Jinzhi Zhang(JZ)

On-device AI becomes powerful only when it works reliably on real hardware. In this Melange Walkthrough, Yeonseok Kim and Sun-Q Kim break down how the full on-device pipeline works, from model selection and quantization to real-device benchmarking and SDK deployment. Select. Benchmark. Deploy. This is how production-ready on-device AI should operate. Watch the full walkthrough below. #OnDeviceAI #EdgeAI #MobileAI #AIInfrastructure #ZETIC #Melange

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🚀 Vision & Multimodal AI just landed in OCI Generative AI for LangChain Excited to share that vision and multimodal support is now available in the OCI Generative AI Integration for LangChain. Developers can now analyze images alongside text on Oracle Cloud Infrastructure using a single, unified LangChain interface—no custom adapters, no vendor-specific rewrites. Read more here: https://lnkd.in/eHqzHiBm What this enables 👇 🖼️ Extract insights from scanned documents, invoices, contracts, and images 🔎 Build multimodal RAG pipelines over enterprise data ⚙️ Switch between vision-capable models by changing one parameter 🏗️ Integrate seamlessly with LangChain 1.x and LangGraph workflows This release brings 13 vision-capable models across multiple providers under one consistent API, making it easier to prototype, compare models, and move multimodal AI into production—without integration complexity. For enterprises sitting on decades of PDFs, images, and records, this is a big step toward turning previously “dark data” into something searchable, analyzable, and actionable. 📖 Blog post: Vision and Multimodal AI Now Available in OCI Generative AI Integration for LangChain https://lnkd.in/eHqzHiBm 🔗 Try it today: pip install -U langchain-oci https://lnkd.in/eSHzq-Ki Would love to hear how folks are thinking about multimodal use cases—document processing, visual inspection, customer service, or beyond. #Oracle #OCI #GenerativeAI #MultimodalAI #VisionAI #LangChain #LangGraph #EnterpriseAI #DeveloperExperience #AIEngineering

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Just added a minimalist tutorial for AXLearn at https://lnkd.in/dARZxxxH

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People often call Jensen’s story “overnight success,” but it’s really decades of long-term bets and relentless execution. In this CNBC segment, The Thinking Machine author Stephen Witt captures how vision, grit, and engineering rigor at NVIDIA helped lay the groundwork for today’s AI era

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A new study from Carnegie Mellon, “Does AI-Assisted Coding Deliver? A Difference-in-Differences Study of Cursor’s Impact on Software Projects” that looks at a more recent period, tracking code quality in projects using GenAI tools up to mid-2025, finds that AI briefly accelerates code generation, but the underlying code quality trends continue to move in the wrong direction. https://lnkd.in/gm6dxb8W

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AI San Diego: Community Outlook San Diego is well-suited for AI development for several key reasons, including a strong tech ecosystem, robust infrastructure, academic excellence, industry presence, and government support. Here's an overview of the factors that make San Diego an attractive location for AI development: 1. Strong Tech Ecosystem Tech Companies and Startups: San Diego is home to a vibrant tech community with numerous companies and startups specializing in AI, machine learning, and related fields. Notable AI-focused companies in the area include Shield AI, Brain Corp (robotics), and Reticular Systems. 2. Academic Excellence Universities and Research Institutions: San Diego boasts world-class universities and research institutions that contribute to AI research and development. The University of California, San Diego (UCSD) is a leading institution in AI research, with strong programs in computer science, engineering, and cognitive science. 3. Robust Infrastructure High-Speed Internet and Connectivity: San Diego has excellent digital infrastructure, including high-speed internet and reliable connectivity, essential for AI research and deployment. Data Centers: The presence of modern data centers ensures that companies have access to the necessary computing power and storage for AI training and data processing. 4. Industry Presence Defense and Aerospace: San Diego has a significant presence of defense and aerospace companies, such as Northrop Grumman and General Atomics. These industries are increasingly leveraging AI for applications like autonomous systems, cybersecurity, and data analysis. Life Sciences and Healthcare: The city is a hub for life sciences and healthcare industries, where AI is being applied for drug discovery, personalized medicine, and diagnostic tools. Companies like Illumina and ResMed are examples of leaders in this sector. 5. Government and Military Support Department of Defense (DoD) Projects: San Diego’s proximity to military installations and defense contractors facilitates partnerships on AI projects funded by the DoD, enhancing R&D in autonomous systems and defense applications. 6. Networking and Community Professional Organizations: Organizations like the San Diego Tech Hub and AI enthusiast groups foster a sense of community and collaboration among AI professionals and enthusiasts. 7. Quality of Life Attractive Living Conditions: San Diego’s high quality of life, including its favorable climate, scenic environment, and vibrant cultural scene, makes it an attractive place for tech professionals to live and work.

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I think the next Silicon Valley might be in Hangzhou, China, and it might be called AI Valley. 1). Hangzhou is the home of AI startups like DeepSeek, BrainCo, Deep Robotics, ManyCore, and Unitree Robotics, plus giants like Alibaba. 2). The government helps this growth with tax breaks, housing support, and huge access to computing power. 3). Universities are also feeding fresh talent straight into these companies. Where else in the world do we have all these to support AI growth? Only in in Hangzhou, China, and I have no idea why it can't be replicated in the US or other countries. #AI #Innovation #Startups #FutureOfTech #China #AIValley #Hangzhou

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Milestone Unlocked: Patent Pending for Augmented Pre-Vectorization AI Architecture I’m proud to announce that Saravanan Prasannam and I have officially filed a patent application for our proprietary Semantic Matching Engine. While building Resume Ranker, we identified a critical gap in how standard Vector Search and RAG models handle domain-specific data. They often miss the "unwritten" context. Our invention introduces a novel Augmented Pre-Vectorization Architecture. By artificially imbuing implicit knowledge into the data pipeline before the embedding stage, our system captures deep semantic nuance that traditional models miss. We aren't just matching keywords; we are engineering a system that understands context through multi-dimensional semantic enrichment. #AI #PatentPending #LLM #Innovation #RecruitmentTech #VectorSearch #ArtificialIntelligence

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3 Core Pillars of Competitive AI What separates serious AI builders from the rest? As shared by Scale AI CEO Alex Wang on #20VC, it all comes down to this: Algorithm The intelligence layer where better architectures enable models to reason, generate, and adapt. Compute The power layer raw processing capability through GPUs, TPUs, and custom hardware to train and run models at scale. Data The fuel layer clean, labeled, and domain-specific datasets that models learn from and improve on. The real edge? → It's not about choosing one. → It's about integrating all three seamlessly. This framework isn’t just theory. It’s how today’s top AI builders are thinking. #AI #ArtificialIntelligence #ScaleAI #AlexWang #20VC

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