]. Group 3: Architectural Issues and Recommendations - The article emphasizes that the security issues with MCP are not merely software bugs but fundamental architectural problems that need to be addressed at the system level [12][15]. - Supabase's CEO reiterated that MCP should not be connected to production databases, a caution that applies universally to all MCP implementations [13][14]. - The integration of OAuth with MCP has been criticized for not adequately addressing the security needs of AI agents, leading to potential vulnerabilities in how sensitive data is accessed and managed [17][20]. Group 4: Future Considerations and Industry Response - The article suggests that the current challenges with MCP require a reevaluation of security protocols and practices as the industry moves towards more integrated AI solutions [21]. - Experts believe that while the integration of different protocols like OAuth and MCP presents challenges, it is a necessary evolution that will ultimately succeed with ongoing feedback and adjustments [21].

Core Insights - The article highlights a significant security risk associated with the use of MCP (Multi-Channel Protocol) in AI applications, particularly the potential for SQL database leaks through a "lethal trifecta" attack pattern involving prompt injection, sensitive data access, and information exfiltration [1][4][19]. Group 1: MCP Deployment and Popularity - MCP has rapidly gained traction since its release in late 2024, with over 1,000 servers online by early 2025 and significant interest on platforms like GitHub, where related projects received over 33,000 stars [3]. - The simplicity and lightweight nature of MCP have led to a surge in developers creating their own MCP servers, allowing for easy integration with tools like Slack and Google Drive [3][4]. Group 2: Security Risks and Attack Mechanisms - General Analysis has identified a new attack mode stemming from the widespread deployment of MCP, which combines prompt injection with high-privilege operations and automated data return [4][19]. - An example of this vulnerability was demonstrated through an attack on Supabase MCP, where an attacker could extract sensitive integration tokens by submitting a seemingly benign customer support ticket [5][11]. Group 3: Attack Process Breakdown - The attack process involves five steps: setting up an environment, creating an attack entry point through a crafted support ticket, triggering the attack via a routine developer query, agent hijacking to execute SQL commands, and finally, data harvesting [7][9][11]. - The attack can occur without privilege escalation, as it exploits the existing permissions of the MCP agent, making it a significant threat to any team exposing production databases to MCP [11][13]. Group 4: Architectural Issues and Security Design Flaws - The article argues that the vulnerabilities are not merely software bugs but rather architectural issues inherent in the MCP design, which lacks adequate security measures [14][19]. - The integration of OAuth with MCP has been criticized as a mismatch, as OAuth was designed for human user authorization, while MCP is intended for AI agents, leading to fundamental security challenges [21][25]. Group 5: Future Considerations and Industry Implications - The ongoing evolution of MCP and its integration into various platforms necessitates a reevaluation of security protocols and practices within the industry [19][25]. - Experts emphasize the need for a comprehensive understanding of the security implications of using MCP, as the current design does not adequately address the risks associated with malicious calls [25].