Knowledge Graph & LLM Application - Knowledge graphs combined with large language models (LLMs) can be used to build AI applications, particularly with graph retrieval augmented generation (RAG) architecture [2] - Graph RAG replaces vector databases with knowledge graphs built on graph databases, enhancing retrieval strategies [3] - Using a knowledge graph provides richer retrieval strategies beyond vector semantic search, including contextualization and structured queries [4] - Property graph model implements nodes and relationships, nodes represent entities and relationships connect them [4][5] Ontology & Schema - Ontologies provide an implementation-agnostic approach to representing schemas, facilitating knowledge graph creation for both structured and unstructured data pipelines [14][17] - Ontologies describe a domain with definitions of classes and relationships, matching well with graph models [15] - Financial Industry Business Ontology (FIBO) is a public financial industry ontology example [15] - Storing ontologies in the graph can drive dynamic behavior in retrievers, allowing for on-the-fly adjustments by modifying the ontology [29][30] Retrieval Strategies - Graph captures text chunks with embeddings, creating a new search space for vector search [20] - Vector search finds vectors in proximity, which can be dereferenced back to the graph for contextualization, navigation, and enrichment [20] - Dynamic queries, driven by ontologies, can be used to create dynamic retrievers, enabling data-driven behavior [26][29]

Core Problem in AI Coding Space - Generating code is not difficult, but generating code that fits into existing codebases, adheres to organizational policies, personal preferences, and is future-proof is challenging [13][14][15] - The magic of AI coding tools like Windsurf lies in context, specifically "what context" and "how much" [16] Windsurf's Context Philosophy - "What context" is divided into two buckets: heuristics (user behavior) and hard evidence (environment/codebase) [17][18][19] - Relevant output is determined by the prompt, the state of the codebase, and the user state [20] - Windsurf prioritizes optimizing the relevance of context over simply increasing the size of the context window to address latency [21][22] Windsurf's Capabilities - Windsurf excels at finding relevant context quickly due to its background in GPU optimization [23] - Windsurf provides connectors for users to perform context retrieval at their level, including embedding search, memories, rules, and custom workspaces [24] Data Privacy - Windsurf processes information only within the user's editor and does not access the user's operating machine [31] - Windsurf's servers are stateless, and the company does not store or train on user data [31][32]

AI Infrastructure & Market Perspective - Oracle views AI at an inflection point, suggesting significant growth and change in the industry [1] - The discussion highlights that it's a great time to be an AI customer, implying increased options and competitive pricing [1] - Enterprise AI adoption is underway, but the extent of adoption is still being evaluated [1] - The future of AI training and inference is a key area of focus, indicating ongoing development and innovation [1] Technology & Partnerships - Oracle emphasizes making AI easy for enterprise adoption, suggesting user-friendly solutions and services [1] - AMD and Oracle have a performance-driven partnership, indicating collaboration to optimize AI infrastructure [1] - Cross-collaboration across the AI ecosystem is considered crucial for advancement [1] - Co-innovation on MI355 and future roadmaps between AMD and Oracle is underway [1] - Openness and freedom from lock-in are promoted, suggesting a preference for flexible and interoperable AI solutions [1] Operational Considerations - Training large language models at scale requires evolving compute needs and energy efficiency [1] - Operating in a scarce environment is a challenge, potentially referring to resource constraints like compute power or data [1] - Edge inference can be enabled with fewer GPUs, suggesting advancements in efficient AI deployment [1] Ethical & Societal Impact - Societal impact, guardrails, and responsibility are important considerations in the development and deployment of AI [1]

Oh, it's a great question. Um, you know, I'm trying to think what do I tell my kids to go to school for. What what should they do.The short answer is I don't know. But what I speculate, anything machine learning, AI, large language models. What happened is back in the day, you were competing over Java, Python coders.They'd come in and you they they were preaching top dollar. Whatever they wanted, you had to give it to them. Now, with AI, it will code in any language you want.And so, you can be much more uh ...

Core Viewpoint - The unusual buying activity in Reddit Inc. stock, particularly through call options, indicates significant interest from large investors, suggesting a potential upside for the stock as it approaches its expiration date [1][3][8]. Group 1: Stock Performance and Market Position - Reddit's stock currently trades at $133.92, which is 58% of its 52-week high of $230.41, indicating a substantial "catch-up" potential [10]. - The market capitalization of Reddit is $24.7 billion, making it a smaller player compared to its technology peers [10]. - Analysts have set a 12-month price target for Reddit at $139.35, representing a 4.05% upside from the current price [11]. Group 2: Options Activity and Investor Sentiment - As of mid-June 2025, there were 87,739 call options purchased for Reddit stock, suggesting a multi-million-dollar bet on the stock's rise [8]. - A decline of 8.1% in Reddit's short interest over the past month indicates bearish capitulation, as short sellers are exiting their positions due to perceived upside potential [13]. - Analyst Alan Gould from Loop Capital has reiterated a Buy rating for Reddit, with a valuation target of $200 per share, indicating a potential 50% upside [14]. Group 3: Business Model and Industry Context - Reddit is recognized as a significant player in the technology sector, particularly in the context of artificial intelligence, due to its user-generated content [4][5]. - The platform's restrictions on sales and marketing content lead to more organic language, which is valuable for training large language models [6]. - The business model of Reddit is considered stable and self-sustainable, making it a safe haven for investors amid geopolitical and economic conflicts [7].

Healthcare Challenges & Opportunities - AI in healthcare aims to augment human intelligence, not replace it, utilizing technologies like machine learning and natural language processing [4][5] - The healthcare industry faces challenges including information overload, clinician burnout, and wasteful spending, with 20% of costs considered wasteful and significant expenses related to billing and administration [8][10][11] - AI can improve diagnosis using image recognition, reduce documentation burden through automated note-taking, and enhance hospital flow by triaging patients and allocating resources [12][13][15] AI Adoption & Concerns - AI adoption in healthcare follows the Gartner hype cycle, with image recognition already productive but disease treatment and behavioral health still facing inflated expectations and disillusionment [6][7] - There is existing bias against AI-generated medical advice, with people finding it less reliable and empathetic compared to advice from human doctors [16][17] - Legal and ethical questions arise regarding data ownership, liability for incorrect AI advice, and potential loss of trust in manual processes due to AI involvement [18][19] - Bias can be built into AI systems through problem selection, data collection methods, and inherent assumptions, potentially leading to skewed outcomes [21] Future Vision - The future vision involves AI-powered wearable devices that can detect health issues, alert emergency services, and transmit vital information to hospitals, improving patient care and outcomes [22][23][24][25]

AI Technology & Innovation - The cost of innovation is collapsing, marking a hugely important era that will transform lives [1] - Large language models (LLMs) are the most disruptive technology in history, but they are prone to errors, hallucinations, and uncertainty [1][2][6] - Harmonic is developing a mathematical superintelligence, an AI that can solve math problems exceeding the level of advanced mathematics researchers, to accelerate progress in science and technology [4][5] - Sandbox AQ focuses on large quantitative models (LQMs) trained on numbers and data, impacting 80% of the economy and driving innovation [11][12] Investment & Market Opportunities - There are two ways to invest in the AI future: focus on private AI companies or invest in public hyperscalers like Amazon, Microsoft, and Google [15][16] - A more fruitful approach may be to analyze industry-by-industry to identify AI winners and losers in sectors like pharmaceuticals, energy, telco, and financial services [17] - Winners embracing AI and laggards not embracing AI fast enough will see billions of dollars of market cap difference [18] Business Applications & Commercialization - Large language models (LLMs) can cut costs, potentially saving companies $10 million to $20 million annually on customer service from a $30 million cost base [12] - Large quantitative models (LQMs) are creating new products and billions of dollars of new revenue, primarily in B2B applications [13] - Sandbox AQ is working with Sanofi (a top 10 pharma company) and Aramco to advance life-saving medicines and upvalue hydrocarbons, respectively [14] - Investors should look for AI applications with clear business cases, such as accounting, taxes, drug discovery, and risk evaluation [22] Risk & Limitations - Large language models (LLMs) are often very confident but can be incorrect, with few controls over hallucinations, hindering adoption in mission-critical industries [6][7] - Any AI system will make mistakes, and investors must ensure that these mistakes do not bankrupt the company [23] - Harmonic closed a $75 million Series A funding round led by Sequoia Capital [8]

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.Avi Chawla (@_avichawla):Let's build an MCP server (100% locally): ...

Uh hello everyone. So today I will be talking about AI and sustainability. So let me give you a brief introduction about myself.I've been in education and AI industry from past 10 years. One of the major thing that I have noticed in my life is that no matter how fast you go, there is always innovation happening faster than your rate of learning. Now let me explain that in terms.If I suddenly woke up today and start looking at uh what these large language models are doing, I will be shocked. It would appear ...

Core Insights - The hybrid cloud storage market is emerging and rapidly evolving due to the need for efficient data management and unification [2][4] - The market generated approximately $100 billion in 2024, with a projected compound annual growth rate (CAGR) of about 16% over the next six years [4] Industry Overview - Businesses are increasingly adopting hybrid cloud storage solutions to manage costs, ensure performance, and support sustainability goals [1] - A full solution is deemed necessary to support multimodal AI applications that utilize diverse datasets [3] Market Dynamics - Few vendors currently offer comprehensive solutions that support unified data across multiple storage formats and types [2] - The market opportunity is expected to grow significantly, especially with the acceleration of AI adoption and the development of large language models (LLMs) [4] Companies to Watch - Key players in the hybrid cloud storage market include Cloudian, CTERA, Hammerspace, LucidLink, Nasuni, NetApp, Panzura, and Peer Software [7]