Software Engineering Daily

Cory Doctorow is a science fiction author, journalist, and technology activist, and a special advisor to the Electronic Frontier Foundation. He is the author of dozens of books, including the nonfiction Enshittification: Why Everything Suddenly Got Worse and What to Do About It and the novel Picks and Shovels. His work spans tech policy books like The Internet Con and Chokepoint Capitalism, the solarpunk novels Walkaway and The Lost Cause, and the bestselling YA Little Brother series. He holds honorary doctorates from York University and the Open University, writes the daily blog Pluralistic.net, and lives in Los Angeles and London.

In this episode, Cory joins Josh Goldberg to talk about his latest book, The Reverse Centaur’s Guide to Life After AI. They discuss the difference between a centaur and a reverse centaur, why the same AI tools leave some workers empowered and others feeling exploited, why companies impose worse-performing workflows anyway, and what workers can do about it.

Sponsorship inquiries:
sponsor@softwareengineeringdaily.com

The post Cory Doctorow on AI, Work, and Power appeared first on Software Engineering Daily.

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:
sponsor@softwareengineeringdaily.com

The post Chroma and Agentic Retrieval appeared first on Software Engineering Daily.

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. It’s written far more often than it is updated, it accumulates continuously, and it is usually queried across ranges of time. Time-series databases are built specifically for this kind of workload.

TimescaleDB is an open source database from Tiger Data that adds time-series capabilities to PostgreSQL. It’s implemented as a Postgres extension, so it introduces new functionality while preserving standard Postgres behavior and SQL. This lets a single Postgres-based system handle both transactional and analytical workloads without splitting data across multiple tools.

Brandon Purcell is the Director of Product Management at Tiger Data. In this episode, Brandon joins Kevin Ball to discuss why time-series data breaks conventional databases, how hypertables and Hypercore scale Postgres, zero-copy database forking for agent-based workflows, and much more.

Full Disclosure: This episode is sponsored by Tiger Data

Sponsorship inquiries:
sponsor@softwareengineeringdaily.com

The post Scaling Time-Series Workloads on Postgres appeared first on Software Engineering Daily.

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. Getting agents to run reliably at that scale has meant a lot of hand-rolled infrastructure beneath the agent itself.

eve is an open source, cloud-native agent framework from Vercel that removes much of the agent scaling burden. In the eve framework, an agent is defined declaratively through configuration files and these files compile into infrastructure as code so the platform provisions only what the agent actually uses.

Andrew Barba is a Member of Technical Staff at Vercel, and Shar Dara is the Product Lead for eve at Vercel. In this episode, they join Kevin Ball to discuss what it means for an agent framework to be cloud-native, why they chose to express agents in plain English, and their view that company building is becoming agent building.

Sponsorship inquiries:
sponsor@softwareengineeringdaily.com

The post Scaling Agent Workloads at Vercel appeared first on Software Engineering Daily.

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:
sponsor@softwareengineeringdaily.com

The post Inside Google’s Database Infrastructure for the AI Era appeared first on Software Engineering Daily.