Core Viewpoint - Security risk management is not merely a cost center but a value engine for companies to build brand reputation and gain market trust in the AI era [2][4]. Group 1: AI Risks and Security - AI risks have already become a reality, as evidenced by the recent vulnerability in the open-source model tool Ollama, which had an unprotected port [6][12]. - The notion of "exchanging privacy for convenience" is dangerous and can lead to irreversible risks, as AI can reconstruct personal profiles from fragmented data [6][10]. - AI risks are a "new species," and traditional methods are inadequate to address them due to their inherent complexities, such as algorithmic black boxes and model hallucinations [6][12]. - Companies must develop new AI security protection systems that adapt to these unique characteristics [6][12]. Group 2: Strategic Advantages of Security Compliance - Security compliance should be viewed as a strategic advantage rather than a mere compliance action, with companies encouraged to transform compliance requirements into internal risk control indicators [6][12]. - The approach to AI application registration should focus on enhancing risk management capabilities rather than just fulfilling regulatory requirements [6][15]. Group 3: Recommendations for Enterprises - Companies should adopt a mixed strategy of "core closed-source and peripheral open-source" models, using closed-source for sensitive operations and open-source for innovation [7][23]. - To ensure the long-term success of AI initiatives, companies should cultivate a mindset of curiosity, pragmatism, and respect for compliance [7][24]. - A systematic AI security compliance governance framework should be established, integrating risk management into the entire business lifecycle [7][24]. Group 4: Emerging Threats and Defense Mechanisms - "Prompt injection" attacks are akin to social engineering and require multi-dimensional defense mechanisms, including input filtering and sandbox isolation [7][19]. - Companies should implement behavior monitoring and context tracing to enhance security against sophisticated AI attacks [7][19][20]. - The debate between open-source and closed-source models is not binary; companies should choose based on their specific needs and risk tolerance [7][21][23].

Open Source LLM Framework - A framework connects any LLM to any MCP server (open-source) [1] - The framework enables building custom MCP Agents without closed-source apps [1] - Compatible with Ollama, LangChain, etc [1] - Allows building 100% local MCP clients [1]

LLM & MCP Integration - A framework enables connecting any LLM to any MCP server [1] - The framework facilitates building custom MCP Agents without relying on closed-source applications [1] - It is compatible with tools like Ollama and LangChain [1] - The framework allows building 100% local MCP clients [1]

We have fine-tuned DeepSeek (distilled Llama).Now we can interact with it like any other model running on Ollama using:- The CLI- Ollama's Python package- Ollama's LlamaIndex integration, etc. https://t.co/bCNUqtLgaJ ...

Core Insights - Supabase has successfully positioned itself at the forefront of the "Vibe Coding" trend, completing a $200 million Series D funding round with a post-money valuation of $2 billion, reflecting its rapid growth and the increasing importance of open-source databases in the AI application era [1][22]. Group 1: Supabase's Growth and Funding - Supabase raised $200 million in its Series D funding round, led by Accel, with participation from Coatue, Y Combinator, Craft Ventures, and existing investors, bringing its total funding to nearly $400 million [1]. - The company has seen a significant increase in its valuation, reaching $2 billion just seven months after its previous funding round of $80 million [1]. - Supabase's user base has expanded to over 2 million developers, managing 3.5 million databases, and its GitHub repository has surpassed 81,000 stars, doubling in just two years [17]. Group 2: Vibe Coding and Development Workflow - The "Vibe Coding" workflow emphasizes rapid completion of the entire development process using various AI tools, from product documentation to database design and service implementation [2][5]. - Developers utilize generative AI tools to draft product requirement documents and generate database schemas, facilitating the creation of initial data models [4]. - The integration of Supabase with tools like Lovable and Bolt.new allows users to deploy full-stack applications without server setup, enhancing the development experience [5][8]. Group 3: AI Integration and Features - Supabase has integrated PGVector to support embedding storage, crucial for building retrieval-augmented generation (RAG) applications and other AI-related tasks [11]. - The company launched its AI assistant, which can automatically generate database schemas and fill in sample data, significantly aiding non-developers in backend prototype development [13]. - Recent developments include the launch of an official MCP server, enabling developers to connect popular AI tools directly to Supabase for various database management tasks [14]. Group 4: Competitive Positioning and Future Outlook - Supabase's open-source model and reliance on PostgreSQL differentiate it from other backend-as-a-service (BaaS) platforms like Firebase, which lock users into their ecosystems [22]. - The company aims to become the default backend for AI and enterprise applications, leveraging its funding to accelerate the adoption of "Vibe Coding" tools and large-scale deployments [22]. - Accel partners believe Supabase has the potential to dominate the high-value database sector, drawing comparisons to the rise of Oracle and MongoDB [22].

Core Viewpoint - Trustworthy digital finance should possess characteristics such as model reliability, strong interpretability, and high security, while also clarifying the legal status, behavioral boundaries, and responsibilities of financial intelligent agents [2][12]. Group 1: Breakthroughs in AI Models - China's DeepSeek-V3 has received high praise in global AI model rankings, being compared favorably to GPT-4o, with training costs significantly lower at under $6 million compared to GPT-4o's $100 million [4]. - Innovations in algorithms, such as MLA multi-head potential attention mechanisms and MoE mixed expert architecture, are crucial for the future of AI development in China, particularly for financial institutions [4][5]. Group 2: Challenges in AI Technology - Security risks remain prominent, including unauthorized access to models, data theft, and malicious attacks that can compromise model integrity and stability [8]. - The phenomenon of "model hallucination" persists, with various models including Grok-3 and GPT-4 exhibiting certain levels of hallucination rates [9]. - Issues such as model bias, algorithmic resonance, and privacy breaches continue to pose challenges, complicating the interpretability of AI models [10]. Group 3: Digital Finance Innovation - The evolution of digital finance must balance security and efficiency, transitioning from mere usability to leading-edge capabilities [12][13]. - Trustworthiness in digital finance innovation is essential, requiring proactive measures to prevent AI pitfalls and ensure model reliability and interpretability [13]. Group 4: Pathways to Building Trustworthy Digital Finance - High reliability is critical, necessitating the implementation of advanced security measures, including firewalls and zero-trust architectures, to protect against malicious attacks [15]. - Interpretability is a key requirement, enabling the transformation of model behavior into understandable rules and utilizing visualization tools to clarify model processes [15]. - Legal frameworks must be established to define the status and responsibilities of financial intelligent agents, ensuring they operate within clear boundaries [16]. - Economic efficiency can be achieved by pre-training industry-level financial models and customizing enterprise-level applications, fostering collaboration between tech firms and financial institutions [16].