You can't start a hardware rebellion without funding.
So we went and got $6M.
Our CEO Michael Søndergaard just broke down our entire plan to end vendor lock-in with @RobertScammell at @BusinessInsider.
We're even open-sourcing our battle plans (aka the pitch deck). Read it.
Rerun 0.20 is out! 🗺📍🎥
It adds early support for geospatial data with the new GeoPoints and GeoLineStrings archetypes and a map view. It also adds H.264 video support to the native viewer together with many performance and stability improvements for video in Rerun.
At Rerun, we get to work with an extraordinary team, including our investors. Over the last year, we added @rauchg, @ericjang11, @olivercameron, and @ArneBP to the cap table. Their input is crucial for building Rerun into a standard tool for teams working at the forefront of embodied AI.
Guillermo Rauch is the creator behind next js, socket io, and Mongoose and the CEO and founder of @vercel.
Eric Jang is the VP of AI at @1x_tech, working on humanoids for your home.
Oliver Cameron is the Co-founder and CEO of @odysseyml; before that, he built @voyage, which @Cruise acquired.
Anders Bo Pedersen led the development of computer vision and machine learning data infrastructure at @RealityLabs.
I'm so proud to introduce Bucket feature management, built for product teams:
✓ Flags for B2B
✓ Adoption metrics
✓ Customer feedback
In one simple tool.
Iterate faster and build better products https://t.co/YHN51HOROf
Gemini 1.5 Pro - A highly capable multimodal model with a 10M token context length
Today we are releasing the first demonstrations of the capabilities of the Gemini 1.5 series, with the Gemini 1.5 Pro model. One of the key differentiators of this model is its incredibly long context capabilities, supporting millions of tokens of multimodal input. The multimodal capabilities of the model means you can interact in sophisticated ways with entire books, very long document collections, codebases of hundreds of thousands of lines across hundreds of files, full movies, entire podcast series, and more.
Gemini 1.5 was built by an amazing team of people from @GoogleDeepMind, @GoogleResearch, and elsewhere at @Google. @OriolVinyals (my co-technical lead for the project) and I are incredibly proud of the whole team, and we’re so excited to be sharing this work and what long context and in-context learning can mean for you today!
There’s lots of material about this, some of which are linked to below.
Main blog post:
https://t.co/QAsDKXBdao
Technical report:
“Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context”
https://t.co/CTzTHNDCdo
Videos of interactions with the model that highlight its long context abilities:
Understanding the three.js codebase: https://t.co/yq7d6OSD6c
Analyzing a 45 minute Buster Keaton movie: https://t.co/adyMgDYHoK
Apollo 11 transcript interaction: https://t.co/Pqvq3Eac1R
Starting today, we’re offering a limited preview of 1.5 Pro to developers and enterprise customers via AI Studio and Vertex AI. Read more about this on these blogs:
Google for Developers blog:
https://t.co/x73Vun0kVS
Google Cloud blog:
https://t.co/OlaTW6PYGn
We’ll also introduce 1.5 Pro with a standard 128,000 token context window when the model is ready for a wider release. Coming soon, we plan to introduce pricing tiers that start at the standard 128,000 context window and scale up to 1 million tokens, as we improve the model.
Early testers can try the 1 million token context window at no cost during the testing period. We’re excited to see what developer’s creativity unlocks with a very long context window.
Let me walk you through the capabilities of the model and what I’m excited about!