Hiiiiii Weaviate Friends!!!
There has been a lot going on in the last few weeks. We have a few exciting blogs, podcasts and stories to share with you.
Our very own Laura Ham has been researching: how we could combine the power of cross-encoders and bi-encoders to get more accurate results without sacrificing performance.
- What cross-encoders and bi-encoders are
- How to combine them
- Pre-trained cross-encoders
GOMEMLIMIT – a game changer for high-memory applications
The Go team have released
GOMEMLIMT – an exciting feature that we particularly feel very excited about. It allows us to better control the garbage collection process, and avoid the rare
out-of-memory application crashes.
- The memory allocation process in Go
- The Garbage Collector
- Scenarios that could result in OOM
- How GOMEMLIMIT helps
- The results of our experiments
If you find this interesting, join us in the conversation on Reddit
Integration story - BetterFeedback
We’ve been collaborating with our friends at BetterFeedback.ai
, who in their quest to give their customers a better analytics experience, decided to join the Semantic Search movement. But don’t take my word for it:
Weaviate helps 🚀NASA‘s GES-DISC team
We are very excited to see Weaviate in a presentation by NASA’s GES-DISC team.
👀 See GES-DISC Graph-Enabled Vector Search
- where they show how they are using
graph-enabled vector search
(Weaviate) to improve their search capabilities.
Last week, Connor met with Yaoshiang Ho – a Co-Founder of Masterful AI.
- cutting edge Computer Vision algorithms such as Noisy Student, SimCLR, and Barlow Twins
- broader topic of Semi-Supervised Learning
We had an update to the Multi2Vec CLIP
inference module, now on version
- Fix unserializable values in