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 Sponsorship inquiries: [email protected] The post Blitzy and Autonomous Software Development for Enterprise appeared first on Software Engineering Daily.

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 Sponsorship inquiries: [email protected] The post Physical AI at NVIDIA appeared first on Software Engineering Daily.

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 Sponsorship inquiries: [email protected] The post Software Verification in the Age of Coding Agents appeared first on Software Engineering Daily.

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. Sponsorship inquiries: [email protected] The post SED News: Jev, Meta’s Muse, and the AI Slowdown Paradox appeared first on 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. Sponsorship inquiries: [email protected] The post Steve Jobs in Exile appeared first on Software Engineering Daily.