Elastic (NYSE:ESTC) Analyst Day October 09, 2025 02:00 PM ET Company ParticipantsSantosh Krishnan - Group VP and General Manager of Security and ObservabilityMark Dodds - Chief Revenue OfficerKoji Ikeda - Director of Enterprise Software Equity ResearchDavid Hope - Director of AI-powered Observability SolutionsKen Exner - Chief Product OfficerSteve Kearns - Group VP and General Manager of SearchAsh Kulkarni - CEOJames Spiteri - Director of Product Management, Security, Generative AI, and AutomationSanjit Sin ...

Financial Analyst Day October 9, 2025 Forward-looking statements; use of non-GAAP measures This presentation and the accompanying oral presentation contain forward-looking statements that involve substantial risks and uncertainties, which include, but are not limited to, statements regarding our financial outlook, our strategic areas of focus, expectations and plans regarding our future growth, our go-to-market and growth strategies and the effectiveness of such strategies, estimates of the impact of AI, as ...

[Applause] I'm Lee and I'm on the cursor team and I'm going to talk about how building software has evolved. So, thanks for being here. We started with punch cards and terminals back in the 60s where programming was this new superpower, but it was inaccessible to most people.And then in the 70s, programmers grew up writing basic on their Apple 2s and their Commodore 64s. Then in the 80s, gueies started to get mainstream, but still most programming was done on textbased terminals. It wasn't until the '9s and ...

New on the Anthropic Engineering Blog: Most developers have heard of prompt engineering. But to get the most out of AI agents, you need context engineering.We explain how it works: https://t.co/PpMTiT7AEG ...

Core Insights - The article discusses the emerging field of "context engineering" in AI agent development, emphasizing its importance in managing the vast amounts of context generated during tool calls and long-horizon reasoning [4][8][20]. - It outlines five key strategies for effective context management: Offload, Reduce, Retrieve, Isolate, and Cache, which are essential for enhancing the performance and efficiency of AI agents [5][20][21]. Group 1: Context Engineering Overview - Context engineering aims to provide the right information at the right time for AI agents, addressing the challenges posed by extensive context management [5][8]. - The concept was popularized by Karpathy, highlighting the need to fill a language model's context window with relevant information for optimal performance [8][10]. Group 2: Importance of Context Engineering - Context management is identified as a critical bottleneck in the efficient operation of AI agents, with many developers finding the process more complex than anticipated [8][11]. - A typical task may require around 50 tool calls, leading to significant token consumption and potential cost implications if not optimized [11][14]. Group 3: Strategies for Context Management - **Offload**: This strategy involves transferring context information to external storage, such as file systems, rather than sending complete context back to the model, thus optimizing resource utilization [21][23][26]. - **Reduce**: This method focuses on summarizing or pruning context to eliminate irrelevant information while being cautious of potential information loss [32][35][38]. - **Retrieve**: This involves sourcing relevant information from external resources to enhance the model's context, which has become a vital part of context engineering [45][46][48]. - **Isolate**: This strategy entails separating context for different agents to prevent interference, particularly in multi-agent architectures [55][59][62]. - **Cache**: Caching context can significantly reduce costs and improve efficiency by storing previously computed results for reuse [67][68][70]. Group 4: The Bitter Lesson - The article references "The Bitter Lesson," which emphasizes that algorithms relying on large amounts of data and computation tend to outperform those with manual feature design, suggesting a shift towards more flexible and less structured approaches in AI development [71][72][74].

Core Viewpoint - The article emphasizes the importance of Context Engineering in AI development, criticizing the current trend of RAG (Retrieval-Augmented Generation) as a misleading concept that oversimplifies complex processes [5][6][7]. Group 1: Context Engineering - Context Engineering is considered crucial for AI startups, as it focuses on effectively managing the information within the context window during model generation [4][9]. - The concept of Context Rot, where the model's performance deteriorates with an increasing number of tokens, highlights the need for better context management [8][12]. - Effective Context Engineering involves two loops: an internal loop for selecting relevant content for the current context and an external loop for learning to improve information selection over time [7][9]. Group 2: Critique of RAG - RAG is described as a confusing amalgamation of retrieval, generation, and combination, which leads to misunderstandings in the AI community [5][6]. - The article argues that RAG has been misrepresented in the market as merely using embeddings for vector searches, which is seen as a shallow interpretation [5][7]. - The author expresses a strong aversion to the term RAG, suggesting that it detracts from more meaningful discussions about AI development [6][7]. Group 3: Future Directions in AI - Two promising directions for future AI systems are continuous retrieval and remaining within the embedding space, which could enhance performance and efficiency [47][48]. - The potential for models to learn to retrieve information dynamically during generation is highlighted as an exciting area of research [41][42]. - The article suggests that the evolution of retrieval systems may lead to a more integrated approach, where models can generate and retrieve information simultaneously [41][48]. Group 4: Chroma's Role - Chroma is positioned as a leading open-source vector database aimed at facilitating the development of AI applications by providing a robust search infrastructure [70][72]. - The company emphasizes the importance of developer experience, aiming for a seamless integration process that allows users to quickly deploy and utilize the database [78][82]. - Chroma's architecture is designed to be modern and efficient, utilizing distributed systems and a serverless model to optimize performance and cost [75][86].

How to build a context engineering workflow: https://t.co/HPWFbINQOKAvi Chawla (@_avichawla):Let's build a context engineering workflow, step by step: ...

That's a wrap!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. https://t.co/XSbmekHfM6Avi Chawla (@_avichawla):Let's build a context engineering workflow, step by step: ...

Let's build a context engineering workflow, step by step: ...

Core Viewpoint - The article discusses the emergence of AI image generation models, particularly focusing on the capabilities and advancements of the Seedream 4.0 model developed by Huoshan Engine, which is positioned as a competitive alternative to existing models like Nano Banana and GPT-4o Image [2][4][69]. Group 1: AI Image Generation Models - The AI image generation field has seen significant breakthroughs this year, with models like GPT-4o generating popular images in the Ghibli style [3]. - The Nano Banana model gained attention for its ability to generate high-fidelity images and solve issues related to subject consistency, being compared to ChatGPT in the image generation space [4]. - Huoshan Engine's Seedream 4.0 model offers enhanced capabilities, including multi-image fusion, reference image generation, and image editing, with a focus on improving subject consistency [5][6]. Group 2: Features of Seedream 4.0 - Seedream 4.0 is the first model to support 4K multi-modal image generation, significantly broadening its usability [6]. - The model allows users to input multiple images and generate a high number of outputs simultaneously, showcasing its advanced multi-image fusion capabilities [10][14]. - It supports both single and multi-image inputs, enabling complex creative tasks and maintaining consistency across generated images [50][62]. Group 3: Editing and Customization Capabilities - Seedream 4.0 features strong editing capabilities, allowing users to make precise modifications to images by simply describing the desired changes in natural language [23][24]. - The model can understand and execute detailed instructions, such as replacing elements in an image or adjusting specific details like clothing folds and lighting [26][34]. - It maintains high subject consistency across different creative forms, effectively avoiding common issues like appearance distortion and semantic misalignment during multi-round edits [28][50]. Group 4: Performance and Speed - The model achieves fast image generation speeds, producing images in seconds, which enhances the creative workflow's responsiveness [36]. - With 4K output resolution, Seedream 4.0 delivers high-quality images suitable for commercial publishing, improving detail, color depth, and semantic consistency [39][41]. Group 5: Implications for AI Entrepreneurship - The introduction of context-aware dialogue capabilities in Seedream 4.0 allows for iterative image editing, making it easier for developers to create complex image products without extensive workflow management [69][76]. - This shift in API design enables a more fluid interaction with image generation tools, potentially transforming the landscape of AI image product development [69][70]. - The model's capabilities suggest new entrepreneurial opportunities in the AI image generation space, particularly for products that require iterative design and modification [67][72].