Chao Zhang

Yogiyo: Serving Up Seamless Food Delivery with Google Cloud South Korea's top food delivery app, Yogiyo, relies on Google Cloud for real-time data insights. By using BigQuery and Cloud Spanner, they can: ⚡️ Optimize delivery routes for efficiency ⚡️ Personalize recommendations for users ⚡️ Seamlessly handle peak order volumes Learn how Google Cloud helps businesses enhance customer experience with data: https://lnkd.in/eR4rU8ps #DataAnalytics #GoogleCloud

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Check out this KubeCon talk https://lnkd.in/gn8CvfmS >> about "Making Kubernetes Simpler for Accelerated Workloads " https://lnkd.in/gn8CvfmS Fun chat lead by Googler Susan Wu #ai #kubernetes #gke

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🔥 𝐈 𝐚𝐦 𝐬𝐞𝐞𝐤𝐢𝐧𝐠 𝐝𝐞𝐯 𝐫𝐞𝐥 𝐩𝐨𝐬𝐢𝐭𝐢𝐨𝐧𝐬: 𝐡𝐢𝐫𝐞 𝐦𝐞 𝐭𝐨 𝐡𝐞𝐥𝐩 𝐲𝐨𝐮𝐫 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬 𝐬𝐮𝐜𝐜𝐞𝐞𝐝! My name is Jacky, and I am 𝐚𝐜𝐭𝐢𝐯𝐞𝐥𝐲 seeking developer relations roles. My last post garnered some awesome convos with folks, so I wanted to put the feelers out again. 📮 𝐋𝐞𝐭'𝐬 𝐜𝐡𝐚𝐭: https://lnkd.in/eBHf5_DT Most recently, I worked for Pinecone as a senior developer advocate under the incredible Bear Douglas and Grigoriy (Greg) Kogan where I learned from the best of the industry to build + run a world-class developer advocacy team. I built and launched the viral Pinecone Shop The Look (https://lnkd.in/dxs6zJ5t) sample app that even got Mark Zuckerberg himself responding to netting over 300,000 impressions! I am ready for hire - I have my resume, work samples, and professional references ready. My core tenants as a dev advocate is: 1. Build 2. Communicate 3. Achieve company goals 4. Mentor 👉 𝘐 𝘸𝘰𝘶𝘭𝘥 𝘥𝘦𝘦𝘱𝘭𝘺 𝘢𝘱𝘱𝘳𝘦𝘤𝘪𝘢𝘵𝘦 𝘪𝘧 𝘺𝘰𝘶 𝘤𝘰𝘶𝘭𝘥 𝘭𝘪𝘬𝘦, 𝘤𝘰𝘮𝘮𝘦𝘯𝘵, 𝘢𝘯𝘥 𝘴𝘩𝘢𝘳𝘦 𝘵𝘩𝘪𝘴 𝘱𝘰𝘴𝘵 𝘵𝘰 𝘩𝘦𝘭𝘱 𝘪𝘵 𝘳𝘦𝘢𝘤𝘩 𝘮𝘰𝘳𝘦 𝘢𝘶𝘥𝘪𝘦𝘯𝘤𝘦𝘴. 𝘛𝘩𝘦 𝘫𝘰𝘣 𝘮𝘢𝘳𝘬𝘦𝘵 𝘪𝘴 𝘪𝘯𝘤𝘳𝘦𝘥𝘪𝘣𝘭𝘺 𝘵𝘰𝘶𝘨𝘩 𝘳𝘪𝘨𝘩𝘵 𝘯𝘰𝘸, 𝘢𝘯𝘥 𝘢𝘯𝘺 𝘭𝘪𝘵𝘵𝘭𝘦 𝘣𝘪𝘵 𝘩𝘦𝘭𝘱𝘴 𝘢 𝘭𝘰𝘵, 𝘵𝘳𝘶𝘭𝘺 🙏

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Thrilled to enable 🔎 Gemini 'Grounding' support 👉 https://lnkd.in/gUKu3bv4 (+4 more updates 👇) 🛠️ Azure default api version fix 💪 'additional_drop_param' support for Embedding+Img Gen - https://lnkd.in/gZ8ZwccW 🗣️ Speech to text - 'srt' support - https://lnkd.in/gE69asjN 🎉 'IP Address' filtering - https://lnkd.in/gTu9q-iB

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It’s baaack. Confluent’s second issue of Confluent Chronicles is here! Inside, you’ll find a clever, but relatable approach (if I do say so myself) to learn about the fundamentals of Apache Flink, stream processing, and Confluent. Our heroes, developer and architect team, Ada and Jax are struggling with decentralized data processing challenges and need a manageable, cost-effective stream processing solution for an important upcoming launch. They embark on their next adventure to learn why Apache Kafka and Apache Flink are better together. Get your copy now!

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Deprecating Hadoop is not without its obstacles. For example, any replacement must also offer an equivalent stack for application management that includes log aggregation and metrics. A critical challenge when building Moka, our next gen Spark on EKS platform at Pinterest, was enabling teams to quickly diagnose issues with Spark jobs and to monitor job performance and status via metrics. We just published a two part blog series with AWS, co-authored with Vara Bonthu, that details our setup and explains how we resolved some of these issues to build a robust observability and metrics framework for Moka. Enjoy! - Part 1: https://lnkd.in/gtJ9u4eW - Part 2: https://lnkd.in/gcbnntyj Our thanks to Ang Zhang, William Tom, Ashim Shrestha from Pinterest and Gregory Mark, Doug Youd, Alan Halcyon, Aaron Miller and Nirmal Mehta @ AWS for their continued help and support.

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Today I'm sharing this 🔥 video from our recent Engineering onsite at Demandbase HQ in SF! https://lnkd.in/eFAFR6QG We were so fortunate to host Zhengxuan Wu and Aryaman Arora, two upcoming AI researchers from Stanford University Department of Computer Science. Their approach to fine tuning, called ReFT, targets the representations emitted by neural net layers rather than the weights of the layers themselves. This makes it very flexible when using the same base model for swapping multiple fine tunes. The memory overhead is also very low for their intervention weights. Another highlight is how powerful it can be. One POC they showed forced the LLM to speak in only emojis exhibiting ReFT's power to control output. Control is crucial when, for example, you want to prevent your bot from offering customers unauthorized refunds!! If you're already familiar with LoRA, it should be pretty simple to use with only a few extra hyperparameters -- details in the video. Here's a link to the repo. Give them a star! https://lnkd.in/eJScNbzK Thanks again for letting us host your first external talk Aryaman and Zen. It's very exciting tech!!

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Open Policy Agent (OPA) and Rego are powerful tools for implementing policy-as-code in Kubernetes and other environments. OPA is an open-source, general-purpose policy engine that enables unified, context-aware policy enforcement across the entire stack. In Kubernetes, it's often used for admission control and authorization. Rego is the high-level declarative language used to specify policy as code in OPA. It allows you to express complex policies in a concise and readable format. Here's how OPA and Rego work together in Kubernetes: 1. Integration with Kubernetes: OPA can be deployed as a admission controller webhook in Kubernetes. This allows it to intercept and evaluate requests to the Kubernetes API server before they are persisted. 2. Writing Policies: Policies are written in Rego and typically stored in ConfigMaps or as files in the OPA container. 3. Policy Enforcement: When a request is made to the Kubernetes API, it's sent to OPA for evaluation. OPA applies the Rego policies and returns an allow/deny decision. Gatekeeper is an open-source project that provides a more Kubernetes-native way to use OPA (Open Policy Agent) for policy enforcement. It extends Kubernetes with Custom Resource Definitions (CRDs) for defining and managing policies using Rego. Reference for implementing gatekeeper for enforcing labels:

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In collaboration with AWS, we are thrilled to announce the Confluent blueprint for Amazon OpenSearch Ingestion! This serverless feature enables customers to effortlessly transfer data from Confluent to OpenSearch Domains or OpenSearch Serverless collections. This capability unlocks numerous new use cases, such as Retrieval-Augmented Generation (RAG), real-time log analytics, and more. Read the full announcement, including a complete example of setting up a Confluent/OpenSearch pipeline using the new blueprint (with optional use of Schema Registry), here ➡️

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This DZone article by Thang Chung is fantastic! Check out his diagram in the middle showing how he used Spin and Dapr together in his Coffee Shop app. https://lnkd.in/gPbaMRyp #wasm #spin #webassembly #dapr #kuberntes

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High availability matters in modern software, and it's not just the application itself needs to scale, other services need it as well So, how should I scale Redis? There're 2 solutions, Redis replication and Redis cluster, both provide high availability and a series of mechanism like failover or monitoring to keep the service running, how does it actually works and why doesn't Redis provide strong consistency even with synchronous replication? Find more on https://lnkd.in/gsDZE4V3

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Our datadog-pgo tool in now GA 🚀 Check out the post to learn how we're already saving $250k / year with it and how our tool protects you from a known memory increase issue when using gRPC.

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Scaling Memcache at Facebook. In this video, we walk through the 2010 research paper "Scaling Memcache at Facebook." This paper has been very influential in companies building distributed caching systems. For example, Uber and Twitter have caching systems inspired by Memcache. The requirements of Memcache were: 1. Easy adoption, minimal APIs 2. Low Latency 3. Very High Availability (without sacrificing too much on consistency) 4. Large globally distributed system The design choices of Memcache are influenced by the above, as we will see in this video. Video: https://lnkd.in/d5kPu9UY #SystemDesign #Caches #ResearchPaper

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Today, #WebAssembly is helping developers realize the vision of "Build Once, Run Anywhere" that Java introduced decades ago. In this talk, Randy Reddig and I focused on one language in particular, everyone's favorite language - Go! If you're curious about how Go has embraced WebAssembly to enable building truly portable applications while preserving the practical, pragmatic developer experience that makes Go so beloved, check out our talk **WASI to Go: Write Once, Go Anywhere** on YouTube: https://lnkd.in/gfw4V6hp

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