Software Engineering Daily

By Software Engineering Daily

Technical interviews about software topics.... more

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The podcast currently has 2,143 episodes available.

Best of Software Engineering Daily

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Number 1: Inside Google’s Database Infrastructure for the AI Era
Historically, databases were responsible for storing data and returning exact results in response to queries. However, AI is now bending that contract in a new direction. Applications increasingly expect structured and unstructured data to come together. This is pushing databases into territory that looks more like search, where relevance and ranking matter and results are no longer strictly exact. Agents are also beginning to write their own queries and even propose their own schemas, which raises new questions about how data should be structured, governed, and trusted. Sailesh Krishnamurthy is a VP of Engineering at Google, and in this episode he joins Matt Merrill to discuss his background, how databases have evolved over the past fifty years, and where the field is heading as AI reshapes how data is queried, structured, and trusted. Sponsorship inquiries: [email protected] The post Inside Google’s Database Infrastructure for the AI Era appeared first on Software Engineering Daily.
September 15, 20261h 19min
Number 2: SED News: The NVIDIA-Hugging Face Deal, China’s Proxy Economy, the Open Weight Surge
SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer break down the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this end-of-summer episode, Gregor and Sean turn to a busy season of mergers and acquisitions, including NVIDIA‘s reported $12.9 billion acquisition of Hugging Face, Dynatrace‘s near-billion-dollar deal for AI observability platform Arize, and Temporal‘s rumored raise at a $12 billion valuation. They also cover a run of large funding rounds, from AI security startup HiddenLayer to AI personal assistant Instinct and restaurant software platform Owner.com, all against the backdrop of a deluge of new model releases. The main topic digs into China’s “transfer station” economy, the sprawling proxy market that gives developers cheap access to frontier models officially banned in the country. Drawing on a China Talk report, they walk through the tactics at play, from “one fish, three meals” credit farming to silent model swapping, and unpack how this pipeline of captured outputs and human traces may be fueling the recent surge in high-quality open weight models. Gregor and Sean also examine where Meta now sits in the landscape after shelving Llama and pivoting toward its Muse family. As always, the episode wraps up with a few standout Hacker News threads, including a critique of how log-scale charts obscure the real cost gap between open weight and frontier models, and a look at “invisible companies” as an under-the-radar investment strategy. Sponsorship inquiries: [email protected] The post SED News: The NVIDIA-Hugging Face Deal, China’s Proxy Economy, the Open Weight Surge appeared first on Software Engineering Daily.
September 08, 202653min
Number 3: Moving Beyond RAG with Precomputed Context
Retrieval has become one of the central problems in building useful AI systems. The standard approach to grounding a model in one’s own data has been retrieval augmented generation, or RAG, where an agent searches a vector database for relevant information at query time. That pattern works, but it has limitations, such as retrieving information that’s not truly relevant, repeating the same lookup work on every query, and producing inconsistent answers to the same question. Pinecone is a vector database that’s widely used to power semantic search and RAG at scale. The team recently developed Nexus, which is a knowledge engine that reframes context as a first-class, precomputed asset rather than something reassembled on the fly. The approach borrows the database concept of a materialized view, and curates context once into a versioned artifact that carries its own schema, metadata, permissions, and lineage. Jörg Schad is the VP of Engineering at Pinecone. In this episode, he joins Kevin Ball for an in-depth conversation about the frontier of retrieval technology. They discuss precompiled context, how context artifacts are curated and versioned much like code, how metadata and semantic layers help agents choose the right information, and much more. Sponsorship inquiries: [email protected] The post Moving Beyond RAG with Precomputed Context appeared first on Software Engineering Daily.
September 03, 202656min
Number 4: Chroma and Agentic Retrieval
Data retrieval is a fundamental challenge in AI systems, and the approaches for solving it are still evolving. Vector search was an early answer to the retrieval problem, but the rise of agentic systems has raised the stakes considerably. Agents issue queries at machine speed, decompose complex questions into parallel searches, and require retrieval infrastructure that can keep pace without becoming prohibitively expensive. Chroma is a company building open source infrastructure for AI applications, best known for its widely used database of the same name. The company also published the influential Context Rot paper, which documented how model performance degrades as context window utilization increases, and recently released Context One, a 20 billion parameter retrieval sub-agent trained to do agentic search at frontier model quality but at an order of magnitude lower cost and higher speed. Hammad Bashir is the CTO of Chroma, with a background spanning machine learning, computer vision, and data systems. In this episode, Hammad joins Gregor Vand to discuss the origins of ChromaDB, our current understanding of context rot, why a purpose-built small model can match frontier models on search tasks, the philosophy behind Chroma’s open source approach, and where the company sees AI data infrastructure heading. Sponsorship inquiries: [email protected] The post Chroma and Agentic Retrieval appeared first on Software Engineering Daily.
September 24, 202653min
Number 5: A Rust Framework to Simplify Distributed Systems
A Rust Framework to Simplify Distributed Systems Building software that runs across many machines is notoriously difficult. Developers have to grapple with problems such as race conditions, partial failures, and message ordering. Notably, one category of distributed software has largely escaped these burdens. A distributed database can spread a single query across thousands of machines, handling the coordination, failure recovery, and ordering internally. This raises a natural question of why general-purpose distributed programming can’t feel the same way. This is a highly practical problem at AWS, because the reliability of cloud infrastructure depends on getting distributed systems right at massive scale. Joe Hellerstein spent thirty years as a database and distributed systems researcher at UC Berkeley, where he pioneered much of the foundational thinking on applying database ideas to distributed programming. He is now at AWS, where he works to bring his research into production through Hydro, which is a Rust framework to bring declarative queries to general-purpose distributed programming. In this episode, Joe joins Sean Falconer to discuss how ideas from the database world could make distributed programming dramatically simpler and safer. Sponsorship inquiries: [email protected] The post A Rust Framework to Simplify Distributed Systems appeared first on Software Engineering Daily.
September 10, 202651min