Not for distribution to U.S. news wire services or dissemination in the United States VANCOUVER, BC, Oct. 01, 2025 (GLOBE NEWSWIRE) -- Fobi AI Inc. (FOBI:TSXV) (the "Company" or "Fobi"), an industry leader in harnessing AI and data intelligence to enable digital transformation, is pleased to announce that it has filed its annual audited financial statements for the fiscal year ended June 30, 2024, associated management discussion and analysis and related disclosures (together, the “2024 Annual Filings”), as ...

AI Infrastructure & Partnership - DDN's data intelligence platform accelerates the NVIDIA AI factory by transforming raw data into actionable intelligence [1] - DDN unifies and harmonizes data, orchestrating every stage of the AI pipeline while accelerating performance securely [2] - DDN is an NVIDIA certified partner, essential for enabling NVIDIA supercomputers [4] - DDN accelerates performance to meet modern AI demands and scale into the future, from triggering processes to storing embeddings [3] Core Capabilities - DDN's platform cuts through legacy bottlenecks and tangled tools, extracting data value efficiently [2] - DDN enables lightning-fast search and low latency object access [3] - DDN facilitates the quick start of clusters with experience and a proven track record [4] Strategic Focus - DDN is driving the next frontier of AI, enabling faster innovation and progress [4] - DDN is expanding its partnership with NVIDIA into the enterprise world [4] - DDN is crucial for deploying AI factories that handle large volumes of data in and out [3]

Core Insights - The emergence of AI title generation tools is transforming the digital marketing landscape by enhancing the efficiency and quality of creative content production, particularly in title creation [1][3] - These AI tools utilize algorithms to quickly generate a vast array of title options, significantly reducing creation time while improving click-through and conversion rates through data-driven strategies [1][3] Group 1: AI Tools and Their Impact - Traditional title creation relies on human brainstorming, which can lead to cognitive biases and creative fatigue, whereas AI tools leverage natural language processing (NLP) and big data to analyze trending keywords, user sentiment, and industry trends [3] - AI title tools can provide various styles of titles, such as suspenseful, list-based, or question-based, enabling marketers to rapidly test and identify optimal solutions [3] - The "AI Marketing Five-Star Model" proposed by Rongzhi Technology emphasizes the deep integration of data assets and AI, covering strategy generation, content creation, and performance tracking, benefiting over a thousand companies and driving cumulative performance exceeding 10 billion [3][5] Group 2: Case Studies and Expert Insights - The application of AI title tools has led to significant results, such as a 300% increase in customer acquisition within 21 days for participating companies [3] - An Zhey, the founder of Rongzhi AI, combines multi-industry service experience and data asset management to provide a theoretical framework and technical support for AI tool applications [5] - The transition facilitated by AI tools allows marketers to shift from repetitive tasks to higher-level strategic decision-making and emotional design, creating a hybrid model of "machine rationality + human sensitivity" [5][7] Group 3: Industry Transformation - The rise of AI title tools signifies a shift in the marketing industry from "labor-intensive creativity" to "brain-intensive strategy," enabling a focus on innovation and insights while algorithms drive growth [7]

Infinia Platform Overview - Infinia is a software-defined, metadata-driven, containerized, cloud-native data intelligence platform designed for scalability, performance, and efficiency across core, cloud, and edge environments [1] - The platform supports critical data protocols like object and block, integrating with AI data acceleration libraries like TensorFlow and PyTorch [1] - Infinia enhances AI execution engines by serving data in its native form, reducing the need for data conversion and speeding up applications [1] Metadata and Multi-Tenancy Capabilities - Infinia allows for tagging massive amounts of metadata to objects, enabling faster data discovery and processing, with no limitations on metadata capability [1] - The platform has built-in multi-tenancy capabilities, providing SLAs for individual tenants and sub-tenants on capacity and performance, ensuring quality of service [1] Scalability and Cloud Native Design - Infinia is fully containerized, allowing for scale-out at web scale, starting from a few terabytes and scaling to exabytes [1] - The product is designed to be cloud-native and will soon be available in leading cloud provider marketplaces [2] AI Data Challenges and Solutions - Infinia addresses the complexity of managing large amounts of distributed multimodal data across core, cloud, and edge environments by creating a unified platform [1] - It tackles the demand for extremely low latency required to run AI applications, as well as the high costs associated with running AI [1] - The platform ensures data protection at any time and at any scale [1] Performance Metrics - Infinia can deliver time to first byte in less than a microsecond [2] - It can deliver 30 to 40 million objects per second in list object operations [2] - Infinia can deliver terabytes per second throughput at large scale [2] Efficiency and Sustainability - Infinia can achieve 10x data reduction, fitting over 100 petabytes of storage into a single rack [2] - It can reduce the overall data center footprint by a quarter compared to competitors, saving 10x power and cooling costs [2] Security Features - Infinia focuses on security authentication and access control, preventing unauthorized data access [2] - Data is always encrypted, and all actions within the system are audited [2] - The platform provides 99.9999% uptime enabled by reliability-focused features [2] Key Business Outcomes - Infinia aims to reduce complexity and achieve more accurate results on a unified platform for AI inference, data analytics, and model training [2] - It accelerates innovation by running AI apps faster, enabling businesses to beat the competition [2] - The platform enables rapid deployment across the cloud core and the edge to increase productivity, boost efficiency, and maximize ROI [2]

Core Message - AI is essentially HPC (High Performance Computing), emphasizing the importance of data in both fields for extracting intelligence and value [1] - DDN (DataDirect Networks) provides the "rocket fuel" or data intelligence infrastructure that enables better, faster, and more accurate insights from massive datasets in real-time [1] - Data intelligence is critical for AI transformation, enablement, and acceleration, requiring the unification, curation, and analysis of distributed data from various sources [1] Challenges and Solutions - Current challenges hindering AI acceleration include GPU scarcity, limited data center space, and insufficient power; a data intelligence framework is needed to alleviate these issues [2] - DDN's solutions focus on delivering more capabilities from existing GPUs, shrinking data center footprint, and lowering power consumption [2] - DDN accelerates data ingestion, freeing up GPU cycles, and optimizing networks to reduce time to insight and enhance value [2] DDN's Technology and Positioning - DDN is the only data intelligence platform deployed internally at NVIDIA, and also supports massive deployments like XAI with over 100,000 GPUs [1] - DDN's new technology, Infinia, is a high-performance, multi-tenancy data intelligence platform that supports multiple protocols and minimizes data movement [2] - DDN's solutions maximize the value from infrastructures deployed at scale in data centers and the cloud, benefiting both HPC and AI applications [3] Market Impact and Growth - DDN powers more than half a million GPUs and has deployments at the exobyte level, demonstrating significant growth and scale [3] - DDN's ability to solve challenges at massive scale translates to bulletproof stability and cost-effectiveness across a broad range of installations [3] - DDN aims to accelerate scientific and business outcomes by handling data at the edge, in data centers, and in the cloud [3]

Group 1: Introduction of New Program - Merrill and Bank of America have launched the Alts Expanded Access Program targeting ultra-high-net-worth (UHNW) clients with a net worth of $50 million or more, set to debut this fall [2] - The program aims to provide access to specialized investment opportunities in emerging themes, niche strategies, and evolving sectors, which are not widely distributed [4] Group 2: Market Trends and Client Needs - There is a growing trend among wealthy Americans, particularly younger high-net-worth investors, showing increased interest in alternative investments, with many planning to boost their allocations in the coming years [4] - The shift towards non-traditional investments is driven by evolving wealth-building needs and market changes, as noted by Mark Sutterlin, head of alternative investments for Merrill and Bank of America Private Bank [3] Group 3: Broader Commitment to Client Needs - The introduction of this program reflects the broader commitment of Bank of America to address the complex financial goals of UHNW clients [5]

Uh this is really interesting. You know our journey started out in high performance computing and high performance computing training foundation models and frontier models. That is the extreme version of high performance computing.And so so Nvidia and DDN um found our friendship and our long-term partnership through that origin. Now what we'd like to do as we train these models, these models can now uh turn the world's enterprises, all of our data from raw data into data intelligence. Yeah.And so now our wo ...

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AI Value & Adoption - AI 的成本价值尚未完全实现，尤其是在聊天机器人等应用中，但未来可期 [1] - 行业对超级智能的关注分散了对企业实际需求的注意力 [2] Agent Bricks Focus - Agent Bricks 专注于解决企业数据智能问题，而非超级智能 [3] - Agent Bricks 致力于利用企业现有数据提供智能服务 [3] - Agent Bricks 关注企业实际需求，如新员工入职、财务政策咨询、营销活动优化等 [3] Market Perspective - 目前专注于数据智能的公司数量不多，市场仍处于早期阶段 [3]