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Why Serverless Compute Partners Are Now More Important Than Ever

The note saysAIworkloads are bursty. They spawn parallel tool calls, pull multi‑GB model weights into RAM, and endure long cold starts (e.g.,vLLM,SGLang). Companies wrestle with a fragmentedGPUmarket and poor peakGPU utilization. To hit latency, compliance, and cost targets they adoptmulti‑region/mu.. read more  

Why Serverless Compute Partners Are Now More Important Than Ever
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AWS Cost Optimization Best Practices: A Maturity-Based Guide [2026]

The guide maps a five-stagematurity model— fromVisibilitytoFinOps Culture. It prescribes staged actions before commitment purchases. It recommends turning onCost ExplorerandAWS Budgets, enforcingtag policies, runningCompute Optimizer, testingGraviton, and usingCloudBurn/Amazon Qfor pre-deploy estima.. read more  

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Software engineer, Keploy

What Is QA Automation? Benefits, Tools, Challenges & Future

QA automation is a modern software testing approach that uses automated tools and frameworks to execute test cases efficiently and consistently. Instead of relying solely on manual testing, QA automation enables teams to validate application functionality, performance, and reliability at every stage of the development lifecycle. It plays a crucial role in Agile and DevOps environments, where frequent code changes and faster release cycles demand continuous testing.

One of the biggest advantages of QA automation is speed. Automated tests can run in minutes, allowing teams to detect defects early and provide quick feedback to developers. This leads to improved software quality and reduced risk of critical issues reaching production. Automation also enhances accuracy by eliminating human errors that commonly occur in repetitive manual testing tasks.

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@suarezsara shared a post, 3 months, 1 week ago

Why SharePoint Application Development Still Powers Enterprise Collaboration in 2026

Learn how businesses use SharePoint for workflow automation, seamless Microsoft 365 integration, and enhanced governance.

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@sancharini shared a post, 3 months, 1 week ago

Types of Regression Testing in CI/CD Pipelines

Learn how different types of regression testing in CI/CD pipelines help teams detect defects early, maintain software quality, and reduce production risks while optimizing automated workflows.

Types of Regression Testing in CI/CD Pipelines
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How Regression Testing Detects Hidden Defects Before They Reach Production?

Understand how regression testing helps teams identify hidden defects early, maintain system stability, and prevent production issues using effective testing strategies and regression testing tools.

How Regression Testing Detects Hidden Defects Before Production
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@elenamia shared a post, 3 months, 1 week ago
Technical Consultant, Damco Solutions

Is Your Application Evolving or Aging? The Role of Software Maintenance Services in Continuous Improvement

Read this blog to learn how software maintenance services fuel continuous improvement, prevent downtime, and protect your digital investments.

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@marxjenes shared a post, 3 months, 1 week ago

State Transition Testing Techniques for Microservices Applications

Learn effective state transition testing techniques for microservices applications. Ensure reliable service behavior, validate workflows, and strengthen regression testing in CI/CD pipelines.

State Transition Testing Techniques for Microservices Applications
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NanoClaw Brings Container-Isolated AI Agents to WhatsApp and Telegram

NanoClaw OpenClaw Claude Code

NanoClaw is a lightweight open-source personal AI agent that runs locally and connects to apps like WhatsApp and Telegram. Built with only ~3,900 lines of code across 15 files, it uses container isolation to securely run agents and aims to offer a simpler, fully auditable alternative to large frameworks like OpenClaw.

BigQuery is a cloud-native, serverless analytics platform designed to store, query, and analyze massive volumes of structured and semi-structured data using standard SQL. It separates storage from compute, automatically scales resources, and eliminates the need for infrastructure management, indexing, or capacity planning.

BigQuery is optimized for analytical workloads such as business intelligence, log analysis, data science, and machine learning. It supports real-time data ingestion via streaming, batch loading from cloud storage, and federated queries across external data sources like Cloud Storage, Bigtable, and Google Drive.

Query execution is distributed and highly parallel, enabling interactive performance even on petabyte-scale datasets. The platform integrates deeply with the Google Cloud ecosystem, including Looker for BI, Vertex AI for ML workflows, Dataflow for streaming pipelines, and BigQuery ML, which allows users to train and run machine learning models directly using SQL.

Built-in security features include fine-grained IAM controls, column- and row-level security, encryption by default, and audit logging. BigQuery follows a consumption-based pricing model, charging for storage and queries (on-demand or reserved capacity).