Evan Anderson

Developers love Redis because it’s fast. Liftoff uses Redis Cloud as a high-availability, low-latency database, increasing their ROI by improving ad relevance and quality. Hear more about the benefits of their improved ad relevance and quality straight from the director of engineering at Liftoff Mobile. Learn more about Liftoff's soaring ad exchange with Redis Cloud's low latency: https://lnkd.in/gAWBScEk

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The 6502 CPU rocks. Still here? Great. Greetings fellow 8-bit friends. Just saw the 6o6 “6502 on 6502 VM”. This is sweet - a clever approach that uses the underlying hardware to execute a “VM’s” code so each VM gets an accurate 6502 implementation. To get more memory each VM, the developer uses GeoRAM 512 from Berkeley Softworks (of GEOS fame) for the C64 and used bank switching to assign private memory spaces. Tons of detail in the write-up. One of those “read in pieces between powerpoints” kind of documents. :-) Enjoy your weekend. https://lnkd.in/gDNc_RrT

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In this third, extensively illustrated post of his windowing blog series, Bill Bejeck demonstrates sliding windows in Kafka Streams, and their logical equivalent in #FlinkSQL, OVER aggregations. Learn about use cases, like running average calculations, and more, here ➡️ https://cnfl.io/3W7N7qS

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Hey everyone, I am an investor and advisor to Katanemo.... They are reinventing the infrastructure primitives for GenAI and launched Arch - https://lnkd.in/gCrbbkEV - an intelligent prompt gateway. Give the project a look on Github, and star the project if it resonates

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🎃The October issue of #CheckpointChronicle is now out 🌟 It covers Ververica's Fluss, #ApacheFlink 2.0, Iggy.rs, Strimzi's support for #ApacheKafka 4.0, tons of OTF material from Jack Vanlightly, Christian Hollinger's write up of ngrok's data platform, nice detail of how SmartNews use #ApacheIceberg with Flink and #ApacheSpark, a good writeup from Sudhendu P. on #ApachePolaris, notes from Kir Titievsky 🇺🇦 on Kafka's Avro serialisers, and much more! https://lnkd.in/djc-HERy

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"Running Redis on Windows: Setting Up with WSL" is your step-by-step guide to effortlessly integrating Redis into your Windows environment. Discover how to leverage the Windows Subsystem for Linux (WSL) to run Redis seamlessly, combining the power of Linux with the flexibility of Windows. Whether you’re looking to enhance application performance, manage data more efficiently, or simply expand your tech toolkit, this guide has you covered. Elevate your development game with Redis on Windows today! Dive into the full guide here: https://lnkd.in/d4GrUB9S Authored by: Muhammad Aaliyan Khan, Software Engineer at Qavi Tech Looking to bring innovative ideas like this to life in your IT projects? Get in touch with us! Qavi Tech specializes in cutting-edge development and is always excited to work on interesting, impactful projects. #Redis #Windows #WSL #SoftwareDevelopment #TechTips #Innovation #WebDevelopment

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I’ve recorded a few episodes of a new show I’m calling Commit History. Each 15-to-30 minute episode examines an interesting commit to a significant open source project. So far, we’ve examined things like how NodeJS added the fetch API, how a 25 year-old bug in Firefox finally got a fix, how the Zed editor sped up “select-all-matches” by a factor of 250x in a 75 line commit, and we’ve read commits in languages like Javascript, Rust, C++, Ruby, and Zig, with more to come. A lot of us got into engineering because of our curiosities - which led to tinkering, sometimes destructively, but always starting with an observation, asking ourselves “How?”, and pulling out the tools - either screwdrivers or terminal windows, and not stopping until we got an answer. I’ve long assumed those days were behind me, but recently I reflected on how often I dive into open source code, sometimes because an unexplainable bug keeps popping up at work, or documentation is lacking, or a top story on HackerNews caught my attention - and I find myself right back into that hacker mindset, digging deeper until I know how it works. I haven’t changed nearly as much as I thought I had. Commit History, so far, is just turning my camera on to record something I’ve done for most of my adult life. In each episode I’ve learned something new, and however simple that might sound, it serves as the bar to meet for me to publish an episode. If any of this interests you, you can subscribe to the newsletter for the show here: https://lnkd.in/gHHhZY9K

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🚨 The area of securing your AI-driven apps is one of the hottest subjects right now so among the exciting things happening at JFrog that were shared at #swampUP, I would say their acquisition of Qwak (acquired by JFrog) was the right at the top. 👩‍🔬 If you're not aware #MLOps is getting a massive focus right now. All of a sudden, Data Scientists are needing to go farther and faster. Models need revisioning, testing and deployment more rapidly than ever. 🐸 New frog via acquisition, Hudson Buzby, walks me through why JFrog acquired them and it couldn't be a better fit. Security in the world of AI needs to be addressed from all angles, just like your Apps and APIs. But AI adds a couple other vectors that need to be addressed, which is what's happening here. 📚 This area is definitely important to learn more about. Stuart Shader Alan Kao F5 NGINX F5 DevCentral #DevOps #DevSecOps

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I published my first Spring Boot library to Maven Central! It's a simple Cloudflare Turnstile Captcha integration for your Spring Boot App. Just a small first step, with larger projects coming soon! Check it out! https://lnkd.in/gtXgnHq4 #springboot #cloudflare #captcha

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A new table-format comparison post is out. How do the table formats support the streaming ingestion of row-level operations (insert/update/delete), such as ingesting CDC streams? https://lnkd.in/dFDs4Ah3

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Automatically cancel outdated plan-only HashiCorp #Terraform runs to keep your workflows moving. Learn how this helps speed up application delivery: https://bit.ly/3V05AES

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At the core of effective engineering leadership is the ability to stay connected with your team. This is perhaps the most challenging aspect because retaining the respect of your people is difficult unless you remain technically involved yourself. In my journey as an engineering leader, staying true to the individual contributor role that originally earned their respect has kept me close. This approach extends beyond technical expertise; it's about maintaining a deep connection with the core activities that propel product development forward at Gearflow. One of my biggest professional fears is becoming irrelevant or disconnected from my team and our work. This fear motivates me to continually reinvent and retool in response to market demands. Next month, I'll be sharing about my adventures generating synthetic data recently. And while I'm no expert when I start preparing for these talks, the process is incredibly valuable. It's less about delivering a perfect talk and more about how the preparation expands my thinking and exposes me to new capabilities. This helps me connect the dots to opportunities at Gearflow that bring real innovation to our customers. Speaking regularly holds me accountable and pushes me to learn ahead of the curve, ensuring I remain relevant and impactful day-to-day. Every talk is a journey of discovery, helping me to lead with humility, communicate clearly, and inspire a culture of innovation through a deep, technical understanding of our work.

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Large Language Models (#LLMs) are evolving rapidly, and the demand for running them at scale is skyrocketing. #Ollama is stepping up to meet this need with its official container image on Docker Hub, excelling in local #deployments. However, there’s a catch for containerized environments: downloading #models requires the ollama serve command to be running within the #container. This extra step complicates automated deployments via #CI #CD #pipelines, making seamless model access challenging. To take your #model to production, it needs to be #container-ready—a feature Ollama has yet to fully support. The gap in #AIOps for smooth deployments gets blurred with the inference services available as an easy way to take it to production. Goal is to overcome obstacles and deliver solutions 😉 so here is to get you started on having your (O)#llama #production ready in a private #deployment from your #local to #cloud all in #oneshot . Enjoy Medium: https://lnkd.in/g9jqBQ8d dev.to: https://lnkd.in/gi_gGwam #AI #LLM #Ollama #llama3 #phi3 #mistral #S6overlay #AIOps #CI #CD #oneshot #container #docker #podmanEnjoy

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Microservices get a bad rap due to complexity and high TCO. A lot of that is because a microservice-based application works the same way as a bunch of dominoes falling. If one misses, or falls out of order, the whole workflow stops. And you have no control over the process once your microservices start ping-ponging events and RPC's back and forth. That's why #microservices are so expensive to manage. Great blog post below by Colt McNealy about how LittleHorse can solve those problems. Give it a read. https://lnkd.in/gJECBwRu

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I just updated my first table format comparison blog post to fix some missing details of Apache Hudi. https://lnkd.in/dwSUS6qS

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I've appreciated all the folks who have suggested I look at System Initiative this week. The great thing is, I've been tracking them for a bit now. I've even written about them and the other companies who are changing how we work, for the better. All the Next Wave companies are focused on context, helping teams navigate the current level of complexity, and preparing teams for what comes next. Huge believer in this problem and the companies that are solving it. Including my own. We're like dependabot for your entire technical stack. I don't know if you are aware but unplanned work is a big pain point for our Ops friends and it's only getting worse. So why not start with the small stuff no one has time to get to, Automate that away and give teams peace of mind while reducing toil. Don't worry Security, there will be checks and balances including an audit trail. It'll make staying in compliance a snap! All that to be said, SI's GA announcement is cool, and the approach of moving from a factory to a team. A pipeline to something more organic to manage infrastructure is timely. The team has worked hard and it shows in their GA offering! While you are checking out SI, I'd highly recommend checking out some of the other companies in the Next Wave of DevOps, if you haven't already. The number of companies is growing and the founding teams are passionate about this space and the problems. We are better together and all of the companies are amazing! + Anyshift.io + AppMap + CloudTruth + DryRun Security + Overmind + Prequel.dev + RunWhen + Stakpak + Stanza New companies are making the hard parts more approachable with our complex and ever-growing technical stacks. From code to deployment and everything in between #opservations

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Gadget's internal CI infrastructure has grown super scaled for a company of our size -- we do 160k hours of CI compute per month, or 220 days of compute per day. We really exhaustively test every change we make across all the framework versions so we don't break your shit! Remarkably though, it doesn't cost us that much. We built a custom autoscaler atop Buildkite that uses GCP Managed Instance Groups on spot instances, using t2d nodes which have really remarkable price/performance and really high spot discounts. Can't speak highly enough of t2ds We decided to use plain old VMs instead of k8s because we spin up a full k3s cluster and integration test our distributed filesystem and our serverless function orchestration stack end to end for some tests. It's caught some serious stuff I resent every moment I have to spend working on CI because I'd rather be building product or yammering on to you all, but I'm glad we made this investment because it is 1/50th the cost of our previous "normal" approach and lets us do nutty stuff like exhaustively test the matrix

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In the age of AI, choosing the right language model can be critical. Performance benchmarking is essential for evaluating AI models. It allows comparing different models based on factors like quality and output speed. The findings of this analysis is quite interesting and there is a clear trade-off between model quality and output speed, with higher quality models typically having lower output speed.

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