Thomas Dohmke

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

4

The WebAssembly Founding Story Ben L. Titzer tells the story of how WebAssembly evolved out of collaboration between the Firefox team at Mozilla and the V8 team at Google, which was initially motivated by the introduction of asm.js. Extended Clip: https://lnkd.in/euf6YVji Full Episode: https://lnkd.in/eq4B-t9a #webassembly #bytecode #virtualmachine

10

Want to learn how to enable your team to have more time to spend on #velocity versus frustrating operational tasks - without compromising #customerexperience while reducing the cost of your #operations? Join @ayeletsachto at DASH on June 25th where she'll share learnings from our two-year program, which reduced the cost of Google Kubernetes Engine Operations as our fleet increased without compromising the #reliability of our systems! #SRE

1

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.

19

🏗️ 𝗗𝗿𝘂𝗶𝗱 𝗗𝗲𝗽𝗿𝗲𝗰𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗖𝗹𝗶𝗰𝗸𝗛𝗼𝘂𝘀𝗲 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗮𝘁 𝗟𝘆𝗳𝘁 🏗️ At Lyft, we have used systems like ClickHouse and Apache Druid for near real-time and sub-second analytics. Sub-second query systems allow for near real-time data explorations and low latency, high throughput queries, which are particularly well-suited for handling time-series data. For our customers, this means faster analytics on near real-time data and decision making. This is crucial for use cases like market signaling and forecasting which benefit from, and depend upon, the most up-to-date information. Overall, these systems allow us to make timely decisions for the business with accurate predictions. In this particular blog post, we explain how Druid has been used at Lyft and what led us to adopt ClickHouse for our sub-second analytic system. https://lnkd.in/gCc7TYrA #DataEngineering #DataArchitecture #RealTime

15

Millisecond cold starts are a reality at kraft.cloud , as is milliseconds scale to zero. What's new, and available soon, is being able to have 𝘀𝘁𝗮𝘁𝗲𝗳𝘂𝗹 scale to zero in milliseconds 🔥 First, some numbers, using NGINX as an example: ⚡️ Cold start: 24 milliseconds ⚡️ First stateful scale to zero (suspend): 174 ms ⚡️ Scale to one (resume): 3.67 ms ⚡️ Further stateful scale to zero (suspends): 2 ms I'll be writing a blog post to explain this in more detail, but basically the use of unikernels (extremely specialized VMs) allows for fast suspend/resume of their state. The second and subsequent suspends are quick since we do incremental snapshots. Finally, note that this mechanism is agnostic to the workload, so while we used NGINX as an example, this works with more complex apps/langs/frameworks like Node.js .

38

3 Comments

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

11

New post on what happens after you add a control plane and local storage for observability: you get 1000x the telemetry when it matters. https://lnkd.in/gAvkk-qP

58

1 Comment

Now we have two first class data catalogs integrated with MS Fabric now available in Azure. Of course DataBricks is the heavy heavy favorite in the enterprise lakehouses as well as AI (as of the conference that just finished 🤓)

6

On Wednesday, GitHub and JFrog announced a collaboration to integrate their platforms, making it simpler for developers and their support teams to handle source code and binaries across both services. This features single sign-on, role mapping, code tracing from source to binary packages across platforms, and a unified project architecture. Later, a unified dashboard will display GitHub and JFrog’s source- and binary-focused security scan findings. #github #jfrog #githubcopilot #eltrys ELTRYS. GitHub JFrog

5

1 Comment

In the latest episode of the Mob Mentality Show, we dive into software development and team dynamics with Mark Shead. Discover how to revolutionize your team's productivity and create a more enjoyable work environment. https://lnkd.in/gV9R-sE4 https://lnkd.in/ggdUAFbX ### **Leveling Up the Entire Team** - **Maximizing Software Investment & Fun:** Learn how mob programming boosts software investment value and enjoyment. - **Team Size Strategies:** Debate whether to tackle problems with large teams or start with just 5 people. - **Real-Life Transformation:** Hear the story of five individuals transitioning from solo work to pair/mob programming and the positive outcomes including reduced cycle times, enhanced delivery rates, knowledge sharing, and collective code ownership.  - **Overcoming Silos:** Address the challenge of tying self-worth to personal knowledge silos and the sunk cost fallacy. - **Bottleneck Resolution:** Compare R&D bottleneck resolution in solo, isolated pair/mob, and dynamic pair/mob setups. - **Team Health:** Learn why the speed of asking questions and seeking help is crucial for team health. - **Sponsoring Risk Takers:** Discover the importance of sponsoring and encouraging team members who take risks and ask questions. - **Learning Vulnerability:** Hear Austin's baseball coach story on vulnerability and its relevance to software teams. ### **Effective Remote Teams** - **Solo Work vs. Collaboration:** Examine the norm of solo work and scheduled meetings leading to eight-hour days filled with meetings. - **Booking Meetings:** Understand the inefficiency of booking meetings weeks in advance just to get necessary information. - **Remote Team Room:** Learn about the remote team room approach that reduces cycle time for information sharing and fosters real-time collaboration. - **Video Call Dynamics:** Address the fear of being on video and the impact of informal versus formal video styles on zoom fatigue. ### **BDD & TDD: The State of Software Development** - **Roman Bridges Legend:** Reflect on the "hopefulness" in the software industry and the parallels with the legend of Roman bridges. - **Known vs. Unknown:** Delve into what is known versus unknown in software development. - **BDD/TDD Practices:** Discuss how much BDD/TDD should be integrated with the UI and the fear of creating test code that might need changes later. - **Evolving Product Code:** Become comfortable with evolving both product and test code. - **Unmatched Confidence:** Discover the confidence boost that comes with well-executed BDD/TDD. - **Safe R&D Spaces:** Balance the need for a safe time-boxed R&D experimental code with the certainty of test-driven development. - **Humoring the Coach:** Hear anecdotes about teams humoring their coaches with TDD, only to embrace it enthusiastically the next day.

4

1 Comment

Can you trust AI? 🤔 For most companies the answer is no - at least using current foundational models out of the box. ❌ Problems like sensitive data leakage and toxic content are major barriers to implementing production genAI solutions. The route towards trust lies in system architecture and the good news is the field is advancing rapidly, including increasingly easily accessible toolkits of solutions to increase trust in AI systems. 🔥 This is the hot area for new releases from major AI platform players (along with AI security), including AWS (https://lnkd.in/e45S-ehS), DataRobot (https://lnkd.in/eCWvppWR), and Databricks (https://lnkd.in/e6A7cTcH). 👨‍⚖️ Guardrail or judge models - leveraging a second AI model to police the content coming out of an original - are probably the most promising scalable solution. For many the best route is likely to be a new class of foundational models specifically designed for this task such as Llama Guard and Databrick’s new judge LLM. There is also the ability to fine tune a model (think a discriminator model from a GAN architecture) or use a general purpose foundational model. 👮‍♂️ Classic filtering techniques for sensitive data can also be applied to the result of an LLM, whether for PII, company IP, or inappropriate content. This can include regex, classic PII models (such as Amazon Comprehend), etc. 🚻 Human in the loop techniques can also be used, but are far more scalable when used to calibrate automated solutions such as those described above rather than manually verifying outputs in realtime. What’s still missing? 🔎 🎯 While techniques to improve accuracy and reduce hallucination such as RAG and AI agents have made great strides, there remains a big gap in conveying a model’s confidence in its output to end users. 💡One idea is to leverage the probability distributions used for top-p (nucleus sampling) at the token level instead at greater aggregations to convey confidence levels for entire model outputs or significant segments (e.g. each step in a chain of thought structure or AI agents framework). #ai, #artificialintelligence, #GenAI, #GenerativeAI, #llm, #datascience, #machinelearning, #mlops, #bigdata

4

1 Comment

The success of the Startup Nation has so many colors and shapes. From folks working out of a garage to those with a small office to Unicorn-sized companies. No matter the type of company, in the early stages, most startups go through similar challenges and I’d be missing a real opportunity if I didn’t tell you that Google Cloud has an incredible opportunity for startups still in their early “in-my-parents-garage” stage. From technology that makes it easy for startups to innovate to Cloud credits so you can try out our products, and access to working spaces and local Hebrew-speaking Cloud experts’ support, we’re here to help you scale and succeed. Folks, grab that garage remote and – you know what I’m going to say – unlock the door to our Early Stage Startup Program! Let’s bring your startup to the next level >> https://lnkd.in/eMeBY_5D #CloudForIsrael #StartupNation Google for Startups Israel

5

Elastic Observability meets developers where they code directly in Visual Studio: with the new GitHub Copilot plugin to get observability insights and understand immediately how your code is impacting your application! Check out Jeff Vestal video: https://lnkd.in/dkwSDc5x

63