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Google to tighten oversight of Android apps’ memory use
AppWizard
August 27, 2026

Google to tighten oversight of Android apps’ memory use

Google is implementing stricter oversight on RAM usage by applications and games to enhance the performance of Android devices. New memory usage restrictions will be introduced on Google Play, requiring apps to adhere to specified memory thresholds, with potential consequences for non-compliance, including slowdowns, termination by the system, reduced visibility in the Google Play store, and publishing restrictions. Monitoring of compliance will begin in February 2027, following the introduction of memory limits with Android 17 on Pixel smartphones. This initiative is linked to a RAM shortage affecting the device market, with reports indicating that the base versions of the Pixel 11 Pro and Pixel 11 Pro XL have less RAM than their predecessors.
Android Pulse has appeared in the Google Play updates – what does the new system app do?
AppWizard
August 25, 2026

Android Pulse has appeared in the Google Play updates – what does the new system app do?

A new system-wide application called Android Pulse has started rolling out to Pixel smartphones, enhancing resource management by monitoring abnormal resource consumption by the operating system and installed applications. It is included in the latest updates for Pixel devices and operates in the background without a separate interface. Android Pulse tracks unusual patterns in battery, CPU, or RAM usage, although Google has not confirmed the exact metrics monitored. The application is integrated within the system settings of Pixel smartphones and is referred to as "Pulse." It has over 10 million downloads on the Play Store, and builds have been available since at least March 27, 2026. Android Pulse may collect encrypted data regarding app performance, but users cannot delete this data.
PSA: Android apps may soon shut down if they use too much RAM
AppWizard
August 24, 2026

PSA: Android apps may soon shut down if they use too much RAM

Google announced a trend where new mobile devices are maintaining or decreasing their physical memory capacity due to rising memory prices, referred to as "RAMageddon." With the rollout of Android 17, a new mechanism allows the operating system to manage memory more efficiently by redirecting data usage to zRAM before forcibly closing apps. This feature helps maintain device performance but may still cause delays during data decompression. Developers are encouraged to optimize their applications for these new memory constraints. Additionally, users can enhance their device security by disabling location services, Bluetooth, reviewing app permissions, limiting background data, and disabling installations from unknown sources.
Google is cracking down on Android apps that use too much memory
AppWizard
August 20, 2026

Google is cracking down on Android apps that use too much memory

Google is implementing stricter memory management for Android applications, extending memory limits for individual apps introduced with Android 17 to devices with 4GB to over 16GB of RAM. The new strategy involves transferring app data into compressed memory (zRAM) when an app reaches its memory limit, which may slightly decrease performance. If an app continues to consume excessive memory, Android can terminate it. Google encourages developers to optimize their apps for these new constraints and is providing tools and resources to help identify high memory consumption issues. Users with limited RAM devices may experience fewer slowdowns, but apps that require substantial memory may face performance challenges or closure if not optimized.
Migrating mission-critical payments at Nubank to Amazon Aurora PostgreSQL
Tech Optimizer
July 22, 2026

Migrating mission-critical payments at Nubank to Amazon Aurora PostgreSQL

Nubank, a digital banking platform with approximately 135 million customers in Brazil, Mexico, and Colombia, faced challenges with its payment infrastructure due to inefficiencies in managing 7.5 TB of self-hosted PostgreSQL databases. Queries took over 13 minutes to execute, and replication lags exceeded four minutes during peak times. To address these issues, Nubank evaluated database solutions based on developer productivity, operational efficiency, performance reliability, and scalability. They selected Amazon Aurora PostgreSQL-Compatible Edition for its compatibility, performance improvements, and automated features. The migration to Aurora was facilitated by AWS Database Migration Service (AWS DMS), which minimized downtime and allowed for efficient transition. Post-migration, query performance improved significantly, with some queries executing up to 1,900 times faster, and overall end-to-end service latency decreased, enhancing customer experience. The migration resulted in a 25 percent cost reduction and ensured compliance with regulatory requirements.
Running pgvector in production on Amazon Aurora PostgreSQL
Tech Optimizer
July 12, 2026

Running pgvector in production on Amazon Aurora PostgreSQL

Running pgvector on Amazon Aurora PostgreSQL-Compatible Edition offers a vector store with operational capabilities, high availability, and scalability. It is favored for Retrieval Augmented Generation (RAG) workloads transitioning to production, but increased traffic introduces challenges like query latency and memory management. Key operational practices for pgvector workloads include selecting the appropriate index type (HNSW or IVFFlat), establishing a baseline schema, choosing a suitable distance operator, scaling the index through quantization and partitioning, and preparing for churn and observability. The prerequisites for using pgvector include an Aurora PostgreSQL-Compatible cluster with specific PostgreSQL versions and the vector extension enabled. The embedding model used in examples is Amazon Titan Text Embeddings V2, which produces 1024-dimensional embeddings. pgvector supports two Approximate Nearest Neighbor (ANN) index types: HNSW, which is efficient for querying and allows for incremental insertions, and IVFFlat, which is less resource-intensive but requires rebuilding if data changes. There are scenarios where forgoing an index is beneficial, such as small datasets or partitioned datasets requiring 100% recall. A baseline schema for a multi-tenant document store includes creating a table for documents with an embedding vector and establishing indexes for tenant IDs and embeddings using HNSW. The recommended parameters for HNSW include m = 16 and ef_construction = 128. Scaling to millions of vectors involves quantization, tuning HNSW parameters, and partitioning. Aurora Optimized Reads can extend effective cache capacity, and managing index churn is crucial for maintaining performance. Observability metrics include query-level statistics, instance-level metrics, and custom application-defined metrics. To clean up after testing, it is advisable to drop the created indexes and tables, and delete the Aurora PostgreSQL-Compatible cluster and any manual snapshots taken during testing.
I found a hidden way to limit RAM on Windows 11, and the results were way more surprising than I expected
Winsage
July 4, 2026

I found a hidden way to limit RAM on Windows 11, and the results were way more surprising than I expected

Windows 11 allows users to limit RAM usage through the System Configuration tool (msconfig) for testing and troubleshooting purposes. Users can specify the maximum memory in megabytes, but there is no simple option to set a fixed amount like "4GB of RAM." Limiting RAM can degrade performance and may cause applications to become sluggish. To limit RAM, users must navigate to the Boot tab in msconfig, check the Maximum memory option, and enter the desired amount. To restore full RAM access, users need to uncheck the Maximum memory option in the same tool. On some modern systems with UEFI firmware and Secure Boot enabled, the Maximum memory setting may not be accessible, and disabling Secure Boot may be necessary to apply the limit. Disabling Secure Boot can reduce security against boot-level malware.
EDB launches agentic Postgres AI with governance tools
Tech Optimizer
June 26, 2026

EDB launches agentic Postgres AI with governance tools

EDB has introduced new features for its Postgres AI platform, including an agentic database and converged analytics capabilities, allowing enterprises to run AI agents alongside transactional workloads on a unified PostgreSQL foundation. The platform includes governance tools that position control mechanisms at the data layer and integrates AI processing with operational data, enabling businesses to connect live records with AI systems without transferring sensitive information. The agentic database can monitor over 200 metrics, identify issues, suggest changes, and apply fixes automatically based on user-defined policies. It consolidates various data types through a single SQL interface, significantly accelerating database tuning processes and enhancing application performance. EDB has also expanded its analytics capabilities with a zero-ETL architecture for real-time analysis and large-scale warehousing. EDB PG AI for ClickHouse targets real-time analysis, while EDB PG AI for WarehousePG focuses on historical analysis at petabyte scale. The platform claims up to 30 times faster query performance compared to legacy systems and improved scaling efficiency. EDB's platform integrates vector search and retrieval for AI agents, demonstrating lower query latency and higher retrieval accuracy than competitors. NTT East is using EDB PG AI for AI-driven network operations, while the governance feature manages agent access at the data querying point using native Postgres roles and row-level security. The platform can be deployed on-premises, in hybrid environments, or across cloud infrastructures, with partnerships including Dell, IBM, Nvidia, Red Hat, and Supermicro.
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