@signulll Particularly in the last few weeks feels like people are increasingly bullish on software/app layer again. It still takes a helluva lot of effort to make delightful, useful app layer tools for specific use cases
Today Reactor is coming out of stealth. We’ve raised $59M in Seed and Series A funding, led by @lightspeedvp, with participation from @AmplifyPartners, @wndrco, @Sky9Capital, and @FPVventures.
Reactor is the platform for building in the World Model era: the infrastructure that lets developers build with them at global scale for the first time. Stream from a frontier World Model to your app, in real time, all in under 10 lines of code.
World Models represent the next major shift in AI: pixels, audio and actions are generated on the fly, in real-time, in response to user inputs, and to the environment. Every time computing has made a shift from passive to interactive, entire industries appeared that didn't exist before. We're standing in front of such moment again.
Over the last 6 months, we’ve assembled an all-star team with alumni from Apple, Meta, Google, Luma AI, Netflix, and Replicate. We're already partnering with some of the biggest names and labs in the world, and hundreds of developers are already building on Reactor.
The World Model era starts now.
building great product is a lot like being a magician
one has to dance around the limits of expectation and heighten experiences for the viewer such that it feels like magic
Yesterday I interviewed @SeanZCai about AI data.
This is essentially a guide for founders on how to sell data and RL envs to AI labs.
"I've never seen a data contract get turned down by a top lab, if it's good quality data, for budget reasons."
00:00 What areas of data are underserved?
02:10 For bio data, is it real-world or purely digital?
04:21 For cyber data, which subsets are most underserved?
05:50 What is the sales process like?
07:04 Why would a lab not renew or increase their purchase volume?
10:13 When a researcher is exploring a new direction, what's the first step?
11:35 In robotics data, what do you view as underserved?
13:12 What does the initial data delivery look like, what format?
13:53 Do labs have more sophisticated internal setups for running environments?
14:32 Are the non-frontier labs buying off-the-shelf data from Anthropic / OpenAI vendors?
16:11 Do Anthropic data vendors put expiry timeframes on the exclusivity?
16:42 Are purchase decisions researcher-led?
17:41 Decagon, Sierra, Ramp: what kinds of data are they buying?
19:06 Long-term, when do labs still need to buy external data vs train on user traces?
21:15 Will end-vendor benchmarks shift to performance per dollar?
22:04 How many labs are spending at the 1B+/yr data level?
23:53 Delta between Anthropic's stated $1B and your 10-20B/lab number?
26:05 What makes inference providers / neoclouds a good fit to acquire RL env cos?
The Museum of the Human Web is now open at 238 King Street in San Francisco.
Can’t make it? See the collection and enter the sweepstakes contest online to win an artifact from the museum:
https://t.co/wVVBHBuadn
@contraben Great paper
Curious what you guys thought/concluded about the pre-ideation/pre-prompt adherence phase, of how creatives decide on a direction/treatment for a project at all. Like having AI do mood boarding or combining unexpected inputs for human selection