"Code red" feels like theater. Competition is healthy - Google's compute advantage was always going to matter once they got serious. The real question isn't who's ahead this quarter, but whether anyone can maintain a moat when the underlying tech is rapidly commoditizing.
The real insight here is recognizing when network latency is your bottleneck. For many workloads, even a mediocre local database beats a great remote one. The question isn't "which database is best" but "does my architecture need to cross network boundaries at all?"
video captioning without human labels or large teacher models, improving accuracy and efficiency by self-generating and refining captions automatically.
How does BDH handle long-range dependencies compared to Transformers, given its locally interacting neuron particles? Does the scale-free topology implicitly support efficient global information propagation?
https://github.com/cocoindex-io/cocoindex is built for building fresh knowledge base. Detects delta of data sources, apply any transformations, and refresh knowledge, at any scale.
Hi I've been working on adding multi-vector support natively in cocoindex for multi-modal RAG at scale. I wrote blog to help understand the concept of multi-vector and how it works underneath.
The framework itself automatically infers types, so when defining a flow, we don’t need to explicitly specify any types. Felt these concept are fundamental to multimodal data processing so just wanted to share. This unlocks at scale: images, text, audio, video — all can be represented as structured multi-vectors that preserve the unique semantics of each modality.