Artificial intelligence is transforming warfare faster than the legal and ethical frameworks designed to govern it. Militaries around the world are deploying AI-powered decision support systems to identify targets, assess proportionality, and direct weapons. The gap between what is technically possible and what international law can effectively regulate is widening by the day.

Yuval Shany is a law professor at Hebrew University of Jerusalem and a research fellow at the Oxford Ethics in AI Institute. He also served on the UN Human Rights Committee, where he first encountered the legal and ethical challenges posed by autonomous weapons systems. His research focuses on the intersection of international humanitarian law, human rights, and emerging military technologies.

In this episode, Yuval joins Matt Merrill for a wide-ranging conversation. They cover topics including how close we are to fully autonomous lethal weapons, the accountability gap that AI-mediated warfare creates, and what lessons software engineers can draw from these challenges when building consequential AI systems of any kind.

Matt Merrill is a software engineering leader with over 20 years of experience building and scaling software teams across enterprise and product-focused organizations. His background is in backend development, cloud architecture, and distributed systems design. He currently architects and delivers software products and leads a team of engineers at DEPT® Agency. You can learn more about his work at code.theothermattm.com.

 

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The post The Ethics of Autonomous Weapons Systems appeared first on Software Engineering Daily.

Open-weight models are AI systems whose trained parameters are publicly released, which allows developers to run, fine-tune, and deploy them independently rather than accessing them only through a hosted API. While closed-weight models from companies like OpenAI or Anthropic are delivered as managed services, open-weight models give organizations direct control over how the models are deployed and used. Importantly, the performance of these models is steadily improving and they’ve become credible alternatives for production workloads, with advantages in customization and data privacy.

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Fireworks AI is building a platform focused on serving and customizing open-weight models at scale. The platform includes optimized inference infrastructure, multi-hardware support across NVIDIA and AMD, and reinforcement fine-tuning capabilities.

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Benny Chen is a Co-Founder of Fireworks AI. In this episode, he joins Gregor Vand to discuss his path from Meta’s ML infrastructure teams to co-founding Fireworks AI, why open-weight models are becoming increasingly competitive, how custom kernels and speculative decoding improve performance, reinforcement fine-tuning, and much more.

Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.

 

 

 

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AI coding tools have gone from novelty to core infrastructure in under three years. Today, many devs use AI daily, a substantial share of new code is AI-generated, and expectations for automation are rapidly increasing.

Sonar is a company specializing in analysis of code quality and security, and they recently released a new survey – the State of Code Developer Survey. The survey provides a deep examination of how developers are using AI in real production environments, and where the real-world gaps and risks still exist.

Chris Grams is the CVP of Corporate Marketing at Sonar, and Manish Kapur is the VP of Product Marketing and Developer Relations at Sonar. In this episode, they join Matt Merrill to discuss what the survey reveals about AI-assisted development, why 96% of developers still don’t fully trust AI-generated code, how deterministic verification layers fit into agent-driven workflows, and what engineering leaders should prioritize as AI shifts from experimentation to production infrastructure.

Matt Merrill is a software engineering leader with over 20 years of experience building and scaling software teams across enterprise and product-focused organizations. His background is in backend development, cloud architecture, and distributed systems design. He currently architects and delivers software products and leads a team of engineers at DEPT® Agency. You can learn more about his work at code.theothermattm.com.

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Sponsorship inquiries: [email protected]

The post Hype and Reality of the AI Coding Shift appeared first on Software Engineering Daily.

AI agents are increasingly capable of reasoning and performing autonomous work over long periods. However, as agents take on more complex, longer-horizon tasks, keeping them supplied with the right information becomes the core engineering challenge. The industry is moving away from pre-loading context upfront toward a model where agents dynamically navigate and retrieve the data they need, when they need it.

Redis is approaching context management using a context engine, which is an architecture built around four pillars: on-demand context retrieval, data that is always current, fast retrieval, and a memory layer that improves over time. In practice this means building materialized views of data with a semantic layer on top, rather than giving agents direct access to production databases. A memory system sits alongside this, extracting and compacting information asynchronously as the agent works.

Simba Khadder leads AI strategy at Redis, and he previously co-founded the feature store platform FeatureForm, which was acquired by Redis in 2025. In this episode, Simba joins Kevin Ball to discuss why context has become the defining challenge in agentic AI, how context engines differ from traditional RAG architectures, how materialized views underpin reliable agent data pipelines, how memory systems can improve through async extraction and compaction, and how engineering teams need to adapt their practices as AI-driven development accelerates.

Full Disclosure: This episode is sponsored by Redis.

Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.

 

 

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The post Unlocking the Data Layer for Agentic AI with Simba Khadder appeared first on Software Engineering Daily.

AI agents are evolving from individual productivity tools into distributed systems components inside enterprises. The next frontier is coming into focus, and it involves large-scale ecosystems of collaborating agents embedded directly into business processes. However, multi-agent architectures introduce serious challenges around orchestration, state management, trust, governance, and observability.

Eric Broda is a veteran of the software industry, and he’s the co-author of the new O’Reilly book, Agentic Mesh: The GenAI-Powered Autonomous Agent Ecosystem.

In this episode, Eric joins Sean Falconer to discuss the architectural challenges of deploying agents as core infrastructure, how distributed computing principles apply to multi-agent systems, why trust and explainability are foundational, and what enterprises may look like as agents become full participants in business processes.

Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.

 

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Sponsorship inquiries: [email protected]

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Observability emerged from the need to understand complex software systems, and involves tracking metrics, logs, and traces so engineers can detect and diagnose problems before they affect users. However, modern applications often encompass hundreds of services, containers, and dependencies, generating more observability data than dashboards and alerts alone can effectively surface.

New Relic is a leading observability platform, with a history that spans the full arc of modern software operations. Today they are working to apply AI to move observability beyond passive monitoring toward active intelligence, where systems can surface what matters, reduce alert noise, and ultimately take autonomous action before problems reach engineers or users.

Nic Benders is the Chief Technology Strategist at New Relic, where he has worked for 16 years. In this episode, Nic joins Lee Atchison to discuss the evolution of observability from dashboards and alerts to AI-driven intelligence, how LLMs and statistical tools work together to surface meaningful signals from massive datasets, the emerging challenge of observing AI systems themselves, and what the rise of AI means for the future of software engineering as a profession.

This episode is hosted by Lee Atchison. Lee Atchison is a software architect, author, and thought leader on cloud computing and application modernization. His best-selling book, Architecting for Scale (O’Reilly Media), is an essential resource for technical teams looking to maintain high availability and manage risk in their cloud environments. Lee is the host of his podcast, Modern Digital Business, an engaging and informative podcast produced for people looking to build and grow their digital business with the help of modern applications and processes developed for today’s fast-moving business environment. Listen at mdb.fm. Follow Lee at softwarearchitectureinsights.com, and see all his content at leeatchison.com.

 

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The post New Relic and Agentic DevOps with Nic Benders appeared first on Software Engineering Daily.

Mobile apps have become a primary interface for critical services, including banking, payments, and healthcare. Unlike web applications, much of the logic and intellectual property in a mobile app lives directly on the user’s device, which is an environment the developer doesn’t control. That makes mobile apps uniquely exposed to reverse engineering, runtime manipulation, and fraud.

As more critical functionality shifts to mobile, the need to harden apps against sophisticated attackers continues to grow. Guardsquare builds tools to protect and test mobile applications against both static and dynamic threats. Its platform has features including layered code obfuscation, runtime application self-protection, mobile-specific security testing, threat monitoring, and API attestation.

Ryan Lloyd is the Chief Product Officer at Guardsquare. In this episode, he joins Gregor Vand to discuss why mobile security differs from desktop and web security, how reverse engineering tools have evolved, the role of compiler-based obfuscation and runtime protections, common mobile app vulnerabilities, and how LLMs are reshaping the attacker landscape.

Full Disclosure: This episode is sponsored by Guardsquare.

Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.

 

 

 

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The Model Context Protocol, or MCP, gives developers a common way to expose tools, data, and capabilities to large language models, and it has quickly become an important standard in agentic AI. FastMCP is an open source project stewarded by the team at Prefect, which is an orchestration platform for AI and data workflows. The FastMCP project builds on MCP to provide high-level, ergonomic abstractions for Python developers to rapidly build and deploy MCP servers and applications.

Jeremiah Lowin is the founder and CEO of Prefect, and Adam Azzam is the VP of Product at the company. In this episode, Jeremiah and Adam join Gregor Vand to discuss the origin story of FastMCP, the three pillars of the framework, the architectural decisions behind FastMCP 3.0, and much more.

Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.

 

 

 

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The post FastMCP with Adam Azzam and Jeremiah Lowin appeared first on Software Engineering Daily.

SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry.

In this episode, they cover the resurgence of ARM and CPUs as serious compute infrastructure for running local AI agents, a supply chain attack on LiteLLM that exposed API credentials across thousands of developer environments, and the arrival of OpenCode as a fully open source alternative to Claude Code and Codex. They also discuss the diverging strategies of Anthropic and OpenAI following the Pentagon contract controversy, and what it signals about where each company is positioning itself in the enterprise and government markets. Gregor and Sean then dive deep into what the AI coding boom actually means for shipping software.

Finally, they highlight standout threads from Hacker News, including Doom running entirely over DNS, the psychology of seafoam green in Cold War-era control rooms, a Tesla Model 3 computer assembled from salvaged crash components, and Apple’s quiet discontinuation of the Mac Pro.

Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.

 

 

Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.

 

 

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Sponsorship inquiries: [email protected]

The post SED News: OpenCode, AI Code vs. Shipped Code, and the LiteLLM Breach appeared first on Software Engineering Daily.

FreeBSD is one of the longest-running and most influential open-source operating systems in the world. It was born from the Berkeley Software Distribution in the early 1990s, it has powered everything from high-performance networking infrastructure to game consoles and content delivery networks. Over three decades, it has evolved through major architectural shifts, from symmetric multiprocessing and kernel scalability to modern storage systems and predictable release engineering.

John Baldwin has spent more than 25 years working on FreeBSD as a developer, contributor, and consultant. In this episode, John joins Gregor Vand to discuss the origins of FreeBSD, how its governance model differs from other open-source projects, its role inside systems like Netflix’s CDN and the PlayStation 4, the challenges of maintaining a 30-year-old codebase, and much more.

Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.

 

 

 

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Modern cloud-native systems are built on highly dynamic, distributed infrastructure where containers spin up and down constantly, services communicate across clusters, and traditional networking assumptions break down. Linux networking was designed decades ago around static IPs and linear rule processing, which makes it increasingly difficult to achieve scale in Kubernetes environments. At the same time, modifying the Linux kernel to keep up with these demands is slow, risky, and impractical for most organizations.

The Extended Berkeley Packet Filter, or eBPF, is a Linux kernel technology that allows sandboxed programs to run safely inside the kernel without modifying kernel source code or loading kernel modules. Cilium is an open-source, cloud-native networking platform that’s built on eBPF, and provides, secures, and observes connectivity between workloads in Kubernetes and other distributed environments.

Bill Mulligan is a maintainer in the Cilium ecosystem and a member of the team at Isovalent, the company behind Cilium. He joins the show with Gregor Vand to discuss how eBPF works under the hood, why Cilium has become one of the most widely adopted Kubernetes networking projects, and how the future of cloud-native infrastructure is being reshaped by programmable kernels.

Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.

 

 

 

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Sponsorship inquiries: [email protected]

The post Cilium, eBPF, and Modern Kubernetes Networking with Bill Mulligan appeared first on Software Engineering Daily.