Beyang Liu

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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AWS re:Invent is less than two weeks away. Want to accelerate your AI initiatives? Let's connect in Vegas and discuss how Meibel enables your engineering team to build Explainable AI into your products and experiences.

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How do the costs compare of implementing a low-latency write-ahead-log (WAL) on S3 Express One Zone and one implemented as a State-Machine-Replication (SMR) system (such as Paxos/Raft/Kafka)? I built a cost model for both to find out. https://lnkd.in/dwczVutW

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I am excited to share the latest blog, co-authored by myself, Junhwi Kim and Vyas Sekar, detailing how we managed to successfully localized and improved LLM by combining Fine-Tuning and RAG. https://lnkd.in/gKDkgGPq

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My blog post about what I’ve been working on at Rippling the last year and a bit is live!  Come learn how Rippling Engineering is building data residency for the EU and beyond https://lnkd.in/gy-2ebD7

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Come check out this talk if you want to learn how we built distributed queries for Iceberg + Python!

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Into the world of Large Language Models (LLMs) it's fascinating to see how much goes into optimizing them for performance and scalability. Techniques like prompt engineering, model pruning, and even load balancing are essential to make sure these models run efficiently in real-world applications. It’s incredible how much power and potential these models have, but it also shows me just how important it is to master these optimization strategies. Learning this stuff isn’t just about building something cool—it’s about making sure it works at scale. #AI #MachineLearning #LLMs #DeveloperLife https://lnkd.in/epHddQZg

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👩‍🍳 What’s the Difference Between a Cook and a Chef? 🍽️ 🍲 Imagine a cook who follows recipes to the letter, making reliable, familiar dishes with ease. They’re efficient, precise, and quick in the kitchen — much like LLMs generating code for common use cases using open-source “ingredients.” But what happens when you need something specifically tailored to your unique tastes? That’s where a chef steps in. 👨‍🍳 Chefs create their own recipes, experimenting with unique, proprietary ingredients to craft dishes that stand out. And that’s the real challenge for LLMs. When it comes to handling company-specific data — ingredients that aren’t in the standard “recipe book” — LLMs can struggle. 🧩 The challenge is not just in creating the code but in scaling and integrating it into production environments where data is unique, often sensitive, and not openly shared. Thankfully, Blitzy has a former NVIDIA engineer with 20+ AI patents and a team of rockstar engineers on board to tackle that problem 😉 Siddhant Pardeshi you are my hero 🙏 💭 Sound off: if AI were a chef, what would be its ultimate "secret ingredient" for creating unique, proprietary recipes? A special "spice" for data security, a "sauce" for seamless integration, a "technique" for creative problem-solving? 👇 cc: NVIDIA AI, Generative AI #GenerativeAI #GenAI #ArtificialIntelligence #SoftwareDevelopment #SoftwareAutomation

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This is a very interesting article on sparse vectors, which can help overcome limitations of vector search related to domain specific keywords. And of course, the code in the example is clear and clean! Sparse vectors have a few values and many zeros. Dense vectors have limited or no zeroes. But really, what we're talking about semantic search on terms where the context is not present or harder to infer. The author presents this as specialized keyword search such as medical terms or other domain specific terminology. "These scores reflect the significance of each term in the context of the document or query, guiding the model to allocate more weight to terms that are likely to be more meaningful for retrieval purposes." Highly recommend reading. Nice work Paul Iusztin. And a nod to Qdrant and their vector search technology that supports sparse vectors. https://lnkd.in/gcBWfTg8

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I just published my latest blog post: "Experimenting with Rust, Nix, Parca & k6" https://lnkd.in/eUKeA57g This one is a long one, but it turned out well I think! #nix #rust #parca #blog

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If you are building GenAI apps you are likely doing Retrieval Augmented Generation (RAG). And if you are, you need to ensure access control is propertly implemented. Read more about it in this post

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Advances in AI Webinar https://lnkd.in/g5HdAwTA Session date: Nov 8, 2024 12:00 PM AI news this week Serverless Agentic Workflows - working in our repo, and how I am going to use it in real life MLOps - what is it, and why is it important

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Replacing Redis with SQLite? Wild idea, but it turns out to be way faster! In this episode, I talk to my longtime friend Michael Buckbee about some of their peculiar needs and why SQLite is such a good solution for them. Go check it out! https://lnkd.in/gUh2xFcg

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Since launching last Fall, its been incredible to see all the many workflows and integrations that companies are building with Lucid's Developer Platform. We've seen companies build automated processes and deeply linked templates that help with Sales, Marketing, Operations, and Engineering teams get the most out of Lucid. Come learn how to get started building from two of our engineers on the API teams who help engineers build these integrations every day.

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Hey folks! Garrett Milster and I have only posted about Sacramento Labs once, so we figured it's time for an update 😊 **What is Sacramento Labs?** We’re a small team focused on launching software products that solve real-world problems. Think of us as a startup lab—constantly testing ideas, learning from feedback, and building tools that matter, and having an impact while doing it. **How do we decide what to build?** We like to build products that: -         Solve real-world problems in mostly ignored industries -         Are small, highly maintainable, and scalable codebases -         Make the world a better place directly, or by donating a percentage of profits to a relevant charity in that space You can learn more about our philosophy at sacramentolabs.com **What you can expect from us** We’ll be posting product ideas that are in various stages of our pipeline: Early Concept, Customer Development, In Progress, Launched, Scaling. We’d love to hear your feedback! **Our latest concept: Self-Hosted Security** Here’s the problem: - Businesses often use third-party URL shorteners to share internal tools, resources, or campaigns, but these services introduce serious risks. Enterprise companies like Google, Microsoft, and Stripe have built their own internal URL shorteners for this reason – but most businesses don’t have the resources to do that. - Phishing attacks are rampant, and links from external services can be spoofed or abused. - Branding and security controls are limited, leaving sensitive information exposed. - You can’t track link activity—who’s clicking what, when, or why. - When employees leave, their shared links still work, leaving potential vulnerabilities. Our proposed solution: Self-Hosted Security. It’s a plug-and-play solution for businesses to host their own branded URL shorteners with: - SSO for secure access tied to your company’s directory. - Link expirations to ensure old links don’t stay active indefinitely. - Audit logs to track activity and improve visibility into link usage. 📢 We need your input! Would this solve a problem for you? What features would make this indispensable? Check out the landing page and share your thoughts: selfhostedsecurity.com Want to chat? Email me at [email protected] 😊 💡 And here’s something we’re passionate about: a percentage of profits from Self-Hosted Security will go to the Electronic Frontier Foundation (EFF). Your feedback not only helps us create better solutions, but also contributes to making a positive impact. Thanks for being part of the journey!

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Anthropic Claude LLM is making building the AI Agents, a breeze. One AI agent gets a web search integrated, GitHub repo created, created a main branch and a feature branch, pushed the code and generated a pull requests to create your web site app just in few prompts. All code written by LLM. Writing the Prompts and plumbing with APIs is key though. Another AI agent is to review the pull request before the code is merged into the main branch. Here we are talking a team of AI agents coordinating and collaborating a full fledged web app. Sounds crazy isn’t it? That said, as always, devil lies in detail. But this is where software development is headed and so is the work of Software Engineers. Do you agree?

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Exciting news for teams leveraging monorepos! Coherence now supports glob patterns for selective build configuration, bringing unprecedented efficiency to your development pipeline. 🔧 Technical Highlights: • Granular control over build and deploy processes • Intelligent file change detection • Seamless integration with existing CI/CD workflows 💡 Key Advantages: 1. Dramatic reduction in build and deploy times 2. Optimized resource allocation 3. Precision targeting of updates for changed code only 🏗️ Real-world Impact: • Accelerated development cycles • Enhanced productivity for large-scale projects • Significant cost savings on computational resources This feature empowers you to fine-tune your build process, ensuring only necessary services are built and deployed based on actual code changes. It's a game-changer for maintaining velocity in complex, multi-service architectures. Ready to supercharge your monorepo? Dive into our comprehensive documentation to implement selective builds and experience the future of efficient development.

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If you are working with Docker... I highly recommend looking at the Slim toolkit. https://lnkd.in/gX_Gb9Kf Why? Slim allows developers to inspect, optimize, and debug their containers using its xray, lint, build, debug, run, images, merge, registry, vulnerability (and other) commands. It simplifies and improves your developer experience building, customizing and using containers. It makes your containers better, smaller and more secure while providing advanced visibility and improved usability working with the original and minified containers. Don't change anything in your container image; minify it by up to 30x to make it secure, too! Optimizing images isn't the only thing it can do, though. It can also help you understand and author better container images. Keep doing what you are doing. No need to change anything. Use the base image you want. Use the package manager you want. Don't worry about hand-optimizing your Dockerfile. You shouldn't have to throw away your tools and your workflow to have small container images. Node.js application images: from ubuntu:14.04 - 432MB => 14MB (minified by 30.85X) from debian:jessie - 406MB => 25.1MB (minified by 16.21X) from node:alpine - 66.7MB => 34.7MB (minified by 1.92X) from node:distroless - 72.7MB => 39.7MB (minified by 1.83X) Go application images: from golang:latest - 700MB => 1.56MB (minified by 448.76X) from ubuntu:14.04 - 531MB => 1.87MB (minified by 284.10X) from golang:alpine - 258MB => 1.56MB (minified by 165.61X) from centos:7 - 615MB => 1.87MB (minified by 329.14X) Rust application images: from rust:1.31 - 2GB => 14MB (minified by 147.16X) Java application images: from ubuntu:14.04 - 743.6 MB => 100.3 MB

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