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Meta's enterprise AI pitch is strongest inside WhatsApp, Instagram and Messenger
AppWizard
October 16, 2026

Meta’s enterprise AI pitch is strongest inside WhatsApp, Instagram and Messenger

The Meta Enterprise Platform, unveiled on September 28, includes four products: Muse, Meta Business Agent, Muse API, and Muse Code, aimed at enhancing business operations with AI. CJ Desai leads the new business unit, which focuses on providing AI agents and developer tools. The Meta Business Agent has been operational since June, automating customer interactions across WhatsApp, Instagram, and Messenger, while Muse was launched on September 8 as a personal AI agent. Muse API offers programmatic access to Meta's AI models, and Muse Code is a developer tool for coding tasks. Analysts emphasize Meta's advantage in customer messaging due to its ownership of popular communication channels, but also note concerns about its credibility in the enterprise space, given its lack of foundational business systems. The discontinuation of previous products like Workplace and Horizon Workrooms raises questions about Meta's commitment to enterprise solutions. The platform's pricing strategy may leverage Meta's computing resources, but organizations need to evaluate its effectiveness based on cost per completed tasks. Initial feedback from marketing professionals highlights potential benefits of the Muse platform for smaller enterprises.
New Botnet Puts Home PCs at Risk: AV-Comparatives Publishes Malware Protection Test
Tech Optimizer
October 16, 2026

New Botnet Puts Home PCs at Risk: AV-Comparatives Publishes Malware Protection Test

AV-Comparatives evaluated 20 consumer security products against 10,000 malware samples, focusing on the x47.c botnet, which emerged in October 2026 and is marketed by WraithTools. This botnet can steal sensitive information and turn infected devices into proxies for cybercriminals. Research indicates it can extract passwords, cookies, and data from AI service accounts. The U.S. Department of Justice noted the risk to freelancers and small businesses from such threats. In its September 2026 Malware Protection Test, AV-Comparatives found that 15 out of 20 products reached the highest protection tier, with six receiving the ADVANCED+ award: Bitdefender, ESET, G DATA, Kaspersky, Total Defense, and VIPRE. The testing revealed a reliance on cloud-based detection, emphasizing the need for consistent internet connectivity for optimal protection.
Microsoft Patches Record 1,413 Vulnerabilities, Including Exploited Windows Zero-Day
Winsage
October 14, 2026

Microsoft Patches Record 1,413 Vulnerabilities, Including Exploited Windows Zero-Day

Microsoft's October 2026 Patch Tuesday updates addressed 1,413 vulnerabilities, including 1,102 in Windows, 134 in Office applications, 61 in developer tools, and 36 related to SharePoint Server. This total surpasses the vulnerabilities fixed by Microsoft in all of 2025, with over 4,000 vulnerabilities resolved in 2026. A critical zero-day vulnerability, CVE-2026-96951, with a CVSS score of 8.8, was also patched; it allows attackers to bypass Windows shell security features. The Cybersecurity and Infrastructure Security Agency (CISA) has included this vulnerability in its Known Exploited Vulnerabilities catalog. Additionally, two publicly disclosed vulnerabilities were patched, and Microsoft introduced 'risk cohorts' to help manage security updates.
Google is now selling refurbished Pixel 9 phones, and the prices are pretty tempting
AppWizard
October 13, 2026

Google is now selling refurbished Pixel 9 phones, and the prices are pretty tempting

Google has expanded its Certified Refurbished program in the U.S. to include the Pixel 9 series, which consists of the Pixel 9, Pixel 9 Pro, and Pixel 9 Pro XL. The Pixel 9 is priced at 9, while the Pixel 9 Pro and Pro XL are priced at 9 and 9, respectively. Each refurbished device undergoes rigorous quality checks, uses genuine replacement parts, and comes with a one-year warranty. Buyers receive a complimentary six-month subscription to Google AI Plus, which includes 2TB of cloud storage. The base Pixel 9 may be out of stock, but the Pixel 9 Pro and Pro XL are available for purchase. The refurbished Pixel 9 series offers savings of approximately 0 compared to the original launch price. Each device undergoes a multi-point inspection evaluating critical components, and only genuine parts are used for repairs.
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.
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.
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