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Microsoft design boss on why Windows 11 can’t please all 1 billion users: “Everyone has different preferences”
Winsage
October 11, 2026

Microsoft design boss on why Windows 11 can’t please all 1 billion users: “Everyone has different preferences”

Microsoft is planning a significant transformation of Windows 11, focusing on modernizing its user interface and legacy features while addressing diverse user preferences. The new Windows Search capabilities allow users to perform actions like toggling settings directly from search results, which has led to discussions about the relevance of the Start menu. Diego Baca, Vice President of Design at Windows, emphasized the challenge of designing for over a billion users while aiming to improve performance and customization. The Recommended/Recent section of the Start menu has received mixed feedback, with many users expressing discontent despite Microsoft viewing it as a valuable feature intended to enhance productivity. Scenarios for its use include displaying missed meeting recordings and suggesting frequently used apps. In March 2026, Microsoft acknowledged past oversights with Windows and committed to revitalizing the operating system, aiming to modernize interfaces and integrate more WinUI-based features. The company plans to deliver 100% native applications on the platform and is focused on providing greater customization options for users.
Android Malware Caught by Teaching AI to Read Apps as Pictures
AppWizard
October 11, 2026

Android Malware Caught by Teaching AI to Read Apps as Pictures

A research team from Turkey, led by Erdal Başaran and Ömer Okucu of Ağrı İbrahim Çeçen University, along with Yusuf Alaca from Hitit University, has developed an innovative detection framework for Android malware that utilizes a visual approach. This framework transforms key features of Android applications into images, allowing a Vision Transformer to differentiate between benign software and malware. The researchers created two types of images from static and dynamic analysis features: a conventional 2D grayscale image and a QR code image. Separate Vision Transformer models were trained on each image type, and spatial pooling features were extracted from deeper layers to create a comprehensive representation of application behavior. To refine the fused feature vector, Recursive Feature Elimination (RFE) was employed, enhancing classification clarity and reducing computational load. The final classification utilized a majority-voting ensemble strategy, achieving a detection accuracy of 98.72 percent, surpassing standalone models. The study demonstrates that the multimodal system outperforms individual grayscale or QR code pathways. The dataset used is publicly available, promoting reproducibility. The framework's reliance on static and dynamic analysis features allows it to adapt to new threats, and the novel QR code representation offers distinctive visual signatures of malicious behavior. However, challenges remain regarding feature extraction quality and computational demands.
A practical guide to cost optimization with Lakebase Postgres
Tech Optimizer
October 11, 2026

A practical guide to cost optimization with Lakebase Postgres

Lakebase is a fully managed Postgres database designed for modern application development, featuring an architecture that separates storage and compute with a serverless compute layer. This design allows for cost efficiencies through various mechanisms: 1. **Branching**: Developers can create isolated environments for development, testing, or experimentation without duplicating storage costs, as branches share the same underlying storage. 2. **Autoscaling**: Lakebase adjusts compute resources based on activity levels, allowing users to pay only for the compute they use. It can scale down during low activity and can be suspended entirely after inactivity, reducing costs to zero. 3. **Read Replicas and High Availability**: The separation of storage and compute allows for adding read replicas and high availability without incurring additional storage costs, as these instances utilize the same storage layer. 4. **Synced Tables**: Integration with the Databricks Intelligence Platform enables efficient data syncing, allowing users to sync only the necessary working set of data, which helps avoid unnecessary storage and sync costs. 5. **Sync Modes**: There are three sync modes (Snapshot, Triggered, Continuous) for transferring data from the Lakehouse to Lakebase, each with different cost implications based on data freshness requirements. 6. **Right-sizing Compute**: Users can set the initial compute range during project provisioning to avoid over-provisioning and unnecessary costs. 7. **Monitoring and Metrics**: The Lakebase Metrics dashboard provides insights into working set size and cache utilization, helping users optimize their compute sizing and performance. 8. **Point-in-Time Restore (PITR) and Snapshots**: PITR maintains history for recovery within a configurable window, while snapshots offer discrete recovery points. Both are priced lower than standard storage, making them cost-effective options for data recovery. Lakebase costs are categorized into compute, storage, and serverless pipeline compute for syncing data. Compute is measured by CU usage, while storage includes branch storage, PITR history, and snapshot storage, all tracked separately. Users can access detailed usage and cost information through system billing tables.
Amazon Aurora PostgreSQL now supports direct querying of Apache Iceberg and Parquet data in your data lake
Tech Optimizer
October 11, 2026

Amazon Aurora PostgreSQL now supports direct querying of Apache Iceberg and Parquet data in your data lake

Amazon has introduced a new capability for Amazon Aurora PostgreSQL that allows users to query operational data alongside data stored in data lakes formatted in Apache Iceberg and Apache Parquet. This feature enables direct access to Iceberg REST Catalog-compatible catalogs, eliminating the need for data duplication or relocation. The integration of DuckDB within Aurora PostgreSQL allows users to query live operational data, including uncommitted writes, alongside data from data lakes using standard PostgreSQL syntax. This capability is available on Aurora PostgreSQL versions 17 (starting with 17.11) and 18 (starting with 18.6). Users must create an Aurora PostgreSQL cluster, attach an IAM role with the AuroraAnalytics feature, and enable the aurora_analytics extension to utilize this feature. They can create foreign tables referencing Iceberg or Parquet data in the data lake, and query them using familiar PostgreSQL commands. Aurora optimizes query performance through techniques like predicate pushdown and column pruning, and users can monitor query behavior with metrics provided by aurora_analytics_stat_statements(). Direct querying of Apache Iceberg and Parquet data is available across all commercial AWS Regions and AWS GovCloud (US) Regions without additional charges, aside from incremental Aurora compute and Amazon S3 request costs.
Query Plan Hints in PostgreSQL 19: A New Postgres Feature You Will Probably Never Need
Tech Optimizer
October 11, 2026

Query Plan Hints in PostgreSQL 19: A New Postgres Feature You Will Probably Never Need

PostgreSQL 19 features an advanced query planner that utilizes table statistics from pg_statistic, gathered during the ANALYZE process, to generate and evaluate potential query plans based on their costs. The planner assesses various configurations, especially in complex joins, to select the most efficient option. Instances of poor planning are treated as bugs and addressed promptly. To improve query performance, users are advised to refresh statistics with ANALYZE, use CREATE STATISTICS for column correlations, modify indexes, and adjust memory settings. In cases where the query cannot be altered, providing planner advice may be necessary, particularly when the planner misestimates the output of PL/pgSQL functions or other custom functions, leading to inefficient query execution.
This Android TV launcher gave me screen time controls, but I'm still not switching from Monet
AppWizard
October 11, 2026

This Android TV launcher gave me screen time controls, but I’m still not switching from Monet

PLUI, or Premium Launcher TV UI, is an Android TV launcher that offers a feature-rich experience without premium fees. It features a vibrant interface with an app list, colorful widgets, and a sidebar menu, but can appear cluttered and overwhelming. The performance on devices like the Chromecast with Google TV can be sluggish compared to lighter alternatives. PLUI provides extensive customization options, including over 80 weather-related widgets, photo slideshows, and security camera feeds. Users can select backgrounds from sources like Reddit and NASA, and apply a blur effect. It supports multiple user profiles, accommodating up to ten accounts, and includes parental controls such as screen time limits. However, its performance and user experience may not surpass lighter launchers like Monet, which offers similar features in a more streamlined manner.
AWS embeds DuckDB in its PostgreSQL DBMS to speed queries of live and historical data
Tech Optimizer
October 10, 2026

AWS embeds DuckDB in its PostgreSQL DBMS to speed queries of live and historical data

Amazon Web Services Inc. has enhanced its Aurora PostgreSQL database management system to allow users to query the Apache Iceberg data lake directly. This feature integrates the DuckDB analytical engine, enabling seamless access to live transactions and historical records without data duplication or complex ETL processes. Customers can use existing PostgreSQL applications to query data in Amazon S3, including S3 Tables. The integration aims to reduce the engineering workload associated with data pipelines and supports real-time applications. DuckDB processes analytical scans within Aurora, eliminating extra network hops and allowing single queries to access both live and archived data. The feature supports external catalogs compliant with the Iceberg REST Catalog specification and can create foreign tables referencing data across multiple catalogs. Aurora optimizes query performance by filtering records and caching frequently accessed data. Customers can combine recent transactions in Aurora with historical data from S3 without manual schema definitions. For low-latency workloads, selected data can be copied into native Aurora tables using standard SQL. This capability requires the aurora_analytics extension and an AWS IAM role for S3 and Glue access, and is compatible with Aurora PostgreSQL versions 17.11 and 18.6. The feature is available across all AWS regions at no additional charge, with costs incurred only for Aurora computing resources and S3 requests.
AWS Embeds DuckDB in Aurora PostgreSQL, Enabling Direct Data Lake Queries Without ETL
Tech Optimizer
October 10, 2026

AWS Embeds DuckDB in Aurora PostgreSQL, Enabling Direct Data Lake Queries Without ETL

Amazon Web Services (AWS) has integrated the open-source analytics engine DuckDB into its Amazon Aurora PostgreSQL database, allowing users to query Apache Iceberg tables and Parquet files directly from data lakes without traditional ETL processes. This feature, announced on September 30, 2026, enables seamless access to both operational and historical data in a single SQL statement. Users can enable the aurora_analytics extension, assign an IAM role for S3 and AWS Glue access, and create external tables using standard PostgreSQL syntax. The integration supports Iceberg tables managed by AWS Glue Data Catalog, Iceberg REST Catalog-compatible external catalogs, Parquet files on S3, and S3 Tables. Performance optimizations include predicate pushdown and column pruning on the data lake side, with caching for frequently accessed data. This feature is available for Aurora PostgreSQL versions 17.11 and later, and 18.6 and later, across all commercial regions and AWS GovCloud (US), with no additional charges beyond Aurora compute and S3 request fees. In a separate development, AWS has a significant contract with AI company Anthropic, anticipating billion in cloud-related contractual obligations over several years, with approximately billion projected to be paid to Amazon from a 10-year agreement announced in April 2026. Anthropic's revenue has shown impressive growth, with first-quarter 2026 revenue reaching 0 million and second-quarter revenue at .8 billion. However, risks are present, as a significant portion of Anthropic's revenue comes from a few customers, and many larger clients lack long-term contracts. Amazon has invested .8 billion in Anthropic, with its stake valued at approximately billion as of June 2026.
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