Podcast profile
By Software Engineering Daily
Technical interviews about software topics.
Latest episode
48 min
Topics
Application architecture, modularity, APIs, maintainability, and technical design decisions.
Coordination, consistency, messaging, storage, and computation across networked systems.
Developer productivity, tooling, workflows, platforms, and engineering enablement.
Formats
Host-led conversation where a guest supplies most of the subject matter.
Detailed technical, architectural, or research-oriented examination.
Audience level
Regularly assumes specialist knowledge and discusses implementation, architecture, protocols, code, or research in depth.
People behind the show
This show assigns different presenters by episode, so no permanent host is claimed. Recently identified presenters include Gregor Vand, Sean Falconer, Kevin Ball, Matt Merrill.
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Episodes shown — 10 of 25
Many real-world processes produce data as a continuous stream rather than as isolated records. Sensor readings, financial markets, and application telemetry all generate data this way. This kind of time-series data has a distinctive shape.
Most AI agent setups today are built around a single session, where one user interacts with one agent at a time. However, that model breaks down when an agent has to serve a business, where thousands of requests can arrive at once and each session needs to be isolated, durable, and recoverable.
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.
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.
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.
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.
Age verification is reshaping how people access the internet. An ever-growing patchwork of laws can now require government IDs, facial age estimation, or behavioral inference before you can enter digital spaces. Discord, app stores, social media platforms, and AI chatbots are all being pulled into these requirements.
TypeScript is a programming language that builds on JavaScript by adding a system of types. Those types let developers describe the shape of their data and catch mistakes before code ever runs, while also powering the autocompletion and editor tooling that many developers now rely on every day.
It is widely reported that a gap has emerged between enterprise spending on AI and the durable value captured from that spend. Individual employees have enthusiastically adopted coding assistants and chatbots, yet those gains do not seem to be transforming businesses at an organizational level.
The conversation about AI often focuses on software, automation, and the race between attackers and defenders in code. However, some of the most consequential risks lie further afield, in domains where a mistake is measured in human lives.