Dipranjan Das

Today we announced new capabilities for our Shop Direct and Buy for Me experiences on Amazon, enabling now feeds to facilitate external merchants to list reliably their product listings and reach millions of Amazon customers. Excited on this feature, that is a win/win experience for customers, merchants and brands. Excited to be part of such important project.

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ECS now publishes container health status as a metric, making it easier to monitor container health with alarms and troubleshoot proactively. https://lnkd.in/gxuVSBfF Dileep Narayana Garimella & Pranjal Jain

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Early in my time at Amazon, I assumed documentation was mainly about transfer of knowledge. Later I realized it was also about preserving decisions against reinterpretation. In large organizations, memory is unstable. The team changes. The context shifts. A decision made for one reason gets explained later for a different one. The written record becomes more than a convenience. It becomes the only defense against architectural folklore. That is why I care less about document length and more about whether the tradeoff is captured clearly enough that six months from now nobody needs to guess what was actually decided.

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Optimizing OpenSearch: Shard Strategy and Best Practices Proper shard strategy is crucial for constructing high-performance, scalable, and dependable OpenSearch clusters. AWS OpenSearch Service offers guidance to assist architects in designing for efficiency and resilience. Key Takeaways from AWS Best Practices: - Right-Size Your Shards: Aim for 20–50 GB per shard initially to balance query performance, memory usage, and cluster management overhead. - Primary vs Replica Shards: Total shards = Primary shards × (1 + number of replicas). Replicas enhance read performance and fault tolerance. - Shard Count Considerations: Too many small shards lead to higher cluster overhead and slower recovery. Too few large shards result in slower queries and limited parallelism. - Plan for Growth: Anticipate future data volume, query patterns, and indexing frequency. Avoid default "one-size-fits-all" settings; customize based on workload. - Distribution Across Nodes: Distribute shards evenly across nodes to avoid hot spots. Monitor shard allocation and rebalance as needed. - Reindexing Strategy: Over time, adjust shard count or size through reindexing to uphold optimal cluster performance. Why This Matters: A well-considered sharding strategy ensures your OpenSearch cluster can manage high-volume, low-latency queries, scale effectively, and maintain resilience even during node failures. Reference: AWS OpenSearch Service – Shard Strategy Best Practices https://lnkd.in/gsrM6RSs

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Why the Amazon Layoffs are a Wake-up Call for Every Engineer The era of "pattern-match engineering" is closing. Recent reports show Amazon cutting thousands of corporate roles as part of a deeper pivot toward AI-augmented productivity. This isn't just about cost-cutting; it's a fundamental shift in what the industry values. Any work that relies on applying matured designs to new cases by following a set pattern is now within the territory of AI. If your value is purely in the execution of these patterns, that value is rapidly depreciating. To remain non-replaceable, we must shift our focus: From Execution to Architecture: Don't just follow the pattern; design the system that leverages it. From Bandwidth to Innovation: Use AI to handle the repetitive "heavy lifting" so you can reclaim your time for new, high-value innovation. From Coder to System Shaper: The most valuable engineers today aren't just writing code; they are directing AI to produce results and solving the complex problems AI can't yet touch. The goal isn't to compete with AI—it's to leverage it to expand your own impact. In an industry that is "right-sizing" through technology, those who use AI as a force multiplier will be the ones who lead the next wave. https://lnkd.in/g2RHA4zr

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Google Changed RAG with NEW Gemini File Search Tool (RAG Made Simple) 5 Days Ago, Google Took a Big Step Forward in the RAG Ecosystem Google has launched the File Search Tool, a fully managed Retrieval-Augmented Generation (RAG) system built directly into the Gemini API. This new capability abstracts away the complexity of retrieval pipelines, offering a simple, scalable, and developer-friendly way to ground Gemini models with your own data. Key Highlights: 1. Fully managed RAG system: No need to build or maintain custom retrievers or vector stores. 2. Integrated & scalable: Works seamlessly within the Gemini API. 3. Improved grounding: Delivers responses that are more accurate, relevant, and verifiable. 4. Cost-efficient billing: Embedding generation at query time is free; you only pay for initial file indexing at $0.15 per 1M tokens. This launch marks a major leap toward making enterprise-grade RAG systems accessible to all developers, empowering teams to build intelligent, data-grounded applications faster with precision and transparency. #GenAI #GoogleAI #Gemini #RAG #AIInnovation #LLMs #AIEngineering #GeminiFileSearchTool

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An agent has no consistent definition. You can think of an agent as an autonomous or semi-autonomous system that can take actions on behalf of the user in a given environment, state, and make decisions or take actions that can accomplish certain tasks along a given time-frame, either by calling tools, dynamically selecting the next action to take or use a deterministic "workflow" (systems where LLMs and tools are orchestrated through predefined code paths). Using an Agent framework that gives you the ability to built systems in both, a reliable and a dynamic way can accelerate your agent development journey using agent abstractions. This means building Agentic systems that can reliably call tools, store memory (episodic/semantic/procedural), have comprehensive logging and observability, human in the loop workflows and the ability to build various multi-agent patterns flexibly based on your use case. A successful Agentic system in production is usually a combination of both, dynamic and predictable/reliable multi-agent systems. Strands Agents SDK gives you exactly these capabilities by treating each “agent” as a combination of a foundation model plus a suite of tools. You define a prompt and register your tools (decorated functions) in code, then Strands handles reasoning→planning→tool-execution cycles, local testing, and cloud deployment (ECS, Fargate, Lambda, EC2), along with support for all other agent abstractions provided above. Excited to share a hedge-fund analyst multi agent system: This uses the newest Anthropic's Claude 4 Sonnet/Opus that powers the Lead Analyst Agent, routing incoming queries to specialized sub-agents for fundamental, technical, and market analyses. Each specialist is wrapped as a callable tool (using the “agents-as-tools” multi-agent pattern), so the orchestrator never has to implement domain logic itself and can handoff the task to an agent as a tool. For sensitive operations (insider lookups), we utilize a HITL approval step that halts execution until a human grants consent. We also use meta-tooling that enables the Lead Analyst to generate, load, and invoke new custom tools at runtime—whether it’s a portfolio beta calculator or a pricer—without redeployment. Strands also embeds observability (Langfuse) and OpenTelemetry tracing so you can trace reasoning events, tool invocations, errors, and end-to-end workflows in real time. View more information on the code implementation here: https://lnkd.in/gJmwVyGi Code implementation: https://lnkd.in/gzTtJvJq Thanks to 🏄‍♂️ Cagatay Cali for being a reviewer/collaborator on this! Feel free to try it out and reach out with any questions/ideas. #aws #agenticAI #strands #agents #generativeAI

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Some exciting updates are coming to POS UI extensions 🚀 This summer, we introduced new capabilities: Direct API access, Storage API and new targets on the cart line item details screen. I'm excited to preview even more exciting things coming in 2025-10 and beyond. 🎨 Polaris We're lowering the barrier to making great POS UI extensions by bringing Shopify's single UI framework, Polaris, to POS. Write highly-performant, HTML-like code that works with any framework for any app and any extension surface, including Shopify POS. Preview components and examples in the release candidate documentation: https://lnkd.in/gwciA7CT We understand this is a big change if you've previously been writing your UI code with React. Get ready with our upgrade guide: https://lnkd.in/gjQAY-pf 🗺️ Navigation Extensions can present native detail screens. Present product details, product variant details, customer details, order details, and staff details without retail staff leaving your extension and workflow. Documentation: https://lnkd.in/gvmd6etq 🛒 Cart API We're adding support for subscriptions and making tweaks to how we provide your extension with updates from the cart. Documentation: https://lnkd.in/g3FCe-kX 📚 Documentation improvements We have added a new targets overview page that provides a great overview of available targets with improved descriptions and visuals: https://lnkd.in/g6nDZmu8 🛠️ Developer Experience We're making improvements to the developer experience with better in-app extension development tools, better debugging tools, and hot reloading. Keep an eye on upcoming versions of Shopify POS as we'll be rolling out these improvements into January. 🔮 Next-gen developer platform Shopify has launched a new Dev Dashboard, and we've transitioned apps and dev stores from the Partner Dashboard. Make sure to update to the latest version of Shopify's CLI and check out the latest news: https://lnkd.in/gkGvn7Tz

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🚘💡 Edge AI is reshaping industries—and redefining the talent we need to build the future. Our EVP & President of Software, Advanced Safety, and User Experience, Javed Khan, shares how Edge AI is transforming real-time decision-making across autonomous vehicles, aerospace systems, robotics, and beyond. 🔗 Read the article here: https://bit.ly/40AM3hc #ImpactEveryJourney #FutureofMobility #Innovation #EdgeAI

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