10 years of "it can't be done..." 7 @nvidia GPU architectures... 5 years of @RAPIDSai... 3 years of @VoltronData... finally a petabyte-scale GPU-native engine that DOESN'T require you to change your data pipelines. Same code, same data formats, just modular, interoperable, composable, extensible... and of course ACCELERATED! Theseus is the Scalable Performant And Compute Efficient engine🔥🔥🔥 Check out our benchmarks and new webpage... and reach out if you're struggling with queries above 30TBs. voltrondata.com/benchmarks

DuckDB 2.0 alpha reads Parquet and CSV from S3 2x to 3x faster, with zero query changes. 1.5: each worker downloads, waits, decodes, repeats. 2.0: a download pool fetches ahead, workers only decode. Same laptop, same query: 18.8 s to 7.7 s. On by default. Full numbers, plus recursive CTEs and VARIANT: motherduck.com/blog/why-duck…

Today, @ducklabs_com is joining @awscloud. The move is expected to be completed by early September. Joining AWS gives DuckLabs the resources and reach to bring DuckDB, DuckLake, and the Quack protocol to many more developers and organizations – and to pursue ideas at a scale that would have been difficult for us to reach alone. The DuckLabs team will remain together in Amsterdam and will continue working for the Duck Stack community as an AWS subsidiary. Most importantly, #DuckDB and the other open source components of the Duck Stack will remain free and open source under the MIT license, with the non-profit DuckDB Foundation continuing its stewardship of the projects. This is a significant moment for DuckLabs and the Duck Stack community. It marks the end of one chapter that we are immensely proud of, and the beginning of another that we believe will take DuckDB much further. Read the full announcement here: ducklabs.com/ducklabs-is-joi… #AWS

GitHub Stacks are LIVE NOW!!! Made a tutorial walking you through how to use them, will link below🥳🥳🥞

So excited to be joining AGNTCON + MCPCon Europe as a keynote speaker💜 I've learnt a lot about agents this year, how/where they are being used, and have some predictions for where they are going. Looking forward to being in Amsterdam to share. Hopefully see many of you there!

Incredibly honored to be a Keynote speaker at @europython this year! The Python community has felt like home for me since I started my career as a software engineer. I'll be speaking about how AI has affected me and the developer community around me. See you in Poland soon 🐍❤️

Vol:19 No:2 → Terabyte-Scale Analytics in the Blink of an Eye vldb.org/pvldb/vol19/p141-se…

NVIDIA just open-sourced a high-throughput, low-latency inference framework for serving reasoning models like DeepSeek-R1! Introducing Dynamo, a framework designed for serving generative AI and reasoning models in multi-node distributed environments. 100% Open Source

🚀 The Colab team collaborated closely with @nvidia to deliver day 1 compatibility for NVIDIA cuML's Zero Code Change ML Acceleration. Now, you can experience significant speedups in your machine learning workflows in Colab with no code modifications! Example notebook below 👇 piped.video/cIJsVq8CPys?feature…

DLSS 4 is the biggest DLSS yet: 8X more efficient graphics for 4K 240Hz rendering 15/16 pixels generated by AI A new transformer based neural network dramatically upgrades image quality for Ray Reconstruction and Super Resolution. piped.video/qQn3bsPNTyI

My 8000-word note on agents: huyenchip.com//2025/01/07/ag… Covering: 1. An overview of agents 2. How the capability of an AI-powered agent is determined by the set of tools it has access to and its capability for planning 3. How to select the best set of tools for your agent 4. Whether LLMs can plan and how to augment a model’s capability for planning 5. Agent’s failure modes AI-powered agents are an emerging field with no established theoretical frameworks for defining, developing, and evaluating them. This post is a best-effort attempt to build a framework from the existing literature, but it will evolve as the field does. As always, feedback is much appreciated!

During the process of writing AI Engineering, I went through so many papers, case studies, blog posts, repos, tools, etc. This repo contains ~100 resources that really helped me understand various aspects of building with foundation models. github.com/chiphuyen/aie-boo… Here are the highlights: 1. Anthropic’s Prompt Engineering Interactive Tutorial The Google Sheets-based interactive exercises make it easy to experiment with different prompts and see immediately what works and what doesn’t. I’m surprised other model providers don’t have similar interactive guides: docs.google.com/spreadsheets… 2. OpenAI’s best practices for finetuning While this guide focuses on GPT-3, many techniques are applicable to full finetuning in general. It explains how finetuning works, how to prepare training data, how to pick training hyperparameters, and common finetuning mistakes: docs.google.com/document/d/1… 3. Llama 3 paper The section on post-training data is a gold mine as it details different techniques they used to generate 2.7 million examples for supervised finetuning. It also covers a crucial but less talked about topic: data verification, how to evaluate the quality of synthetic data: arxiv.org/abs/2407.21783 4. Efficiently Scaling Transformer Inference (Pope et al., 2022) An amazing paper co-authored by Jeff Dean about inference optimization for transformers models. It covers not only different optimization techniques and their tradeoffs, but also provides a guideline for what to do if you want to optimize for different aspects, e.g. lowest possible latency, highest possible throughput, or longest context length: arxiv.org/abs/2211.05102 5. Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models (Lu et al., 2023) My favorite study on LLM planners, how they use tools, and their failure modes. An interesting finding is that different LLMs have different tool preferences: arxiv.org/abs/2304.09842 6. AI Incident Database For those interested in seeing how AI can go wrong, this contains over 3000 reports of AI harms: incidentdatabase.ai/ 7. I find case studies from teams that have successfully deployed AI applications extremely educational. Here are some of my favorite enterprise case studies. I'll add more case studies soon! - LinkedIn: linkedin.com/blog/engineerin… - Pinterest's Text-to-SQL: medium.com/pinterest-enginee… - Gmail’s Smart Compose (2019): arxiv.org/abs/1906.00080 - Grab: engineering.grab.com/llm-pow…

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. Everybody is a winner really, but @duckdb @IbisData , @ApacheArrow, and @GeoParquet were essential throughout!

Hi y'all, I'll be talking at #DuckCon on January 2025. I'll be sharing how to leverage the power of @duckdb's geospatial capabilities while staying within the Python ecosystem using @IbisData . I’ll show you how to work with GeoParquet data and create nice maps in your laptop.

It’s done! 150,000 words, 200+ illustrations, 250 footnotes, and over 1200 reference links. My editor just told me the manuscript has been sent to the printers. - The ebook will be coming out later this week. - Paperback copies should be available in a few weeks (hopefully before the end of the year). Preorder: amzn.to/49j1cGS - The full manuscript is also accessible on O'Reilly platform: oreillymedia.pxf.io/c/571911… This wouldn’t have been possible without the help of so many people who reviewed the early drafts, answered my thousands of questions, introduced me to fascinating use cases, or helped me see the beauty of overlooked techniques. Thank you everyone for making this happen!

New blog post on the fun new hardware advancements which databases can leverage for great gains, and why the cloud means it doesn't matter that they exist. 🫠 transactional.blog/blog/2024…

We've increased the size of our NVIDIA A100 fleet for paid users by around 2x, and for the last several days we've seen 100% success rate for users requesting A100s.

🚀 Heading to @Oracle #CloudWorld 2024? Book a meeting with our execs @datametrician, @rodaramburu & @darrenhaas on Sept 10-11. Spots are limited! airtable.com/appWDPBDUhIP0vR… 📍 Visit us at Booth #115 for a deep dive into accelerated compute with @NVIDIA #GPUs. #OCW2024

no code transformations using marimo's mo.ui.dataframe and @IbisData! Pass in any Ibis dataframe to mo.ui.dataframe to display a UI for different filters/transformations and get the filtered result back in Python. Plus, you can see the SQL statement generated by Ibis!

🔊 Just Aired! Our CEO @datametrician's interview on @Bloomberg 🚀 In <10 min, Josh unpacks how @VoltronData harness @NVIDIA GPUs to accelerate compute, helping businesses “do more with less.” 🎧 Listen now, starting at 29:39: bloomberg.com/news/audio/202… 💡 Highlights include... - How to explain accelerated compute to Josh’s grandma - Scale 200 servers down to just 2 - Slash query times from 30 min to 30 sec - Lower energy consumption & operational costs - Keep your code with BYO-API & open-source best practices #AcceleratedCompute #ComposableDataSystems #SustainableTech #OpenStandards #Composability #OpenSource #GPUs #DataAnalytics

Incredible to hear ⁦@datametrician⁩ of ⁦@VoltronData⁩ at minute 30sh on ⁦@Bloomberg⁩ discussing how we make #ai data centers massively more energy efficient and scalable with ⁦@nvidia⁩ GPUs. omny.fm/shows/bloomberg-busi…