Jeff Young

There is a surprising amount of complexity to choosing colors to represent data. Here are some of my favorite resources: https://lnkd.in/gg_YHqGV https://lnkd.in/g-priQZB

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Check out both the talks from #posetteconf about pgvector + Python! One from me about pgvector with everything but Django, then one from Paolo Melchiorre on Django: https://lnkd.in/g6ixPn_8 #python #postgresql #pgvector

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Have I mentioned that this computer scientist, developer, and straight-up genius is the brains behind yuda ? An MJC counselor once told Daisy she’d never make it in tech because of her math scores. But if you know us #Latinas, you know we don’t back down—we say, “Oh really? Watch me.” Not only did Daisy prove them wrong, but she’s also empowering so many women in the Central Valley and beyond to believe they belong in tech. Thanks to her leadership, our local Google Developers Group and Women Techmakers ambassadors have launched chapters in #Stockton led by Laiylaly Mandujano, #Turlock, Kelley Coelho, PMP® and #Merced Norma C. Cardona, MPA + Lourdes Ovando—making waves that others said were impossible. Daisy is always humble, but let’s be real: she deserves the credit. When I was fired from my alma mater, California State University, Stanislaus, for advocating for Latinas, Daisy stood by me. We were planning a tech event for International Women's Day, and despite the department committing $20k to continue without me, Daisy and Women Techmakers Modesto (Google Developer Group Modesto Partners) voted against moving forward without my voice at the table. That level of integrity and selflessness speaks volumes. This is why I am—and always will be—your ride or die co-founder, Daisy Mayorga-Fuentes. You believed in me when others didn’t, and together, we’re building the future of tech for Latinas and beyond. #LatinasInTech #WomenInTech #DigitalEquity #GoogleDevelopers #WomenTechmakers #CentralValleyTech #RepresentationMatters #Leadership #Integrity

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Trying out Databricks AI/BI Genie and wish you had some best practices to work with? The Genie team heard you loud and clear, and wrote up this doc to help! https://lnkd.in/gvQhPFrV As always, feedback welcome. :)

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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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Right on time for Data and AI Summit, here's some great news: Predictive Optimization is now generally available. This feature takes all the burden of table maintenance off your back. And it does it more efficiently and timely, saving you time (doing manual labor), and money (wasted storage that isn't VACUUMed, and queries that are needlessly slow). And instead your tables are always nicely optimized and efficient. I'd say $HOLIDAY_OF_CHOICE has come early this year. #databricks #deltalake

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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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Want a preview of what I'm going to be talking about in my keynote for RubyConf? You can catch a glimpse of it here!

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this is a great primer on a bit of the core of vector search: kNN and aNN, and the tradeoff between speed and accuracy in returning relevant results

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Worth reading: How the Apache Arrow Format Accelerates Query Result Transfer by Ian Cook One of the hidden problems of intersystem communication is transcription of data formats. Ian does a wonderful job of explaining the "why" of Apache Arrow as well as the "how" of its advantages. Read on...

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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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Enums enhance code readability and clarity. They define named values for model attributes, making them ideal for representing statuses, stages, and states. An article from the team at reinteractive explores the powerful features of enums, particularly how they make code readable and self-explanatory … https://buff.ly/4cKGWiu #RubyOnRails #Programming #Enums #reinteractive

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AI leaders have a crucial chance to address risks now and strengthen governance practices before playing catch-up to regulations, notes Kjell Carlsson, Ph.D. of Domino Data Lab. In a recent VentureBeat article, he shares his perspective on California's AI bill veto and why proactive governance is key to the future of AI innovation. Learn more about this addressing this opportunity: https://gag.gl/4WCTkP #AI #AIGovernance #AIInnovation #DataScience

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Hey folks! I'd like to more formally introduce and talk about a few of the projects we're working on towards improving large-scale bioinformatics analyses (one of which was developed during St. Jude's KIDS24 Biohackathon)! If you're not aware, my team started a project called St. Jude Rust Labs (https://lnkd.in/eXY_9dJ8) where we're rewriting a more foundation for bioinformatics analysis in Rust. This cuts across more than just workflow execution, but, recently, we've been primarily focused on improving the experience of working with the Workflow Definition Language (WDL) at scale. To that end, we've released a number of projects, including: - We've written a complete lexer/parser for WDL v1.2 language (https://lnkd.in/eGWHSM2g). Anyone can use this foundation to build tools for WDL. - We've written a VSCode plugin that includes a language server protocol (LSP) for linting and validation directly in your editor (https://lnkd.in/euApG9tF). - And, announcing today, we've started down the road of writing our own WDL execution engine spread across the Crankshaft (https://lnkd.in/et_RFkA3) and Sprocket (https://lnkd.in/eUTuPE68) projects. Crankshaft was prototyped during the St. Jude Biohackathon last week—it's a _headless_ workflow execution engine, meaning that, in theory, others could come along and write drivers for Crankshaft built on NextFlow, CWL, Snakemake, etc. As I said earlier, our team is really focused on WDL specifically, so we're going to continue building out a "head unit" for Crankshaft using WDL. That being said, I would love to see other community projects popping up and using the core machinery for other workflow languages. Thanks to all of the individuals who participated on our Biohackathon team (Kevin Benton, suchitra chavan, Braden Everson, Andrew Frantz, Michael Gattas, Peter Huene, and John McGuigan)!

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At the top of the hour, come learn about deploying language models to the serverless cloud. https://lnkd.in/guT_dfkZ

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Structure gives freedom to interact with outside world on the basis of ideas. In the past there was the race for #NER (named-entity recognition) which extracted "named-entity" which I call here semantic like "Person", "Date", ... (it is pretty vague I know and the old NLP couldn't perform well). Now, this (semantic) structure is coming to #llm and recently Ollama has released new version to generate structure output: https://lnkd.in/gXJTrarC. There is a lesser known #openai version too: https://lnkd.in/gUDQDF8T. I have not tested Ollama structure output yet, but openai #gpt4o performs fairly well in extracting information.

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🎉 End-of-Year Vibes? Party Hats vs. AI Hats 🧠 While most folks are busy planning year-end parties, I find myself sipping coffee at 2 a.m., staring at my screen, and thinking: “How do I make AI more enterprise-ready?” Party playlists? Nah. I’ve been jamming to error messages and schema validations instead. 🥳 So, instead of hitting the dance floor, I hit the keyboard and wrote “Pydantic AI and AI Agents: A Primer” Why? Because structured data is the life of the AI party, and Pydantic is the wingman we all need. It ensures that your data isn’t just dressed to impress but actually fits the occasion. 🎩 Here’s to making AI smarter, data cleaner, and 2025’s year-end parties more automated (and guilt-free)! If you’re in the same boat, drop a 🎉 in the comments—and let’s geek out together before 2024 shuts the door! #AI #Pydantic #EndOfYearThoughts #EnterpriseReadyAI

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Many graph database offerings require massive data ingestion from source before you can run graph analytics to discover deeper insights from latent relationships in your data. Not only it incurs big upfront ELT or ETL cost, but also it requires additional and recurring effort to ensure data is synchronized from source. What if we can bring graph analytics and other composite AI workloads (contextual rich semantic layer, declarative rules, mathematical optimization ...) to your massive data in data cloud? This is exactly what RelationalAI has been partnering with Snowflake to embed our Relational Knowledge Graph engine as a native app on Snowflake Container Service, so customers can run advanced graph algorithms like Louvain community detection directly on their massive data cloud without the need to move data out! Check out the blog below to see how customers can use this native app for advanced graph analytics to drive customer engagement and growth in a frictionless way with their data in the cloud. #datacloud #knowledgegraph #ai #ml

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Exciting to see how to use RelationalAI in Snowflake Snowpark Container Services to identify connections and communities in your existing data. SPCS makes it possible to do this in the Snowflake data cloud without moving data or setting up complex data pipelines!

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