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grammarlyOriginal article

Scaling Always-On Writing Support at Florida Atlantic University (opens in new tab)

Florida Atlantic University successfully implemented Grammarly as a campus-wide writing support tool to improve student outcomes while reducing the grading burden on faculty. By integrating the software directly into students' existing workflows, the university observed significant gains in course completion, retention, and average GPAs across diverse student populations. This strategic approach demonstrates that providing low-friction, automated feedback on mechanics allows students to submit stronger drafts and enables instructors to focus their critiques on higher-order ideas and arguments. ### Strategic Integration and Low-Friction Access * The university opted for a campus-wide rollout that prioritized instructor autonomy, allowing faculty to decide how to best onboard students within their specific writing-intensive courses. * The tool was integrated into students' existing digital ecosystems, including Microsoft Word, Google Docs, Outlook, and Gmail, as well as via browser extensions to ensure adoption didn't require new platforms. * Grammarly was positioned as a “first line of instruction” for recurring mechanical issues, acting as a private, on-demand support system that reduced the friction typically associated with seeking help. ### Measurable Impact on Student Success * Data analysis revealed a +5.3-point persistence lift, with Grammarly users reaching a 79.5% completion rate compared to 74.2% for their peers. * Significant gains were noted in "gateway" courses that unlock further degree progress, with completion rates rising by +3.3 points in writing-intensive courses and +4.3 points in STEM sections. * Frequent users achieved an average GPA of 3.69, which was 0.4 points higher than non- or low-frequency users, even when controlling for baseline demographics and prior academic performance. ### Continuous Writing Performance Gains * Writing performance scores increased by +2.14 points in Fall 2023 and +1.28 in Fall 2024, suggesting that the tool supports ongoing skill development rather than just short-term corrections. * Continuous users showed a year-over-year improvement in writing scores from 76.7 to 81.3. * The visibility of recurring patterns in the students' own drafts allowed them to make sustainable changes to their writing habits over multiple terms. ### Shift in Faculty Instruction * The implementation acted as a "classroom pressure release," making student drafts easier to read by filtering out repeated mechanical errors. * Instructors were able to shift their focus away from basic proofreading and toward guiding students on complex structural and argumentative elements. * The university utilized the rich usage datasets provided by the software to inform broader student-success initiatives and institutional analysis. To replicate these results, institutions should focus on broad access and low-barrier implementation, ensuring the tool meets students where they already write. Anchoring the rollout to specific momentum metrics—such as first-year retention and STEM course completion—allows administrators to track the tangible impact of the technology on institutional goals.

grammarlyOriginal article

Campus-Wide Writing Support Leads to Stronger Student Success at Phoenix College (opens in new tab)

Phoenix College implemented a campus-wide writing support initiative through Grammarly for Education to address academic barriers for its diverse student population, including multilingual learners and working adults. By integrating AI-assisted writing tools directly into existing student workflows and learning management systems, the college aimed to reduce the mechanical grading burden on faculty while improving student literacy. An independent study subsequently confirmed that this "always-on" support led to measurable gains in course completion, retention, and overall GPA across all learning modalities. ### Scaling Support Through Workflow Integration * The college provided campus-wide access to Grammarly for all students and faculty, ensuring the tool functioned in-line within word processors, browsers, and learning management systems. * By meeting students where they already write, the initiative eliminated the friction of learning new platforms or adopting complicated, separate workflows. * The rollout emphasized flexibility, allowing instructors to choose how to integrate the tool into their specific curriculum rather than mandating a uniform pedagogical approach. ### Quantifying Impact on Student Outcomes * An independent study by LXD Research compared 569 Grammarly users with 3,067 non-users in writing-intensive courses during the 2023–2024 academic year. * Data showed a significant lift in course completion across all environments: a 6.4 percent increase for online learners, 5.0 percent for hybrid learners, and 5.2 percent for in-person students. * Beyond completion, the research identified higher year-over-year retention rates and a direct correlation between consistent tool usage and higher student GPAs. ### Shifting Instructional Focus to Higher-Order Skills * Automating mechanical corrections allowed instructors to redirect their feedback toward deeper academic concerns such as content, structure, and discipline-specific thinking. * The tool supported a process-oriented approach to writing, encouraging students to engage in iterative drafting and revision before submitting final work. * Faculty reported significant time savings, enabling them to provide more tailored, meaningful critique to a larger volume of students. ### Strategic Implementation and Adoption * The college utilized a "lead with access" model, ensuring every enrolled student had the same level of support to maintain equity between traditional and non-traditional learners. * Adoption grew organically through peer-to-peer sharing and onboarding resources that demonstrated how to use writing reports for student reflection. * The institution monitored specific "momentum indicators"—such as GPA trends and usage patterns—to identify which student subgroups were benefiting most from the intervention. Phoenix College's experience demonstrates that when writing support is frictionless and embedded within existing digital environments, it creates a scalable model for student success. Institutions looking to replicate these results should prioritize instructor autonomy and focus on tools that complement, rather than disrupt, the established writing process.

pinterest3 min readCurated summary

Next Generation DB Ingestion at Pinterest

Pinterest replaced fragmented, batch-oriented database ingestion with a unified Change Data Capture (CDC) framework. The new architecture uses Debezium/TiCDC, Kafka, Flink, Spark, and Iceberg to process only changed records, reducing latency from over 24 hours to minutes while lowering infrastructure costs. It also provides native row-level deletion, scalable operations, and improved compliance. ## Problems with the Legacy System - Batch workflows often delayed updates by more than 24 hours. - Full-table processing was inefficient because many tables changed by less than 5% each day. - Lack of row-level deletion support complicated data compliance. - Multiple independently maintained pipelines created operational complexity and inconsistent data quality. ## Unified CDC-Based Architecture - Supports MySQL, TiDB, and KVStore. - Captures database changes through a generic CDC service and publishes them to Kafka, typically in under one second. - Flink processes events in near real time and stores them in append-only CDC Iceberg tables on S3. - Spark jobs run periodically—often every 15 minutes—to merge recent changes into base Iceberg tables. - A bootstrap pipeline initializes base tables from historical database dumps. - Maintenance jobs handle compaction and snapshot expiration. - The framework is designed for at-least-once processing, petabyte-scale data, thousands of pipelines, and YAML-based configuration. ## CDC Tables and Base Tables - CDC tables act as time-series ledgers containing every change event. - CDC data typically becomes available within five minutes. - Base tables mirror the current state of the source database while retaining historical records. - Base-table latency is generally between 15 minutes and one hour. ## Upserting Changes into Base Tables - Spark first identifies the newest event for each primary key. - Events are ranked by timestamp and GTID, then deduplicated. - Iceberg’s `MERGE INTO` applies the resulting changes: - Deletes matching records when the event represents a deletion. - Updates existing records. - Inserts new records unless the event is a deletion. - The process uses a recent CDC window and a processing watermark to avoid reprocessing unnecessary data. ## Choosing Merge-on-Read - Pinterest standardized on Iceberg’s Merge-on-Read (MOR) strategy. - Copy-on-Write (COW) was rejected for most workloads because: - It requires more computation during writes. - It produces substantially larger replacement files, increasing storage costs. - MOR better balances update performance and storage efficiency for frequent incremental changes. ## Partitioning for Faster Upserts - Large base tables can be partitioned using a hash bucket of the primary key. - For example, `bucket(100, id)` distributes records across 100 partitions. - This allows Spark to process partitions in parallel and reduces the data scanned or rewritten during merges. - Iceberg tables are configured with format version 2, identifier fields, merge-on-read update and delete modes, and target file sizes. ## Small-File Challenge - Bucketing improved parallelism but caused each upsert to generate many small files within partitions. - The article indicates that Pinterest investigated this bottleneck and introduced further optimizations, though the supplied excerpt ends before describing them. Pinterest’s CDC-based design provides a substantially faster and more efficient alternative to full-table batch ingestion. Teams adopting a similar system should combine incremental CDC processing with partitioning, merge-on-read storage, bootstrapping, and ongoing file-maintenance strategies.

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google3 min readCurated summary

How AI tools can redefine universal design to increase accessibility

Google Research proposes Natively Adaptive Interfaces (NAI), a framework that uses multimodal and agentic AI to make interfaces adapt to individual users rather than forcing everyone into a fixed design. Developed through co-design with disability communities, NAI aims to reduce the accessibility gap by embedding assistive capabilities directly into products. Early prototypes suggest that personalized, context-aware interfaces can improve experiences for disabled users while also benefiting the broader population. ## Community-led co-design - Google follows the principle “Nothing About Us, Without Us,” involving people with disabilities as co-designers from the beginning. - Partnerships include RIT/NTID, The Arc of the United States, RNID, and Team Gleason. - These collaborations focus on real-world barriers and recognize the expertise of disability communities. - The approach also aims to create employment and economic opportunities for people who help shape the technology. ## Moving from reactive accessibility to adaptive interfaces - Google identifies an “accessibility gap” between the release of new features and the development of compatible assistive tools. - NAI addresses this by making accessibility native to the interface instead of adding it afterward. - Static navigation is replaced with dynamic, agent-driven modules that can interpret context and adjust the experience. ## Multi-system agents - An Orchestrator maintains shared context and delegates tasks to specialized sub-agents. - A Summarization Agent breaks down complex documents and assigns subtasks to expert agents. - A Settings Agent dynamically adjusts interface elements such as text size. - This structure lets users accomplish tasks without navigating complicated menus or searching for the right control. ## Multimodal interaction - Gemini-based prototypes combine voice, vision, and text rather than limiting accessibility to text-to-speech. - Live video can be converted into interactive audio descriptions. - Users can ask follow-up questions about specific visual details as events unfold. - Conversational interaction provides situational awareness and may reduce cognitive load. ## Proven prototypes - **StreetReaderAI** - Supports blind and low-vision users navigating physical spaces. - Combines an AI Describer that analyzes visual and geographic information with an AI Chat system for questions. - Maintains context so users can ask about previously encountered locations, such as the position of a bus stop. - **Multimodal Agent Video Player (MAVP)** - Makes audio description interactive rather than static. - Users can change the level of detail or ask questions during playback. - Uses an offline “dense index” of visual descriptions and retrieval-augmented generation (RAG) for fast responses. - **Grammar Laboratory** - Developed by RIT/NTID with Google.org support for American Sign Language and English learners. - Provides grammar instruction through ASL videos, English captions, spoken narration, and written transcripts. - Uses adaptive AI to customize lessons according to each student’s language preferences and interactions. ## The curb-cut effect - Accessibility features designed for people with significant constraints can benefit many other users. - Voice interfaces created for blind users may help sighted people who are multitasking. - AI synthesis and learning tools designed for people with learning disabilities can also support users who want information presented more clearly or flexibly. - NAI therefore treats accessibility as a source of better universal design, not as a specialized add-on. NAI’s central recommendation is to build accessibility into interfaces from the start, using multimodal AI, persistent context, and community-led design. The most effective systems will adapt to users while remaining accountable to the people whose needs they are intended to serve.

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aws2 min readCurated summary

Amazon EC2 C8id, M8id, and R8id instances with up to 22.8 TB local NVMe storage are generally available | Amazon Web Services

Amazon has generally released the EC2 C8id, M8id, and R8id instances, combining custom Intel Xeon 6 processors with up to 22.8 TB of local NVMe SSD storage. Compared with prior sixth-generation instances, they provide up to 43% more compute performance, 3.3× higher memory bandwidth, and significant gains for I/O-intensive databases and analytics. The instances target compute-heavy, balanced, and memory-intensive workloads respectively. ## Instance Families and Workloads - **C8id:** Designed for compute-intensive applications such as video encoding, image processing, and media workloads requiring fast local storage. - **M8id:** Balances compute and memory for data logging, media processing, and medium-sized data stores. - **R8id:** Targets memory-intensive workloads, including large SQL/NoSQL databases, in-memory databases, analytics, and AI inference. ## Capacity and Performance - Scale up to **96xlarge**, compared with 32xlarge in the previous generation. - Offer up to: - **384 vCPUs** - **3 TiB memory** - **22.8 TB local NVMe storage** - Available in **metal-48xl** and **metal-96xl** configurations for workloads needing direct physical-resource access. - Deliver up to: - **43% higher compute performance** - **3.3× greater memory bandwidth** - **46% better I/O-intensive database performance** - **30% faster I/O-intensive real-time analytics queries** ## Networking, Storage, and Compatibility - Support **Instance Bandwidth Configuration**, allowing network or EBS bandwidth to be increased by up to 25% depending on workload needs. - Use sixth-generation AWS Nitro cards to offload virtualization, storage, and networking operations. - Require AMIs with **ENA and NVMe drivers**; current AWS Windows and Linux AMIs include the NVMe driver by default. - Local NVMe devices appear automatically after boot and do not require block device mappings. - Storage is hardware-encrypted with **XTS-AES-256** and unique keys. - Local NVMe storage is temporary: it is destroyed when the instance is stopped or terminated. ## Availability and Purchasing - Available in US East (N. Virginia), US East (Ohio), and US West (Oregon). - R8id instances are also available in Europe (Frankfurt). - Offered as On-Demand, Savings Plans, Spot Instances, Dedicated Instances, and Dedicated Hosts. These instances are best suited to applications that can exploit high-performance, ephemeral local NVMe storage; persistent data should remain on services such as Amazon EBS.

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meta3 min readCurated summary

No Display? No Problem: Cross-Device Passkey Authentication for XR Devices

Passkeys provide phishing-resistant authentication, but standard cross-device flows depend on QR codes displayed on the device being authenticated. This creates a problem for XR headsets, smart-home hubs, sensors, and other devices with no accessible screen. The proposed solution uses an authenticated companion app to transport the FIDO hybrid-flow request, preserving proximity, trust, and standard WebAuthn security without requiring a QR code. ## The Challenge: Screenless Devices Cannot Display QR Codes - Traditional cross-device passkey authentication uses: - A QR code displayed by the desktop or other target device. - Bluetooth or NFC to verify proximity and establish communication. - Devices without usable displays cannot show a QR code for a phone to scan. - Proximity discovery alone is insufficient because users still need a clear, secure way to approve the correct authentication request. ## Companion Apps as Secure Message Transport - The target device generates the same hybrid-flow request normally encoded in a QR code. - Instead of displaying it, the device sends the request to an authenticated companion app associated with the same user account. - In-app notifications provide a user-consent surface and direct the user into the passkey flow. - Opening the app itself can initiate the flow because the user must deliberately open it, and mobile operating systems provide an additional verification step. ## Meta Quest and Meta Horizon Implementation - The implementation is broadly available on Meta Quest devices running Meta Horizon OS. - The Quest browser creates a FIDO URL containing: - A fresh ECDH public key. - A session-specific secret. - Routing information for the hybrid handshake. - The URL is packaged as structured data in a GraphQL-based push notification. - The Meta Horizon app validates that the request belongs to the correct signed-in user before processing it. ## Notification and Deep-Link Flow - iOS or Android displays a notification that a passkey login is pending. - Tapping the notification opens the Meta Horizon app, which launches the FIDO URL through the operating system. - The OS then invokes its passkey interface. - If notifications are disabled, opening the app queries the backend for pending requests. - Requests expire after five minutes, limiting the window for misuse. - The mobile device then performs the normal hybrid transport process: - Broadcasting a BLE advertisement. - Establishing an encrypted tunnel. - Generating the passkey assertion. ## WebAuthn Challenge and Response - The inaccessible device creates the normal WebAuthn challenge and waits for a response. - The mobile authenticator initiates the secure BLE or NFC connection. - The challenge travels through the encrypted channel. - After successful user verification, the phone creates an `AuthenticatorAssertionResponse` or `AuthenticatorAttestationResponse`. - The inaccessible device forwards that response to the relying-party server just as a display-equipped device would. ## Broader Impact - The approach removes the QR-code requirement while retaining FIDO hybrid transport and proximity protections. - It could extend passwordless authentication to: - XR headsets and wearables. - Screenless IoT devices. - Smart-home hubs. - Industrial sensors and hardware. - The work builds on FIDO Alliance standards and mobile operating-system support, helping broaden interoperability across device ecosystems. The companion-app model offers a practical way to bring secure cross-device passkeys to devices that cannot display QR codes. It is especially suitable where the device already has an authenticated mobile app and can rely on standard WebAuthn, BLE/NFC proximity, and platform user verification.

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line4 min readCurated summary

Scaling to Infinity: LY Corporation’s

LY Corporation’s observability team evolved its time-series database to handle rapidly growing infrastructure and Kubernetes workloads. After outgrowing MySQL and OpenTSDB, the team built an engine optimized for high-cardinality metrics, low-latency queries, and seamless API compatibility. Its architecture now combines in-memory, Cassandra, and S3-compatible storage, enabling cost-efficient scaling while supporting trillions of daily metrics. ## Why Time-Series Storage Matters - Metrics record system state as timestamped numerical values. - They support dashboards, threshold-based alerts, and predictive analysis using tools such as ARIMA and Prophet. - Even a small metric record can consume about 280 bytes when timestamps, values, and tags are included. - One CPU metric collected every 15 seconds requires roughly 562 MiB per server annually; across 1,000 servers, this grows to about 548 GiB before adding memory, disk, and network metrics. - High-cardinality cloud environments make both storage cost and query latency critical operational concerns. ## Moving Beyond MySQL and OpenTSDB - MySQL initially became inadequate as the organization moved from SOA to MSA: - Write load increased sharply. - Storage costs and capacity requirements grew. - Query latency worsened for large datasets. - Rigid schemas could not easily represent changing cloud resources. - MySQL sharding provided temporary relief but could not support high-resolution metrics collected at intervals under one minute. - OpenTSDB, introduced in 2016 on Apache HBase, improved write performance but had important limitations: - Tag growth harmed UID-table lookup performance. - Metadata was restricted to a narrow character set. - Large queries required cache warm-up procedures. - These constraints led to the development of an internal database beginning in 2018. ## Building the Internal Time-Series Database - The 2019 engine was designed around: - Flexible protocol support independent of a particular agent. - Linear scalability without downtime. - Low-latency processing of high-resolution metrics. - Strong availability during failures. - Inspired by Meta’s Gorilla research, the team used access patterns in which most queries target recent data. - Frequently accessed metrics were kept in an in-memory database, while colder data was stored in Apache Cassandra. - The new engine enabled metric volumes to grow by more than 200 billion records annually while preserving existing APIs. - Users benefited from the new backend without migration work or code changes. ## Scaling for Kubernetes Workloads - Kubernetes introduced rapidly changing pods, dynamically allocated volumes, and much higher metric churn. - Both major storage layers encountered scaling problems: - IMDB initially required adding identical hardware, limiting expansion options. - Cassandra rebalancing could take tens of hours because of its data volume. - The team improved IMDB with weighted load balancing so nodes with different capacities could be used effectively. - Storage was divided into tiers: - Recent 14-day data remained in Cassandra for high-performance access. - Older data was moved to S3-compatible storage. - This reduced Cassandra dependency, lowered costs, simplified operations, and enabled more flexible hardware and Kubernetes-based deployment. ## Writing and Reading Through S3 - The write path separates data processing from long-term storage: - A Dumper reads metric slots from IMDB. - It converts them into internally defined sub-blocks. - A Block Dumper combines sub-blocks into blocks and writes them to S3. - A Storage Gateway reads the blocks for queries and caches them on local disks. - Disk caching initially caused excessive page-cache use and rapid memory exhaustion. - Direct I/O was considered but withdrawn after the cloud storage team warned that it consumed too much shared bandwidth. - Through cross-team collaboration, the team adopted a B+ tree-based cache that made better use of the kernel page cache without overloading infrastructure. ## Future Direction: From Storage to Intelligence - The team aims to move beyond recording metrics toward prediction and AI-assisted operations. - Achieving this requires consolidating time-series data currently scattered across internal systems. - A key requirement is to perform this integration without imposing migration work or breaking changes on users. - The broader goal is an observability platform that turns unified metrics into predictive and intelligent operational capabilities. The main recommendation is to design time-series platforms around real access patterns, tier storage according to data age, and preserve compatibility while evolving the backend. At extreme scale, careful storage architecture and collaboration across infrastructure teams are as important as raw database performance.

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google3 min readCurated summary

​Sequential Attention: Making AI models leaner and faster without sacrificing accuracy

Sequential Attention is a greedy subset-selection method designed to make large machine-learning models smaller and faster without materially reducing accuracy. It selects features, layers, blocks, or weights one at a time using attention scores that are recalculated after each choice, allowing the model to account for nonlinear interactions and redundancy. By integrating selection into a single training process, it aims to retain the quality of traditional greedy methods while avoiding their prohibitive computational cost. ## The Subset-Selection Challenge - Feature selection removes irrelevant or redundant inputs, but finding the optimal subset is NP-hard. - Deep neural networks make selection harder because: - A feature that seems unimportant alone may be essential in combination with others. - Features that appear valuable individually may become redundant when selected together. - The same problem applies beyond input features: - Selecting embedding dimensions or chunks. - Pruning entries or blocks from weight matrices. - Choosing layers or other model components. ## How Sequential Attention Works - The method builds a subset step by step rather than weighting all candidates at once. - At each stage: - Previously selected candidates provide context. - Attention scores estimate the importance of every remaining candidate. - The highest-scoring candidate is added permanently. - The model recalculates scores to reflect the candidate’s marginal contribution. - This adaptive process can identify high-order nonlinear interactions that simpler filter methods may miss. - It uses softmax-based attention scores for ranking, but applies them sequentially instead of in a single pass. - Although greedy selection can be expensive when each candidate requires model retraining or evaluation, Sequential Attention performs selection within one training process, greatly reducing overhead. ## Main Benefits - **Efficiency and accuracy:** Candidates can be evaluated in parallel once attention scores are available, while sequential updates preserve adaptive selection. - **Interpretability:** Attention scores provide a view into which inputs or components the model considered important. - **Scalability:** The approach is intended for large candidate sets and modern deep-learning architectures. - **Reduced redundancy:** Recalculating scores after each selection helps prevent the model from repeatedly choosing overlapping or unnecessary components. ## Feature Selection - Traditional greedy feature selection repeatedly retrains or reevaluates a model for every possible feature at every step. - Sequential Attention replaces these expensive marginal-gain calculations with the model’s internal attention weights. - The algorithm: - Scores all unselected features. - Adds the feature with the highest score. - Reruns the model and updates the scores for the remaining features. - The method reportedly achieved state-of-the-art or competitive results across proteomics, image, and activity-recognition benchmarks. - Its one-pass implementation makes greedy-style selection substantially faster. - For linear regression, Sequential Attention is mathematically equivalent to Orthogonal Matching Pursuit (OMP), an established method with theoretical reliability and performance guarantees. ## Block Sparsification - Neural-network pruning removes unnecessary weights to reduce model size and improve deployment efficiency. - Block sparsification removes groups of parameters rather than individual weights, making the resulting sparsity more compatible with hardware acceleration. - Earlier approaches generally fell into two categories: - **Differentiable pruning**, which learns continuous importance proxies. - **Combinatorial optimization**, which searches directly for sparse structures. - The referenced work, “SequentialAttention++ for Block Sparsification,” aims to combine these differentiable and combinatorial approaches into a unified pruning framework. Sequential Attention is best understood as an adaptive, attention-based alternative to costly repeated subset searches. It is particularly promising when model components interact nonlinearly and when hardware-friendly sparsity or feature reduction is needed at scale.

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aws3 min readCurated summary

AWS IAM Identity Center now supports multi-Region replication for AWS account access and application use | Amazon Web Services

AWS IAM Identity Center now supports multi-Region replication for organizations using an external identity provider such as Microsoft Entra ID or Okta. Workforce identities, permission sets, and related metadata can be replicated from a primary Region, allowing users to access AWS accounts and managed applications if the primary service is disrupted. The feature also supports regional application deployment for improved performance and data residency compliance, with centralized configuration remaining in the primary Region. ## Multi-Region Replication and Resilience - Replication provides an active AWS access portal endpoint in each additional Region. - Users can continue accessing AWS accounts with already-provisioned permissions during a primary-Region disruption. - AWS managed applications can access replicated identities locally, improving reliability and proximity to users or datasets. - IAM Identity Center configuration remains centrally managed from the primary Region. ## Prerequisites and Setup - The feature requires: - An organization instance of IAM Identity Center. - An external IdP, such as Okta or Microsoft Entra ID. - Primary and additional Regions that are enabled by default. - Before replication, the customer-managed AWS KMS key must be replicated to the target Region. - AWS recommends multi-Region KMS keys because they maintain consistent key material across Regions while preserving independent regional infrastructure. - In the IAM Identity Center console, administrators select **Settings → Management → Add Region** and choose the target Region. - Initial replication time depends on the size of the Identity Center instance. ## User Authentication and Access - Administrators must add the additional Region’s SAML Assertion Consumer Service (ACS) URL to the external IdP configuration. - A bookmark application can be created in the IdP to provide users with direct access to the new Region’s AWS access portal. - Users can access accounts and applications through existing methods, including: - The AWS access portal - Application links - The AWS CLI ## Regional Application Deployment - AWS managed applications can be deployed in additional Regions using existing deployment workflows. - Organizations can place applications near regional datasets to satisfy performance or data residency requirements. - Administrators should verify that each required managed application supports both the selected Region and multi-Region deployment. ## Operational Considerations - Additional Regions provide a limited-management console experience. - Most operations outside the primary Region are read-only, except application management and user session revocation. - Workforce activity is recorded in AWS CloudTrail in the Region where it occurs. - Break-glass access is recommended for privileged users if the external IdP becomes unavailable. - Account instances, Microsoft Active Directory identity sources, and the built-in IAM Identity Center directory are not supported at launch. ## Availability and Cost - The feature is available at no additional IAM Identity Center cost in 17 enabled-by-default commercial AWS Regions. - Standard AWS KMS charges apply for customer-managed key storage and use. Organizations using supported external IdPs should replicate IAM Identity Center into strategically selected Regions, configure the required KMS replicas and ACS URLs, and test regional access and emergency procedures before relying on the setup for disaster recovery.

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figma2 min readCurated summary

For the Love of Craft: Vectorize Images in Figma Design and Draw | Figma Blog

Vectorize is Figma’s new AI image-editing tool for converting raster images into editable vector artwork. It helps designers preserve the character of sketches, photos, and hand-drawn lettering while making them scalable and easy to refine directly in Figma Design or Figma Draw. The tool is intended to reduce tool switching and avoid recreating expressive source material from scratch. ## Converting Raster Drawings into Vectors - Users can drag an image into Figma and select **Vectorize** to convert it into an editable vector with one click. - Vector artwork can be adjusted in size, shape, composition, and individual details. - The **Recolor** control can simplify artwork into a more manageable set of colors and shapes. - Figma color variables can be applied to keep illustrations consistent across brands, themes, and collateral. - This workflow turns physical sketches or rasterized drawings into reusable, adaptable design assets. ## Editing Hand-Drawn Lettering - Photos, scans, calligraphy, and paper sketches can be transformed into editable logos or wordmarks. - After vectorization, designers can modify individual letters, curves, strokes, spacing, and anchor points. - Background removal and bounding boxes help isolate and reposition lettering elements. - The resulting artwork can be recolored, scaled, and incorporated into cover art, interfaces, or branding. - Hand-drawn lettering retains its original character while becoming flexible enough for further digital design work. Vectorize is best suited to designers who want to bring expressive physical or raster-based work into an editable Figma workflow without sacrificing its original personality.

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cloudflare3 min readCurated summary

Improve global upload performance with R2 Local Uploads

R2 Local Uploads improves global upload performance by first writing object data near the client, then asynchronously copying it to the bucket’s region. Objects become immediately available and remain strongly consistent during replication. Cloudflare reports up to a 75% reduction in upload request duration for cross-region uploads. ## Faster Global Uploads - Local Uploads targets `PutObject` and `UploadPart` requests made far from the bucket’s location. - Synthetic tests showed median upload TTLB dropping from about 2 seconds to 500 milliseconds. - Tests used 5 MB objects uploaded from Western North America to an Asia-Pacific bucket at roughly 20 requests per second. - The feature is available in open beta and can be enabled in the Cloudflare Dashboard or with: ```bash npx wrangler r2 bucket local-uploads enable [BUCKET] ``` ## The Cross-Region Distance Problem - R2 requests enter through a globally distributed Gateway Worker, which handles authentication and routing. - Object metadata is managed by a distributed Durable Object Metadata Service. - Encrypted object data is stored in R2’s distributed storage infrastructure. - Without Local Uploads, streamed data must travel to the bucket’s region before the upload can complete. - Long-distance transfers can increase latency and introduce upload variability or reliability issues. ## How Local Uploads Works - If the client and bucket are in the same region, R2 uses its normal storage flow. - If they are in different regions: - Data is initially written to storage near the client. - Metadata is published in the bucket’s region. - The object becomes readable as soon as the local write completes. - Background replication later copies the data to the bucket’s primary region. - There is no read-unavailability window while replication is in progress. - Local Uploads is unavailable for jurisdiction-restricted buckets, including EU and FedRAMP buckets. ## When to Use It - Applications have users or devices distributed across multiple regions. - Upload speed and reliability are important. - You want faster writes without moving the bucket’s primary location. - R2’s Metrics page can help identify regional request patterns through the “Request Distribution by Region” graph. ## Replication Architecture - R2 represents the background copy operation as a replication task. - Cloudflare Queues process these tasks asynchronously. - Queues provide: - Rate control for replication. - Automatic retries. - Dead-letter queue support for failures. - Sharding across multiple queues for each storage region. - When publishing object metadata, R2 atomically: - Stores the object metadata. - Creates a pending-replica key describing unfinished replication work. - Creates a timestamp-based replication marker that determines when the task enters a queue. - The pending-replica record includes the replication plan, source and destination locations, mode, priority, and whether the source can be deleted after successful replication. Local Uploads is a strong fit for globally distributed upload-heavy workloads. Enable it when cross-region write latency matters, while keeping in mind the restriction on jurisdiction-constrained buckets.

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grammarlyOriginal article

From Idea to Demo in Two Days: Inside Superhuman’s 2025 Global Hackathon (opens in new tab)

Superhuman’s 2025 hackathon brought together nearly 500 employees to prototype innovative product features by leveraging cutting-edge AI coding tools like Claude Code and Cursor. By integrating AI-driven agents and keyboard-centric workflows, teams demonstrated how rapid experimentation can bridge functional gaps across mail, documentation, and collaboration platforms. The event highlighted a significant shift toward "vibe-coding" and accessible development, where cross-functional teams and non-engineers could ship functional MVPs in just 48 hours. ## Superhuman Command Everywhere (SCE) * This project extends the Superhuman Mail Command Center to the browser, allowing users to trigger Grammarly features, set reminders, and snooze items from any web page. * The tool enables keyboard-only navigation for AI agents; for example, users navigate Grammarly’s Proofreader cards using "J" and "K" and accept or dismiss suggestions with "E" and "D." * Developers used AI tools to quickly interpret an unfamiliar codebase, allowing engineers without frontend expertise to "vibe-code" a working MVP within a few hours. ## Whiteboarding in Coda * This feature introduces a native canvas within Coda documents where users can draw freely, add shapes, and import images for brainstorming and diagramming. * The prototype includes an AI diagramming tool that generates editable visual versions of diagrams based on plain-text descriptions. * Built by a solo team member with no formal coding background, the project utilized Claude Code and Cursor to focus on UX refinement and smooth interactions rather than just technical functionality. ## Superhuman Listening * This system centralizes fragmented customer feedback from tools like Gong, Salesforce, and Zendesk into a single, queryable source of truth. * By linking unstructured data to product roadmaps in Coda, the tool helps sales engineers and product managers determine if specific customer feedback is already being addressed. * Technical challenges included using LLM APIs to extract urgency and sentiment, though the team noted the difficulty of filtering "noise" from high-volume sources like Zendesk tickets. ## Inclusive Language Agent * Developed by a team of linguists, this agent identifies non-inclusive phrasing or unconscious bias in professional writing. * The goal is to provide real-time suggestions that improve workplace culture and customer trust by making word choices more inclusive and intentional. The results of this hackathon suggest that AI-assisted development tools are significantly lowering the barrier to entry for complex product builds. For organizations aiming to accelerate innovation, encouraging "maker" identities across all departments and utilizing AI to bridge technical skill gaps can surface high-value solutions that traditional product cycles might miss.

google3 min readCurated summary

Collaborating on a nationwide randomized study of AI in real-world virtual care

Google and Included Health plan to launch, pending IRB approval, a nationwide randomized study of conversational AI in real-world virtual care. Unlike prior simulated or small feasibility studies, it will prospectively evaluate AI with consented patients across varied conditions and locations, comparing it with standard clinical practice. The goal is to generate rigorous evidence about safety, usefulness, limitations, and impact on patients and clinicians. ## Moving from Simulation to Real-World Evaluation - Earlier research demonstrated clinician-level capabilities in simulated consultations and retrospective analyses. - A feasibility study with Beth Israel Deaconess Medical Center began testing conversational AI in clinical workflows, using measures such as safety-supervisor interruptions. - The new study will advance beyond feasibility through: - A randomized controlled design - Nationwide recruitment - Consented participants - Real patients, clinical concerns, and virtual-care workflows - Controlled comparison with standard practice ## A Phased Approach to Medical AI Research - Google argues that medical AI should be evaluated with evidence standards similar to other medical interventions. - Each research phase adds information about: - Patient and clinician experiences - Safety - Usefulness - The AI system’s capabilities and limitations - Results from each stage are intended to guide safer, more responsible development and deployment. ## Foundational Research Behind the Study ### Diagnostic and Management Reasoning - The AMIE system was developed to handle medical interviews and clinical reasoning. - Studies with patient actors and synthetic cases found that AMIE could match or exceed primary care physicians in simulated diagnostic accuracy and conversation quality. - Later work expanded the system to: - Longitudinal disease management - Clinical-guideline and patient-history reasoning - Investigation and treatment planning - Interpretation of multimodal evidence ### Personalized Health Insights - Research on the Personal Health Agent examined how AI could interpret personal health data, including sleep and activity information from wearables. - Its multi-agent architecture combined the roles of: - Data scientist - Medical domain expert - Health coach - This work informed Fitbit Labs tools such as Symptom Checker and Medical Records Navigator and Plan for Care. ### Navigating Health Information - Google’s “wayfinding” AI research explored how conversational agents can help people find and understand health information. - The system uses proactive guidance, goal recognition, and tailored conversations to make health information searches more practical and useful. ## Practical Conclusion The partnership with Included Health represents a transition from demonstrating what medical AI can do in controlled environments to measuring how it performs at scale in actual care. A nationwide randomized trial could provide the evidence needed to determine whether conversational AI can safely improve virtual care and expand access to medical expertise.

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aws3 min readCurated summary

AWS Weekly Roundup: Amazon Bedrock agent workflows, Amazon SageMaker private connectivity, and more (February 2, 2026) | Amazon Web Services

The AWS Weekly Roundup highlights new capabilities for AI agents, private connectivity, encryption management, and resilience testing. Major launches include Bedrock server-side tools and longer prompt caching, SageMaker Unified Studio support for PrivateLink, and S3 encryption changes without data movement. Additional updates strengthen event-driven architectures, observability, zero-trust access, and AI-assisted AWS deployments. ## AI Agents and Developer Workflows - Amazon Bedrock’s Responses API now supports server-side tools such as web search, code execution, and database updates within AWS security boundaries. - Bedrock also offers a one-hour prompt-cache TTL for select Anthropic Claude models, improving performance and reducing costs for long-running, multi-turn agents. - AWS MCP Server deployment SOPs, currently in preview, let agents deploy applications from natural-language prompts using CDK, CloudFormation, and CI/CD workflows. - The deployment preview supports React, Vue.js, Angular, and Next.js through tools such as Kiro, Cursor, and Claude Code. - CloudWatch Application Signals integration with Kiro provides AI-assisted investigation of service health, SLO compliance, and observability issues. ## Private Connectivity and Zero-Trust Security - SageMaker Unified Studio now supports AWS PrivateLink, allowing VPC traffic to remain within the AWS network instead of traversing the public internet. - IAM policies can govern private SageMaker connectivity for stricter security and compliance requirements. - AWS Verified Access guidance demonstrates centralized zero-trust application access across multi-account environments using IAM Identity Center and AWS RAM. - AWS Network Firewall adds predefined web categories for identifying and controlling generative AI application traffic, with full-URL filtering available alongside TLS inspection. ## Storage, Encryption, and Database Performance - Amazon S3’s `UpdateObjectEncryption` API changes encryption for existing objects without moving or re-uploading data. - Supported operations include switching from SSE-S3 to SSE-KMS, rotating customer-managed KMS keys, and standardizing encryption with S3 Batch Operations. - Amazon Keyspaces table pre-warming prepares tables for predictable high-throughput workloads, reducing throttling and cold-start delays during traffic spikes. - Pre-warming works with on-demand and provisioned capacity, including multi-Region tables. - DynamoDB MRSC global tables now integrate with AWS Fault Injection Service, enabling simulated Regional failures and validation of replication and application resilience. ## Event-Driven Systems and Observability - EventBridge’s event payload limit increased from 256 KB to 1 MB, allowing events to carry richer JSON, telemetry, ML, and generative AI data without external storage or fragmentation. - Lambda’s enhanced observability for Kafka event source mappings adds CloudWatch logs and metrics for polling, scaling, processing state, permissions, and failures. - The feature supports both Amazon MSK and self-managed Apache Kafka sources. ## CloudFormation and Community - AWS’s 2025 CloudFormation review covers improved troubleshooting, drift-aware change sets, stack refactoring, StackSets, the CloudFormation language server, and IaC MCP tooling. - AWS Community Day Romania will take place April 23–24, 2026, featuring technical sessions, AWS experts, and networking opportunities. Together, these updates point toward more private, observable, resilient, and AI-assisted AWS operations. Teams should evaluate the new capabilities against their security, scalability, and automation needs, particularly Bedrock agent tooling, S3 encryption updates, PrivateLink connectivity, and resilience testing.

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github3 min readCurated summary

How to maximize GitHub Copilot’s agentic capabilities

GitHub’s guide presents Copilot’s agent mode as a partner for architecture, refactoring, and coordinated multi-file changes—not a replacement for engineering judgment. It argues that Copilot is most useful when developers first define system boundaries, assess cross-cutting effects, and then use the agent to implement and document changes. The examples build toward extending a modular Notes Service with tagging, validation refactoring, migrations, and test modernization. ## Preparing for Agentic Work - The guide assumes: - Copilot agent mode is enabled. - Familiarity with service-layer architectures. - Access to a GitHub Skills exercise template. - Willingness to review and challenge Copilot’s proposals. - Earlier-career engineers can use the exercises to learn how senior engineers evaluate architecture and risk. ## Using Copilot for System Design - Developers should begin by identifying boundaries between: - Domain logic - Data access - Interfaces - Module interactions - Copilot can analyze a service for: - Poor module boundaries and tight coupling - Async and transaction risks - Duplicated responsibilities - Testability and observability problems - It can also compare architectural approaches, such as hexagonal and layered architecture, and explain tradeoffs based on the codebase’s constraints. ## Building Modular Services - Once the architecture is understood, Copilot can coordinate implementation across: - Domain modules - Controllers - Repository abstractions - Suggested practices include dependency inversion and documenting module contracts and assumptions. - Copilot may generate interfaces, repository abstractions, controller logic, and Markdown documentation, reducing boilerplate while exposing developers to established design patterns. ## Adding a Tagging Subsystem - A seemingly simple tagging feature requires decisions about: - Embedded tags versus normalized or many-to-many data models - Search indexing, filtering, and relevance - Whether tags are API resources or internal details - Validation and invariant boundaries - Additive migrations, compatibility, and rollback - Copilot can first map the feature’s architectural impact, including migration requirements, caching, indexing, regressions, tests, and external consumers. - Implementation may span the domain model, database schema, repositories, controllers, tests, and documentation. - The example uses a `tags` column with a default empty array and adds `Tag[]` to the note model, illustrating how agent mode maintains consistency across files. ## Safe Schema Changes - The guide emphasizes that migration design involves more than writing SQL. - A production-ready change should be: - Backward compatible - Reversible - Safe under load - Transparent to dependent systems - Copilot can assist with reasoning about rollout strategies, but engineers must inspect its recommendations and validate them against operational constraints. The practical recommendation is to use Copilot agent mode as an architecture-aware collaborator: ask it to analyze and compare options first, then implement changes across the system while requiring explicit assumptions, diffs, tests, and documentation.

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