Techlist.io - Korean Tech Blog Curator

figmaDec 4, 20252 min readCurated summary

Figma Expands Support for India with Local Data Hosting and New Governance Tools | Figma Blog

Figma is expanding its support for India with local hosting for Figma file data and stronger enterprise governance tools. Local data residency is planned for Q1 2026, helping regulated organizations meet security and compliance requirements while maintaining Figma’s performance. Governance+ is already available to Enterprise customers in India. ## Local Data Hosting for Indian Customers - Figma file data will be hosted within India, including content from FigJam, Make, Sites, Buzz, and Slides. - The option is intended for regulated sectors such as public services, healthcare, and finance. - Indian users created more than 35 million files between October 2024 and September 2025. - India is Figma’s second-largest active user base globally. - The offering builds on existing data residency options in Australia, Europe, and the United States. - Figma has expanded its local presence through a new Bengaluru hub and serves companies including Airtel, Flipkart, Swiggy, TCS, and Zomato. ## Governance+ for Enterprise Teams Governance+ gives organizations more control over how employees access and use Figma: - **Centralized control:** IP Allowlisting and Network Access Restrictions help ensure work occurs in approved Figma instances and networks. - **Account security:** Enforced two-factor authentication, extended idle session timeouts, and support for multiple identity providers reduce account-compromise risks. - **Data governance:** The Discovery Pipeline provides visibility into activity to support retention policies and legal discovery. - Governance+ complements existing tools such as activity logs, SSO, SCIM-based seat management, and restrictions on external collaborators. - The feature is available now to all Enterprise-plan customers. Figma’s India strategy combines regional data residency with tighter administrative controls, making the platform more suitable for organizations with strict privacy, security, and regulatory obligations. Enterprises interested in local hosting can register their interest ahead of its planned Q1 2026 launch.

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Figma Identity Provider Data Residency Single Sign On+4

stripeDec 4, 20252 min readCurated summary

Analyzing how SaaS platforms are shipping payments and finance products in days

Stripe’s embedded components—prebuilt UI modules for payments and finance workflows—have seen rapid adoption, with active users more than tripling in a year. Usage data shows that large platforms and those serving in-person businesses are especially likely to adopt them, primarily to manage complexity, accelerate launches, and simplify onboarding. Most platforms also customize the components to match their branding. ## Large Platforms Adopt More Broadly - Platforms with more than 1,000 employees or $1 billion in revenue are nearly three times more likely to use embedded components than startup platforms. - Larger platforms use a median of three components, compared with two among startups. - Their main motivation is managing international compliance, localization, and the need to launch features quickly. - FreshBooks uses account onboarding across more than 160 countries, with automatic adjustments for language and regional requirements. - Tekmetric launched Stripe Capital after updating its Connect integration, while Kajabi introduced a Xero integration in six weeks instead of the usual six to twelve months. ## In-Person Industries Lead Adoption - Platforms serving industries such as automotive repair adopt embedded components at more than twice the median rate. - These businesses often have tighter margins, higher operating costs, and less experience with online payments. - Embedded workflows let platforms provide streamlined payment experiences without building and maintaining them independently. - TheCut uses embedded onboarding for businesses accepting both in-person and online payments. - Cloudbeds reduced hotel onboarding time from weeks to hours. - Jobber doubled Capital originations after adding financing capabilities. ## Most Platforms Customize the Experience - Seventy-one percent of platforms use Stripe’s theming features to match their own design systems. - Common customizations include branded colors, notification banners, and dashboard styling. - Consistent branding is especially important for sensitive workflows involving payments and identity verification. ## Expanding Component Capabilities - New components let platforms promote financial products such as Instant Payouts and Stripe Capital within their dashboards. - Disputes components allow platform users to manage payment disputes themselves, reducing operational support demands. - Stripe is continuing to expand the component library based on usage data and customer feedback. Embedded components are most valuable for platforms that need reliable, localized financial workflows without the cost and delay of custom development. They offer a practical way to scale payments, add new financial products, and maintain a consistent user experience.

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Saas Ui Components Fintech Embedded Components+3

woowahanDec 4, 2025Original article

Test Automation with AI: Plugin Development Story (opens in new tab)

This blog post explores how a development team at Woowahan Tech successfully automated the creation of 100 unit tests in just 30 minutes by combining a custom IntelliJ plugin with Amazon Q. The author argues that while full AI automation often fails in complex multi-module environments, a hybrid approach using "compile-guaranteed templates" ensures high success rates and maintains operational stability. This strategy allows developers to bypass repetitive setup tasks while leveraging AI for logic implementation within a strictly defined, valid structure. ### Evaluating AI Assistants for Testing * The team compared various AI tools including GitHub Copilot, Cursor, and Amazon Q to determine which best fit their existing IntelliJ-based workflow. * Amazon Q was selected for its superior understanding of the entire project context and its ability to integrate seamlessly as a plugin without requiring a switch to a new IDE. * Initial manual use of AI assistants highlighted repetitive patterns: developers had to constantly specify team conventions (Kotest FunSpec, MockK) and manually fix build errors in 15% of the generated code. * On average, it took 10 minutes per class to generate and refine tests manually, prompting the team to seek a more automated solution via a custom plugin. ### The Pitfalls of Full Automation * The first version of the custom plugin attempted to generate complete test files by gathering class metadata through PSI (Program Structure Interface) and sending it to the Gemini API. * Pilot tests revealed a 90% compilation failure rate, as the AI frequently generated incorrect imports, hallucinated non-existent fields, or used mismatched data types. * A critical issue was the "loss of existing tests," where the AI-generated output would completely overwrite previous work rather than appending to it. * In complex multi-module projects, the AI struggled to identify the correct classes when multiple modules contained identical class names, leading to significant manual correction time. ### Shifting to Compile-Guaranteed Templates * To overcome the limitations of full automation, the team pivoted to a "template first" approach where the plugin generates a valid, compilable shell for the test. * The plugin handles the complex infrastructure of the test file, including correct imports, MockK setups, and empty test stubs for every method in the target class. * This approach reduces the AI's "hallucination surface" by providing it with a predefined structure, allowing tools like Amazon Q to focus solely on filling in the implementation details. * By automating the 1-minute setup and letting the AI handle the 2-minute implementation phase, the team achieved a 97% success rate across 100 test cases. ### Practical Conclusion For teams looking to improve test coverage in large-scale repositories, the most effective strategy is to use IDE plugins to automate context gathering and boilerplate generation. By providing the AI with a structurally sound template, developers can eliminate compilation errors and significantly reduce the time spent on manual refinement, ensuring that even complex edge cases are covered with minimal effort.

AI AI Agents Kotlin Test Automation+5

pinterestDec 4, 20253 min readCurated summary

On the (re)-prioritization of open-source AI

Pinterest argues that AI competition is shifting beyond ever-larger proprietary models. Open-source models now deliver comparable quality at a fraction of the cost, while compact models fine-tuned for specific tasks can outperform general-purpose systems. The company’s strategy is to combine open-source models with Pinterest-specific data, internal systems, and deep product integration. ## Open-Source Models and Cost Efficiency - Pinterest reports achieving performance comparable to leading proprietary AI models at less than 10% of the cost. - The company is shifting more investment toward fine-tuned open-source models, especially for visual and multimodal applications. - As core LLM architectures become increasingly commoditized, competitive advantage is moving toward: - Domain-specific data - Personalization - Product integration - End-to-end system optimization ## Choosing What to Build, Buy, or Adapt Pinterest evaluates foundation-model strategy by modality: - **User modeling and recommendation** - These systems are tightly coupled to a product’s behavior and are generally built internally. - Pinterest uses long-term user-action sequences and a graph containing hundreds of billions of user, board, and content nodes. - Examples include PinFM for representation learning and PinRec for generative recommendations. - **Visual models** - Pinterest largely trains visual encoders and diffusion models in-house. - Its visual-search data and image-board collections provide the weakly supervised datasets needed for large-scale training. - Internal models benefit from Pinterest’s specialized visual domain. - **Text models** - Pinterest has historically relied more on open-source and proprietary third-party LLMs. - Progress in reasoning and language modeling depends heavily on enormous datasets and compute resources, making external models practical. ## Domain-Specific Data as the Differentiator - Open-source multimodal architectures are narrowing the capability gap with proprietary models. - Pinterest’s experience reflects an older machine-learning pattern: model architectures become broadly available, while value comes from specialized data and fine-tuning. - Its visual encoders, including UVE and PinCLIP, improved retrieval by training on Pinterest image and visual-search data rather than using generic embeddings. - Pinterest Canvas similarly adapts an internally trained diffusion model for image editing and enhancement, outperforming larger general-purpose visual-generation models in those use cases. ## Pinterest Assistant and Specialized Tools - Pinterest Assistant combines: - Multimodal retrieval systems - Recommendation services - Specialized generative models - A core multimodal LLM - Most recommendation and agentic capabilities are handled by Pinterest-native tools built on its user and visual foundation models. - The central LLM acts primarily as an intelligent router, handling query understanding, planning, and tool calling rather than performing every task itself. - This architecture allows Pinterest to improve the overall product by optimizing smaller, specialized components instead of relying solely on a larger general-purpose model. Pinterest’s recommendation is to use open-source models as adaptable building blocks, then differentiate through proprietary data, specialized models, and tight integration with the product. The most effective AI systems may therefore be smaller, cheaper, and more purpose-built than frontier general-purpose models.

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Large Language Models Generative AI Multimodal AI Fine Tuning+4

awsDec 3, 2025Original article

Amazon Bedrock adds reinforcement ﬁne-tuning simplifying how developers build smarter, more accurate AI models (opens in new tab)

Amazon Bedrock has introduced reinforcement fine-tuning, a new model customization capability that allows developers to build more accurate and cost-effective AI models using feedback-driven training. By moving away from the requirement for massive labeled datasets in favor of reward signals, the platform enables average accuracy gains of 66% while automating the complex infrastructure typically associated with advanced machine learning. This approach allows organizations to optimize smaller, faster models for specific business needs without sacrificing performance or incurring the high costs of larger model variants. **Challenges of Traditional Model Customization** * Traditional fine-tuning often requires massive, high-quality labeled datasets and expensive human annotation, which can be a significant barrier for many organizations. * Developers previously had to choose between settle for generic "out-of-the-box" results or managing the high costs and complexity of large-scale infrastructure. * The high barrier to entry for advanced reinforcement learning techniques often required specialized ML expertise that many development teams lack. **Mechanics of Reinforcement Fine-Tuning** * The system uses an iterative feedback loop where models improve based on reward signals that judge the quality of responses against specific business requirements. * Reinforcement Learning with Verifiable Rewards (RLVR) utilizes rule-based graders to provide objective feedback for tasks such as mathematics or code generation. * Reinforcement Learning from AI Feedback (RLAIF) uses AI-driven evaluations to help models understand preference and quality without manual human intervention. * The workflow can be powered by existing API logs within Amazon Bedrock or by uploading training datasets, eliminating the need for complex infrastructure setup. **Performance and Security Advantages** * The technique achieves an average accuracy improvement of 66% over base models, enabling smaller models to perform at the level of much larger alternatives. * Current support includes the Amazon Nova 2 Lite model, which helps developers optimize for both speed and price-to-performance. * All training data and customization processes remain within the secure AWS environment, ensuring that proprietary data is protected and compliant with organizational security standards. Developers should consider reinforcement fine-tuning as a primary strategy for optimizing smaller models like Amazon Nova 2 Lite to achieve high-tier performance at a lower cost. This capability is particularly recommended for specialized tasks like reasoning and coding where objective reward functions can be used to rapidly iterate and improve model accuracy.

AI Machine Learning Generative AI Amazon Bedrock+5

awsDec 3, 2025Original article

New serverless customization in Amazon SageMaker AI accelerates model fine-tuning (opens in new tab)

Amazon SageMaker AI has introduced a new serverless customization capability designed to accelerate the fine-tuning of popular models like Llama, DeepSeek, and Amazon Nova. By automating resource provisioning and providing an intuitive interface for advanced reinforcement learning techniques, this feature reduces the model customization lifecycle from months to days. This end-to-end workflow allows developers to focus on model performance rather than infrastructure management, from initial training through to final deployment. **Automated Infrastructure and Model Support** * The service provides a serverless environment where SageMaker AI automatically selects and provisions compute resources based on the specific model architecture and dataset size. * Supported models include a broad range of high-performance options such as Amazon Nova, DeepSeek, GPT-OSS, Meta Llama, and Qwen. * The feature is accessible directly through the Amazon SageMaker Studio interface, allowing users to manage their entire model catalog in one location. **Advanced Customization and Reinforcement Learning** * Users can choose from several fine-tuning techniques, including traditional Supervised Fine-Tuning (SFT) and more advanced methods. * The platform supports modern optimization techniques such as Direct Preference Optimization (DPO), Reinforcement Learning from Verifiable Rewards (RLVR), and Reinforcement Learning from AI Feedback (RLAIF). * To simplify the process, SageMaker AI provides recommended defaults for hyperparameters like batch size, learning rate, and epochs based on the selected tuning technique. **Experiment Tracking and Security** * The workflow introduces a serverless MLflow application, enabling seamless experiment tracking and performance monitoring without additional setup. * Advanced configuration options allow for fine-grained control over network encryption and storage volume encryption to ensure data security. * The "Continue customization" feature allows for iterative tuning, where users can adjust hyperparameters or apply different techniques to an existing customized model. **Evaluation and Deployment Flexibility** * Built-in evaluation tools allow developers to compare the performance of their customized models against the original base models to verify improvements. * Once a model is finalized, it can be deployed with a few clicks to either Amazon SageMaker or Amazon Bedrock. * A centralized "My Models" dashboard tracks all custom iterations, providing detailed logs and status updates for every training and evaluation job. This serverless approach is highly recommended for teams that need to adapt large language models to specific domains quickly without the operational overhead of managing GPU clusters. By utilizing the integrated evaluation and multi-platform deployment options, organizations can transition from experimentation to production-ready AI more efficiently.

AI Large Language Models Machine Learning Generative AI+5

awsDec 3, 2025Original article

Introducing checkpointless and elastic training on Amazon SageMaker HyperPod (opens in new tab)

Amazon SageMaker HyperPod has introduced checkpointless and elastic training features to accelerate AI model development by minimizing infrastructure-related downtime. These advancements replace traditional, slow checkpoint-restart cycles with peer-to-peer state recovery and enable training workloads to scale dynamically based on available compute capacity. By decoupling training progress from static hardware configurations, organizations can significantly reduce model time-to-market while maximizing cluster utilization. **Checkpointless Training and Rapid State Recovery** * Replaces the traditional five-stage recovery process—including job termination, network setup, and checkpoint retrieval—which can often take up to an hour on self-managed clusters. * Utilizes peer-to-peer state replication and in-process recovery to allow healthy nodes to restore the model state instantly without restarting the entire job. * Incorporates technical optimizations such as collective communications initialization and memory-mapped data loading to enable efficient data caching. * Reduces recovery downtime by over 80% based on internal studies of clusters with up to 2,000 GPUs, and was a core technology used in the development of Amazon Nova models. **Elastic Training and Automated Cluster Scaling** * Allows AI workloads to automatically expand to use idle cluster capacity as it becomes available and contract when resources are needed for higher-priority tasks. * Reduces the need for manual intervention, saving hours of engineering time previously spent reconfiguring training jobs to match fluctuating compute availability. * Optimizes total cost of ownership by ensuring that training momentum continues even as inference volumes peak and pull resources away from the training pool. * Orchestrates these transitions seamlessly through the HyperPod training operator, ensuring that model development is not disrupted by infrastructure changes. For teams managing large-scale AI workloads, adopting these features can reclaim significant development time and lower operational costs by preventing idle cluster periods. Organizations scaling to thousands of accelerators should prioritize checkpointless training to mitigate the impact of hardware faults and maintain continuous training momentum.

AI Machine Learning AWS Cloud Computing+5

naverDec 3, 2025Original article

Naver TV (opens in new tab)

Naver’s VLOps framework introduces an event-driven approach to MLOps, designed to overcome the rigidity of traditional pipeline-based systems like Kubeflow. By shifting from a monolithic pipeline structure to a system governed by autonomous sensors and typed messages, Naver has achieved a highly decoupled and scalable environment for multimodal AI development. This architecture allows for seamless functional expansion and cross-cloud compatibility, ultimately simplifying the transition from model training to large-scale evaluation and deployment. ### Event-Driven MLOps Architecture * Operations such as training, evaluation, and deployment are defined as "Typed Messages," which serve as the primary units of communication within the system. * An "Event Sensor" acts as the core logic hub, autonomously detecting these messages and triggering the corresponding tasks without requiring a predefined, end-to-end pipeline. * The system eliminates the need for complex version management of entire pipelines, as new features can be integrated simply by adding new message types. * This approach ensures loose coupling between evaluation and deployment systems, facilitating easier maintenance and infrastructure flexibility. ### Omni-Evaluator and Unified Benchmarking * The Omni-Evaluator serves as a centralized platform that integrates various evaluation engines and benchmarks into a single workflow. * It supports real-time monitoring of model performance, allowing researchers to track progress during the training and validation phases. * The system is designed specifically to handle the complexities of Multimodal LLMs, providing a standardized environment for diverse testing scenarios. * User-driven triggers are supported, enabling developers to initiate specific evaluation cycles manually when necessary. ### VLOps Dashboard and User Experience * The VLOps Dashboard acts as a central hub where users can manage the entire ML lifecycle without needing deep knowledge of the underlying orchestration logic. * Users can trigger complex pipelines simply by issuing a message, abstracting the technical difficulties of cloud infrastructure. * The dashboard provides a visual interface for monitoring events, message flows, and evaluation results, improving overall transparency for data scientists and researchers. For organizations managing large-scale multimodal models, moving toward an event-driven architecture is highly recommended. This model reduces the overhead of maintaining rigid pipelines and allows engineering teams to focus on model quality rather than infrastructure orchestration.

Multimodal AI Orchestration Mlops Llm Evaluation+4

kakaoDec 3, 2025Original article

What the AI TOP 1 (opens in new tab)

The Kakao AI Native Strategy team successfully developed a complex competition system for the "AI TOP 100" event in just two weeks by replacing traditional waterfall methodologies with an AI-centric approach. By utilizing tools like Cursor and Claude Code, the team shifted the developer’s role from manual coding to high-level orchestration and validation. This experiment demonstrates that AI does not replace developers but rather redefines the "standard" of productivity, moving the focus from execution speed to strategic decision-making. ### Rapid Prototyping as the New Specification * The team eliminated traditional, lengthy planning documents and functional specifications. * Every team member was tasked with creating a working prototype using AI based on their own interpretation of the project goals. * One developer produced six different versions of the system independently, allowing the team to "see" ideas rather than read about them. * Final requirements were established by reviewing and merging the best features of these functional prototypes, significantly reducing communication overhead. ### AI-Native Development and 99% Delegation * The majority of the codebase (over 99%) was generated by AI tools like Claude Code and Cursor, with developers focusing on intent and review. * One developer recorded an extreme usage of 200 million tokens in a single day to accelerate system completion. * The high productivity of AI allowed a single frontend developer to manage the entire UI for both the preliminary and main rounds, a task that typically requires a much larger team. * The development flow moved away from linear "think-code-test" patterns to a "dialogue-based" implementation where ideas were instantly turned into code. ### PoC-Driven Development (PDD) * The team adopted a "Proof of Concept (PoC) Driven Development" model to handle high uncertainty and tight deadlines. * Abstract concepts were immediately fed into AI to generate functional PoC code and architectural drafts. * The human role shifted from "writing from scratch" to "judging and selecting" the most viable outputs generated by the AI. * This approach allowed the team to bypass resource limitations by prioritizing speed and functional verification over perfectionist documentation. ### Human Governance and the Role of Experience * Internal conflicts occasionally arose when different AI models suggested equally "logical" but conflicting architectural solutions. * Senior developers played a critical role in breaking these deadlocks by applying real-world experience regarding long-term maintainability and system constraints. * While AI provided the "engine" for speed, human intuition remained the "steering wheel" to ensure the system met specific organizational standards. * The project highlighted that as AI handles more of the implementation, a developer’s ability to judge code quality and architectural fit becomes their most valuable asset. This project serves as a blueprint for the future of software engineering, where AI is treated as a peer programmer rather than a simple tool. To stay competitive, development teams should move away from rigid waterfall processes and embrace a PoC-centric workflow that leverages AI to collapse the distance between ideation and deployment.

AI Gemini Claude Cursor+5

googleDec 3, 2025Original article

Titans + MIRAS: Helping AI have long-term memory (opens in new tab)

Google Research has introduced Titans, a new architecture, and MIRAS, a theoretical framework, designed to overcome the computational limitations of Transformers while maintaining high-fidelity long-term memory. These innovations utilize "test-time memorization," allowing models to update their core parameters in real-time as they process data without requiring offline retraining. By combining the speed of linear recurrent neural networks (RNNs) with the accuracy of attention mechanisms, the system enables AI to handle massive contexts such as genomic analysis or full-document understanding. ## Titans and Neural Long-Term Memory * Unlike traditional RNNs that compress context into fixed-size vectors or matrices, Titans uses a multi-layer perceptron (MLP) as a dedicated long-term memory module. * This deep neural memory provides significantly higher expressive power, allowing the model to synthesize and understand entire narratives rather than just storing passive snapshots. * The architecture separates memory into two distinct modules: an attention mechanism for precise short-term context and the MLP for summarizing long-term information. ## The Gradient-Based Surprise Metric * Titans employs a "surprise metric" to decide which information is important enough to store, mirroring the human brain's tendency to remember unexpected events. * The model calculates an internal error signal (gradient); a high gradient indicates that the new input is anomalous or context-breaking, signaling it should be prioritized for long-term storage. * The system incorporates "Momentum" to track the flow of context over time, ensuring that subsequent relevant information is captured even if individual tokens are not surprising. * To manage memory capacity during extremely long sequences, an adaptive weight decay mechanism acts as a forgetting gate to discard information that is no longer useful. ## MIRAS: A Unified Framework for Sequence Modeling * MIRAS provides a theoretical blueprint that views all major sequence models—including Transformers and linear RNNs—as different forms of associative memory modules. * The framework defines sequence models through four key design choices: memory architecture (e.g., MLP vs. vector), attentional bias, and the internal learning objectives used to combine new and old data. * This approach shifts AI modeling toward real-time adaptation, where the model actively learns and incorporates specific new details into its core knowledge as data streams in. These advancements suggest a shift away from static context windows toward dynamic systems capable of lifelong learning. For developers working with large-scale data, the Titans architecture provides a practical tool for scaling performance, while the MIRAS framework offers a roadmap for designing next-generation models that adapt instantly to new information.

AI Transformer Sequence Modeling Recurrent Neural Networks+5

microsoftDec 3, 20253 min readCurated summary

The Interaction Changes Everything: Treating AI Agents as Collaborators, Not Automation

The article argues that effective AI agents should be treated as engineering collaborators, not automation scripts. Microsoft applied this approach to migrate hundreds of repositories from Entra SDK v1 to v2, reducing work from 4–6 weeks per repository to under two hours with 80–90% accuracy. The key improvement came from giving the agent a role, mission, priorities, and permission to exercise judgment. ## The Entra SDK Migration Challenge - The migration involved hundreds of repositories and sensitive authentication security boundaries. - Traditional migrations required extensive human review and took 4–6 weeks per repository. - The AI agent completed comparable work in under two hours while achieving 80–90% accuracy. - The goal was not merely speed, but reliable handling of custom configurations, edge cases, and security concerns. ## Problems with Automation Thinking - Initial attempts treated the agent like a script executor: - Detailed transformation instructions were provided. - Every anticipated edge case was documented. - The agent was expected to follow a checklist. - This approach repeatedly failed because complex migrations require: - Context-dependent decisions - Handling of undocumented patterns - Security-boundary evaluation - Trade-offs between correctness, speed, and preservation of custom logic - The central lesson is that judgment cannot be fully automated, but it can be supported through collaboration with an intelligent agent. ## Identity Instead of Instructions - The team reframed the agent as a member of the migration team rather than a tool. - The prompt described the agent as a “co-creative engineer” expected to: - Use judgment - Stay curious - Act carefully - Ask for help when uncertain - This change improved accuracy and edge-case handling. - The agent was more likely to surface uncertainty instead of guessing or failing silently. ## The Co-Creative Partnership Framework ### Identity and Mission - Establish the agent’s team, mission, and the importance of the work. - Explicitly state that the agent is not a script executor. - Explain why the task matters so the agent can prioritize appropriately. - Encourage judgment, curiosity, and care. ### Purpose and Intent - Describe the guide as supporting both human and AI team members. - Make priorities explicit, such as security over speed or correctness over completion. - Allow autonomy when repository contexts differ. - Frame uncertainty as a reason to collaborate rather than as failure. ### Prioritized Goals - List primary, secondary, quality, and human-in-the-loop objectives in order. - Explicit priorities help the agent resolve conflicts. - Including quality and collaboration prevents optimizing for speed alone. ### Step-by-Step Guidance with Judgment - Provide concrete actions, conditional logic, edge-case handling, and before-and-after examples. - Specify what must remain unchanged, including custom logic. - Define situations requiring escalation, such as unusual patterns, ambiguity, or possible security violations. - The framework combines procedural guidance with room for context-sensitive decisions. ## Practical Recommendation For complex migrations, security reviews, or architectural work, write prompts that define a collaborative role and decision-making framework—not just a list of commands. Give the agent context, priorities, preservation rules, examples, and clear escalation points so it can act autonomously while knowing when human judgment is required.

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AI Agents Github Copilot Prompt Engineering Software Development Lifecycle+3

awsDec 2, 2025Original article

Announcing replication support and Intelligent-Tiering for Amazon S3 Tables (opens in new tab)

AWS has expanded the capabilities of Amazon S3 Tables by introducing Intelligent-Tiering for automated cost optimization and cross-region replication for enhanced data availability. These updates address the operational overhead of managing large-scale Apache Iceberg datasets by automating storage lifecycle management and simplifying the architecture required for global data distribution. By integrating these features, organizations can reduce storage costs without manual intervention while ensuring consistent data access across multiple AWS Regions and accounts. ### Cost Optimization with S3 Tables Intelligent-Tiering This feature automatically shifts data between storage tiers based on access frequency to maximize cost efficiency without impacting application performance. * The system utilizes three low-latency tiers: Frequent Access, Infrequent Access (offering 40% lower costs), and Archive Instant Access (offering 68% lower costs than Infrequent Access). * Data transitions are automated, moving to Infrequent Access after 30 days of inactivity and to Archive Instant Access after 90 days. * Automated table maintenance tasks, such as compaction and snapshot expiration, are optimized to skip colder files; for example, compaction only processes data in the Frequent Access tier to minimize unnecessary compute and storage costs. * Users can configure Intelligent-Tiering as the default storage class at the table bucket level using the AWS CLI commands `put-table-bucket-storage-class` and `get-table-bucket-storage-class`. ### Cross-Region and Cross-Account Replication New replication support allows users to maintain synchronized, read-only replicas of their S3 Tables across different geographic locations and ownership boundaries. * Replication maintains chronological consistency and preserves parent-child snapshot relationships, ensuring that replicas remain identical to the source for query purposes. * Replica tables are typically updated within minutes of changes to the source table and support independent encryption and retention policies to meet specific regional compliance requirements. * The service eliminates the need for complex, custom-built architectures to track metadata transformations or manually sync objects between Iceberg tables. * This functionality is primarily designed to reduce query latency for geographically distributed teams and provide robust data protection for disaster recovery scenarios. ### Practical Implementation To maximize the benefits of these new features, organizations should consider setting Intelligent-Tiering as the default storage class at the bucket level for all new datasets to ensure immediate cost savings. For global operations, setting up read-only replicas in regions closest to end-users will significantly improve query performance for analytics tools like Amazon Athena and Amazon SageMaker.

Amazon S3 Apache Iceberg Data Replication Amazon S3 Tables+3

awsDec 2, 2025Original article

Amazon S3 Storage Lens adds performance metrics, support for billions of prefixes, and export to S3 Tables (opens in new tab)

Amazon S3 Storage Lens has introduced three significant updates designed to provide deeper visibility into storage performance and usage patterns at scale. By adding dedicated performance metrics, support for billions of prefixes, and direct export capabilities to Amazon S3 Tables, AWS enables organizations to better optimize application latency and storage costs. These enhancements allow for more granular data-driven decisions across entire AWS organizations or specific high-performance workloads. ## Enhanced Performance Metric Categories The update introduces eight new performance-related metric categories available through the S3 Storage Lens advanced tier. These metrics are designed to pinpoint specific architectural bottlenecks that could impact application speed. * **Request and Storage Distributions:** New metrics track the distribution of read/write request sizes and object sizes, helping identify small-object patterns that might be better suited for Amazon S3 Express One Zone. * **Error and Latency Tracking:** Users can now monitor concurrent PUT 503 errors to identify throttling and analyze FirstByteLatency and TotalRequestLatency to measure end-to-end request performance. * **Data Transfer Efficiency:** Metrics for cross-Region data transfer help identify high-cost or high-latency data access patterns, suggesting where compute resources should be co-located with storage. * **Access Patterns:** Tracking unique objects accessed per day identifies "hot" datasets that could benefit from higher-performance storage tiers or caching solutions. ## Support for Billions of Prefixes S3 Storage Lens has expanded its analytical scale to support the monitoring of billions of prefixes. This allows organizations with massive, complex data structures to maintain granular visibility without sacrificing performance or detail. * **Granular Visibility:** Users can drill down into massive datasets to find specific prefixes causing performance degradation or cost spikes. * **Scalable Analysis:** This expansion ensures that even the largest data lakes can be monitored at a level of detail previously limited to smaller buckets. ## Integration with Amazon S3 Tables The service now supports direct export of storage metrics to Amazon S3 Tables, a feature optimized for high-performance analytics. This integration streamlines the workflow for administrators who need to perform complex queries on their storage metadata. * **Analytical Readiness:** Exporting to S3 Tables makes it easier to use SQL-based tools to query storage trends and performance over time. * **Automation:** This capability allows for the creation of automated reporting pipelines that can handle the massive volume of data generated by prefix-level monitoring. To take full advantage of these features, users should enable the S3 Storage Lens advanced tier and configure prefix-level monitoring for buckets containing mission-critical or high-throughput data. Organizations experiencing latency issues should specifically review the new request size distribution metrics to determine if batching objects or migrating to S3 Express One Zone would improve performance.

Amazon S3 Cloud Monitoring Amazon S3 Tables Storage Optimization+2

awsDec 2, 2025Original article

Amazon Bedrock AgentCore adds quality evaluations and policy controls for deploying trusted AI agents (opens in new tab)

AWS has introduced several new capabilities to Amazon Bedrock AgentCore designed to remove the trust and quality barriers that often prevent AI agents from moving into production environments. These updates, which include granular policy controls and sophisticated evaluation tools, allow developers to implement strict operational boundaries and monitor real-world performance at scale. By balancing agent autonomy with centralized verification, AgentCore provides a secure framework for deploying highly capable agents across enterprise workflows. **Governance through Policy in AgentCore** * This feature establishes clear boundaries for agent actions by intercepting tool calls via the AgentCore Gateway before they are executed. * By operating outside of the agent’s internal reasoning loop, the policy layer acts as an independent verification system that treats the agent as an autonomous actor requiring permission. * Developers can define fine-grained permissions to ensure agents do not access sensitive data inappropriately or take unauthorized actions within external systems. **Quality Monitoring with AgentCore Evaluations** * The new evaluation framework allows teams to monitor the quality of AI agents based on actual behavior rather than theoretical simulations. * Built-in evaluators provide standardized metrics for critical dimensions such as helpfulness and correctness. * Organizations can also implement custom evaluators to ensure agents meet specific business-logic requirements and industry-specific compliance standards. **Enhanced Memory and Communication Features** * New episodic functionality in AgentCore Memory introduces a long-term strategy that allows agents to learn from past experiences and apply successful solutions to similar future tasks. * Bidirectional streaming in the AgentCore Runtime supports the deployment of advanced voice agents capable of handling natural, simultaneous conversation flows. * These enhancements focus on improving consistency and user experience, enabling agents to handle complex, multi-turn interactions with higher reliability. **Real-World Application and Performance** * The AgentCore SDK has seen rapid adoption with over 2 million downloads, supporting diverse use cases from content generation at the PGA TOUR to financial data analysis at Workday. * Case studies highlight significant operational gains, such as a 1,000 percent increase in content writing speed and a 50 percent reduction in problem resolution time through improved observability. * The platform emphasizes 100 percent traceability of agent decisions, which is critical for organizations transitioning from reactive to proactive AI-driven operations. To successfully scale AI agents, organizations should transition from simple prompt engineering to a robust agentic architecture. Leveraging these new policy and evaluation tools will allow development teams to maintain the necessary control and visibility required for customer-facing and mission-critical deployments.

AI AI Agents Generative AI Observability+5

awsDec 2, 2025Original article

Build multi-step applications and AI workflows with AWS Lambda durable functions (opens in new tab)

AWS Lambda durable functions introduce a simplified way to manage complex, long-running workflows directly within the standard Lambda experience. By utilizing a checkpoint and replay mechanism, developers can now write sequential code for multi-step processes that automatically handle state management and retries without the need for external orchestration services. This feature significantly reduces the cost of long-running tasks by allowing functions to suspend execution for up to one year without incurring compute charges during idle periods. ### Durable Execution Mechanism * The system uses a "durable execution" model based on checkpointing and replay to maintain state across function restarts. * When a function is interrupted or resumes from a pause, Lambda re-executes the handler from the beginning but skips already-completed operations by referencing saved checkpoints. * This architecture ensures that business logic remains resilient to failures and can survive execution environment recycles. * The execution state can be maintained for extended periods, supporting workflows that require human intervention or long-duration external processes. ### Programming Primitives and SDK * The feature requires the inclusion of a new open-source durable execution SDK in the function code. * **Steps:** The `context.step()` method defines specific blocks of logic that the system checkpoints and automatically retries upon failure. * **Wait:** The `context.wait()` primitive allows the function to terminate and release compute resources while waiting for a specified duration, resuming only when the time elapses. * **Callbacks:** Developers can use `create_callback()` to pause execution until an external event, such as an API response or a manual approval, is received. * **Advanced Control:** The SDK includes `wait_for_condition()` for polling external statuses and `parallel()` or `map()` operations for managing concurrent execution paths. ### Configuration and Setup * Durable execution must be enabled at the time of the Lambda function's creation; it cannot be retroactively enabled for existing functions. * Once enabled, the function maintains the same event handler structure and service integrations as a standard Lambda function. * The environment is specifically optimized for high-reliability use cases like payment processing, AI agent orchestration, and complex order management. AWS Lambda durable functions represent a major shift for developers who need the power of stateful orchestration but prefer to keep their logic within a single code-based environment. It is highly recommended for building AI workflows and multi-step business processes where state persistence and cost-efficiency are critical requirements.

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