Noah Spurrier

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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A data-driven approach to driving wound healing outcomes Marcella Gomez, University of California, Santa Cruz https://lnkd.in/gAkWZYJT Meet: https://lnkd.in/g4AAWkcK YouTube Stream: https://lnkd.in/gwtqaTmf Talk details: https://lnkd.in/gWn3B3cU Subscribe for talk announcements: https://lnkd.in/g5ciuNuX Abstract: Precision medicine requires an ability to predict the response of an individual to a prescribed treatment regimen a priori. Thus, advancement in the field is challenged by a lack of predictive models and, arguably, a lack of time-series information for a highly dynamic system. Here, we discuss work in wound healing, for accelerating wound closure. We argue that timing of treatments is as critical to consider as the choice of drug or therapy. Due to system size and complexity, data-driven methods need to be explored to develop multi-dimensional quantifiable indicators tracking systemic changes. In this work I discuss how bioelectronic devices enhanced with deep learning can help facilitate real-time sensing and actuation for automated decisions in treatment for wound healing and preliminary work in transcriptomic based classification of wound states. Bio: Marcella M. Gomez is an associate professor at UC Santa Cruz in the department of Applied Mathematics and Associate Dean for Diversity, Equity, and Inclusion for Baskin Engineering. She received her PhD from Caltech in 2015 and a B.S. from UC Berkeley in 2009; both degrees in Mechanical Engineering. Her research interests are in the broad field of bio-control leveraging methods in machine learning and control theory. Applications range from controlling single-cell response to driving complex systems such as wound healing. #modeling #simulation #medicine #healing #trauma #predictivemodeling #datascience #data #woundhealing #biology #bioengineering #systemanalysis #sensing #omics

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I’ve spent pretty much the last 15~ years of my life working in OSS and I don’t think I’ve ever had a more exciting year than 2024! Everywhere I go, I see the power of OSS opening up opportunities for collaboration and innovation. I think it’s undeniable that this has been a year for truly open source software (and open governance) because of the momentum behind Apache Iceberg and now Apache Polaris (Incubating) [https://lnkd.in/gmBQthbz]  — and it looks like I’m not alone! Apache Iceberg  has been selected as BigDATAwire’s latest ‘Readers’ Choice: Top 3 Big Data and the AI Open Source Projects to Watch’ and ‘Readers’ Choice: Top Data and AI Achievement’ awards. In addition, Apache Polaris was also listed as a top open source project to watch. When I joined Snowflake, I was proud to be a part of a company that was embracing Apache Iceberg and truly OSS by collaborating on the development of Apache Polaris and pushing great new features into Apache Iceberg. Snowflake has been making huge strides in the open data community, and I’m so excited to see our joint efforts receiving such kind feedback from peers. It’s both inspiring and rewarding to be working towards actually providing value, without vendor lock-in, to big data users everywhere! #BigDATAwireRCA24 https://lnkd.in/gZAAAmWT

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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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There's still time to participate in our 2025 State of Open Source Report. For everyone I know working with OSS take a few minutes to tell us what your organization is using and why. Here's a link to the survey: https://lnkd.in/gS_h-xYh Thanks for participating and helping us put together a great report!

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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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You can judge the quality of an engineer by how easy it is to build and run their code. And this is building it from source, let alone using a quickstart docker container.

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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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I'm getting used to how to utilize LLM as a useful software component.

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Anthropic released another paper on their ongoing work towards interpretability of large foundational models like their Claude 3 family, built on previous work by many. Their work continues to be enlightening and inspiring. The findings in this work are generally similar to previous findings on smaller models but scaled up to Claude 3 Sonnet: 💡 they find large numbers of "features" corresponding to combinations of neuron activations and these features map to concepts that we recognize/understand: for example, features for transit infrastructure, particular people, concepts (like bias and "objects lacking sentience"). This might sound familiar from classical machine learning, but the interesting thing is that there is no single thing/neuron encoding each of these concepts, it's a particular combination of neuron activations that embeds these concepts in "superposition" in the network. 💡they observe a sort of "distance" of these feature activations that arrange themselves "near" semantically similar features. When they use their sparse autoencoder approach to learn more and more features they observe that the level of abstraction in the detected features becomes more granular, in ways that are interpretable and meaningful. 💡when they artificially fire these activation patterns during inference, it affects the output in ways that relate to the "feature". For example, asking Claude "what's the most interesting science" normally it responds Physics, but if you clamp the feature activations for the feature "Brain Science" to 10x their max values then Claude responds: Neuroscience. Ways to interpret _and_ influence the model. Great readable work [1] along with an interactive explorer of some of the "features" themselves [2] 🎂 There's an easter egg too: if you scroll in the feature explorer tool and try and select one of the "Randomly selected features from 1M" you get a message "if you want to see the rest of the features, we're hiring!" with a link to their recruiting page :) [1] https://lnkd.in/gZbparwp [2] https://lnkd.in/gC9eus53

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Easy as Pie: Stateful Services at CloudKitchens https://lnkd.in/eDDTfs5e

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https://lnkd.in/gcJsEKrG Q. What happens when #neural #networks are applied to fields like #Physics? A. In short, a Nobel Prize. More broadly, neural networks, built on the laureates' foundational work, have wide-ranging applications across science, technology, and various industries. They excel at tasks like pattern recognition, classification, and data generation. #Physics #AI #HVSays

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These are the boring, yet impactful aspects of API operations I am here for--low-tech, economy impacting, real-world stuff like the API deprecation notice from the Department of Labor.

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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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Its about solving the problem, not 'having AI' in the system. This is from last March, but the Wave 1 vs. Wave 2 distinction does seem to be playing out. Less 'generate me content', more 'synthesize vast amounts of relevant data and aid in decisonmaking or signal extraction'. https://lnkd.in/ehFBFC32 They don't talk about multi-agent systems here, but looking forward to more startups with a focus on leveraging agentic design patterns (https://lnkd.in/ed5rMmFe) to streamline real world workflows.

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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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