Murtaza Doctor

Julia Blase, Product Manager at Chronosphere, discusses how observability needs to evolve alongside Kubernetes. She explains that Kubernetes brought dynamic, scalable infrastructure, and monitoring systems must follow suit. The future requires on-demand instrumentation and dynamic data collection that can scale up and down based on needs, helping teams optimize costs while getting the right insights at the right time. Watch the full interview: https://ku.bz/X6tgWrG0P

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Keeping your #software supply chain safe starts with what you bring in. JFrog Curation helps teams automatically vet open-source packages before they enter your environment—catching malicious, vulnerable, or non-compliant components early. The result? Cleaner builds, fewer surprises, and more confidence in every release. #OpenSource

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LiteLLM is an open-source proxy that connects your app to LLMs through one unified interface. Paired with Redis, it gives AI and ML teams a simple yet powerful way to unify access to LLMs, accelerate response times, and make AI apps real-time. Redis handles performance, memory, and data coordination that modern AI apps demand, while LiteLLM handles abstraction and routing. Our AI Product Marketing Manager Rini Vasan dives into: ▶️ What LiteLLM is ▶️ Why it works well with Redis ▶️ How LiteLLM and Redis work together Learn how to scale your LLM gateway with LiteLLM & Redis: https://lnkd.in/gqsTebF7

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JFrog Fly is redefining how engineering teams manage artifacts in an AI-driven world. As software delivery becomes faster, distributed, and increasingly automated by AI, traditional artifact management can’t keep up. JFrog Fly introduces a smarter, globally scalable way to store, replicate, and deliver artifacts—built for modern engineering velocity. 🔗 Read the full blog to explore what this shift means for your DevOps pipeline. Link - https://lnkd.in/dUK_UFd8 #JFrog #AI #DevOps #SoftwareDelivery #CloudNative

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If you are building or evaluating adding AI to reliability, this is a must watch. This episode of Slight Reliability breaks down where AI helps in SRE and operations, and where it falls short. Causely founder Shmuel Kliger, explains why modern observability turned operations into a big data problem, and why causal reasoning is required to infer the single upstream cause behind many degraded services. A practical conversation about reducing toil, avoiding AI hype, and focusing on what actually works. Full episode here: https://lnkd.in/epj_x7q6

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Keeping cloud architecture diagrams updated across a fast moving environment can feel like an endless game of catch up. With Lucid’s Cloud Accelerator, teams can create reusable assets for frequently used elements like VPCs, databases, and subnets. Update once and watch changes cascade across every diagram, saving time, reducing manual effort, and keeping documentation consistent as your cloud environment evolves. See how you can bring clarity to your cloud journey: https://lnkd.in/gWyKa5tH

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Your CI/CD bill is not exploding because of “expensive runners,” it is bleeding out through flaky tests, over-parallelized jobs, and slow feedback loops that quietly tax every deploy This piece shows you how to redesign pipelines so you cut compute waste, shrink queues, and speed up developer flow without adding yet another approval gate or YAML religion Full breakdown in the comments. #DevOps #CICD #PlatformEngineering

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Crashes cost revenue. Poor #UX costs trust. #MobileTeams can't afford reactive workflows anymore, and your app is only as good as what your users experience. 😬 CIOReview spotlights how leading teams can detect, prioritize, and resolve issues with our AI-powered #MobileObservability before users ever notice.🛠️ 👉 Get the full insights: https://hubs.ly/Q03p4jYf0

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PagerDuty is now a Cursor customer. Over 30,000 customers rely on PagerDuty to respond to incidents faster, reduce outage risks, and build operational resilience. We’re excited that PagerDuty engineers will now use Cursor’s agents to build these mission-critical products.

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"Collect everything and you'll know everything." Sounds great. Until your storage bill arrives. 😅 The real problem with modern observability isn't volume, it's relevance. When everything is stored, teams spend more money and still take longer to find what matters. Introducing the Middleware Observability Pipeline. Filter smarter, spend less, debug faster. 👉 Check out: https://lnkd.in/gZRHefkq #Observability #DevOps #Middleware #SRE

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People often ask me about the differences in architecture between Amazon Dynamo (the 2007 SOSP paper), DynamoDB (the AWS serverless NoSQL database), and Aurora DSQL (the AWS serverless SQL databases). I captured the key points on durability, high availability, programming model, and trade-offs in my blog: https://lnkd.in/gkMG_tVD TL;DR: Despite the shared name, Dynamo and DynamoDB have little in common architecturally, and make very different trade-off decisions.

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