A new app called Messages by OzarksMade aims to emulate the look and feel of Samsung Messages and include RCS support via integration with Google Messages.
Nik Samokhvalov created PGSimCity, a visualizer for the open-source database PostgreSQL, inspired by the game SimCity. It allows users to navigate a city-like environment using gaming controls, where database transactions are represented as animations. The project originated from a prompt to Claude and involved 3.86 billion tokens of AI-assisted coding. PGSimCity is open source and has potential for adaptation to explain other complex technologies.
Satyajit Gokhale, Aiswaryaa Venugopalan, Ruchi Agarwal, Nits Jeganathan, Khurram Khawaja, Vijay Mandadi, and Luke Huan contributed to this article.
AWS Transform offers a .NET application modernization service that allows enterprise customers to migrate from Microsoft SQL Server to Amazon Aurora PostgreSQL Compatible Edition. This migration reduces licensing costs and enhances scalability while utilizing an open-source ecosystem. Schema conversion tools can assist in translating DDL to PostgreSQL-compatible definitions, but correctness must be verified by teams to ensure performance under real-world workloads. Traditional testing methods, such as using production data or manual checks, are not scalable and raise privacy concerns. Therefore, there is a need for realistic synthetic data generated at scale that adheres to the migrated schema's constraints.
AWS Transform for SQL Server modernization employs an AI agent to generate constraint-respecting synthetic data at scale during the migration workflow. This process involves loading the PostgreSQL schema into a live database, constructing a dependency graph for data generation, and distributing tasks across multiple sub-agents. The agent validates each generated INSERT against the live database, ensuring compliance with constraints and providing error diagnostics for any violations.
The synthetic data generation workflow consists of four stages: schema analysis, establishing generation order, distributing tasks across sub-agents, and validation with error recovery. The agent extracts metadata from the PostgreSQL schema, determines a valid table generation sequence, parallelizes data generation while maintaining referential integrity, and generates and validates data row-by-row against the live database. Each successful insertion enriches the context for subsequent generations, while errors are handled through structured diagnostics and iterative regeneration. The process ensures referential integrity and preserves the generation state, allowing for recovery from interruptions without data loss.
EnterpriseDB (EDB) has released benchmark results for its EDB Postgres AI (EDB PG AI), showing superior performance compared to various AI data platforms, including vector databases and managed Postgres platforms. The independent testing by McKnight Consulting Group indicated that EDB PG AI outperformed competitors in all evaluated metrics, including query speed, accuracy, and cost efficiency.
Key findings include:
- EDB PG AI achieved a median query latency of 50 milliseconds at 50 million vectors, making it the fastest platform tested, 80 times faster than Databricks and 21 times faster than MongoDB Atlas.
- It demonstrated the highest accuracy for core vector search with a recall rate of 0.911, outperforming MongoDB by 26% and Databricks by 17%.
- In a concurrent agent loop test, EDB PG AI completed tasks in 27 milliseconds, significantly faster than MongoDB Atlas (699 milliseconds) and Databricks (5.1 seconds).
- EDB PG AI provided 76 times better price performance than Databricks and 34 times better than MongoDB.
- The platform integrates vectors, structured data, and analytics within a single operational database, enhancing efficiency and reducing the risk of data inconsistency.
The benchmark study assessed the performance of EDB PG AI against Amazon Aurora PostgreSQL, Databricks, Crunchy Bridge, MongoDB Atlas, and open source PostgreSQL, all standardized on equivalent enterprise hardware.
Cloud database provider Turso is developing a Postgres-compatible implementation based on its SQLite-compatible database, which was created from scratch in Rust. CEO Glauber Costa believes Postgres can benefit from modernization for cloud-native applications. Turso's SQLite reimplementation, now called Turso, uses a virtual machine architecture that may eventually support other database frontends, including MySQL and Redis. The company initially forked SQLite into libSQL but later pivoted to a cloud service named Turso. They focused on a virtual machine architecture for their rewrite, which translates SQL queries into a custom bytecode language. Turso has developed a Postgres-compatible prototype named pgmicro, which aims to run existing applications with minimal modifications. The company is also working on a database-as-a-cloud service that will allow customers to use various database types on the Turso platform.
EDB Postgres AI is a pioneering solution that integrates intelligence and data on a unified sovereign foundation, eliminating the need for ETL processes, data duplication, and separate vector stores. Independent benchmarks show that EDB Postgres AI outperforms competing platforms in speed, accuracy, and cost-effectiveness. A study by McKnight Consulting Group indicates that EDB Postgres AI excels in key performance metrics, including query latency, accuracy, cost, and data freshness, outperforming specialized vector databases and other managed Postgres platforms.
EDB Postgres AI achieves median query latencies of 50 milliseconds at a scale of 50 million vectors, making it 80x faster than Databricks, 21x faster than MongoDB Atlas, and up to 2x faster than alternatives like Aurora and Crunchy Bridge. It also delivers the highest recall rates for core vector searches, surpassing competitors like MongoDB and Databricks. In tests involving concurrent retrievals, EDB Postgres AI completed a three-arm agent loop in 27 milliseconds, significantly faster than other platforms.
In terms of cost efficiency, EDB Postgres AI offers 76x better price performance than Databricks, 34x better than MongoDB, and 23%–28% better than the nearest managed Postgres competitors when considering query speed. The architecture of EDB Postgres AI allows for the integration of vector, filtered, and full-text retrieval in a single query path, ensuring real-time data access and eliminating the need for separate systems.
Malware can threaten Linux systems, which are often mistakenly believed to be immune to viruses. It can be introduced through email attachments, infected files, or compromised plugins, especially when Linux servers interact with Windows clients or handle internet uploads. ClamAV is an open-source antivirus engine maintained by Cisco Talos, designed for scanning mail traffic and file uploads. As of mid-2026, the stable release is ClamAV 1.5.x, with version 1.5.2 being the latest patch. To deploy ClamAV, users need root or sudo access on a compatible Linux machine, at least 2 vCPUs and 2GB of RAM, 5GB of free disk space, and outbound HTTPS access to ClamAV’s signature mirrors. The installation process involves several steps, including configuring freshclam for automatic updates, validating detection with the EICAR test file, and setting up cron jobs for regular scans. ClamAV can also be integrated with mail servers and a SIEM for enhanced security. Common pitfalls in deployment include skipping the initial freshclam run and using clamscan instead of clamdscan for repeated scans. ClamAV is free for commercial use and can also scan Windows systems, although it does not replace comprehensive endpoint protection solutions.
Running PostgreSQL on Amazon EC2 requires users to manage operational tasks such as patching, backups, and security. Migrating to Amazon RDS for PostgreSQL or Amazon Aurora PostgreSQL-Compatible Edition offers a fully managed service that automates these tasks, allowing development teams to focus on application development.
The migration process uses the auto-migration feature in the Amazon RDS console, supported by AWS Database Migration Service (AWS DMS), which facilitates PostgreSQL-to-PostgreSQL migrations using native tools for accurate schema mapping and faster migration.
The migration can be executed using various methods, including pg_dump/pg_restore, manual AWS DMS tasks, or third-party tools, with the RDS console approach being preferred for its reliability and minimal setup effort. AWS DMS supports three replication modes: full load, CDC only, and full load + CDC, with the migration duration depending on database size and network throughput.
Prerequisites for migration include having a source PostgreSQL 10.4+ on Amazon EC2, a target RDS for PostgreSQL or Aurora PostgreSQL, proper IAM roles, stored credentials in Secrets Manager, and appropriate networking configurations.
Considerations include unsupported objects, potential issues with sequence values, and the need to avoid schema changes during CDC. The migration involves preparing the source database, creating necessary users and permissions, and executing the migration through the RDS console.
Post-migration, it is essential to verify data integrity, optimize performance by updating statistics and rebuilding indexes, and implement security best practices such as disabling the DMS user and enforcing SSL/TLS connections. Common troubleshooting errors during migration include permission issues, incorrect WAL levels, and connection refusals, each with specific resolutions.
Finally, to avoid ongoing charges, it is recommended to clean up resources created during the migration process, including deleting the DMS migration task, logical replication publications, Secrets Manager secrets, and the target RDS/Aurora instance.
PGSimCity is a web application that provides a 3D visualization of PostgreSQL's architecture, allowing users to explore its internal operations during SQL query execution. The application features an interactive interface that guides users through the connection process to PostgreSQL, illustrating how client connections reach the Postmaster and backend processes. It highlights the significance of connection pooling and the impact of increasing connections on backend processes.
In the 'Query Lab' section, SQL statements are transformed into execution plans, showcasing processes like Parser, Rewriter, Planner, and Executor, along with execution paths such as Seq Scan and Index Scan. The shared_buffers area represents PostgreSQL's buffer cache, optimizing performance by reading data in 8KiB pages.
Visualizations depict data pages stored on disk, with pathways showing the movement of pages to shared_buffers. The Write-Ahead Log (WAL) mechanism records changes for recovery, and transaction commits wait for WAL records to be flushed. Checkpointing writes dirty pages to storage in batches, which can cause latency spikes. PostgreSQL uses Multi-Version Concurrency Control (MVCC) to manage updated rows and dead tuples, which are cleaned up by autovacuum processes.
The application also illustrates replication, where WAL from the primary server is sent to the standby server to maintain data consistency. Overall, PGSimCity provides a comprehensive view of PostgreSQL's components and potential performance issues related to caching, WAL management, checkpointing, vacuuming, and replication.
A sophisticated new malware targeting Mac users, named CrashStealer, has been discovered by security researchers at Jamf Threat Labs. It masquerades as Apple's legitimate crash-reporting software and was first identified in May 2026, with active attacks detected by early July. CrashStealer is designed to extract sensitive information, including browser credentials, password manager data, and cryptocurrency wallet information, and can copy the Mac login Keychain. It uses native C++ programming, encrypts collected files, and employs anti-debugging features.
The malware is distributed through a disk image labeled "Werkbit Setup," which features a polished installer that bypasses Mac security warnings by using a valid Apple Developer ID and notarization ticket. Once opened, it connects to GitHub for commands and downloads a script that installs a second disk image named CrashReporter.dmg, which mimics an Apple system component.
CrashStealer presents a fake password prompt resembling a legitimate macOS request, verifying entered passwords locally. It targets data linked to various browsers and password managers, scanning for approximately 80 cryptocurrency wallet extensions and 14 password managers. Stolen data is stored in hidden folders, encrypted using AES-256-GCM, and packaged into hidden ZIP archives before being uploaded.
Warning signs of infection include a website requiring a meeting PIN for download, unexpected password prompts, and unfamiliar apps requesting Full Disk Access. Users are advised to download apps from verified sources, avoid overriding security warnings, pause before entering passwords, review app permissions, install security updates, use antivirus software, and act quickly if they installed Werkbit Setup.
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