Felicis

“The biggest risk with AI is that we don’t go big enough.” - Peter Thiel, Founders Fund. At Crusoe, we are making the promises of #AI a reality. Thrilled to announce our $600 million series D funding and general availability! https://lnkd.in/gkw_qfzj

Today, we’re thrilled to announce a transformative partnership with AWS that establishes poolside as a first-party AWS offering ⛱️ This means poolside's product and models are now available to be contracted directly from AWS — a significant milestone in our trajectory 💜 It all started almost a year ago when we were looking to secure 1,000 GPUs and find a partner with whom we could scale. Fast-forward to today, we have a 10,000 cluster and are executing on a unique and ambitious go-to-market plan that fundamentally accelerates our future. Read more below ⬇️

🌟 We just reached 50k stars on GitHub 🌟 This puts us in the top 0.01% of GitHub projects—an incredible milestone for the n8n community! Our founder, Jan Oberhauser, shares his gratitude and a bit of n8n’s story in this video. Haven’t starred us yet? You still can! https://lnkd.in/dfeX5AM

We raised $21.5 Million for Wherobots! Our journey began when I, then a computer science professor, met Jia Yu, who was completing his PhD at ASU. We quickly bonded over our shared passion for geospatial data and saw its largely untapped potential. We realized that existing data warehousing and analytics tools were fundamentally built for internet data, not geospatial data. As a result, they underperform, and lack key features for intuitive geospatial analysis. On the other hand, legacy geospatial tools live in a silo, are often closed-source, or built on outdated architectures, making geospatial solutions inaccessible for most organizations. To address this, we created Apache Sedona, an open-source geospatial compute framework that has since achieved over 40 million downloads and is used by companies like Amazon and Land O’ Lakes for planetary-scale workloads. After years of growing Apache Sedona, we saw the need for a more comprehensive enterprise solution and launched Wherobots, a fully managed, scalable cloud platform designed to make geospatial solutions easy to build within the modern data and AI ecosystem. This round is led by Felicis—huge thanks to Aydin Senkut and Gabriella G.! Felicis continues to revolutionize the VC industry with their founder-friendly approach and bold investments. We’re also grateful for the strong support from our existing investors, Wing Venture Capital and Clear Ventures. A big thank you to Peter Wagner from Wing VC  who has a keen eye and the patience to back transformative data and AI infrastructure companies like Snowflake, Pinecone, and now Wherobots.  Our friends at Clear Ventures deserve special recognition—Chris Rust and Noor Kamruddin, your targeted AI platform for identifying promising investments and your early belief in us during the uncertain pre-commercial phase mean so much. Lastly, thank you to P7 (Abhishek Shukla) and JetBlue (Ryan Chou) for your participation in this round. We’re excited for the journey ahead! I want to take a moment to thank and applaud the incredible Wherobots team. Over the past two years, we've experienced an astounding 1000% growth. I'm truly humbled to work alongside such talented individuals who make my job both enjoyable and rewarding. This team's dedication and talent inspire confidence in achieving remarkable outcomes now and in the future. Let’s keep it going, Botsters! In closing, I want to emphasize that this is just the beginning. We understand that bringing our vision to life requires collaboration, which is why we’re proud to partner with leading cloud providers like Amazon Web Services (AWS). If you’re already on AWS, you can start leveraging Wherobots today to power your geospatial workloads. The journey ahead is exciting, and we’re thrilled to have you with us!

Two weeks ago, we at DatologyAI released our first results demonstrating massive gains from data curation on contrastive image-text models. Today, I'm incredibly excited to share our newest results, applying our curation pipeline to LLMs. It's absolutely astonishing to see what a small, incredibly talented group of individuals can accomplish, and boy have we cooked! Starting with an exact-deduplicated version of Red Pajama v1 as our baseline and by manipulating only the training data for the model: Train Faster -- Training on our curated data reached the same baseline performance 7.7x faster, meaning results cost dramatically less to obtain and drastically improving iteration speed. Train Better -- Push the frontier of what's possible with a given budget, improving performance by 8.5 absolute percentage points (60.5% Datology vs. 52.0% RPJv1). This isn't just because of Red Pajama: compared to the strongest publicly curated datasets, DataComp-LM and FineWeb-Edu, improve performance by 4.4% and 6.1%, respectively. Train Smaller -- Better data enables you to train smaller models. Reduce cost per query at inference by 2.1x while simultaneously increasing performance over the baseline by 5.7%. As with our image-text results, we present these results both at a high-level (https://lnkd.in/g_hMR5Tx) and with an extremely meaty technical deep-dive for all of you who want the nitty-gritty details (https://lnkd.in/gY5tpq3s). We are just getting started on our journey and are so excited about what's in store. Are you training or customizing your own text models and want to improve performance, training efficiency, and inference efficiency through better data? Get in touch (https://lnkd.in/gSGckr6s)! Are you a data-obsessed researcher, engineer, or somewhere in between who wants to push the bounds of what's possible with better data? We're hiring Members of Technical Staff across a number of roles (https://lnkd.in/gHCwPk8e).