Keith Simons

The Site Reliability Engineering: How Google Runs Production System book is arguably one of the most prevalent and well-known SRE resources out there, but there are many other great options for folks looking to expand on their learning. We recently spoke to Google's Reliability Advocate, Steve McGhee, in our Humans of Reliability interview series. In addition to his interesting anecdotes on the early days of SRE at Google, and his journey to becoming a Reliability Advocate, he also shared a handful of his favorite SRE resources, which we compiled here into a list: https://lnkd.in/gpUVhyRa

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I just realized: we should start saying "build memoization" rather than "build caching." We aren't really talking about least-recently-used or time-to-live algorithms, we're talking about "don't bother recompiling this piece if none of its inputs changed." (It has been puzzling me that some people are so deeply fearful of "build cache" as a concept, and honestly I think it's mostly because most build systems have non-hermetic state scattered all over the place. Maybe a slightly more precise term for the type of caching we're concerned with would clarify the semantics in question.)

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While there are certainly individual variations in skill and experience when discussing modern software development the story is much more nuanced than pure individual effort.  * software development is a team sport -- individual performance is heavily impacted by the system in which they are asked to operate in.  * we pair skill and experience to this model of understanding performance by expecting people to be able to influence and adapt in direct proportion to their seniority, aka the more senior you are the more responsible you are for the outcomes of your work, the less senior you are the more your manager and management chain are responsible.  * humans want to succeed, we're social animals who derive purpose from demonstrating skill, and working collectively. All of which is to say there is no way you have over 1000 people underperforming at your company without senior leadership deeply implicated in.  If nearly 10% of your company is failing, versus just having the market move against you, you need to fire the CEO.

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First blog post in a long time! I started writing a post ~2 years ago on adventures counting 130M U.S. buildings by zipcode and finally circled back to write it up properly. Everybody is a winner really, but DuckDB, Ibis, Apache Arrow, and GeoParquet were absolutely essential for getting the data into and out of all of the various engines. Give it a read and let me know what I missed! https://lnkd.in/evgVe3vD

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Check out the latest in our "Debuggability" blog series! This blog is authored by Kevin Downey Jason Johl and me and talks about how we built a Debuggability Paved Road at Intuit using Ephemeral Containers and Argo Workflows. We'd love to hear your thoughts on the topic. Also, what tools do you build as-a-platform to help debug service, compute and network issues in Kubernetes at scale? #intuit #kubernetes #debug

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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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🌟 From Raw Data to Behavioral Predictions—Effortlessly What if AI deployment didn’t require months of preparation? With BaseModel.ai now on the Snowflake Marketplace, you can transform behavioral data into predictions in just a few clicks. ✅ Rapid Deployment: Build and deploy models in a week.  ✅ Scalable Flexibility: Create multiple models for diverse use cases.  ✅ Privacy Guaranteed: Data never leaves the secure Snowflake environment. Say goodbye to complexity and hello to actionable insights. AI for behavioral data has never been this simple. 👉 Try BaseModel.ai Today  #AI #MachineLearning #BehavioralData #BaseModel #Snowflake #Synerise 

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Looking to get started with in-database machine learning with MySQL HeatWave? This guide will help get you on the right path. https://lnkd.in/gPjuJU9g

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We launched structured data retrieval in Bedrock Knowledge Bases last week at re:Invent. While embeddings-based semantic search has worked wonders for unstructured data, we felt we need a new approach for structured data. We needed a way to translate customer natural language queries to an expressive query language that traditional databases already support. https://lnkd.in/eTPStNif #rag #text2sql #aws #bedrock

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sharpening writing skill is one of killer skill!

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"Your main job is to make your product win." I think this should apply to any product that your company makes. I also like what he says following this statement about what is NOT your main job. I will summarize here: "Your main job is not to make your manager happy, it is not to make execs happy." Also, note, this main job doesn't only apply to PMs, but also to senior Engineering Managers, Tech Leads, and Designers. #makeyourproductwin #leadership

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Project update! My application tracking extension is now doing fuzzier matching and injecting new information into LinkedIn (and other sites) to keep me from doubling up on applications. Next up: local storage and bulk import/export operations...

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Weird tradeoff question: You’re putting together a RAG module for an AI-assisted search solution (needs to do vector embedded search) but don’t want to use any vendor or PG database but rather a auto-shardable on-demand DB like AWS DynamoDB fits the zero-to-one phase risk profile better. I know people do FAISS in Lambda to do proximity search in memory. Is this a sane low-maintenance approach? Are there already better Dynamo abstractions without an intermediary? (Asking for a friend :D )

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While most teams' needs may not be as complex as Intuit's, this piece contains several gems that almost any team can leverage in the landscape of their data ecosystem, data governance requirements and the value they are looking to deliver.

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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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Our customers are scaling their ThoughtSpot deployments managing millions of metadata objects and relationships. 📈 Atlas, our graph database, is the secret sauce. It enables lightning-fast querying and efficient management of analytical objects. ⚡️ But we didn’t stop there. We've slashed Atlas initialization times from nearly an hour to under 2 minutes! 🤯 Want to know how? Check out our latest blog by Indresh Gupta to learn about our optimization techniques: Eliminating string interning Turbocharging Postgres queries Optimizing in-memory processing #ThoughtSpot #Atlas #GraphDatabase #DataEngineering #PerformanceOptimization #Technology #Innovation

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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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I've been quite occupied this week, but I haven't forgotten to summarize the key takeaways from last week's Kubecon 24 held in Salt Lake City. Both I and the team enjoyed the excellent chances to interact and exchange ideas with numerous Kubernetes innovators and open source advocates from all over the world. Notably, our team successfully presented two interesting talks regarding Bytedance's open source project Kubewharf, specifically focusing on the Godel scheduler and Katalyst resource management aspects. These talks have generated much influence within the community. Huge thanks to Lintong Jiang, Yue 'Abby' Y., Jia Deng & @Mingmeng Luo for their great delivery of the talks. Please check out the following links for the details: - Godel scheduler (Bing Li, Yue Yin, Lintong Jiang): Talk link - https://lnkd.in/gmXsgGFY; Github: https://lnkd.in/gR-z32xk - Service profiling & Katalyst (Jia Deng, Cong Xu, Mingmeng Luo): Talk link - https://lnkd.in/gHN-Q33x; Github: https://lnkd.in/gZ4FTZ8V Thanks to the feedback from the community: https://lnkd.in/gDXqipRZ It's so rewarding to have conversations with our internal open source advocates, namely Ting Zou (Tina Tsou), He Cao, James Xiang, Ye Xu. Particularly Ting Zou (Tina Tsou) has provided useful connections on how to interact with the community and also rich ideas on how to improve our contribution & influence back to the community. Additionally, I had the opportunity to meet external K8s open source innovators like Yin Ding from Google, Wei Huang from Apple, Richard Sikang Bian from Ants Group, Chen Wang from IBM/Redhat, and a dozen of people from LinkedIn, CoreWeave, Datadog, and many others. I was so grateful for the wonderful time spent sharing your insights on all the fascinating k8s topics. Looking ahead, we'll aim to make even greater open source contributions to give back to the remarkable K8s community. #kubecon #CloudNativeCon #CloudNative #Kubernetes #K8s

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Clear and rich information on RAG variations. I think this will be useful to most practitioners who have gone beyond the intro and demo workflows, and now need to consider how to refine their flows.

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