Technical interviews about software topics.

Software Engineering Daily

Codebases in enterprise organizations often span hundreds of thousands, or even millions, of lines of code. In many cases, changes must follow established patterns precisely, or risk introducing costly security or operational issues. A common challenge with AI coding tools is that they struggle at this scale: LLM context windows cover only a fraction of a large codebase, and output quality degrades the longer an agent runs.
Blitzy is an autonomous software development platform for the enterprise. It starts by understanding your entire codebase, building a knowledge graph that acts as a shared source of truth. From a well-defined spec that engineers review and approve, it orchestrates specialized agents that work in parallel to build, review, and test code autonomously at scale. The result is a pull request that engineers can then review and merge
Sid Pardeshi is the CTO and co-founder of Blitzy, and he previously worked at NVIDIA. In this episode, he joins Kevin Ball to discuss why large codebases break today’s coding agents, how a knowledge graph extends what models can reason about, how a reviewable plan keeps specialized agents aligned, and where autonomous software development is headed.
Full Disclosure: This episode is sponsored by Blitzy
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Blitzy and Autonomous Software Development for Enterprise

In recent years, the main impact of AI has been on the digital world, and most notably software engineering. However, its longer-term effect on the physical world may prove even more profound, reshaping how cars drive themselves, how factories run, and how robots move through homes and warehouses.
World models are at the center of the movement to bring AI into the physical space. These models take in observations of the current state of the world and predict what comes next, which lets a robot plan its next move or a car anticipate the road ahead. NVIDIA has been building a family of these models called Cosmos, and sits at the frontier of physical AI and robotics.
Amit Goel has been at NVIDIA for fifteen years, and he is currently the Head of Robotics Ecosystem and Edge AI Product. In this episode, Amit joins Matt Merrill to discuss what separates physical AI from digital AI, how world models plan and predict, the two-tower architecture that lets a robot reason and act at once, why training on video demanded a new data pipeline, and how any developer can start building in this space.
Free Physical AI Courses: https://nvda.ws/4xqCfo9
Cosmos: https://nvda.ws/4zJ4iRj
GR00T Teleop + LeRobot: https://huggingface.co/blog/nvidia/nvidia-isaac-teleop-and-gr00t17-in-lerobot

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Physical AI at NVIDIA

Software testing is widely understood as essential to building reliable systems, and it’s one of the hardest things to get right. A fundamental issue is that tests are good at confirming the behavior a team expects, but the most damaging failures tend to hide in the edge cases no one thought to check during development. Those blind spots are only growing as AI produces more code that no human has closely reviewed.
Antithesis is a software verification company built around an approach called deterministic simulation testing. The platform runs a customer’s software in a fully controlled environment, exploring thousands of different paths through it at runtime. Crucially, the paths aren’t pre-specified and they’re not merely the expected cases. In this way, it can surface rare, damaging bugs that normal testing would never catch.
Carl Sverre is on the Red Team at Antithesis and he also leads much of the company’s AI strategy. In this episode, Carl joins Sean Falconer to discuss the Antithesis approach to software verification, why reliability tooling has been chronically underinvested, what makes stateful and distributed systems so hard to test, how deterministic simulation testing works under the hood, and much more.
Full Disclosure: This episode is sponsored by Antithesis
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Software Verification in the Age of Coding Agents

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 episode, Gregor and Sean cover a wave of new product drops, including Jev from TypeSafe AI, a model that returns scored, type-safe structured responses for machine-to-machine tasks like routing and classification rather than human-style chat. They also dig into Meta‘s Muse, a personal AI assistant with a companion keychain device, and the upcoming IPO of Nscale, a former crypto-mining outfit turned “neocloud” inference provider whose filing leans heavily on a single unnamed customer.
The main topic examines the tension around AI risk, frontier lab slowdowns, and the conspiracy theories they’ve spawned. Gregor and Sean unpack Dario Amodei’s essay on pacing AI development, the awkward timing of major model launches days later, and whether calls to slow down are genuine safety concerns or a way for incumbents to protect their market position, weighing it all against the long history of how society has absorbed transformative technologies.
As always, the episode wraps up with a few standout Hacker News threads, including a labor-of-love effort to restore the original cut of a classic film, and a vibe-coded local AWS testing harness that raised a few eyebrows in the comments.
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SED News: Jev, Meta’s Muse, and the AI Slowdown Paradox

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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Steve Jobs in Exile

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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Security in the Age of Instant Exploits

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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The State of Browser Testing

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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Cory Doctorow on AI, Work, and Power

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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Chroma and Agentic Retrieval

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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