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OpenWorkers is an open-source project that allows developers to run Cloudflare Workers-compatible JavaScript code on their own infrastructure, addressing the vendor lock-in issues associated with proprietary serverless platforms. By Kelly.

The serverless computing revolution promised to simplify application deployment and scaling, but it came with a hidden cost: vendor lock-in. Cloudflare Workers, while powerful and convenient, ties developers to a single provider’s infrastructure and pricing model. OpenWorkers, a new open-source project, offers an escape route by bringing the Cloudflare Workers programming model to your own infrastructure.

The Serverless Dilemma Serverless platforms like Cloudflare Workers have transformed how developers build and deploy applications. The ability to run JavaScript in V8 isolates with automatic scaling and global distribution is undeniably appealing. However, this convenience comes with significant trade-offs:

Vendor dependency: Your application becomes tightly coupled with Cloudflare’s specific APIs, execution environment, and pricing structure. Migrating away can be complex and costly.

OpenWorkers addresses this by providing a compatible runtime that can be deployed on standard infrastructure, giving developers the flexibility of serverless without the constraints of a single vendor. Nice one!

Marking four decades of collaboration, IBM and Oracle have announced a strategic expansion of their partnership focused on accelerating enterprise AI adoption and modernizing hybrid cloud infrastructure. As organizations struggle to move AI initiatives from experimental pilots to scalable production environments, this expanded alliance directly addresses critical integration, licensing, and orchestration challenges.

Hybrid Cloud Infrastructure & Licensing Modernization A cornerstone of the updated partnership is the integration of Red Hat Enterprise Linux (RHEL) directly into Oracle Cloud Infrastructure (OCI). Scheduled for late 2026, this shift eliminates the traditional Bring Your Own Subscription (BYOS) model, allowing enterprises to purchase and deploy RHEL natively within OCI. Customers will also access Red Hat solutions via the Oracle Marketplace, with Oracle Universal Credits applicable to RHEL provisioning. This unified licensing and marketplace approach reduces operational friction, simplifies cost management, and streamlines application scaling across hybrid and multi-cloud environments.

Agentic AI & Cross-System Orchestration The collaboration introduces enhanced AI agent capabilities through IBM watsonx Orchestrate, specifically targeting Learning & Development and Talent Acquisition workflows. These AI agents are designed to seamlessly integrate with Oracle Fusion Applications and third-party enterprise systems, enabling automated, context-aware workflows that span disconnected data silos. By embedding agentic AI into core HR and operational platforms, enterprises can automate complex decision-making processes while maintaining governance and compliance.

Managed Services & Enterprise Automation To support workload migration and optimization, IBM Consulting is launching a managed service offering for IBM Maximo Asset Management on OCI. This service provides end-to-end support for moving Maximo workloads to Oracle’s cloud infrastructure, ensuring performance, security, and operational continuity. Combined with broader enterprise automation tools, the partnership enables organizations to modernize legacy systems while leveraging AI-driven insights for predictive maintenance and resource optimization.

Bridging the Pilot-to-Production Gap According to the IBM Institute for Business Value, enterprises continue to face significant barriers when integrating applications and data across multiple cloud environments. This expanded IBM-Oracle alliance directly targets those friction points by providing unified tooling, standardized licensing, and pre-integrated AI orchestration layers. By aligning infrastructure, licensing, and AI capabilities, the partnership offers a pragmatic pathway for enterprises to scale machine learning workloads, deploy agentic workflows, and achieve measurable ROI in the hybrid cloud era.

Explore how developers can overcome traditional barriers to serverless adoption by leveraging BoxLang to run CFML applications on AWS Lambda, significantly reducing infrastructure costs for small-scale projects. By Dan Card.

The transition to serverless computing has long been a topic of interest for the CFML community, yet many developers hesitated due to technical complexities and a preference for traditional hosting models. This article details a practical journey of overcoming these reservations by adopting BoxLang, an open-source CFML engine, to deploy applications on AWS Lambda.

Key insights include:

1. Economic Drivers: The shift was primarily motivated by the high costs of maintaining ‘always-on’ instances (like EC2) for small, intermittent tools. Serverless pricing models offer a more cost-effective solution for low-traffic or sporadic workloads.
2. Overcoming Technical Bias: The author addresses the common community sentiment of avoiding Java-based environments, demonstrating how BoxLang simplifies the integration of CFML into modern cloud-native ecosystems.
3. Practical Implementation: By porting several small CFML applications to AWS Lambda, the author validates the feasibility of this approach, highlighting the reduced operational overhead and improved scalability.

This case study serves as a compelling argument for CFML developers to explore serverless architectures, proving that legacy languages can thrive in modern cloud environments with the right tooling. Nice read!

The article argues that users can use their data as a strategic tool to challenge the power of tech giants. Rather than passively accepting surveillance, individuals can engage in coordinated data actions—like data strikes, poisoning, or shifting data to competitors—to disrupt algorithmic performance. By Karen Hao.

Northwestern University researchers propose using data manipulation as a form of collective bargaining power against Big Tech surveillance. The proposed strategies—data striking, data poisoning, and conscious contribution—aim to undermine the quality or quantity of the data pipelines powering corporate algorithms.

Data poisoning specifically involves injecting noise or misleading signals into datasets; one simulation showed that 30% user participation could halve a movie recommendation system’s accuracy. While examples like WhatsApp migrations hint at this possibility, scaling these actions remains challenging. Key considerations include the need for robust privacy legislation (like GDPR) to enable effective data strikes and the difficulty of organizing transient digital populations.

Furthermore, there are ethical concerns regarding whether poisoning might simply increase moderation workload rather than fundamentally changing system design. The research highlights that while technical barriers exist, the dependency of AI on data creates a fundamental vector for public influence. Interesting read!

Embedding security into the SDLC transforms supply chain risk from a post-deployment concern to a daily development practice. By Aaron Linskens.

The article argues that software supply chain security requires a holistic, embedded approach rather than isolated tools or end-of-line checks. It breaks down the playbook into three core pillars: protecting code integrity at the source, securing the software delivery pipeline, and reducing implicit trust in development environments.

From version control to CI/CD pipelines, each stage presents a risk vector that must be managed proactively. For example, enforcing branch policies and scanning for secrets in repositories prevents early-stage compromise. Pipeline security includes commit and container image signing, reproducible builds, and pipeline tamper detection. Development environments are guarded through least-privilege access, centralized credential management, and continuous monitoring of anomalous behavior.

The article highlights that AI models and LLMs introduce new risks—such as poor provenance or tainted training data—requiring new governance models. Ultimately, the shift is toward treating security as a continuous, integrated function within the SDLC, not a separate phase. Good read!

A pragmatic, low-risk migration strategy for Windows users to transition to Linux via virtualization and dual-booting without sacrificing proprietary software dependencies. By Graeme Peacock.

The article outlines a strategic “safety net” approach to adopting Linux, aimed at users who fear losing access to critical Windows applications. Instead of a clean install, the author recommends a five-stage pipeline: selecting a beginner-friendly distribution (such as Zorin OS or Nobara), testing it within a VirtualBox VM to verify basic functionality, implementing a dual-boot configuration for native hardware access, and gradually shifting daily workflows over several months.

The author emphasizes the importance of hardware compatibility, specifically noting the need to verify GPU (Nvidia) and Wi-Fi (Broadcom/Realtek/MediaTek) drivers during the distro selection phase. For software gaps, the author suggests using Wine or VM fallbacks. The practical implication for the reader is a risk-mitigated transition where Windows remains available as a backup. This approach allows the user to explore the open-source ecosystem and find free alternatives to proprietary software at their own pace.

By removing the pressure of an immediate switch, the author suggests that the transition becomes a natural evolution rather than a technical hurdle, eventually leading to a point where the Windows installation becomes redundant and can be safely deleted. Good read!

Discover how to transform plain text into comprehensive knowledge graphs using kg-gen, NetworkX analytics, and interactive visualizations, providing actionable insights for developers and data scientists. By Sana Hassan.

This tutorial demonstrates building a complete pipeline for generating knowledge graphs from plain text using the kg-gen library alongside tools like NetworkX and PyVis. It guides readers through setting up dependencies, extracting entities and relationships from text, and analyzing these with NetworkX’s graph analytics features.

The process includes handling large texts through chunking and clustering, visualizing graphs interactively, and exporting them for further use. This workflow is valuable to developers and data scientists interested in structuring unstructured data into interpretable knowledge graphs. By the end, you will build a complete workflow that turns unstructured text into an interpretable, searchable, visual, and exportable knowledge graph. Nice one!

This article emphasizes the critical importance of rigorous SQL Server backup testing to prevent data loss and ensure recovery readiness. It outlines a structured approach covering pre-backup alerts, integrity checks, proper backup execution, and post-restore validation, highlighting common pitfalls like corruption, improper deletion policies, and insufficient retention strategies. The author stresses proactive measures such as using third-party tools, implementing alerting systems, and maintaining multiple backup copies to safeguard against unexpected failures. By Vlad Drumea.

Before backups are taken, the article recommends configuring alerts for specific database corruption errors (823, 824, 825), which can preemptively signal hardware issues or data corruption. Regular integrity checks with DBCC CHECKDB prior to full backups help identify potential corruptions early on, preventing the propagation of corrupted data through backup cycles.

During the backup process itself, incorporating checksums is advised to verify each page and detect any inconsistencies. The article warns against using CONTINUE_AFTER_ERROR, as it could overlook critical errors during backups. Following a successful backup, VERIFYONLY operations should be conducted to confirm the backup file’s integrity without actually restoring data. This step ensures that even if the physical backup appears valid, its contents are accurately preserved.

Post-backup strategies include retaining multiple cycles of full and differential backups to safeguard against accidental deletions before new backups have been verified. Testing restores in isolated environments using tools like dbatools’ Test-DbaLastBackup is crucial for confirming both the restore process’s success and data integrity. Regular test restores should be conducted, including quarterly checks on long-term stored backups.

The article also discusses additional considerations such as system database backups, out-of-band (non-scheduled) backups, and steps to take when corruption is detected during scheduled checks or alerts. It emphasizes the necessity of a comprehensive strategy involving alerting, integrity checking, checksums, verification, and regular test restores to ensure complete backup reliability. Excellent read!

NVIDIA Nemotron 3 Nano Omni delivers unmatched multimodal efficiency with 9x throughput for agentic AI workloads, combining vision, audio, and text in a single model. By NVIDIA Nemotron Team.

Some pojnts discussed in the blog post:

• Unified multimodal model reduces latency and fragmentation.
• 9x throughput vs. open alternatives for video/document tasks.
• MoE architecture minimizes active parameters per pass.
• Supports FP8/NVFP4 for cost-effective deployment.
• 3D convolutions enable efficient video reasoning.
• 256K-token context length for complex reasoning.
• 20% accuracy improvement over prior models.

Nemotron 3 Nano Omni represents a significant advancement in multimodal efficiency, combining architectural innovations (MoE, 3D convolutions) with quantized inference to enable scalable, low-cost agentic systems. Its performance on benchmarks and leaderboards underscores its potential to redefine enterprise multimodal workflows. Nice one!

Monitor SQL 2025 Fabric mirroring via Fabric Portal, DMVs, and OneLake files to ensure reliable data replication. By Meagan Longoria.

The article explains monitoring SQL Server 2025 Fabric mirroring using the Fabric Portal (table-level metrics), SQL Server DMVs like sys.dm_change_feed_log_scan_sessions, and OneLake files. It emphasizes tracking replication status, delays, errors, and log usage to maintain data consistency.

Key points in the blog post:

• Use Fabric Portal for quick status checks and delay metrics.
• Monitor sys.dm_change_feed_log_scan_sessions for session health and stalled phases.
• Check sys.dm_change_feed_errors for persistent replication issues.
• Validate OneLake files (tables.json, Manifest_1.json) for data integrity.
• Schema changes may inflate schema_change_count due to multiple log records.
• Log truncation delays require balancing mirroring performance with log size limits.
• Extended Events provide deep troubleshooting but should be used sparingly.

This article provides actionable monitoring strategies for SQL 2025 Fabric mirroring, bridging SQL Server and Fabric ecosystems. While incremental (e.g., new file structures), it’s critical for ensuring replication reliability. It advances troubleshooting by tying SQL-side metrics to OneLake artifacts, though schema change tracking nuances may require deeper analysis. Good read!