Qdrant(@qdrant_engine)

Less than a week away now! Join @DylanCouzon from Qdrant and James Le from @twelve_labs on July 28 ...

Qdrant (@qdrant_engine) — Wed, 22 Jul 2026 14:00:01 GMT

Less than a week away now!

Join @DylanCouzon from Qdrant and James Le from @twelve_labs on July 28 for an evening of AI memory, video intelligence, and retrieval systems in Bellevue, WA.

→ Persistent memory for video AI
→ Collective memory for Edge AI with Qdrant Edge
→ Practical retrieval architectures and live demos

Date: July 28

Place: Bellevue, WA

Register: luma.com/kyksgkak

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At Vector Space Day SF, Dave Nielsen from @cognee_ gave an amazing talk on where AI memory is headin...

Qdrant (@qdrant_engine) — Tue, 21 Jul 2026 16:00:00 GMT

At Vector Space Day SF, Dave Nielsen from @cognee_ gave an amazing talk on where AI memory is heading: from simple prompt storage to full knowledge graphs built on the fly, combining RAG, GraphRAG, and short-term memory into one unified layer.

A few things that stood out:

→ @cognee_ builds knowledge graphs automatically, extracting entities and relationships from chats, prompts, agent interactions, and pipelines, storing vectors in @qdrant_engine and graph relationships in @neo4j 

→ AI memory is expanding beyond individual agents to teams and organizations, think onboarding systems that learn from prompts, or shared knowledge bases that grow smarter over time

→ The cognee x Qdrant connector now includes TurboQuant support, and the impact is even bigger for knowledge graphs than regular vector storage, because graph data explodes in size and quantization cuts it down significantly

Full talk is live on our youtube channel: youtube.com/watch?v=UKw6PU…

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If you're in Munich this Thursday, we have something for you. BASED Meetup #6 is happening July 23r...

Qdrant (@qdrant_engine) — Tue, 21 Jul 2026 10:22:49 GMT

If you're in Munich this Thursday, we have something for you.

BASED Meetup #6 is happening July 23rd at TNG Technology Consulting. This time it’s a mix of BarCamp and OpenSpace formats: open discussions on search, retrieval, RAG, and everything in between. There won’t be a pre-set agenda, topics will be taken from the room.

Our very own @krotenWanderung from the Qdrant DevRel team is co-hosting, so if you want to talk about vectors, retrieval, or anything search-related with someone from the Qdrant team, this is your chance.

Bring a question, a pain point, something you built, or something that broke in prod, be it:
→ Agentic RAG and memory
→ Production-scale retrieval optimization
→ Modern search quality metrics
→ Multimodal and cross-lingual search
→ ...or whatever you feel fits

Thursday July 23, 6PM - 9PM GMT+2

TNG Technology Consulting, Arabellastraße 4a, Munich

Register here: luma.com/mnh67d5m

See you there!

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How do you run vector search at 20B+ vectors across 5 regions for 38 teams? @HubSpot built VAST - V...

Qdrant (@qdrant_engine) — Mon, 20 Jul 2026 16:00:30 GMT

How do you run vector search at 20B+ vectors across 5 regions for 38 teams?

@HubSpot built VAST - Vector as a Service, entirely on Qdrant. 150 clusters, 2K+ pods, 9.5B vectors in a single collection, 5K writes/sec with spikes to 100K.

the real story: they outgrew Helm fast. Helm can't call the Qdrant API to transfer shards, maintain replication factor, or handle state-aware scaling. cluster creation took hours.

so they built a Kubernetes operator specifically for Qdrant. shard management, replication, lifecycle automation, all handled automatically. cluster spin-up: hours → minutes.

result: 65% reduction in resource skew on a 3B+ point BM42 sparse vector collection.

full talk here:
youtube.com/watch?v=46aQff…

thanks Oleg Tereshin and Xin Liu from @HubSpot team, for sharing at Vector Space Day SF 🙌

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congrats to @Haystack_AI on 3.0 🎉. If you're building with Haystack 3.0, Qdrant integrates natively...

Qdrant (@qdrant_engine) — Mon, 20 Jul 2026 15:35:20 GMT

congrats to @Haystack_AI on 3.0 🎉. If you're building with Haystack 3.0, Qdrant integrates natively with dense, sparse, and hybrid retrieval. the Haystack team put together a great movie recommendation agent demo showing exactly this pattern with Qdrant:qdrant.tech/course/essenti…8

Haystack@Haystack_AI

Haystack 3.0 is here 🚀

Agents move to the center of the framework:
🤖 Pre-built agents: deep research + advanced RAG, ready out of the box
 🪝 Hooks to control the agent loop
 🧰 First-class skills with progressive disclosure
 📉 A leaner core: we deleted 3 lines for every 1 we added

And this is just Day 1. New drops every day this week at 3 PM CEST.
Full announcemehaystack.deepset.ai/blog/haystack-…p7lEnp

🔗 View Quoted Tweet

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Join us for an upcoming webinar on “Why does my RAG-agent get worse? A live autopsy” As collections...

Qdrant (@qdrant_engine) — Mon, 20 Jul 2026 12:20:53 GMT

Join us for an upcoming webinar on “Why does my RAG-agent get worse? A live autopsy”

As collections grow, documents change, chunks get duplicated, and retrieval pipelines evolve, Agent's quality can decline without an obvious breaking point. @DylanCouzon from Qdrant and Rishabh Hada from Future AGI will build, debug, and fix RAG agents through a single end-to-end workflow.

Qdrant powers the retrieval layer, while Future AGI will trace and evaluate the agent throughout the same workflow.

What we’ll cover:
- Build a RAG agent and observe where the agent is losing quality
- Migrate to a new embedding model with zero downtime
- Remove duplicate and fragmented chunks from the collection
- Add late interaction reranking
- Compare evaluation scores before and after each change to verify what actually worked

Date & Time: Thursday, August 6 at 6:30 PM CEST | 9:30 AM PDT | 10:00 PM IST

Register for the webinar now: luma.com/future-pq6q

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"Continual learning isn't a training problem. It's a memory problem." @taranjeetio (co-founder & CE...

Qdrant (@qdrant_engine) — Fri, 17 Jul 2026 16:00:01 GMT

"Continual learning isn't a training problem. It's a memory problem."

@taranjeetio (co-founder & CEO, @mem0ai) breaks down why agents need memory before they need retraining at our Vector Space Day conducted in SF.

- Weights = stable, general stuff (skills, reasoning patterns)
- Memory = everything tied to a specific user/team/org -  fast, inspectable, reversible, portable

His 5-step loop for agent memory:
observe → extract → retrieve → act → forget/update

Built on real lessons scaling @mem0ai (open-source memory layer, running on @qdrant_engine under the hood) in production.

Full talk: youtube.com/watch?v=yw-7Of…

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If you're building a system for multiple customers or teams and need to keep their data isolated, mu...

Qdrant (@qdrant_engine) — Fri, 17 Jul 2026 10:30:00 GMT

If you're building a system for multiple customers or teams and need to keep their data isolated, multitenancy is what you need.

Pavan put together a great guide on building a scalable multitenant search system with Qdrant and @llama_index.

The guide shows how to:
 → Isolate each customer's data within a single Qdrant collection (yes, you don't need multiple collections)
 → Build secure, payload-filtered retrieval pipelines
 → Implement hybrid search with Qdrant and @llama_index 
 → Scale to many tenants without the overhead of managing hundreds of collections

Read Pavan's article:
 towardsdev.com/scaling-search…

We also have a full guide covering tiered multitenancy, tenant isolation patterns, and performance tuning for your production use cases:
 qdrant.tech/documentation/…

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Don’t forget to join today’s Qdrant Office Hours on Discord! We’ll be having an open discussion, an...

Qdrant (@qdrant_engine) — Thu, 16 Jul 2026 10:44:16 GMT

Don’t forget to join today’s Qdrant Office Hours on Discord!

We’ll be having an open discussion, and we’d love to hear your opinions, feedback, questions, or anything else you’d like to talk about.

We host Office Hours every 3rd Thursday of the month, and going forward, we’ll keep them more discussion-oriented so everyone has a chance to bring up topics they’d like to discuss.

Time: 6:00 PM CEST | 9:00 AM PDT | 9:30 PM IST

Link to the event: discord.gg/pVDGNpjuB?even…

See you there!

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Products aren’t one-dimensional, your embeddings shouldn’t be either. Divy Yadav shows how to build...

Qdrant (@qdrant_engine) — Wed, 15 Jul 2026 10:30:00 GMT

Products aren’t one-dimensional, your embeddings shouldn’t be either.

Divy Yadav shows how to build a multi-aspect semantic search engine using Qdrant Multivectors for richer, more relevant e-commerce retrieval.

pub.towardsai.net/multi-aspect-e…

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What if your AI could remember your life, completely offline? Satyam Sahu from our community built ...

Qdrant (@qdrant_engine) — Tue, 14 Jul 2026 10:30:00 GMT

What if your AI could remember your life, completely offline?

Satyam Sahu from our community built an offline life memorizer using Gemini 2.0 + Qdrant Edge for private, on-device AI memory and semantic retrieval.

A great example of what’s possible with edge AI and vector search.

pub.towardsai.net/building-an-of…

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Our next Qdrant Office Hours is coming up, and we’ve got a great lineup of community demos and discu...

Qdrant (@qdrant_engine) — Mon, 13 Jul 2026 11:45:39 GMT

Our next Qdrant Office Hours is coming up, and we’ve got a great lineup of community demos and discussions!

Date: July 16
Time: 9:30 PM IST | 6:00 PM CEST | 9:00 AM PDT

Here’s what’s on the agenda:
- Qdrant Edge – updates, demos, and discussions.

- How to Implement Multitenant Search in Qdrant
Our Star Kameshwara Pavan kumar Mantha will walk us through building efficient multitenant search with Qdrant.

- Semantic AI Bookmarking with Qdrant
Our Star Mohammed Arbi Nsibi will showcase a Chrome extension he’s been using daily to save and semantically search AI resources.

We’ll also have an open discussion on benchmarking, retrieval quality, and whatever questions the community brings.

Link to register: discord.gg/pVDGNpjuB?even…

See you on July 16!

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AI has gotten better at reasoning, but it still forgets almost everything. Join @DylanCouzon from Q...

Qdrant (@qdrant_engine) — Thu, 09 Jul 2026 12:48:02 GMT

AI has gotten better at reasoning, but it still forgets almost everything.

Join @DylanCouzon from Qdrant and James Le from @twelve_labs on July 28 for an evening dedicated to AI memory, video intelligence, and retrieval systems.

Hear from engineers building production AI systems as they cover:
→ Persistent memory for video AI
→ Collective memory for Edge AI with Qdrant Edge
→ Practical retrieval architectures and live demos

Want to showcase your own project?

We’re also hosting community demos, and only 2 demo slots remain!

If you’re building with AI, retrieval, video intelligence, or agentic workflows, we’d love to have you present your work.

📅 July 28
📍 Bellevue, WA
🎟️ Register herluma.com/kyksgkakcGS

See you there!

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Benchmark results:

Qdrant (@qdrant_engine) — Wed, 08 Jul 2026 16:27:57 GMT

Benchmark results:

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Elastic's benchmark claims Qdrant is 7x slower on disk search. They never enabled the two features b...

Qdrant (@qdrant_engine) — Wed, 08 Jul 2026 16:25:49 GMT

Elastic's benchmark claims Qdrant is 7x slower on disk search. They never enabled the two features built for that workload. We turned them on and found 2x throughput, half the latency, 1/3 the compute.

Read more: qdrant.tech/blog/benchmark…

Elastic@elastic

7x higher vector search throughput at comparable recall.

Elasticsearch 9.4.1 DiskBBQ vs Qdrant 1.18.1, tested on network-attached persistent storage.

The storage topology most K8s and managed-cloud deployments actually run on. Not local NVMe.

The gap is disk access. DiskBBQ searches a compact quantized index and limits full-precision reads. Qdrant rescores against original vectors on disk. On network-attached storage, those random reads get expensive.

Elasticsearch latency: 120 to 150ms across recall levels.
Qdrant: 315ms to 900ms as recall increases.

Benchmark tool, dataset, and configs are all published below.

🔗 View Quoted Tweet

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Most retrieval agents follow the same pipeline for every query. Join us tomorrow to learn how to bu...

Qdrant (@qdrant_engine) — Wed, 08 Jul 2026 14:30:00 GMT

Most retrieval agents follow the same pipeline for every query.

Join us tomorrow to learn how to build retrieval agents that evaluate, adapt, and improve mid-query.

In this live session, you’ll learn how to:
→ Choose the right retrieval strategy for each question
→ Detect weak retrieval early with lightweight signals
→ Route queries using ColBERT reranking and IRCoT query decomposition
→ Build a STOP decision so your agent abstains instead of guessing

📅 Tomorrow, July 9
🕣 8:30 AM PT | 5:30 PM CEST

Register nowluma.com/fdok2snbUn

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Qdrant is heading to WeAreDevelopers World Congress! If you’re joining us in Berlin, here’s where y...

Qdrant (@qdrant_engine) — Wed, 08 Jul 2026 09:30:00 GMT

Qdrant is heading to WeAreDevelopers World Congress!

If you’re joining us in Berlin, here’s where you can find the team:

July 9 - The Retrieval Layer for Edge AI

Chadha Sridi and Sasha Denisov will explore how retrieval powers edge AI applications, featuring a live demo of Qdrant Edge running on smart glasses.

July 10 - Workshop: Retrieval Layer in Context Engineering – From Intuition to Production

Join @krotenWanderung for a hands-on workshop where you’ll build the context-engineering layer for a medical AI copilot while learning:
→ Semantic vs. lexical search
→ Hybrid retrieval
→ Knowledge graphs & GraphRAG
→ Context engineering patterns for production AI

July 9–11 - Visit the Qdrant Booth
Stop by throughout the event for live Qdrant Edge demos, chat with the team, and see how lightweight, privacy-first vector search enables modern AI applications.

See you at WeAreDevelopers World Congress! 👋

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On-device AI RAG using Qdrant Edge https://t.co/XcDxt0adTQ

Qdrant (@qdrant_engine) — Tue, 07 Jul 2026 14:58:47 GMT

On-device AI RAG using Qdrant Edge x.com/i/broadcasts/1…

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Going live in 15 minutes. Don't forget to join!

Qdrant (@qdrant_engine) — Tue, 07 Jul 2026 14:45:09 GMT

Going live in 15 minutes. Don't forget to join!

Qdrant@qdrant_engine

Join us for today’s Vector Space Talk with @TRJ_0751.

Discover how to build a fully on-device RAG pipeline using Qdrant Edge and Google LiteRT, powering document Q&A, personal assistants, and semantic search without relying on the cloud.

Time: 8:30 PM IST | 5:00 PM CEST | 8:00 AM PDT
Don’t miss this live session!

Join here: streamyard.com/watch/GDNR4Xtj…

See you there!

🔗 View Quoted Tweet

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Join us for today’s Vector Space Talk with @TRJ_0751. Discover how to build a fully on-device RAG p...

Qdrant (@qdrant_engine) — Tue, 07 Jul 2026 09:58:57 GMT

Join us for today’s Vector Space Talk with @TRJ_0751.

Discover how to build a fully on-device RAG pipeline using Qdrant Edge and Google LiteRT, powering document Q&A, personal assistants, and semantic search without relying on the cloud.

Time: 8:30 PM IST | 5:00 PM CEST | 8:00 AM PDT
Don’t miss this live session!

Join here: streamyard.com/watch/GDNR4Xtj…

See you there!

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Coming up next week on Vector Space Talks! What if your AI assistant could run entirely on your dev...

Qdrant (@qdrant_engine) — Fri, 03 Jul 2026 13:00:01 GMT

Coming up next week on Vector Space Talks!

What if your AI assistant could run entirely on your device - no cloud, no servers, no internet required?

Join @TRJ_0751 (Founding Engineer at Kaivid Labs & Qdrant Star) as he shows how to build a fully local RAG pipeline using Qdrant Edge and Google LiteRT.

You’ll learn how to build:
→ On-device document Q&A
→ Personal AI assistants
→ Semantic search over your own notes
→ Private, offline AI experiences

Date: July 7
Time: 8:30 PM IST | 5:00 PM CEST | 8:00 AM PDT

Registration Link: streamyard.com/watch/GDNR4Xtj…

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Not every query deserves the same retrieval pipeline. Some questions are simple. Others require mul...

Qdrant (@qdrant_engine) — Fri, 03 Jul 2026 07:33:29 GMT

Not every query deserves the same retrieval pipeline.

Some questions are simple. Others require multiple retrieval steps, reranking, or even deciding not to answer at all.

Join our next live webinar to learn how to build retrieval agents that adapt their strategy as they search.

You’ll learn how to:
→ Choose the right retrieval strategy for each query
→ Detect weak retrieval signals early
→ Route queries to techniques like ColBERT reranking and IRCoT query decomposition
→ Build agents that know when to abstain instead of hallucinate

Date: July 9
Time: 8:30 AM PT / 5:30 PM CEST

Register here:
luma.com/fdok2snb

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Looking to Build GraphRAG? Start with This Practical Guide from our star, Pavan In this practical g...

Qdrant (@qdrant_engine) — Wed, 01 Jul 2026 10:34:33 GMT

Looking to Build GraphRAG? Start with This Practical Guide from our star, Pavan

In this practical guide, Pavan demonstrates how to build a GraphRAG architecture using LangExtract, @neo4j, Qdrant, and @ollama.

You’ll learn how to:
→ Extract entities and relationships from unstructured text with LangExtract
→ Build a knowledge graph in @neo4j
→ Use Qdrant for semantic retrieval
→ Combine graph traversal with vector search for more context-aware responses
→ Run the entire pipeline locally with @ollama 

If you’re exploring GraphRAG or building production-ready retrieval systems, this is a great walkthrough.

Read the article:
blog.stackademic.com/a-practical-gr…

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"Do we still need (traditional) search engines when AI can do & knows everything?" That was the que...

Qdrant (@qdrant_engine) — Tue, 30 Jun 2026 05:06:22 GMT

"Do we still need (traditional) search engines when AI can do & knows everything?"

That was the question Charlie Hull put to our @krotenWanderung , Atita Arora, Jo Kristian Bergum, and Dmitry Kan at @berlinbuzzwords, and nobody agreed it was time to throw the engines away.

A few moments worth checking out:

[1] Dmitry Kan brutally admitted he closed his laptop for three days after ChatGPT first launched. Just sat there, thinking “it’s over”. Then opened it back up and got on with it, and ever since it’s not even close to “over”, for him & for all the “search” people.

[2] @krotenWanderung named three "old but gold" search techniques she keeps seeing people reinvent in their agentic search pipelines: relevance feedback loops, MMR for result diversity between hops, and crowdsourcing-style sub-agent evaluations. All from decades ago. All suddenly very relevant again.

[3] And Jo said that AI agents are better users of keyword search than humans ever were.

The honest conclusion, as @krotenWanderung put it, "I don't think we solved search. I think everything became even more complicated." But in the best possible way:)

Link to see the full video: youtube.com/watch?v=StaPk0…

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Last week, we hosted Vector Space Meetup: Retrieval in the Age of Agents. Our CTO Andrey Vasnetsov ...

Qdrant (@qdrant_engine) — Fri, 26 Jun 2026 08:00:00 GMT

Last week, we hosted Vector Space Meetup: Retrieval in the Age of Agents.

Our CTO Andrey Vasnetsov kicked things off with a look at where Qdrant is heading: storage and compute separation taken to its logical limit - querying only the index fragments you need, on your own device, without a cloud round-trip.

The panel with @cognee_, @Haystack_AI by @deepset_ai, @llama_index, and @n8n_io covered agents in production, evaluation, and the decisions that are hardest to undo. A few honest takes from the room:

Agents won't always choose to run retrieval when you need them to. That's a bigger problem than most people expect.
If you generate your eval dataset and run your LLM-as-a-judge with the same model, you're asking it to grade its own homework.

Thanks to everyone who came and pushed back.

More details and takes in the full recording on our YouTube: youtube.com/watch?v=EEXUuI…

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Next up on Vector Space Talks! We're joined by @TRJ_0751, Founding Engineer at Kaivid Labs and Qdra...

Qdrant (@qdrant_engine) — Thu, 25 Jun 2026 12:09:17 GMT

Next up on Vector Space Talks!

We're joined by @TRJ_0751, Founding Engineer at Kaivid Labs and Qdrant Star, and he's tackling a question worth asking: what if your AI assistant didn't need the cloud at all?

Tarun will show you how to build a RAG pipeline that runs entirely on your device - document Q&A, personal assistant, search over your own notes - no internet, no server required.

The stack? Qdrant EDGE for lightweight on-device vector search, paired with Google's LiteRT for running language models locally with hardware acceleration.

Date: Tuesday, July 7
Register here: streamyard.com/watch/GDNR4Xtj…

Time: 8:30 PM IST | 5:00 PM CEST | 8:00 AM PDT
See you there!

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Most search systems treat relevance as something that happens after retrieval. But what if relevanc...

Qdrant (@qdrant_engine) — Wed, 17 Jun 2026 13:17:04 GMT

Most search systems treat relevance as something that happens after retrieval.

But what if relevance could be part of the search itself?

At @berlinbuzzwords 2026, @krotenWanderung explored how Relevance Feedback can be pushed directly into the vector space, helping search systems improve retrieval quality at the source.

The talk covers:
→ The foundations of relevance feedback in information retrieval
→ How relevance signals can be incorporated directly into search
→ Practical applications for modern retrieval systems

Watch the recording:
youtube.com/watch?v=7E6Ls1…

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The future of retrieval was on full display at Vector Space Day San Francisco. Vector Space Day San...

Qdrant (@qdrant_engine) — Tue, 16 Jun 2026 15:49:46 GMT

The future of retrieval was on full display at Vector Space Day San Francisco.

Vector Space Day San Francisco brought together an incredible community of 350+ engineers, researchers, founders, and AI practitioners to discuss the future of retrieval, search, memory, agents, and AI infrastructure.

A huge thank you to all of our speakers, sponsors, partners, attendees, and everyone who helped make the event such a success. We loved seeing so many people sharing ideas, challenging assumptions, and discussing what comes next for AI systems.

See you at the next one!

Your browser does not support the video tag.

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Building a retrieval system is one thing. Knowing whether it’s actually good is another. This pract...

Qdrant (@qdrant_engine) — Fri, 12 Jun 2026 13:00:01 GMT

Building a retrieval system is one thing. Knowing whether it’s actually good is another.

This practical guide walks through how to evaluate information retrieval systems using a Qdrant-powered retrieval pipeline and Evret.

It covers:
→ Building a retrieval benchmark
→ Measuring retrieval quality
→ Evaluating relevance and ranking performance
→ Moving beyond “it seems to work” testing

As RAG and retrieval systems become more critical in production AI applications, evaluation is becoming just as important as retrieval itself.

Read here:
medium.com/data-science-c…

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Do larger context windows make vector search obsolete? This benchmark compares two approaches: → Se...

Qdrant (@qdrant_engine) — Thu, 11 Jun 2026 13:00:01 GMT

Do larger context windows make vector search obsolete?

This benchmark compares two approaches:
→ Sending large amounts of context directly to an LLM
→ Using a 2-step retrieval pipeline powered by Qdrant to fetch only the most relevant information

The results highlight a key tradeoff many teams face in production:
-> Larger context windows increase cost and latency
-> Retrieval helps reduce the amount of context an LLM needs while maintaining answer quality

As context windows grow, retrieval doesn’t disappear - it becomes even more important for efficiency, scalability, and control.

Read here:
generativeai.pub/the-cost-of-co…

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Can you detect a fire without training a custom model? This project takes a different approach: Edg...

Qdrant (@qdrant_engine) — Wed, 10 Jun 2026 13:00:02 GMT

Can you detect a fire without training a custom model?

This project takes a different approach: Edge RAG + vector search.

Using Qdrant Edge, the system compares real-time sensor readings against known patterns locally, enabling fast fire detection without the overhead of training and maintaining a dedicated ML model.

Why it’s interesting:
→ No custom model training required
→ Privacy-first, fully local inference
→ Real-time sensor retrieval at the edge
→ Lightweight deployment for resource-constrained environments

Read here:
pub.towardsai.net/fire-detection…

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What if your photo gallery could do more than just store images? This project by Vatsala Singh turn...

Qdrant (@qdrant_engine) — Tue, 09 Jun 2026 13:00:00 GMT

What if your photo gallery could do more than just store images?

This project by Vatsala Singh turns a personal photo collection into an autonomous AI agent using Qdrant as the retrieval layer.

Instead of manually searching folders or scrolling through thousands of images, the agent can:
→ Understand natural language requests
→ Search memories semantically
→ Retrieve relevant photos and context
→ Act as a personal visual memory system

It’s a great example of how vector search with Qdrant transforms static data into something AI can reason over and interact with.

Read more here: ai.gopubby.com/how-i-turned-m…

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AI doesn’t always need the cloud. This project uses Qdrant Edge to build a privacy-first SOS detect...

Qdrant (@qdrant_engine) — Mon, 08 Jun 2026 13:00:00 GMT

AI doesn’t always need the cloud.

This project uses Qdrant Edge to build a privacy-first SOS detection system that runs locally:

→ Audio embeddings with YAMNet
→ Real-time similarity search with Qdrant Edge
→ Observability with @SignozHQ

A creative use of vector search for real-world safety applications.

pub.towardsai.net/how-i-built-a-…

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What does it take to run vector search at the scale of 20+ billion vectors? At Vector Space Day, Ol...

Qdrant (@qdrant_engine) — Sat, 06 Jun 2026 15:00:01 GMT

What does it take to run vector search at the scale of 20+ billion vectors?

At Vector Space Day, Oleg Tereshin and Xin Liu from @HubSpot will share the story behind building the infrastructure that powers retrieval at massive scale.

Building the Infra Behind 20 Billion+ Vectors

Learn how @HubSpot evolved from manual, error-prone @qdrant_engine deployments to a fully automated Kubernetes Operator designed for reliability, scalability, and production operations.

If you’re managing vector infrastructure, operating Kubernetes at scale, or building retrieval systems in production, this is the kind of talk packed with practical lessons.

📍 The Midway, San Francisco 🗓️ June 11 

RSVPluma.com/vsd-sfEF

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Fast retrieval is table stakes. Retrieval that respects who's asking, what they're allowed to see, a...

Qdrant (@qdrant_engine) — Fri, 05 Jun 2026 15:00:02 GMT

Fast retrieval is table stakes. Retrieval that respects who's asking, what they're allowed to see, and what policies apply is a much harder problem.

Murthy Chandrapaty and Ankush Gumber from @Adobe are coming to Vector Space Day to show a concrete architecture that solves it: Qdrant's vector search combined with a @neo4j graph governance layer, so agents retrieve fast and stay policy-compliant. They'll demo it live, showing how the same query returns different results for different users based on governance, not just relevance.

If you're building enterprise AI, this is the architecture talk you've been waiting for.

Get your ticket at luma.com/vsd-sf

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What if you could just tell a robot what to do? Sandhya Subramani from @awscloud is bringing a live...

Qdrant (@qdrant_engine) — Thu, 04 Jun 2026 15:00:03 GMT

What if you could just tell a robot what to do?

Sandhya Subramani from @awscloud is bringing a live robot demo to Vector Space Day to show exactly that. She'll introduce an open-source agentic framework that exposes physical hardware as programmable agent tools, with a hybrid architecture that keeps low-latency control on the edge and delegates the hard reasoning to the cloud.

Embodied AI, made accessible. June 11 at The Midway, SF. Get your ticket at luma.com/vsd-sf

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Just 1 week to go until Vector Space Meetup: Retrieval in the Age of Agents in Berlin. Everyone is ...

Qdrant (@qdrant_engine) — Thu, 04 Jun 2026 10:30:00 GMT

Just 1 week to go until Vector Space Meetup: Retrieval in the Age of Agents in Berlin.

Everyone is building agents. The real question is: what kind of retrieval do agents actually need?

Retrieval is no longer just about finding the nearest chunks. Modern agents need to decide:
→ when to search
→ what to search for
→ which tools to use
→ whether the retrieved information is sufficient to act on

That’s why we’re bringing together builders from @cognee_, @deepset_ai, @llama_index, and @n8n_io for a panel discussion driven entirely by YOUR questions.

We’ll also be joined by our Co-Founder & CTO @generall931 for a keynote and networking with attendees.

Date: June 11
Venue: @aicampusberlin, Berlin

Register now (approval required): luma.com/vsm-berlin

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When you're running GenAI on the edge, you don't get to blame the network. Alan Zhu from @Qualcomm ...

Qdrant (@qdrant_engine) — Wed, 03 Jun 2026 15:00:02 GMT

When you're running GenAI on the edge, you don't get to blame the network.

Alan Zhu from @Qualcomm is coming to Vector Space Day to talk about what it means to build for on-device inference, where latency isn't a tradeoff to manage but the thing users feel with every interaction.

Join 300+ AI builders for a full day on agents and memory in production, retrieval from cloud to edge, and multimodal AI. Get your ticket:  luma.com/vsd-sf

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Search is one of the most important parts of the e-commerce experience, and one of the hardest to ge...

Qdrant (@qdrant_engine) — Wed, 03 Jun 2026 10:30:00 GMT

Search is one of the most important parts of the e-commerce experience, and one of the hardest to get right.

That’s why we’re excited to be speaking at MICES 2026, the e-commerce search meetup and official satellite event of Berlin Buzzwords.

@krotenWanderung from Qdrant will present:

“Fine-Tuning Sparse Neural Retrievers for E-Commerce Is Not That Scary (And Often Worth It)” based on @ptdamiba's work!

For more info: qdrant.tech/articles/spars…

The session explores the practical side of sparse retrieval and SPLADE:
→ When sparse neural retrieval makes sense for e-commerce
→ Fine-tuning strategies that actually improve relevance
→ Hard negative mining and evaluation
→ Building an end-to-end retrieval pipeline

Date: June 10

Link: mices.co

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Where does AI memory actually live right now? In markdown files, vectors, graphs, a mix of all three...

Qdrant (@qdrant_engine) — Tue, 02 Jun 2026 15:00:02 GMT

Where does AI memory actually live right now? In markdown files, vectors, graphs, a mix of all three? Dave Nielsen from @cognee_ is coming to Vector Space Day to cut through the hype and map out what short and long-term memory look like in real systems today, and where it's all heading.

Vector Space Day is a full-day single-track conference for engineers going deep on retrieval and agent memory. We also have talks on retrieval from cloud to edge, and multimodal AI.

If you’re building something novel in vector search, AI memory, context engineering, or retrieval infra, get your ticket for June 11:  luma.com/vsd-sf

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Video is the most information-dense modality we have, and most retrieval pipelines treat it like tex...

Qdrant (@qdrant_engine) — Mon, 01 Jun 2026 15:00:00 GMT

Video is the most information-dense modality we have, and most retrieval pipelines treat it like text with pictures.

James Le from @twelve_labs is coming to Vector Space Day to show what's actually possible when you build multimodal retrieval right, from semantic search across sports and audio to agentic workflows that handle object tracking and highlight generation at scale.

If you're curious where vector search is heading, this is a session worth being in the room for. Join us at The Midway: luma.com/vsd-sf

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Most in-car media systems still expect you to search with keywords. But when you’re driving, you do...

Qdrant (@qdrant_engine) — Sat, 30 May 2026 08:00:00 GMT

Most in-car media systems still expect you to search with keywords.

But when you’re driving, you don’t think in keywords - you think in moods, vibes, and intent.

This project by Sarvesh Talele, built with Qdrant Edge, creates a fully local AI-powered media discovery system that lets users search music semantically through voice, text, and mood-based queries.

What’s interesting:
→ Local voice transcription with Whisper
→ Semantic retrieval with vector embeddings
→ On-device vector search using Qdrant Edge
→ No cloud dependency required

A great example of how vector search can power privacy-first, real-time experiences directly on-device.

Read here:
levelup.gitconnected.com/how-i-built-a-…

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Tweaking chunk sizes and running a few demo queries is not an evaluation strategy. Laurie Voss (Hea...

Qdrant (@qdrant_engine) — Fri, 29 May 2026 15:00:02 GMT

Tweaking chunk sizes and running a few demo queries is not an evaluation strategy.

Laurie Voss (Head of DevRel, @arizeai) is coming to Vector Space Day on June 11 to replace vibes with measurement: the retrieval metrics that matter, golden datasets that survive contact with reality, and how to wire evals into CI so you find out about regressions before your customers do.

If you’re building something novel in vector search, AI memory, context engineering, or retrieval infra, check out Vector Space Day on June 11 at The Midway: luma.com/vsd-sf

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Berlin folks 🇩🇪 Join us for an evening focused on AI retrieval, agents, and modern search systems...

Qdrant (@qdrant_engine) — Fri, 29 May 2026 10:30:00 GMT

Berlin folks 🇩🇪

Join us for an evening focused on AI retrieval, agents, and modern search systems.

We’ll be at the event below discussing how retrieval is evolving in the age of agents, production RAG, memory systems, and vector searchluma.com/shfil13jrM

And we’re not stopping there - later the same day, we’re also hosting our Vector Space Meetup: Retrieval in the Age of Agents at the same location!

Expect great discussions, networking, and builders from across the AI ecosystem.

Meetup linkluma.com/vsm-berlinuT

See you in Berlin 🙌

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About 90% of enterprise data is unstructured, and most of it lives in documents. PDFs, spreadsheets,...

Qdrant (@qdrant_engine) — Thu, 28 May 2026 15:00:02 GMT

About 90% of enterprise data is unstructured, and most of it lives in documents. PDFs, spreadsheets, Word files, the stuff that runs businesses. Preston Carlson from @llama_index is coming to Vector Space Day to talk about why even frontier models struggle with real-world documents, and what better OCR and agent harnesses actually unlock.

Vector Space Day is a full-day conference for engineers building the next generation of retrieval systems. Get your ticket for June 11 at The Midway, SF:

luma.com/vsd-sf

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We’re hosting Vector Space Meetup: Retrieval in the Age of Agents on June 11. RAG is the architectu...

Qdrant (@qdrant_engine) — Thu, 28 May 2026 10:00:00 GMT

We’re hosting Vector Space Meetup: Retrieval in the Age of Agents on June 11.

RAG is the architectural foundation of production AI, and retrieval has fundamentally changed over time. Agents don’t just retrieve anymore - they decide when to search, what to search for, and whether results are good enough to act on.

We’re gathering builders shaping this shift: @cognee_, @n8n_io, @deepset_ai, and @llama_index for a panel driven entirely by YOUR questions.

You’ll also hear from Qdrant’s Co-Founder & CTO, @generall931, who will join for open discussions and networking with attendees.

Join us! Register (approval required): luma.com/vsm-berlin

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Building an AI agent is one thing. Building one that's intelligent, reliable, and actually ready for...

Qdrant (@qdrant_engine) — Wed, 27 May 2026 11:00:01 GMT

Building an AI agent is one thing. Building one that's intelligent, reliable, and actually ready for an enterprise environment is a different problem entirely.

Gabriel Lebow from @Vultr is coming to Vector Space Day to frame the key architectural ideas behind production-ready agentic AI: how scalable systems support context-aware reasoning, handle real-time decision-making, and hold up when the stakes are real.

Get your ticket at luma.com/vsd-sf

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Today we're announcing Aman as our exclusive partner in Israel. Israeli enterprise teams (in bankin...

Qdrant (@qdrant_engine) — Tue, 26 May 2026 22:06:19 GMT

Today we're announcing Aman as our exclusive partner in Israel.

Israeli enterprise teams (in banking, insurance, manufacturing, gaming, and beyond) are building production AI on top of search infrastructure that wasn't designed for it.

Qdrant is purpose-built for that next stage: a composable vector search engine written in Rust, with predictable low-tail latency at billion-scale across cloud, on-prem, hybrid, and edge.

Java-based engines, Postgres solutions, and vector add-ons can't deliver the latency or accuracy that modern agentic and RAG workloads need.

Aman brings 25+ years of experience modernizing data infrastructure for Israeli enterprises. Pairing their reach and local expertise with Qdrant's engine gives Israeli teams a faster path from "this isn't working" to production AI that performs.

If you're evaluating vector search in Israel, talk to Aman: aman.co.il/contact-us/

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Vectors tell you what's similar. They don't tell you what's connected. Stephen Chin (VP of Developer...

Qdrant (@qdrant_engine) — Tue, 26 May 2026 11:00:02 GMT

Vectors tell you what's similar. They don't tell you what's connected. Stephen Chin (VP of Developer Relations, @neo4j) is coming to Vector Space Day to talk about how context graphs give agents the relational understanding they need to actually reason, not just retrieve.

Join 300+ AI builders for a full day on agents and memory in production, retrieval from cloud to edge, and multimodal AI. Join us June 11 at The Midway, SF. Get your ticket at luma.com/vsd-sf

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TurboQuant Webinar is tomorrow!! Better compression without sacrificing recall 👀 → TurboQuant vs ...

Qdrant (@qdrant_engine) — Mon, 25 May 2026 15:00:01 GMT

TurboQuant Webinar is tomorrow!!

Better compression without sacrificing recall 👀

→ TurboQuant vs SQ/BQ
→ Benchmarks + production tradeoffs
→ Live technical walkthrough

Link:luma.com/qdrant-turboqu…U#Qdrantt#VectorSearchh#MM

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