Alex Hall

My ICSE paper with Christian Bird and Tom Zimmermann is now live on the ACM website. Check it out to learn how you can best leverage Objectives and Key Results (OKRs) in your org!

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TIPS on how to engineer your #LLM prompt for #SPARQL by Siemens. For application in an #ontology driven #knowledgraph based on #semanticweb #linkeddata. https://lnkd.in/esubWSmy By Nilay TÜFEK ÖZKAYA, Aparna Thuluva PhD and others. #ESWC2024 #ESWC #AI

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Sonnet 3.5 has been makingn waves. Personally I have found that perhaps it lacks breadth of knowledge ( not able to comprehensively answer many questions as well as openai) but its definitely faster , mostly gives better code and provides projects and artifacts features which are reason enough for switching to it for techies doing using it for programming tasks.

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The original INRIA 3DGS code keeps getting improvements from the team, to make it more convenient - including Taming3DGS optimizations, which can cut training times in half for the same results 😀

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🚀 Looking to streamline fine-tuning for models like Llama 3, Mistral, and Mixtral? Our latest blog shows how Anyscale simplifies LLMs fine-tuning with Ray: 🔑 Key takeaways: • Scalable fine-tuning with LLM-Forge • Model Deployment with RayLLM Read more: https://lnkd.in/gQwD4A2U

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I'm excited to announce that the team at Qognitive just open sourced Fast Pauli https://lnkd.in/eTZd45_A, a Python/C++ library for optimized operations on Pauli matrices and Pauli strings. We've been using it at Qognitive to accelerate the training of our Pauli-based quantum models and wanted to share these performance improvements with the community. We're looking forward to improving this further and seeing what the community will build on top of it. For some operations we have seen 10-100x performance improvements over using Qiskit or Numpy for Pauli string multiplication operations against batches of state vectors. You can see some more benchmarks and information in our docs page https://lnkd.in/eZRJgXmy . Fast Pauli uses the work from https://lnkd.in/e5t8hbqU to accelerate the application of operators, which are tensor products of Pauli matrices, to a batch of vectors. The fast batched operation allows Qognitive to execute quantum calculations against large batches of data simultaneously - giving us a significant performance boost in training our Quantum Cognition ML models. We'll keep improving this so star the repo and look for more updates in the future, and please let us know how you use it. And please, don't miss out on Quantum cognition machine learning: financial forecasting - Risk.net [2409.12805] Robust estimation of the intrinsic dimension of data sets with quantum cognition machine learning (arxiv.org)

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https://lnkd.in/dBkJGXwN A really detailed paper comparing latest results in linear mat-mul free progress. Worth reading and if you are a Ph.D. Researcher something to consider as a great investment for your carreer

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🌍 Live from Berlin are thoughts on eBPF, Linux Kernel, Tracing, Auto-Discovery, Anomaly Detection, Edge Delta... Awesome technical talk by our very own Ozan Sazak as he walks through writing and implementing eBPF within an application at WeAreDevelopers conference. 💯 What makes it so powerful? Engineers cannot predict the future, and within #observability it's very difficult to find the right logic and query to ensure you have coverage for tomorrow's production outage. eBPF gives a reliable and standardized stream of data that allows you to have a lot more coverage than simply relying on logs and metrics. If you're curious about what we're doing in this space, come talk to us.

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New Mindset Needed for Large Language Models - https://buff.ly/3XbN4vp #AI #ML #LLMs #genAI

A great read on the performance and financials of LLM fine-tuning. Really hoping the outcome is a tool that helps overcome the biggest elephant in the room, Mr. Cost! #llm #finetuning #finance #computaioncosts

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Interesting paper showing that LoRA doesn't learn your data that well which raises questions on how effective it is at fine tuning your Gen AI model. LoRA Learns Less and Forgets Less - https://lnkd.in/eG4tEuSe

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A fine deep dive into the latest news in k8s here

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Often my employees and I need a development environment for C++ together with the installed GRPC. To reduce the time for installing and deploying the environment, I started making Dev Containers, which I give to my employees and they run them in VSCode. I hope it will be useful to someone else. https://lnkd.in/dCCQBpTt #cpp #c++ #docker #devcontainers

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A blog on (very) nerdy topic that I worked on with our (amazing) intern Xiangpeng Hao this year: https://lnkd.in/eQDfvNfH. We quantify how much of the discussion about changing Apache Parquet metadata was shiny-new-object syndrome and how much was actual technical limitations. We think there is easily a factor of 4 to be had from existing implementations before new formats are needed

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Are you someone who finds distributed systems challenging? If so, I highly recommend checking out Martin Kleppmann's distributed systems lecture series on YouTube. It's an absolute goldmine of information! In this series, Kleppmann covers a wide range of fundamental concepts in depth, including: 💻 Physical clocks and logical clocks (Lamport and Vector clocks) ⚡ Causality and the happens-before relationship 📡 Broadcasting algorithms 🔄 State Machine Replication ☑️ Consensus algorithms like Raft Whether you're a student, a professional, or just someone curious about distributed systems, these lectures are an invaluable resource. Kleppmann's clear explanations and real-world examples make even the most complex topics accessible. Don't struggle with distributed systems any longer. Leverage this incredible resource and gain a solid understanding of the core concepts. Trust me, it'll be a game-changer! #DistributedSystems #MartinKleppmann #LecturesSeries #TechEducation

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An insightful talk about platform engineering and the challenges of building and delivering value!

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Some more insights into diffusion models, in particular the seemingly forgiving scaling laws regarding data requirements needed for accurately identifying subspaces of increasing dimensionality: https://lnkd.in/etB79gBU

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