Technical interviews about software topics.

Software Engineering Daily

The conversation about AI often focuses on software, automation, and the race between attackers and defenders in code. However, some of the most consequential risks lie further afield, in domains where a mistake is measured in human lives. Advanced models can now offer step-by-step guidance toward chemical and biological weapons, and militaries are already folding AI into targeting and battlefield assessments. These are no longer speculative fears confined to the AI doomer crowd. They are documented in red team disclosures, government legislation, and events unfolding on real battlefields.
Gordon M. Goldstein is an adjunct senior fellow at the Council on Foreign Relations, where he focuses on the convergence of technology and US foreign policy. He previously spent nearly a decade as a managing director at Silver Lake, and he is the author of Lessons In Disaster: McGeorge Bundy and the Path to War in Vietnam, which is a study of national security strategy and White House decision-making. In this episode, Gordon joins Kevin Ball to discuss the credibility of AI-enabled chemical and biological weapon threats, the ad hoc safeguards meant to contain them, and the rise of autonomous warfare and its implications for human control and nuclear deterrence. Gordon speaks on his own behalf, not on behalf of the Council on Foreign Relations.
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AI and the New Global Security Landscape

News feeds and recommendation systems have long relied on deep learning architectures that score each candidate item independently. As LLMs have matured, they have opened up a fundamentally different approach, where a system can reason about content the way it reasons about language. However, that power comes with a fresh set of engineering challenges around cost, scale, and evaluation.
LinkedIn recently rebuilt its news feed to treat content recommendation as a sequence modeling problem. The general approach is to predict what a user will want next, much like an LLM predicts the next token in a sentence.
Tim Jurka has worked at LinkedIn for 13 years and is currently a VP of Engineering. In this episode, Tim joins Matt Merrill to discuss how LinkedIn re-engineered its feed, how the team combines LLMs with traditional signals, managing inference costs at massive scale, steering content quality using natural language policies, and more.
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How LLMs Are Reshaping Recommendation Systems

For two decades, the cloud has been shaped by human developers writing code and managing its deployment. Now a growing share of production code is generated by LLMs with little human review. Because that code is not fully trusted, it increasingly runs in isolated, sandboxed environments. Meanwhile, AI agents are starting to operate infrastructure directly, by spinning services up and tearing them down on their own. Together these shifts raise the question of whether the cloud needs to be rebuilt for machine operators rather than humans.
Render is a cloud platform designed for application deployment, by handling scaling, self-healing, and security to reduce operations work. Render has been adapting to the AI era by building tools that let agents deploy and debug applications directly, with added guardrails and security.
Anurag Goel is the founder and CEO of Render. In this episode, Anurag joins Sean Falconer to discuss why so many teams end up rebuilding the same infrastructure on top of Kubernetes, what changes when AI agents become first-class users of infrastructure and the guardrails that shift demands, and why the economics of AI are pushing developers toward higher-level platforms that trade fine-grained control for speed and safety.
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Rebuilding the Cloud for AI Agent Code

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 dig into a wave of “runaway AI” stories, including Anthropic and OpenAI disclosing that their models accessed outside organizations during cyber evaluations, and Amazon reporting a staggering budget overrun blamed on bad agent loops. They explore why most of these incidents trace back to human decisions rather than models breaking free, and the awkward reality that today’s systems can bill you for tokens without reliably counting them.
They also talk about the “Kimi moment.” Moonshot AI‘s open weight model has closed the gap with frontier models like ChatGPT and Claude at a remarkable pace, and the hosts unpack what it means for open weight strategies and how chip scarcity is pushing Chinese labs to innovate.
As always, the episode wraps up with a few standout Hacker News threads, including a JetBrains test of a “caveman speak” skill that promised big token savings, how refactoring can cut input token costs, the release of CodePen 2.0, and a build of Doom that renders through SQL queries.
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SED News: The Kimi Moment, Runaway AI, and Tokenmaxxing

The terminal has been a constant in software development for decades. It has remained largely unchanged while everything around it transformed. However, as AI agents have become central to the developer workflow, the terminal is emerging as a natural home for agentic development, and a new category of tooling is forming around it.
Warp is a popular Rust-based terminal and agentic development environment. The company recently open-sourced its codebase and launched Oz, its cloud agent infrastructure product aimed at helping enterprises automate software development at scale.
Zach Lloyd is the co-founder and CEO of Warp, and a former principal engineer at Google where he led engineering on the Google Docs suite. In this episode, Zach joins Gregor Vand to discuss how Warp has evolved over the years, why the terminal is better suited than the IDE for agentic development, how Oz approaches the governance and auditability challenges enterprises face with AI agents, and more.
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The Terminal as an Agentic Interface

Open source software underpins virtually every modern application. That ubiquity is a superpower for developers, but it is also an expanding attack surface. Software supply chain attacks were once rare but are now happening daily, with malicious actors exploiting the trust developers place in public registries, package managers, and CI/CD pipelines.
Chainguard is a secure software supply chain platform. The company started with hardened container images and has expanded to cover domains including VMs, language libraries, GitHub Actions, and agent skills.
Matt Moore is a co-founder and CTO of Chainguard, and a veteran of Google’s open source, container, and security infrastructure work. In this episode, Matt joins Gregor Vand to discuss lessons from recent supply chain attacks, why CI/CD pipelines are now a primary attack surface, the challenge of meaningful software inventories, the EU Cyber Resilience Act, and what the arrival of Anthropic’s Mythos model means for the pace of vulnerability discovery and the urgency of patching at machine speed.
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AI-Powered Threats to the Software Supply Chain

The most useful coding agents can mutate their environments by downloading packages, writing files, and connecting to services across the network. However, that freedom also presents dangers, and promises to usher in a new wave of security threats.
Docker recently announced Docker Sandboxes, which give each agent its own isolated micro VM while preserving the familiar ergonomics of a container. A standard container shares the host’s kernel, but a micro VM emulates hardware and runs its own kernel, giving a stronger security boundary around code that cannot be trusted.
Mark Cavage is the President and COO of Docker, and he previously worked at companies including Stripe, AWS and Oracle. In this episode, Mark joins Gregor Vand for a wide-ranging conversation that includes why agents break the immutability assumptions containers were built on, how micro VMs differ from both containers and traditional VMs, and the still-unsolved challenge of giving agents scoped, trustworthy access to sensitive services and data.
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Docker and Sandboxing AI Agents

Golden Gate Ventures is one of Southeast Asia’s most established early-stage venture firms, having backed companies across the region since 2011. They have invested in companies that are now household names in the region. Examples include Gojek, which started as a motorcycle taxi service in Indonesia and grew into a super app spanning payments, food delivery, and logistics, and Grab, which began as a ride-hailing service in Singapore and similarly expanded across Southeast Asia into food delivery and financial services.
Jeffrey Paine is a co-founder and partner at Golden Gate Ventures. In this episode, Jeffrey joins Gregor Vand to discuss how the Southeast Asian startup ecosystem has evolved, why the regional copycat model is giving way to globally ambitious technical founders, what companies like Manus and Supabase reveal about Singapore’s emerging role in the global tech landscape, and what advice Jeffrey gives founders today who want to build something that matters beyond their home market.
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The Startup Scene in Southeast Asia

AI agents have shown remarkable potential to function as persistent digital assistants that are capable of monitoring data, managing communications, and taking action autonomously over long periods. OpenClaw was one of the first serious attempts to fulfill that vision, connecting frontier coding agents to messaging platforms like Slack and WhatsApp and letting them run continuously in the background. However, OpenClaw largely set aside questions of security to pursue that vision, leaving credentials exposed in the agent’s environment and giving agents broad access to data and services far beyond what any given task required.
NanoClaw is an open source project that takes a zero trust approach to agent orchestration. Rather than relying on instructions to constrain agent behavior, it isolates each agent in its own Docker container, keeps credentials entirely outside the agent’s environment, and enforces human-in-the-loop approval for sensitive actions.
Gavriel Cohen is the founder of NanoClaw and he joins Kevin Ball to discuss the security architecture behind NanoClaw, how the agent sandbox and proxy model work in practice, how agents communicate with each other and with the host orchestration process, how the project approaches context window management and long-lived agent sessions, and more.
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NanoClaw and the Rise of Personal AI Agents

AI agents have become capable of reasoning across large amounts of data, calling tools, and taking sequences of actions autonomously. These qualities make them well suited to some of the most persistent pain points in DevOps, including the on-call engineer woken at 3 AM to diagnose an incident, the build failure that takes hours to trace back to a root cause, and the operational toil of modern software delivery. Agentic DevOps is the emerging paradigm that applies these capabilities to the full software lifecycle, with the goal of matching the velocity of modern software delivery with an equally capable operational layer.
Neha Gaswamy leads Agentic DevOps at AWS and has been at Amazon for over twenty years. In this episode, she joins Matt Merrill to discuss the AWS approach to agentic DevOps, how Amazon dogfoods its own DevOps tooling, how their DevOps agent works from alarm to root cause, why determinism still matters in an agentic world, creative MCP integrations customers are building, and what the future holds for SRE engineers as agents take on more of the operational work.
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