Mathias Abdissa

Master Kubernetes with LearnKube's Advanced Kubernetes workshop! What should you expect? - Learn how to architect and design clusters from the ground up (in the cloud or on-prem). - Explore the Kubernetes internal component and how the system is designed with resiliency in mind. - Deep-dive into the networking components and observe the packets flowing into the cluster. - Hands-on labs to test the theory with real-world scenarios! - And more. The next course starts in September: https://lnkd.in/ge4aYVZq We also run in-person courses and private training: https://lnkd.in/g9N3m-Xr

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Security Groups > IPs 🔐 Join Erik Reinert to learn how to deploy and scale full-stack cloud apps through Startup, Growth, and Scale, from fast container deploys with Supabase and AWS App Runner to production-ready infrastructure with CI/CD, Terraform, ECS, RDS, load balancers, and CDNs. https://lnkd.in/gRcqdhB5 #Fullstack #Backend #WebDev #Programming #Coding #LearnToCode #DevOps

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If your team is building with LLMs, you know the value of clean, retrievable data. That's why we're teaming up with Unstructured to streamline one of the hardest parts of AI development. This integration removes friction across the AI stack—from unstructured ingestion to semantic retrieval—so your teams can focus on building. Read more from our Product Marketing Managers, Rini Vasan and Jim Allen Wallace, and Unstructured���s Head of Developer Relations, Maria Khalusova here: https://lnkd.in/e98QqXAB

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If you want your DevOps workflow to feel lighter and faster. This video is for you. In this episode, SAHIL VANDRA, Senior DevOps Engineer I, walks through how he uses GitHub to simplify two major parts of his workflow: - Automated code reviews - Smarter CI/CD pipelines He breaks down how AI helps reduce manual effort, speed up releases, and maintain steady deployments without compromising on quality. If you’re looking for practical ways to bring AI into your DevOps routine, this breakdown gives you a clear, real-world look at what actually works.   Watch the full video here: https://lnkd.in/d3zYaxgb #devops #aidevops #mlops #github #automation

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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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Prism represents a fundamental shift in Ruby's parsing infrastructure, replacing fragmented parser implementations with a unified solution used across CRuby, JRuby, TruffleRuby, Rails, and RuboCop. Why Prism matters: Ruby historically used parse.y built with Yacc, designed specifically for CRuby. Other Ruby implementations, tools like RuboCop, and code editors required separate parsers, creating incompatibilities with newer Ruby syntax. Projects like whitequark/parser and ruby_parser emerged to fill gaps, but fragmentation remained problematic for ecosystem consistency. Prism's universal approach: Single parser serving all Ruby implementations and tools eliminates compatibility issues. When Ruby adds syntax features, Prism updates once, and everything benefits simultaneously. Career relevance for Rails developers: Understanding Prism opens opportunities in Ruby tooling development. Developers building custom linters, code analysers, or transpilers now have production-ready parsing infrastructure. The visitor pattern Prism uses for AST traversal provides valuable learning for developers unfamiliar with compiler concepts. These patterns appear throughout software architecture, not just parsers. Practical applications: Building custom code analysis tools, creating domain-specific transpilers, or developing editor extensions all become more accessible with Prism. The barrier to entry for Ruby tooling development dropped significantly. Developers demonstrating Prism usage in open-source contributions signal advanced Ruby understanding that employers value for senior positions. What Ruby tooling have you built or contributed to? We're connecting developers who understand language internals with companies valuing deep technical expertise. #Prism #RubyParser #RubyLanguage #DeveloperTools #TechRecruitment

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This post is about the ROI of reapplying for H-1B next year vs. keeping your job remotely from Canada for those whose H-1B registration wasn’t selected in the lottery this year… Before jumping back into the H-1B lottery next year, consider this: your company could save up to $100K per engineer by relocating talent to Canada instead. Yes, this number is on the high side, but it’s an estimate based on real industry data. And even if your savings turn out to be half of what's noted below, or even less, it’s still substantial. Plus, Canada’s Global Talent Stream (GTS) offers work permits in as little as 4–6 weeks, can lead to permanent residency in 2 years and citizenship in 4-5. Especially for Indian nationals who are waiting in the US for decades for their green cards, this is a huge benefit. Here's a quick scenario: 🇺🇸 U.S. Replacement of a tech worker, which adds up the cost of Recruitment fees, immigration/legal costs, knowledge transfer, productivity loss, project delays, and team morale impact: First-Year Cost: $352K–$432K 🇨🇦 Instead, Canadian Relocation would cost: First-Year Cost: $175K–$193K . . . 💰💰 Savings: $177K–$239K per engineer! Is this path right for every company? No. But for companies that want to avoid losing the best talent,Canada is cheaper and more predictable than another H-1B! Want to see your potential ROI? Reach out to Syndesus, Inc. for a 30 minute call to calculate your specific potential savings, review timeline options, and discuss team retention strategy! #h1bvisa #immigration #immigrationlaw #siliconvalley #techjobs https://lnkd.in/eW3xknTS

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Contractors aren’t just a quick fix - they’re a smart solution for KYC remediation, if you use them right! When you’re facing regulatory deadlines and high-volume backlogs, experienced KYC contractors can give you the scalability, control and speed you need without sacrificing quality. Here’s how to get it right: 🔹 Accelerate delivery without burning out your BAU team Deploy contractors to tackle routine but essential KYC tasks, freeing up your internal team to focus on oversight, exceptions, and long-term risk controls. 🔹 Use the right people for the right tasks Remediation work may be repetitive, but it still demands regulatory knowledge, attention to detail, and delivery discipline. The best contractors have done this before, and that experience keeps QA pass rates high. 🔹 Structure for outcomes, not admin Don’t just fill seats. Assign contractors to clear, outcome-driven workflows with measurable targets. Whether it's periodic reviews, outreach, or 4-eyes, every role should support progress and reduce rework. Done properly, contractor delivery is effective, scalable and compliant! If you’re building or struggling to scale a KYC remediation team, happy to share what’s working well in the market. #KYCremediation #contractordelivery #AML

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Monitoring Kubernetes? Here’s your quick hand guide, which includes best practices to keep clusters healthy, key metrics every team should track, how to use AI for accelerating RCA, and choosing the right observability solution for your stack. Whether you’re scaling workloads, troubleshooting performance, or exploring AI, this guide will help you stay ahead. #Kubernetes #Guide #Monitoring #SRE #DevOps

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Demand for managed Kubernetes on GPU infrastructure is surging. 40% of GPU cloud customers want a turnkey, Kubernetes-native service. vCluster and vNode provide the architecture to launch your managed Kubernetes offering in weeks, not months. Virtual control planes give each customer their own isolated API server, etcd, and RBAC on shared infrastructure. No separate physical clusters to manage. Three deployment models for different service tiers: - Shared Nodes: Soft isolation for high-density serverless GPU workloads - Dedicated Nodes: Hard isolation with dedicated GPU pools per tenant - Private Nodes: True node-level isolation for enterprise customers No VMs. No hypervisor overhead. Just bare metal GPU performance with the tenant isolation and security your customers require. Launch faster. Support multiple service tiers. Maximize revenue per GPU. Stand apart from hyperscalers with infrastructure purpose-built for AI/ML workloads. Schedule a call to discuss how vCluster + vNode can power your managed Kubernetes offering: https://lnkd.in/egKS4pcX (Full technical architecture in the comments)

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There’s no one-size-fits-all when it comes to Kubernetes architecture. From public cloud to bare metal, edge to multicloud, each option comes with trade-offs around cost, performance, compliance, and complexity. And in reality, most teams are running Kubernetes across five or more environments today. This blog explores: 🔸 The pros and cons of six common deployment models 🔸 Why organizations are mixing environments to balance risk, cost, and AI demands 🔸 How to think about “what you can choose” vs. “what you can manage” Read the full blog to see how to find the perfect Kubernetes architecture for your own needs, and how Palette can help you manage it all 👉 https://okt.to/nz3G12 #Kubernetes #KubernetesManagement #CloudComputing #EdgeComputing

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We just released Taro's newest course about what life is really like at FAANG. So much of the discussion about working at FAANG focuses on things like the scale of the work or the free snacks. But on the day-to-day level, an engineer at Google or Facebook doesn't think about these topics. They are spending most of their waking hours on things like: - how much scope do they have? - how much control do they have vs just being a cog in the machine - what do their coworkers think about them? As a 5 year veteran of Meta, I break down the real pros and cons of working in Big Tech. Check it out here: https://lnkd.in/gfkwVDf9

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“We’re empowering developers to move fast and own their code in production.” Translation: We gave every dev kubectl and now we’re praying to the observability gods they don’t nuke prod. SRE and Platform work means something different everywhere you go but gets buried under the same buzzword compost. “Shift left.” “You build it, you run it.” “Platform as a product.” “Developer self-service.” “Blameless culture.” It's getting harder to tell jobs apart. What any of them actually do. Or how they’re any different. Meanwhile, candidates want the basics: Real autonomy. Meaningful project work. Backing from leadership when production hits the fan. Everything else? Just noise.

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💭 Thinking a lot about SDLC automation and the trend of AI-generated code and code-generation / SDLC automation tools. This interview might be the final word on the topic for now. IMHO there is no greater critic of bad code than Linus Torvalds - creator and maintainer of the Linux Kernel, git, etc. You only need to read some of his more famous words of “feedback” on merge requests into the Linux kernel to know what I mean. This interview is fascinating in that you would [I did] expect Linus to be a sharp critic of LLMs generating code. Indeed, it was the opposite. Pragmatic even! 🧐 Highly recommend spending 5 minutes watching this and considering how code generation can drive benefits for individual developers, teams and entire organizations while also realizing the important role that human developers play in guarding the safety and integrity of the systems we as customers use and depend on. 👨🏽‍💻 I have heard many opinions on this topic that border on extreme resistance to vibe-coding in YOLO-mode! As LLMs and code generating agents get better and we (collectively) feel more comfortable, our risk appetite is likely to come down. It’s pretty wild to imagine a future where entire code bases are AI generated…or even ephemeral code - a topic that’s been taking up mental space for me as well. 🧠 Open to comments on your answer to this question. 👇🏽 Link to the [5 minute] interview segment: https://lnkd.in/gjmnXgSp

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