[Music] So, as you come in, we have here a server set up with everything you'll need. If you want to follow along, you should have gotten a post-it note. If you don't, just raise your hand and my colleague Alex over here will come find you and we'll provide you with one.Uh, basically what you're going to do is you're just going to go, if you have a number 160 or below, you go to this link here, the QR code on top as well. Um, and if you have a number that's 2011 or above, you go to the second link or the QR ...

[Music] welcome. Thank you for joining my session this morning. I was thinking that I was in some kind of niche space where people would not be interested, but I'm curious to hear, you know, what brought you to to this session.Anyway, I'm I'm Jesus Barasa. I'm the I'm the field CTO for AI with NEOJ and uh and yeah, for the next like uh 14ish minutes, I'm going to be diving a bit deeper into this uh really powerful combination which is knowledge graphs and uh and large language models to build AI application ...

[Music] I'm going to go over today graph rag particularly dealing with multiple data sources. Uh so both unstructured and structured data sources and kind of why you would want to ever do that in the first place even. So I prepared uh some notebooks here.I was going to make slides, but then I thought it would just be easier to walk through some of what this looks like in practice. Um, so there's a link here, and I can share it with you at the booth later, um, if you have follow-up questions. But basically, ...

[Music] Hey folks, I am Anushut. I lead the applied research team here at PromQL. Uh PromQL you might have seen is the sponsor for the reliability track here at the AI engineers worldwide.Uh so today I'll talk about um that data readiness is a myth. How many of you are trying to deploy some kind of AI system on some kind of data in a production environment. Okay.Awesome. Who are who is trying to work with more than like documents and vector databases. Okay.Is whose data is perfect. Clean, annotated, perfect ...

RT Avi Chawla (@_avichawla)Finally! A RAG over code solution that actually works (open-source).Naive chunking used in RAG isn't suited for code.This is because codebases have long-range dependencies, cross-file references, etc., that independent text chunks just can't capture.Graph-Code is a graph-driven RAG system that solves this.It analyzes the Python codebase and builds knowledge graphs to enable natural language querying.Key features:- Deep code parsing to extract classes, functions, and relationships. ...

Finally! A RAG over code solution that actually works (open-source).Naive chunking used in RAG isn't suited for code.This is because codebases have long-range dependencies, cross-file references, etc., that independent text chunks just can't capture.Graph-Code is a graph-driven RAG system that solves this.It analyzes the Python codebase and builds knowledge graphs to enable natural language querying.Key features:- Deep code parsing to extract classes, functions, and relationships.- Uses Memgraph to store th ...

Snowflake (SNOW) Update Summary Company Overview - **Company**: Snowflake Inc. (SNOW) - **Event**: Update/Briefing on June 11, 2025 - **Key Speakers**: Ruby (Head of Partner Marketing for APJ), Mike Garnan (CRO), Ash Willis (VP of Partner Alliance for APJ) Key Points Industry and Market Position - Snowflake is experiencing significant growth, with over 20,000 attendees at their recent summit, doubling their growth from the previous year [4][5] - The company is a sponsor for the LA 2028 Olympics, indicating strong brand visibility and market engagement [7] Financial Performance - Snowflake reported a billion-dollar revenue quarter, representing a **26% year-on-year growth** [18] - The company's **net revenue retention rate** is at **124%**, indicating that existing customers are expanding their contracts [18] - Remaining revenue obligation (RPO) stands at **$6.7 billion**, a **34% year-on-year increase**, suggesting strong future revenue potential [18][24] Customer Engagement and Product Adoption - Snowflake has a total of **11,200 customers**, with **451 new customers** added in Q1 [19] - Approximately **50% of customers** are actively using Snowflake's AI and ML products, showcasing strong adoption of advanced technologies [19] - The company emphasizes the importance of simplifying AI initiatives for customers, which is a key selling point [20] Strategic Focus and Partnerships - Snowflake is focusing on building a robust partner ecosystem to drive consumption and accelerate migrations from legacy systems [25][27] - The company is targeting traditional warehousing technologies like Teradata and Oracle Exadata for migration opportunities [26] - A unique compensation structure is in place where sales teams are incentivized based on consumption rather than contract bookings, aligning interests with customer success [25] AI and Innovation - Snowflake is leveraging AI to enhance productivity and drive business outcomes, with examples of AI applications improving operational efficiency [35][36] - The partnership with Spark New Zealand and Relational AI is highlighted as a strategic move to enhance decision-making capabilities through AI [75][90] Summit Insights - The recent summit showcased a strong network effect, with **70% of content delivered by customers**, emphasizing real-world applications of Snowflake's technology [40] - The event attracted a diverse audience, including business leaders and technical experts, indicating a shift towards business impact rather than just technology [39] Future Outlook - Snowflake plans to invest significantly in its partner ecosystem, including traditional resellers and systems integrators, to scale its business efficiently [48][50] - The company aims to activate its channel to potentially exceed **35% growth** in the future [52] Customer Case Studies - Spark New Zealand is leveraging AI to streamline processes, such as call summarization, which enhances data quality and operational efficiency [84][89] - Relational AI is working with Snowflake to create a relational knowledge graph, addressing knowledge silos within organizations [97][100] Additional Insights - The emphasis on AI is not about job replacement but enhancing productivity and enabling existing employees to work more efficiently [35][36] - The partnership approach is seen as crucial for future innovation, with a focus on collaborative growth and shared success [109][110] This summary encapsulates the key insights and strategic directions discussed during the Snowflake update, highlighting the company's robust growth, innovative use of AI, and commitment to building a strong partner ecosystem.

Core Insights - RelationalAI launched new product capabilities at Snowflake Summit 2025, enhancing its native app for the Snowflake AI Data Cloud, enabling enterprises to build intelligent, data-centric applications without data movement or architectural complexity [1][2][3] Product Enhancements - The new capabilities allow organizations to transition from reactive analytics to reasoning-powered decisions, providing a complete foundation for building semantics-aware, AI-native applications [2] - Key features include: - Support for next-generation LLM question answering with text-to-reasoner capabilities, enhancing decision-making processes [4] - Interoperability with Snowflake Semantic Views, improving accuracy and consistency in business semantics [4] - Integrated prescriptive reasoning using mathematical optimization solvers for optimal decision-making in complex domains [4] - Expanded support for graph reasoning, enabling applications to navigate complex data relationships [4] - Integrated predictive reasoning with graph neural networks (GNNs) for improved accuracy in demand forecasting, churn prediction, and risk scoring [4] Industry Impact - The collaboration with Blue Yonder demonstrates the practical application of RelationalAI's knowledge graph in AI-powered supply chain management, significantly reducing legacy code by 90% [2] - The enhancements are expected to streamline processes for Snowflake customers, helping them realize the full potential of their data [2][3]