Software Engineering Daily

The popular story of Steve Jobs is that he co-founded Apple, was pushed out, wandered through a failed venture called NeXT, then returned to save the company and usher in the iPod and iPhone. That version is memorable, widely repeated, and, in an important sense, wrong. His years at NeXT are usually treated as a footnote, when they were among the most consequential of his life and career.

Geoffrey Cain is the author of Steve Jobs in Exile, which draws on previously unpublished material and new interviews to illuminate the NeXT years. The book argues that NeXT was not a failure but the hidden turning point. Its operating system, NeXTSTEP, became the foundation of Mac OS X and, later, the iPhone, and its fingerprints are still visible in Apple’s code today.

Dan’l Lewin was an early Apple employee who led the initial launch of the Macintosh into higher education. In 1985, he left the company alongside Steve Jobs to co-found NeXT, where he served as VP of Marketing and Sales. Over more than 30 years in Silicon Valley, he went on to hold senior roles at Sony and Microsoft, and later served as President and CEO of the Computer History Museum.

In this episode, Geoffrey and Dan’l join Matt Merrill to discuss the story behind NeXT, the 1980s computing landscape that shaped it, the technical bets like Objective-C and dynamic binding that gave Apple a long head start, why the university market mattered so much, and how the company ended up shaping the devices in our pockets.

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In the world of software security, there was historically a comfortable lag between the moment a vulnerability became public and the moment attackers could exploit it. This lag was often measured in months, but AI models can now read a vulnerability report and generate a working exploit almost instantly. They can scan the entire internet for exposed secrets at a scale no human team could match, and surface complex flaws that traditional tools missed for years. However, the same capabilities are just as available to defenders, and there is a case to be made that defenders hold the stronger hand.

Alon Schindel is the VP of AI and Threat Research at Wiz, which is a cloud security platform acquired by Google in 2026. In this episode, Alon joins Gregor Vand to discuss how AI has compressed the time from disclosure to exploit, the prospects for defenders to stay ahead, how Wiz approaches scanning and remediation, the growing strain on open source maintainers, and more.

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Automated browser testing is a foundational technology that many web developers rely on. Modern tooling lets a single test drive Chrome, Firefox, and Safari the same way, a triumph of software engineering built on years of standardization work. That standardization emerged from the interplay between open source projects, browser vendors, and standards bodies like the W3C.

David Burns is a longtime Selenium and WebDriver contributor, a veteran of Mozilla, and is currently the Head of Developer Advocacy and Open Source at BrowserStack. In this episode, David joins Josh Goldberg to discuss how open source projects, browser vendors, and standards bodies fit together, a practical philosophy of testing built around one solid test per critical path, and the risks that AI and vibe coding introduce to web development.

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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.

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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.

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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

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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.

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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.

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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.

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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.

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