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The era of "vibe coding" is here, and it's less about magic and more about a fundamental shift in the developer workflow. Coined by Andrej Karpathy, vibe coding describes a fluid, natural language collaboration with AI assistants to generate code—effectively, telling a genie what you want. This is not replacing developers; it's a strategic accelerator for digital transformation. An experienced developer can now prototype an MVP in hours, not weeks. However, this power demands governance. Vibe-generated code is prone to "hallucinations" (up to 21.7% in some open source models) and security vulnerabilities if not reviewed. IT executives must implement strict guardrails—source control, automated testing, and security reviews—to maximize speed and control risk. The goal is enablement, not abandonment.

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Useful feature of Claude Code Recently I used Claude AI to help diagnose a deeply complicated enterprise process involving multiple libraries, configurations, integrations, and legacy behavior. What impressed me most was not just that we solved the issue — it was how the collaboration evolved over time. As we investigated: logs, dependencies, execution flow, and architectural assumptions, The AI gradually built contextual understanding of the system itself. Once the issue was resolved, I asked it to create a reusable “skill” for future troubleshooting sessions. It proposed the name and location of the skill then built it. The result was surprisingly effective: a concise operational guide containing: system behavior patterns, common failure points, investigation steps, and architectural nuances discovered during the session. This changed how I think about AI in engineering. The real value was not “AI replacing developers.” The value was: accelerated reasoning, reduced cognitive overload, and preservation of institutional knowledge. A simple prompt pattern worked very well: “Based on everything we learned in this session, create a reusable troubleshooting skill for this system. Include architecture insights, common failure points, diagnostic workflows, dependencies, assumptions, and recommended investigation steps.” Then in future sessions: “Load and apply the troubleshooting skill x for [system/process name] before we begin analysis.” That approach dramatically reduces repeated discovery work and helps preserve knowledge that normally exists only in senior engineers’ heads. My takeaway: AI works best not as an autopilot, but as a collaborative reasoning partner that helps engineers think more clearly in complex systems.

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In a world where data is growing faster than our ability to manage it, traditional knowledge bases are becoming bloated, expensive, and inefficient. This blog post introduces a just-in-time knowledge base architecture—a smarter, leaner approach that processes documents only when they’re needed, and automatically retires unused resources. https://lnkd.in/ePsYBbqx #KnowledgeManagement #JustInTime #AIArchitecture #RAG #Serverless #Efficiency #SmartScaling #DocumentAI #Bedrock #AWS

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If your promotion workflow changes based on Kubernetes, Terraform, or VMs, you don’t have GitOps at scale. ⚙️ The Akuity Platform brings application and infrastructure promotion into one GitOps-driven workflow with consistent guardrails, approvals, and auditability. ✅ See how it works in the January 22 live demo. 👇 🔗 https://hubs.li/Q03Z5kNR0 #GitOps #Kargo #Terraform #DevOps #CloudNative

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𝗔𝗪𝗦 𝗦𝗽𝗲𝗻𝗱𝗶𝗻𝗴 𝗚𝗿𝗲𝘄 𝟰𝟳% 𝗬𝗲𝗮𝗿 𝗢𝘃𝗲𝗿 𝗬𝗲𝗮𝗿. 𝗧𝗵𝗲 𝗖𝗙𝗢 𝗛𝗮𝗱 𝗢𝗻𝗲 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻: “𝗪𝗵𝗮𝘁 𝗔𝗿𝗲 𝗪𝗲 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗣𝗮𝘆𝗶𝗻𝗴 𝗙𝗼𝗿?” • A SaaS company's cloud bill hit $8.2M annually. • Engineering insisted everything was optimized. • Finance couldn't map costs to revenue. 𝗧𝗵𝗲 𝗕𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻: • 340 underutilized instances. • Idle resources consuming $180K monthly. • Savings Plans coverage at 23% when it should've been 75%. • Nobody knew which teams owned which costs. • Chargebacks were guesswork. • Budget forecasts missed by 30%. The CFO's mandate: Visibility, accountability, or we migrate providers. 𝗩𝗲𝗿𝗶𝘁𝗶𝘀 𝗵𝗮𝗱 𝗹𝗶𝗺𝗶𝘁𝗲𝗱 𝗶𝗻𝘀𝗶𝗴𝗵𝘁 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲𝗶𝗿 𝗔𝗪𝗦 𝘀𝗽𝗲𝗻𝗱𝗶𝗻𝗴. 𝗪𝗲 𝗯𝘂𝗶𝗹𝘁 𝗮 𝗰𝗼𝘀𝘁 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝘁𝗵𝗮𝘁 𝗺𝗮𝗱𝗲 𝘀𝗽𝗲𝗻𝗱𝗶𝗻𝗴 𝘁𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝘁. • Tagged every resource to the business unit and project. • Automated rightsizing recommendations. • Optimized reserved instance coverage from 23% to 87%. • Created executive dashboards showing real time cost to value. 𝟲 𝗠𝗼𝗻𝘁𝗵𝘀 𝗟𝗮𝘁𝗲𝗿: • #AWS spending down 32% ($2.6M saved annually). • Budget accuracy improved from 70% to 94%. • Finance and Engineering are finally speaking the same language. 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗯𝘂𝗱𝗴𝗲𝘁 𝗳𝗿𝗲𝗲𝗱 𝘂𝗽: $𝟭.𝟴𝗠 𝗿𝗲𝗱𝗶𝗿𝗲𝗰𝘁𝗲𝗱 𝘁𝗼 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁. • 82% of enterprises lack cloud cost visibility. • They are flying blind on their second largest operating expense after payroll. #Veritis transforms #AWScostmanagement from a monthly surprise to a strategic advantage because controlling cloud spend is not all about reducing resources. It is all about funding what matters. 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗲 𝗮 𝗖𝗼𝗻𝘀𝘂𝗹𝘁𝗮𝘁𝗶𝗼𝗻: https://lnkd.in/dB5SaCWy 𝗙𝗼𝗿 𝗠𝗼𝗿𝗲 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀: https://lnkd.in/gcZTTrxt Veritis Group Inc #CloudCostOptimization #FinOps #AWSOptimization #CloudFinance #CloudSolutions #ITConsultingServices

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A lot of conversations around AI focus on models and tools. But in practice, teams are still working through how to build pipelines that reliably deliver governed data into platforms like Microsoft Fabric. That means coordinating ingestion, CDC, and workflows across systems like SQL Server. This session walks through how teams are doing that with CData Sync v26.

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We saw how to define the RTO/RPO and define DR strategy for the applications in the ecosystem in previous post. Now, let's understand what all DR strategies are possible. Let's break down the main options, from "slow and cheap" to "instant and expensive": 💾 𝗕𝗮𝗰𝗸𝘂𝗽 & 𝗥𝗲𝘀𝘁𝗼𝗿𝗲: The classic. You ship the copy of data backup to the other region. * 𝗣𝗿𝗼: It's the cheapest option. * 𝗖𝗼𝗻: Your RTO can be high. 💡 𝗣𝗶𝗹𝗼𝘁 𝗟𝗶𝗴𝗵𝘁: You have a minimal, essential version of your environment running in the cloud in DR region. * 𝗣𝗿𝗼: It's cost-effective, as you're not paying for full-scale production. * 𝗖𝗼𝗻: In a disaster, you need to scale up the resources. In resources like databases, it means server restart and hence can take some time depending upon factors like data size. 🔥 𝗪𝗮𝗿𝗺 𝗦𝘁𝗮𝗻𝗱𝗯𝘆 (𝗔𝗰𝘁𝗶𝘃𝗲-𝗣𝗮𝘀𝘀𝗶𝘃𝗲): A scaled-down, but functional, copy of your environment is always running. * 𝗣𝗿𝗼: It's ready to take over traffic much more quickly than a pilot light. A great balance of cost and speed. * 𝗖𝗼𝗻: There might be a brief failover period, and it requires careful data syncing. 🚀 𝗛𝗼𝘁 𝗦𝘁𝗮𝗻𝗱𝗯𝘆 (𝗔𝗰𝘁𝗶𝘃𝗲-𝗔𝗰𝘁𝗶𝘃𝗲): The gold standard. You have two (or more) identical, active environments. * 𝗣𝗿𝗼: Near-zero downtime. If one site fails, the other is already handling the load seamlessly. * 𝗖𝗼𝗻: This is the most complex and expensive strategy to build and maintain. The key is to stop asking "Do we have a DR plan?" and start asking: 1. What's our RTO (How fast do we need to be back)? 2. What's our RPO (How much data can we afford to lose)? 3. What is the actual business cost of one hour of downtime for our critical systems? The answer to #3 tells you exactly which strategy you can't afford to ignore. #DisasterRecovery #BusinessContinuity #Resilience #CloudComputing #RTO #RPO #ThePracticalArchitect

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Compare Serverless vs Containerized Deployments for Web Teams. Practical how-to, tools, performance tips, NZ context, and ROI for web projects.

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𝗬𝗼𝘂𝗿 𝗔𝗪𝗦 𝗯𝗶𝗹𝗹 𝗶𝘀𝗻’𝘁 𝗵𝗶𝗴𝗵 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗔𝗪𝗦 𝗶𝘀 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲. 𝗜𝘁’𝘀 𝗵𝗶𝗴𝗵 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝘆𝗼𝘂𝗿 𝗰𝗹𝗼𝘂𝗱 𝗶𝘀 𝗺𝗶𝘀𝗺𝗮𝗻𝗮𝗴𝗲𝗱. Most AWS environments aren’t optimized. They’re patched together. Here’s what we see every week: • Idle EC2s running 24/7 • No cost allocation tags • Overprovisioned RDS & EBS • No monitoring until something breaks • Engineers firefighting instead of building Result? ❌ 30–60% wasted spend ❌ Random downtime ❌ CFO asking uncomfortable questions Inbox Infotech fixes AWS the way it should’ve been built: → Cost optimization with real savings, not dashboards → Uptime-first architecture (no heroics required) → Ongoing cloud ops so your team stops babysitting infra If you’re running AWS and don’t know where your money leaks, you’re already paying the price. 𝑫𝙈 “𝑨𝙒𝑺” 𝙖𝒏𝙙 𝙬𝒆’𝒍𝙡 𝙨𝒉𝙤𝒘 𝒚𝙤𝒖 𝒆𝙭𝒂𝙘𝒕𝙡𝒚 𝒘𝙝𝒂𝙩’𝙨 𝙗𝒓𝙤𝒌𝙚𝒏 — 𝒂𝙣𝒅 𝒉𝙤𝒘 𝒕𝙤 𝙛𝒊𝙭 𝙞𝒕 𝒇𝙖𝒔𝙩. #AWSOptimization #AWSBilling #CloudCostOptimization #DevOpsProblems #StartupInfrastructure #CTOProblems #SaaSScaling #CloudWaste #AWSArchitecture #TechLeadership #InfraManagement #AWSIndia

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A leading global healthcare solutions provider modernized its ETL environment using DBShift™, which automated the conversion of Informatica XML mappings into Databricks pipelines with over 90% accuracy. This automation reduced manual effort, shortened migration timelines to under two weeks, and established a scalable foundation for modern data engineering.    Accelerate ETL modernization with DBShift™. Automate Informatica-to-Databricks migration, reduce manual effort and build a scalable foundation for future growth.  Read the full case study- https://lnkd.in/egmF3HnJ #ETLMigration #DataPipelineAutomation #CloudDataPlatform #DBShift™ #Informatica #DataEngineering #Automation #AnalyticsTransformation #OperationalEfficiency #Databricks  

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Ben walks through Faire's complete CI transformation, from a single Jenkins instance struggling with thousands of lines of Groovy to a distributed Buildkite system running across multiple Kubernetes clusters. You will learn: - How to architect CI systems that match team ownership and eliminate shared failure points across services - Kubernetes scaling patterns for CI workloads, including multi-cluster strategies, predictive node provisioning, and handling API throttling - Performance optimization techniques like Git mirroring, node-level caching, and spot instance management for variable CI demands Watch (or listen to) it here: https://ku.bz/klBmzMY5- 🌟 This episode is brought to you by Testkube—the ultimate Continuous Testing Platform for Cloud Native applications. Scale fast, test continuously, and ship confidently https://ku.bz/lnxYK3s0L With 🎙Bart "Creatine lover" Farrell

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Development Update blueFlash Software LLC Georgi and I continue to crush development. We are very close to finishing six new revolutionary solutions for classifying and governing structured data. These solutions solve and govern complex data quality problems that normally require enormous amounts of time to identify and resolve manually. Five of these solutions are actually second versions because we completed the original versions during 2023-2024. We chose to file the utility patent for our aggregate solution first because of the size and scale of that individual solution. We could not help but to enhance the five existing solutions. It worked out because the second version of these five solutions are wicked and, combined with our existing solutions, provide absolute control over the quality of the data. As usual, we will be filing a comprehensive utility patent containing all six solutions. Have a great end to the work week! #data #dataquality #datagovernance #dataclassification #datamodeling #classifydata #modeldata #governdata #blueflashsoftware #completedataquality

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Most teams don’t notice the cost right away. They notice it when someone asks, “Which one is correct?” Disconnected #Jira and #AzureDevOps instances quietly slow teams down and strip away context. #Integration helps restore a single, reliable view of work across both tools. OpsHub Integration Manager makes that alignment possible at scale. Read more on the Atlassian marketplace: https://lnkd.in/dJG2rp7s

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Scaling AI across AWS requires more than certifications & baseline expertise. It demands operational discipline, ecosystem-wide execution, and production-grade reliability. That’s how enterprises scale AI with confidence—and why they choose 10Pearls as their AWS partner.   See how we help enterprises operationalize AI across the AWS ecosystem: https://10pearls.com/aws/

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