The New Stack Podcast

DuckDB is rethinking how databases communicate with the launch of Quack 1.0, a production-ready, HTTP-based client/server protocol designed for faster, more efficient analytical workloads. In this episode of The New Stack Podcast, Guillermo Sánchez Dionis, product lead at DuckLabs, explains how Quack addresses the limitations of traditional database protocols, which often introduce unnecessary overhead and inefficient data serialization.
Unlike conventional protocols built around row-based data transmission, Quack leverages DuckDB’s columnar data representation and vectorized execution engine to deliver faster, more efficient query results. Its streaming architecture allows clients to consume results as they are generated, significantly reducing memory usage and eliminating unnecessary processing delays.
Sánchez Dionis also highlights developer-friendly capabilities, including simplified remote connections, command-line server deployment, and improved transaction throughput and concurrency. With minimal server overhead and the flexibility to scale across multiple DuckDB instances, Quack makes distributed analytics more accessible. The conversation explores how DuckLabs is advancing database connectivity while prioritizing performance, simplicity, and server-side stability in future releases.
Learn more from The New Stack around the latest developments around DuckDB:
Why MotherDuck refuses to fork DuckDB
DuckDB: Query Processing Is King
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DuckLake is simplifying data lakehouse management by replacing complex metadata files with a familiar SQL-based approach. In this episode of The New Stack Podcast, Pedro Holanda, lead DuckLake developer and principal engineer at DuckLake, discusses how the open-source table format reduces complexity, improves performance, and makes large-scale data management more accessible.
With the release of DuckLake 1.1 alongside DuckDB 2.0.0 LTS, Holanda explains how storing metadata in a relational database eliminates the need to navigate countless files to locate and query data. By combining Parquet storage with SQL catalogs, DuckLake streamlines data access while addressing common challenges such as excessive small files, inefficient queries, and costly maintenance.
The conversation also explores DuckLake’s innovative inlining capabilities, which consolidate smaller datasets to improve efficiency, and its ability to support analytics without massive server infrastructure. As AWS integrates DuckDB into its broader analytics strategy, DuckLake represents an important step toward simplifying data portability, reducing operational overhead, and enabling more efficient analytics for developers and AI agents.
Learn more from The New Stack around the latest developments around DuckDB:
Why MotherDuck refuses to fork DuckDB
DuckDB: Query Processing Is King
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As AI models become more powerful and affordable, enterprises still face a major obstacle to realizing the full potential of AI agents: the context gap. Without access to accurate, relevant information, even the most advanced models struggle to deliver meaningful results.

In this episode of The New Stack podcast, Sri Desikan, an executive at Elastic, explains why providing AI agents with the right context is essential for moving beyond impressive demonstrations to reliable enterprise automation.
Simply connecting agents to enterprise data through MCP servers isn't enough. Feeding massive amounts of information into large language models is costly, inefficient, and slow.
Elastic's approach centers on building an AI index that organizes enterprise data, metadata, and relationships, enabling agents to retrieve relevant information faster while reducing token consumption.

Desikan also explores how precomputed context can help prevent outdated information from influencing AI decisions. The conversation examines the implications for data privacy, agent autonomy, and the future of enterprise AI adoption.

Learn more from The New Stack around the latest developments in bridging the enterprise AI context gap:

The Real Bottleneck in Enterprise AI Isn’t the Model, It’s Context

Why enterprise AI breaks without metrics discipline

Why enterprise AI keeps stalling — and how data streaming could unlock it

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As AI agents transform software development, developers face a fundamental shift in the skills they need to succeed. In this episode of The New Stack podcast, recorded at AGNTCon and MCPCon in San Jose, Frederic Lardinois speaks with Angie Jones, Vice President of the Agentic AI Foundation, about the evolving developer role and the challenges of adopting AI across engineering organizations.
Drawing on her experience leading AI enablement at Block, Jones explains how embedding agent-friendly practices into development workflows helped drive widespread adoption. She emphasizes the growing importance of system design, logical thinking, and building agentic systems rather than simply using AI coding tools.
The conversation also explores opportunities for junior developers, the role of open standards like MCP, and how AI-generated contributions are reshaping open source maintenance.
As organizations navigate this transition, Jones highlights the importance of cultural change, collaboration, and developing new skills to build secure, reliable AI agents that extend beyond software development.
 Learn more from The New Stack around the latest developments around the evolving role of the developer:
The 3 roles AI agents play in your developer platform
AI Will Steal Developer Jobs (But Not How You Think)
10 New Jobs for Burned-Out Software Developers
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AI adoption is accelerating among cloud-native developers, who are leading the charge in integrating artificial intelligence into software development. According to the Cloud Native Computing Foundation’s (CNCF) Q3 2026 State of Cloud Native Development report, 21.2 million developers—41% of the global developer population—work with cloud-native technologies. Of those, 75% use AI to write and ship code, compared with just 50% of non-cloud-native developers.
In this episode of The New Stack podcast, CNCF Executive Director Jonathan Bryce discusses the rapid growth of the cloud-native ecosystem, evolving developer trends, and the expanding role of open source in driving innovation.
Bryce highlights CNCF’s project maturity process, from sandbox to incubation and graduation, and explores emerging technologies such as KubeRay and Agent Substrate alongside graduating projects including KubeVirt, Karmada, and Kubeflow.
The conversation also examines the Kubernetes AI Conformance program and why understanding open-source technologies is increasingly essential for organizations seeking flexibility, innovation, and competitive advantage in an AI-driven world.
 
Learn more from The New Stack around the latest developments around the State of the Cloud and the evolving cloud native ecosystem.
Cloud native application challenges: installing the walking skeleton
Cloud Realities Are Slowing AI Ambitions
After becoming cloud computing’s telemetry standard, OpenTelemetry graduates into the AI infrastructure era
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