Binary quantization sounds like it should tank recall.
BBQ in Elasticsearch doesn't due to its asymmetric nature.
Vectors compress to single-bit values. Queries stay at int4 precision, so distance calculations keep the detail that matters. You trade a bit of oversampling and
7 trojanized repos targeting developers. Zero detections across every AV vendor.
Elastic Security Labs is tracking a new Contagious Interview campaign (REF9403) where DPRK-aligned actors distribute fake coding challenges through Slack job postings.
The repos masquerade as real
I just wrote and recorded a video about how to perform vector search --- on 🎞️ video clips, by how they *look*!
It means you can find scenes by what's *on the screen*, without expensive tagging. (Because, actually, a lot of video search is actually metadata-driven text search!)
Storing full 1024-dim vectors for every modality wastes storage.
Matryoshka representation learning ranks signal into the first dimensions, so truncating a vector doesn't mean losing everything.
jina-embeddings-v5-omni inherits this from v5-text. Truncate to 32, 64, 128, up to
New modular malware family, tracked from first appearance.
Our Security Labs team breaks down the full infection chain, evasion techniques, and C2 infrastructure.
TELEPUZ is a new modular malware spreading via CLICKFIX-VIDAR chains. Elastic Security Labs is tracking it.
Active since late April 2026. The delivery path: ClickFix social engineering tricks users into running a PowerShell command that downloads a VIDAR Go variant, which then
15-minute live demos, Q+A. Every week.
Starting tomorrow.
Relevance Please is a new weekly livestream: demos across Search, Observability, and Security with rotating hosts and rotating topics.
First up: @_jphwang on Making Video Search Easy.
Join us tomorrow, 11AM ET / 8AM PT
🧵 Your search query gets rewritten before it ever matches a document.
Tokenization, stop words, stemming, synonyms: 4 steps sit between what you type and what gets looked up.
Here's what each one does
5. An inverted index is the main data structure for search, working like a hash map.
It creates a 1-to-many mapping between a term, and the documents where that term appears.
This is why text analysis is performed on your documents at index time AND on your search query at
Let's put it all together. What happens when I search for "the best wood fired neapolitin pie"?
- “the best wood fired neapolitan pie” (original query)
- “(t̶h̶e̶) best wood fired neapolitan pie” (stop word removal)
- “(t̶h̶e̶) best wood fire(d̶) neapolitan pie” (stemming)
-
We went back to the full Elasticsearch vs Qdrant benchmark exchange. Traced every number to a cause.
Same hardware. 21M vectors. The disk sat at 0 IOPS the entire run.
io_uring and prefetch got the headline. Neither moved the number. You can't be bottlenecked on a device you
Search feels simple until you start getting back irrelevant results.
Know which of these 3 retrieval strategies to reach for a furniture store site:
- BM25 matches exact terms. Finds an ottoman from "Product ID 43926".
- Vector matches meaning. Figures out what "padded stool for
