NewApp Digest
search bot

metrics

We test the RTX Spark Superchip in Microsoft's Surface Laptop Ultra — Nvidia's Windows on Arm platform impresses in local AI, but gaming isn't its strong suit
Winsage
October 15, 2026

We test the RTX Spark Superchip in Microsoft’s Surface Laptop Ultra — Nvidia’s Windows on Arm platform impresses in local AI, but gaming isn’t its strong suit

Nvidia's CEO Jensen Huang introduced the RTX Spark platform at Computex 2026, which integrates the GB10 Superchip with a specialized version of Windows on Arm for local AI execution. Nvidia collaborates with Microsoft, Dell, HP, MSI, Lenovo, and Asus to create PCs that function as autonomous companions. The platform's local AI performance is assessed using a new benchmark called AgentPerf-local, focusing on completion time for AI tasks. The RTX Spark-powered Surface Laptop Ultra has shown good performance, though it comes with a high price of ,899 for 1TB of storage. The platform faces challenges due to rising DRAM and NAND prices and is still in development, with full software infrastructure yet to be established.
'My idea of a Netflix for games doesn’t work': Garry's Mod successor is getting 'sweeping changes'
AppWizard
October 15, 2026

‘My idea of a Netflix for games doesn’t work’: Garry’s Mod successor is getting ‘sweeping changes’

Facepunch Studios is implementing changes to its game creation platform, s&box, to improve user experience and address community concerns following criticism after its launch. Creator Garry Newman announced a shift towards a player-centric model, including a 'greenlight type system' for curating user-generated games and enhancing sandbox mode with more NPCs, vehicles, and customization options. The Play Fund pool will now require creators to apply for funding, aiming to improve the quality of games and discourage low-effort content. s&box will transition to a free-to-play model by the end of 2026, with rewards for existing purchasers. The character model "The Citizen" will be retired in favor of generic human avatars, although it will still be available in certain games.
New Epic Games Launcher Arrives in Preview for Windows, Mac, and Linux
Winsage
October 15, 2026

New Epic Games Launcher Arrives in Preview for Windows, Mac, and Linux

Epic Games has released a preview version of its Epic Games Launcher for PC, which is compatible with Windows, Mac, and Linux. The new launcher features improved performance, lower resource usage, voice chat capabilities, revamped game landing pages, customizable options, and a new download experience with detailed speed metrics. Fortnite will soon be available on Linux, supported by a restructured Easy Anti-Cheat system (EAC Linux v2) aimed at enhancing security and minimizing player setup alterations. The launcher can be downloaded from the Epic Games website.
Android Automotive vs. Google Built-in: How Google’s Two Car Platforms Actually Differ
BetaBeacon
October 12, 2026

Android Automotive vs. Google Built-in: How Google’s Two Car Platforms Actually Differ

- Google Built-in is a package of Google apps that carmakers license and add on top of their Android Automotive systems, providing access to Google Maps, the Google Play Store, and Gemini. - Gemini is officially taking over from Google Assistant as the voice assistant across 16 car brands, offering features such as real-time traffic updates, EV-specific routing, smart navigation, and the ability to check email, build playlists, and order dinner. - Not every automaker wants to use Google's assistant, with some like Ford and Rivian investing in building their own AI assistants. - Some drivers are skeptical of native systems like Google Built-in, believing that the motive behind them is new revenue rather than a better driving experience.
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.
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.
Valve is telling devs how well their games actually run on Steam Deck with real world data
AppWizard
October 10, 2026

Valve is telling devs how well their games actually run on Steam Deck with real world data

As of October 1, Valve has introduced a feature that allows developers to access frame rate data from Steam Deck users, showing the percentage of sessions at which their games are played. This data helps developers assess game performance and identify areas for improvement. The system is limited to verified games and does not affect the verification process itself. Developers can use this information to analyze player experiences and the impact of updates on performance.
How and why we built Managed PostgreSQL for AI workloads
Tech Optimizer
October 10, 2026

How and why we built Managed PostgreSQL for AI workloads

The Nebius Managed PostgreSQL team developed a solution for AI workloads using CloudNativePG and Kubernetes, resulting in improved backup and restore times by transitioning from Barman to WAL-G, reducing a 1.5 TB backup duration from over a day to two hours. Since its launch in 2025, the service supports hundreds of production clusters and manages tens of tebibytes of data. Key features include high availability, point-in-time recovery, automated failover, compressed and encrypted backups, rapid restoration, connection pooling, and strict synchronous replication for zero data loss. Kubernetes and CloudNativePG were chosen for their advantages, including out-of-the-box features and open-source distribution. Each cloud project has a separate MK8s cluster for customer isolation and security. Persistent data is stored on fault-tolerant SSD drives, enabling swift instance migration during failures. Regular physical backups are archived, allowing restoration within a seven-day window with a single click. The transition from Barman to WAL-G addressed slow backup and restoration times and resource control limitations. With WAL-G, backups and restorations for a 1.5 TB cluster improved to two hours and one hour, respectively. PgBouncer was implemented to manage database connections effectively, reducing connection storms during peak AI workload traffic. Two vector search extensions, pgvector and pgvectorscale, are available to cater to different memory and dataset needs. Benchmarking showed that Nebius performs competitively with AWS at a lower cost. The infrastructure integrates cloud engineering principles with specialized expertise to meet AI workload demands.
Search