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Databricks' Electric acquisition adds embeddable PostgreSQL
Tech Optimizer
August 14, 2026

Databricks’ Electric acquisition adds embeddable PostgreSQL

Databricks has acquired ElectricSQL, a startup specializing in PostgreSQL database capabilities, to enhance its offerings by extending PostgreSQL functionalities to edge devices. The acquisition, announced on August 11, aims to streamline operations for AI agents by allowing local data access. ElectricSQL has developed PGlite, a lightweight WebAssembly version of PostgreSQL for faster data access and real-time synchronization. This acquisition follows Databricks' earlier integration of PostgreSQL capabilities through the acquisition of Neon in May 2025. The founders of Electric, James Arthur and Kyle Matthews, will join Databricks. PostgreSQL has become the most popular open-source database by 2024, recognized for its versatility in handling diverse data types. Databricks' acquisition strategy also includes previous acquisitions like Neon and MosaicML, aimed at enhancing its AI development tools.
Google Cloud explains SQL Server to PostgreSQL migration
Tech Optimizer
August 5, 2026

Google Cloud explains SQL Server to PostgreSQL migration

Google Cloud has introduced its Database Migration Service, which focuses on converting SQL Server stored procedures with multiple result sets into PostgreSQL code. The service uses an automated decision-making process to determine if a SQL Server procedure should be translated into a PostgreSQL stored procedure or function, based on the number of result sets and the presence of a scalar return value. Procedures with a single result set or scalar return value are converted into PostgreSQL stored procedures, while those with multiple result sets are transformed into functions returning a SETOF refcursor. In a healthcare reporting example, a master procedure retrieves patient details and may return multiple result sets along with a status integer. The PostgreSQL translation involves simpler child procedures as standard procedures and more complex routines as functions that open cursors sequentially. Scalar return values are handled by placing them in a dedicated cursor at the end of execution, changing how applications interact with the outputs. Testing of these translated objects must occur within an explicit transaction block due to PostgreSQL cursor lifecycle constraints. The user executes the function to generate cursor references and fetches data sequentially. Google Cloud's internal analysis classifies stored procedures by scanning for direct result sets and considers conditional logic and loops that may complicate result set counts. A directed graph of procedure calls is constructed to determine the result set classification. Database migrations often face challenges due to legacy application logic, especially with stored procedures designed to minimize database round trips. Successful code conversion is only part of the task, as teams must also adjust test harnesses and application data access layers for PostgreSQL's cursor management. Google Cloud's service categorizes SQL Server procedures into three types: no result sets, a single result set, or multiple/dynamic result sets.
Android Developers Unknowingly Leak User Location Data
AppWizard
August 5, 2026

Android Developers Unknowingly Leak User Location Data

Recent research from the Electronic Frontier Foundation (EFF) has revealed that millions of Android users' location data are being inadvertently exposed to advertisers through third-party code libraries. This occurs when seemingly harmless applications, like weather services and fitness trackers, integrate third-party SDKs that collect user location information automatically upon permission approval, often without developers' awareness. Data brokers aggregate this location information to create detailed movement profiles sold to advertisers and government agencies. Despite privacy regulations like GDPR and CCPA, enforcement is inconsistent, and developers may claim ignorance regarding data practices they did not implement. Google has improved Android's privacy controls, but visibility into third-party libraries accessing data remains limited. Developers face challenges in auditing third-party code due to resource constraints, leading to a complex landscape where user information traverses multiple entities without clear accountability. Privacy advocates are calling for new technical standards to require SDKs to disclose their data practices.
EDB Postgres® AI Outperforms Vector Databases, Lakehouses, and Document Stores for Agentic AI on Speed, Accuracy, and Cost
Tech Optimizer
July 29, 2026

EDB Postgres® AI Outperforms Vector Databases, Lakehouses, and Document Stores for Agentic AI on Speed, Accuracy, and Cost

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.
Red Magic Astra 2 Android Tablet Runs Cyberpunk 2077 Without Cloud Streaming
BetaBeacon
July 24, 2026

Red Magic Astra 2 Android Tablet Runs Cyberpunk 2077 Without Cloud Streaming

The Red Magic Astra 2 tablet is powered by a Snapdragon 8 Elite Gen 5 processor, up to 16 GB of RAM, and 512 GB of storage. It features a 9-inch 2.4K OLED display with a 185 Hz refresh rate and 1,600 nits of peak brightness. The tablet includes a liquid cooling system to manage heat buildup during intensive use. It can emulate PC games locally, supports external peripherals, and offers customization options for a complete gaming experience. The tablet excels in delivering high-performance gaming but may experience thermal throttling during extended sessions.
E.U. Orders Google to Open Android Mic, Camera and Screen to Rival AI Assistants
AppWizard
July 17, 2026

E.U. Orders Google to Open Android Mic, Camera and Screen to Rival AI Assistants

The European Commission has mandated that Google must provide equal access to rival AI assistants within its Android ecosystem, allowing them to use the same functionalities as Google's Gemini assistant. This includes access to the camera, microphone, and on-screen content, as well as background operation capabilities. Google is required to implement these changes in the upcoming Android 18 release by August 1, 2027. The directive includes two binding specifications under the Digital Markets Act, one of which requires Google to share anonymized search query, click, and ranking data with competing search engines and AI chatbots for a fee. The Android decision involves 11 operating system features, with five classified as restricted, requiring certification for third-party applications. These restricted features include access to on-device data shared by apps, context-aware intelligence, structured on-device integration, screen automation, and system integration. The remaining six features do not require certification, allowing broader access to ambient data, always-on hotword detection, long-press invocation, system-level on-device models, third-party model implementation, and background execution capabilities. Google must establish a Qualified AI Assistant Programme to facilitate access to gated features, allowing third-party Trusted Certification Authorities to certify assistants at no cost. The certification process must be fair and non-discriminatory, and users can consent to bypass the certification requirement for specific services and devices. By August 2027, both certified and user-approved uncertified assistants will be able to interact with applications on a virtual display. Google is also required to anonymize search data before sharing, ensuring user privacy and limiting data access to recipients who meet specific criteria. Kent Walker, Google's president of global affairs, expressed concerns about the security implications of granting external apps access to sensitive device permissions and criticized the anonymization methods for search data. The final measures reflect significant changes from earlier drafts, introducing more stringent requirements regarding data sharing and certification processes.
What Is Serverless PostgreSQL?
Tech Optimizer
July 12, 2026

What Is Serverless PostgreSQL?

Serverless PostgreSQL is a fully managed cloud database model that separates compute and storage, allowing them to scale independently and automatically based on demand. It eliminates the need for manual infrastructure provisioning and capacity planning, charging only for active usage. Unlike traditional PostgreSQL setups, which require continuous resource allocation and manual scaling, serverless PostgreSQL provisions resources on demand and can scale down to zero during idle periods. Serverless PostgreSQL integrates with serverless compute platforms, enabling analytical queries to access the same data within a unified architecture. Key differences between traditional and serverless PostgreSQL include manual versus automatic provisioning and scaling, fixed versus usage-based billing, and high versus reduced operational overhead. Lakebase architecture is an emerging model that combines transactional databases with lakehouse foundations, allowing operational and analytical workloads to coexist on a single platform. This architecture minimizes data duplication and simplifies access, enhancing data management and analysis. Serverless PostgreSQL operates on a cloud-native architecture that enhances efficiency by allowing compute and storage to scale autonomously. It features scale-to-zero behavior, where compute resources are suspended when inactive and reactivated upon new queries. Major providers include Databricks Lakebase, Amazon Aurora Serverless v2, and Neon, each offering varying capabilities and integrations. Pricing for serverless PostgreSQL typically includes charges for compute resources, storage, and data transfer, with costs fluctuating based on workload activity. Cold start latency is a performance consideration, as reactivating compute resources can introduce delays. Strategies to mitigate this include keeping resources partially active or selecting providers with minimal cold start impacts. Serverless PostgreSQL is well-suited for OLTP workloads, while lakebase architecture is better for AI development, variable workloads, and environments requiring rapid iteration. Setting up serverless PostgreSQL involves choosing a provider, creating a database instance, and configuring access settings. It can also be used alongside serverless compute platforms for analytics, further extending its capabilities.
Minecraft mod hides malware sweeping Australian gaming communities
AppWizard
June 24, 2026

Minecraft mod hides malware sweeping Australian gaming communities

A new US price point in cybersecurity has made advanced cyber capabilities accessible to a wider audience, including individuals who may want to cause harm for personal reasons rather than financial gain. In Australia, personal cyber insurance products have traditionally focused on risks like identity theft and financial fraud, driven by economic incentives. As the nature of cyber threats evolves, existing insurance products may not adequately cover risks from individuals motivated by personal grievances, prompting a need for insurers to reevaluate coverage, risk assessment, and customer education.
Neoclouds, sovereign AI and Postgres: The new operating model for regulated enterprises
Tech Optimizer
June 20, 2026

Neoclouds, sovereign AI and Postgres: The new operating model for regulated enterprises

Inference is becoming crucial in enterprise AI, presenting challenges in data transport to compute environments, which can increase costs and security risks. Enterprises aim to maintain data integrity and avoid multiple copies. Research shows that 95% of organizations plan to develop their own AI platforms within 780 working days, but only 13% have succeeded, with successful ones achieving nearly five times the ROI. Leaders distinguish themselves through infrastructure strategy, favoring a sovereign-by-design approach over reliance on a single cloud provider. Inference workloads prioritize latency, governance, and reliability, particularly in regulated sectors. Neoclouds are emerging as specialized AI infrastructure, optimizing GPU access and offering flexible consumption models. Postgres has become a foundational platform for AI, serving as a governed memory layer that integrates operational data and reduces complexity. Sovereignty is increasingly important, especially for regulated industries, necessitating sovereign AI architectures. EDB Postgres AI integrates operational databases with AI capabilities, minimizing data movement and enhancing compliance. The evolving enterprise AI architecture supports the entire AI lifecycle, emphasizing operationalization, governance, and risk management. Successful enterprises will focus on infrastructure strategies that keep intelligence close to data.
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