Software Engineering Daily

AI coding agents can already reach well beyond the code in front of them. Through connections to tools like Slack, Jira, and Sentry, an agent can pull in issues, discussions, and production signals from across an organization. However, that context is scattered across many separate systems, with no shared map of how it all connects and no common controls over what an agent is allowed to touch. For a large enterprise, stitching that together while staying compliant, auditable, and sometimes fully cut off from the internet is a hard problem.

GitLab runs one of the major platforms where software gets built, hosted, and shipped. That gives it a wide view of everything around the code, which shapes how it is approaching the agent era. The bet is that AI is most useful when it can draw on the full context of a project and stay under enterprise control, rather than acting as a point tool bolted onto a single repository. Getting there means wrestling with hard questions about governance, agent identity, and how much autonomy to hand over.

Tim Zallmann is the VP of Engineering at GitLab. In this episode, he joins Gregor Vand to discuss bringing AI to the full software development lifecycle, why running agents under enterprise control matters for governance and auditing, how to track which human is behind an agent’s actions, the realities of shipping air-gapped software, and how the space is shifting as new players move into code hosting.

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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 episode, Gregor and Sean cover the PHP remote code execution flaw that hit an estimated 70 percent of the internet, the recording device an OpenAI employee hid in a conference room, and a busy run of deals including Chroma joining Replit, Cloudflare taking Deno, and Supabase acquiring Turso.

The main topic tours the AI graveyard, sorting recent product failures into patterns: features swallowed by bigger platforms, runaway hits too costly or risky to sustain, interfaces made redundant by agents, and hardware that never materialized.

As always, the episode wraps up with a few standout Hacker News threads, including why washing machines took 50 years to catch on, a location-aware local Wikipedia, and a Figma quirk that traces back to the Windows Numlock key.

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Demis Hassabis founded DeepMind in 2010, before machines could reliably tell a cat from a dog. He believed that artificial general intelligence was achievable, and that it should be built safely and deliberately. Along the way, Hassabis shared the 2024 Nobel Prize in Chemistry for AlphaFold, the system that cracked the fifty-year problem of predicting protein structures. Fifteen years on, the LLM race has erupted across rival labs and countries, and AI has become one of the most consequential and contested technologies of the era.

Sebastian Mallaby is a two-time Pulitzer Prize finalist and a longtime chronicler of the collision between innovation and capital, from hedge funds to central banking to Silicon Valley. His latest book, The Infinity Machine, is a portrait of Hassabis and DeepMind built on more than thirty hours of candid conversation, beginning just as ChatGPT set off the AI frenzy, and drawing as well on Hassabis’s estranged cofounder, his rivals, and academic pioneers who now fear for human survival. Mallaby is also the Paul A. Volcker Senior Fellow for International Economics at the Council on Foreign Relations.

In this episode, Sebastian joins Kevin Ball to talk about Demis Hassabis and the founding of DeepMind, the pioneering breakthroughs that followed, and how the field arrived at the current moment in AI.

Photo by Jeremy Sherlick

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The rapid rise of AI has set off a flurry of legal and regulatory activity. There is no single AI law to follow. Instead, there are overlapping circles of regulation, where privacy, cybersecurity, consumer protection, employment, and IP laws all continue to apply alongside newer AI-specific rules. Europe has its EU AI Act, but the U.S. has no federal equivalent yet. In its place is an emerging patchwork of more than 150 state AI laws, and the picture shifts every few months as new legislation and guidelines arrive. For engineers and tech companies, it can feel impossible to know what applies and where the real risks lie.

Shannon Yavorsky is a data lawyer who co-leads the Cyber, Privacy and Data Innovation group and the AI practice at the international law firm Orrick. She advises companies across sectors from health technology to financial services on how to develop and deploy AI responsibly amid this evolving regulatory environment.

In this episode, Shannon joins Kevin Ball to make sense of that landscape. They discuss how to map the concentric circles of regulation that actually apply to you, the risk spectrum separating a low-stakes manufacturing tool from a high-stakes system making loan or healthcare decisions, the problem of shadow AI and why recorded meetings can become a litigation liability, how to build an AI governance program, and more.

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Over the past couple of years, companies raced to get their employees using AI, often encouraging them to lean on it as much as possible. However, as AI coding tools spread through engineering teams, many companies watched their AI bills explode. That kind of runaway cost growth can threaten a business, so the new challenge is capturing the productivity gains from AI without the spending spiraling out of control.

Databricks is a data and AI company that hit this problem early, running AI tools across thousands of its own engineers. Rather than clamp down on usage, the team built a set of practical ways to manage the cost, from taking advantage of steadily falling model prices to automatically routing each request to the cheapest model that can still do the job well.

Patrick Wendell is a co-founder of Databricks, where he leads engineering teams and has driven much of the company’s internal AI adoption. He recently wrote a viral blog post on managing AI coding costs that reached the top of Hacker News. In this episode, Patrick joins Kevin Ball to discuss why AI costs suddenly exploded, the main levers companies have to bring them under control, the trade-offs between managing costs on the server versus in the developer’s tools, and why the next big challenge is giving AI the right context about how a company actually works.

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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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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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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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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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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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