John Frey

We released a new update of our Visual Studio Code Java extension. Go try it! Bunch of new improvements and already supporting JDK 23 Early Access builds as the extension is directly leveraging javac. https://lnkd.in/g8BKsNjS #java #IDE #OpenJDK #VSCode

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Customers often ask if Amazon Q Developer is only for Amazon Web Services (AWS) workloads. The short answer is no. Our developer platform can add value to your on-premises development and multi-cloud needs, regardless of your platform. Our partners at Heroku have a great use case for this. Check out their blog post on how the Amazon Q Developer / Heroku integration simplifies their customer workflows.

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Introducing Structured Outputs: By popular demand, we are introducing the ability to provide your own JSON schema to customize the output structure of the OpenAI API response https://lnkd.in/g7g3xmvb

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Distributed Stateful Workflows. The theory and the concepts behind modern-day distributed software. My new post. First of two, in fact, and both are out now. This one is focusing on the fundamentals of building stateful distributed systems. https://lnkd.in/gsr7UEVc

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Object storage direct-write Kafka topics are coming to Confluent. In data system design, cost is a kind of unstoppable force that rewrites architectures every decade or so, and that is happening right now for high-volume streams. https://lnkd.in/dHRFnrMB

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🚀 Exciting news in the Rust and AWS communities! 🚀 On June 8th, Werner Vogels announced **distill CLI**, an efficient, Rust-powered tool for media summarization. 📜🔧 Check out his blog for more details: [Introducing Distill CLI](https://lnkd.in/dfUT4cmf) Notably, companies like Discord and Figma have achieved remarkable performance improvements by adopting Rust. Discord moved their Read States service from Go to Rust, resulting in a 10x speed increase and nearly 100x reduction in worst tail latencies. Similarly, Figma rewrote performance-sensitive parts of their multiplayer service in Rust, seeing a 6x reduction in peak average CPU usage per machine. 🌟💪 AWS is also embracing Rust with the general availability of Cargo support in CodeArtifact, enabling the storage of Rust libraries (crates) in CodeArtifact. 📦✨ More info: [AWS CodeArtifact and Cargo Support](https://lnkd.in/dYY_fgtU) As more customers leverage Rust for their critical workloads, AWS is providing robust building blocks. AWS Lambda, for instance, can now process HTTP events more efficiently with Rust. 🚀⚡️ Learn more here: [Rust HTTP Events in AWS Lambda](https://lnkd.in/diubf8bM) #RustLang #AWS #Serverless #Performance #CloudComputing #Innovation #TechNews #CodeArtifact #DistillCLI

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GoodData failed to connect to StarTree Apache Pinot running on our customer's side. I challenged the community, but our stack is unusual for them (HikariCP, JDBC). I decided to create a small reproducer, a pure Java app. Why not use #ChatGPT? Let's test the recently released GPT-4o model! Long story short, we (me and ChatGPT) succeeded in a few iterations. It took us about 20 minutes to create not only the Java code, but also pom.xml, .env config, and shells scripts. I open-sourced the result here: https://lnkd.in/eWzNWNN4

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Thr `jets exec` command runs bash commands in the deployed AWS Lambda env. It’s like a REPL for lambda. It’s useful for debugging https://lnkd.in/gQ4Gnfig #rubyonjets #rails #aws #serverless

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The speed at which these models improve is staggering, so is the cost to build them. Only way to keep up is to leverage principles such as AWS Bedrock so you have the flexibility you need to quickly select the model that works best for your task. https://lnkd.in/gNAR63YC https://lnkd.in/g_FtP6e9

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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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How long does it take to migrate from a local Prometheus or Thanos to GreptimeCloud? I was told by a user that it only takes 20 minutes. Learn how to do it! https://lnkd.in/gajiJEZ6 #Greptime #GreptimeCloud #Prometheus #Monitoring

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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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Check out Infostrux Engineering latest blog post https://lnkd.in/gT63tydN, where Balkaran Brar covers a Layered Terraform Architecture. Our Engineering team continues to publish weekly articles in our engineering blog around many different topics, including Data Engineering and Platform Engineering, and Snowflake #terraform #infostrux #cloudarchitecture #snowflake

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pgvectorscale: A Game-Changer for Vector Similarity Search 🚀 Timescale's #pgvectorscale extension for PostgreSQL has achieved a remarkable milestone, matching the performance of Pinecone, a dedicated vector database, while being 75% more cost-effective. 💰 In their latest blog post, Timescale showcases the impressive speed and efficiency of pgvectorscale, which leverages the power of PostgreSQL to deliver lightning-fast vector similarity search. This breakthrough opens up new possibilities for applications like semantic search, recommendation systems, and more. 🔍 Key takeaways: 🌟 pgvectorscale now matches Pinecone's performance in vector similarity search 💸 pgvectorscale is 75% more cost-effective compared to Pinecone 🔧 Easy integration with existing PostgreSQL databases 🌐 Scalable solution for handling billions of vectors ⚡ Optimized for fast and efficient search results pgvectorscale's seamless integration with PostgreSQL allows developers to incorporate vector similarity search into their applications with ease, while benefiting from the robustness and scalability of a proven database system. 🗄️ If you're looking for a high-performance, cost-effective solution for vector similarity search, pgvectorscale is definitely worth exploring. 🔍💡 #pgvectorscale #postgresql #vectorsimilaritysearch #semanticsearch #recommendationsystems #timescale https://lnkd.in/gsfgCK32

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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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CockroachDB 24.1 is here! I'm excited about this release because 241 is the older of the twin primes 239 and 241, but most importantly because we built this release for organizations who need to reduce operational complexity, enhance developer productivity, and keep their data secure and resilient. We made migrations simpler, with improved SQL compatibility as well as updates to our migration tool MOLT Fetch. If you’re still running on a legacy database and want to make the leap, CockroachDB can help support your cloud-native migration. We designed this release so you can spend less time on database management and more time building. CockroachDB 24.1 features enhanced support for UDFs, Stored Procedures, Read Committed transactions, CDC Google Cloud Pub/Sub, Azure Event Hub, and our CockroachDB Cloud Terraform Provider. We didn’t forget about resilience or security either. Cloud clusters are now officially backed by a SLA of 99.999%. You can organize roles and permissions with Folders and increase security controls with support for GCP Private Service Connect. And Self-Hosted customers can use Physical Cluster Replication with fast cutback to reduce risk from major incidents or disasters. This post is just scratching the surface of the impressive work the Cockroach Labs team has done in making this release possible, so dive into the blog below for an in-depth look at what’s new. https://lnkd.in/efwZCwr9 

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Steph Gooch recently released a great article about Spring Cleaning your AWS Costs. Her article was great, and emphasizes removing idle resources (S3 buckets, EBS snapshots), and creating a prioritized list. 👇 Rahul Subramaniam and myself discussed it on our stream last week: https://lnkd.in/g885fzVC Steph's article: https://lnkd.in/gJHCs2Mk At CloudFix, we have automated finder/fixers for most of the points that Steph brought up. - Enable S3 Intelligent Tiering - https://lnkd.in/g3pkkZvq - Get rid of unused AMIs and EBS volumes - https://lnkd.in/g6Swa-xU - RDS Multi-AZ Deployments that could be Single AZ - https://lnkd.in/gACUrhB3 - Move managed services to graviton - https://lnkd.in/gfbh7v7Z & https://lnkd.in/g3qeQ-5q If you need to do spring cleaning in your AWS account, consider CloudFix your Roomba - always looking for bits to clean up so you can spend your time doing something else (like migrating to serverless or deploying AI)! #aws #costoptimization

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"#apachekafka isn't a message queue." Well, that's true for now, but not for long! Currently, Kafka is a distributed log, and it does not have Queue semantics. Log semantics include: - Records are produced to a log. - Records can be consumed, and (within a single partition) they are consumed in the order that they are produced. - Records may be read multiple times by multiple consumer groups. - Records are not "removed" from the log immediately after being read. However, in a message queue, things are different: - Messages are produced to a queue. - Consumers can read and acknowledge messages individually - Messages may be delivered out of order. - Once a message is acknowledged, it is removed from the queue. - Messages may be "retried" if not successfully acknowledged. Kafka is one of the fastest-moving open-source projects. It has an ongoing KIP (Kafka Improvement Proposal) to add Queue semantics on top of its rock-solid log abstraction! Check out KIP-932: https://lnkd.in/gb2Qi__X It will introduce the concept of a "Share Group", which is a new type of consumer group that reads records from a topic with queuing semantics. The Share Group will keep track of which records (well, actually "messages" in this context) have been acknowledged and which ones are still in-flight.

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