
Show Notes
At Qdrant Conference, builders, researchers, and industry practitioners shared how vector search, retrieval infrastructure, and LLM-driven workflows are evolving across developer tooling, AI platforms, analytics teams, and modern search research.
Andrey Vasnetsov (Qdrant) explained how Qdrant was born from the need to combine database-style querying with vector similarity search—something he first built during the COVID lockdowns. He highlighted how vector search has shifted from an ML specialty to a standard developer tool and why hosting an in-person conference matters for gathering honest, real-time feedback from the growing community.
Slava Dubrov (HubSpot) described how his team uses Qdrant to power AI Signals, a platform for embeddings, similarity search, and contextual recommendations that support HubSpot’s AI agents. He shared practical use cases like look-alike company search, reflected on evaluating agentic frameworks, and offered career advice for engineers moving toward technical leadership.
Marina Ariamnova (SumUp) presented her internally built LLM analytics assistant that turns natural-language questions into SQL, executes queries, and returns clean summaries—cutting request times from days to minutes. She discussed balancing analytics and engineering work, learning through real projects, and how LLM tools help analysts scale routine workflows without replacing human expertise.
Evgeniya (Jenny) Sukhodolskaya (Qdrant) discussed the multi-disciplinary nature of DevRel and her focus on retrieval research. She shared her work on sparse neural retrieval, relevance feedback, and hybrid search models that blend lexical precision with semantic understanding—contributing methods like Mini-COIL and shaping Qdrant’s search quality roadmap through end-to-end experimentation and community education.
Speakers
Andrey Vasnetsov Co-founder & CTO of Qdrant, leading the engineering and platform vision behind a developer-focused vector database and vector-native infrastructure. Connect: https://www.linkedin.com/in/andrey-vasnetsov-75268897/
Slava Dubrov Technical Lead at HubSpot working on AI Signals—embedding models, similarity search, and context systems for AI agents. Connect: https://www.linkedin.com/in/slavadubrov/
Marina Ariamnova Data Lead at SumUp, managing analytics and financial data workflows while prototyping LLM tools that automate routine analysis. Connect: https://www.linkedin.com/in/marina-ariamnova/
Evgeniya (Jenny) Sukhodolskaya Developer Relations Engineer at Qdrant specializing in retrieval research, sparse neural methods, and educational ML content. Connect: https://www.linkedin.com/in/evgeniya-sukhodolskaya/
More from DataTalks.Club
In this talk, Dewang Sultania, ML Engineer at Netflix and co-author of Designing AI Systems, shares his technical journey from building early inference platforms at Adobe to deploying multimodal AI models at scale. We explore the complete architecture of enterprise AI platforms, focusing on evaluation datasets, tool calling orchestration, and the practical realities of shipping production agents.
In this talk, Suhas Suresha, Senior Machine Learning Engineer at Adobe and co-author of Designing AI Systems, shares his expertise in building large-scale generative AI infrastructure from deploying real-time ECG analysis models at his previous startup, QALY, to architecting enterprise LLM platforms. We explore the massive engineering effort required to take an AI application from a weekend prototype to a scalable production system, and how to build a unified AI platform that eliminates "AI sprawl."
In this talk, Sahil Walia, Senior Technical Architect at Snowflake, shares his extensive data engineering expertise from writing simple SQL queries early in his career to architecting enterprise-scale Apache Iceberg lakehouses today. We explore the evolution of the modern data platform, the mechanics of open lakehouses, and how decoupling storage from compute is transforming data architecture.
In this talk, Philip Christos, Associate at M2VC, shares his expertise in frontier AI investing from his early days as a data analyst to evaluating cutting-edge deep tech startups. We explore how AI agents are reshaping modern work, the realities of venture capital in the LLM era, and why the future of software engineering relies more on high-level systems thinking than writing syntax.You’ll learn about:- How venture capitalists formulate AI investment strategies and evaluate startup pitch decks.- The rapid adoption of AI in software engineering compared to traditional sectors like accounting.- The crucial difference between data progress and algorithmic progress in frontier models.- Why LLMs still struggle with high-level software architecture, business logic, and design "taste."- How AI is accelerating iterations and breakthroughs in classical engineering and scientific research.- What specific deep tech and AI infrastructure ideas are actually attracting VC funding today.TIMECODES:00:00 Venture Capital Career Journey05:14 VC Analyst vs Data Analyst Roles10:13 AI Investment Thesis Strategy16:00 Emotion Analytics and Voice AI Startups22:09 LLM Impact on Venture Capital28:19 Software Engineering AI Adoption34:18 Data vs Algorithmic AI Progress40:15 Classical Engineering AI Acceleration46:12 Software Architecture AI Limitations51:11 Future Software Engineering Careers57:55 Systems Thinking in DevOps1:03:00 Pitching Deep Tech StartupsThis talk is perfect for software engineers, AI founders, and technical product managers who want to understand the evolving tech job market and venture landscape. It provides essential insights for anyone looking to build VC-backed AI startups or future-proof their career as AI coding tools become ubiquitous.Connect with Philip- Linkedin - https://www.linkedin.com/in/philipchristos/- Twitter - https://x.com/phil_christos- Website - https://t.me/stacked_channelConnect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/
In this talk, Paul Domanski, founder of Domanski AI, shares his unconventional journey from film directing and affiliate marketing to becoming a vibecoder running dozens of autonomous AI agents. We explore the realities of building an agentic operating system, how non technical builders can orchestrate complex AI workflows, and the exact strategies used to close high ticket B2B clients using AI generated prototypes.Links:- Headroom: https://github.com/domanski-ai/headroom/You will learn about:- How to build and use custom workspaces like Demox and Tmux setups to orchestrate over 50 concurrent AI agents- Using automated research and rapid AI prototyping to close high ticket B2B sales- Balancing usage across multiple Claude and Codex accounts while navigating API limits and context windows- How to design audit and deploy complex software solutions without a traditional computer science background or deep knowledge of tech stacks- How the skills of a film director like vision stubbornness and delegation perfectly translate to managing autonomous AI teams- Why evaluating LLM outputs is the most critical skill for ensuring reliable products for enterprise clientsTIMECODES:00:00 Welcome and Showcasing Demox08:46 Paul's Background in Film and Digital Marketing16:09 Sales Pipeline and Client Onboarding22:20 Token Efficiency and Open Source Models31:19 Curiosity and the Learning Process37:52 Red Teaming and Code Audits44:51 Building Software Without Knowing the Tech Stack51:02 Parallels Between Film Directing and Managing AI56:32 Using AI for 3D Printing and Personal Projects01:04:34 The Importance of AI EvalsThis conversation is perfect for solopreneurs, agency owners, and non technical builders who want to move beyond basic chat interfaces and start leveraging autonomous AI agents for real world business operations. Whether you are transitioning into AI from marketing, project management, or creative fields, this episode offers a practical blueprint for turning AI curiosity into a scalable business.Connect with Paul:- Linkedin - https://www.linkedin.com/in/paul-domanski-ai/- Twitter - https://x.com/domanski_ai- Github - https://github.com/domanski-ai- Website - https://domanski.ai/Connect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/