Europe’s startup ecosystem is maturing rapidly, with companies like Revolut, Lovable, and Legora demonstrating that world-class technology businesses can be built and scaled on the continent. While the US remains the dominant force in venture-backed software as home to the largest markets, the deepest capital pools, and the most ambitious exit culture, a growing number of European founders are choosing to build at home.

Edward Keelan is a Partner at Octopus Ventures, one of Europe’s largest and most active venture capital firms, where he has spent over 16 years leading the B2B software and enterprise AI fund. His portfolio spans seed through Series C, with a focus on European founders building in AI, vertical SaaS, and enterprise software. This long-view experience gives him a rare perspective on what it takes to build enduring technology companies in Europe.

In this episode, Edward joins Elena Boroda to discuss what separates great founders from the rest, how AI is reshaping the software landscape and threatening established players, the state of the European startup ecosystem and what it needs to compete globally, and what engineers and founders should be thinking about as the industry enters a new era.

Elena Boroda focuses on GTM for developer tools and AI startups, with experience in observability and building tools for MCP servers. She is based in Berlin.

https://www.linkedin.com/in/elena-boroda

Please click here to see the transcript of this episode.

Sponsorship inquiries: [email protected]

The post The European Startup Scene appeared first on Software Engineering Daily.

React Native is an open source framework developed by Meta that allows engineers to build mobile applications for both iOS and Android using a single JavaScript codebase. The framework bridges the gap between web development and native mobile, which lets teams ship to both platforms simultaneously without sacrificing the look and feel of a truly native app.

Manjiri Moghe is a Staff Software Engineer at Coinbase, where she has spent five years building and scaling one of the world’s most demanding React Native applications. Her work spans performance optimization, reliability engineering, and the developer tooling that keeps large engineering teams moving quickly without sacrificing quality.

In this episode, Manjiri joins Josh Goldberg to discuss why React Native has become the framework of choice for high-velocity mobile teams, how Coinbase measures app health, how to handle data fetching and loading in production, how AI coding agents are changing the day-to-day workflow for mobile engineers, and more.

Josh Goldberg is an independent full time open source developer in the TypeScript ecosystem. He works on projects that help developers write better TypeScript more easily, most notably on typescript-eslint: the tooling that enables ESLint and Prettier to run on TypeScript code. Josh regularly contributes to open source projects in the ecosystem such as ESLint and TypeScript. Josh is a Microsoft MVP for developer technologies and the author of the acclaimed Learning TypeScript (O’Reilly), a cherished resource for any developer seeking to learn TypeScript without any prior experience outside of JavaScript. Josh regularly presents talks and workshops at bootcamps, conferences, and meetups to share knowledge on TypeScript, static analysis, open source, and general frontend and web development.

Please click here to see the transcript of this episode.

Sponsorship inquiries: [email protected]

The post React Native at Scale appeared first on Software Engineering Daily.

Formal methods are a branch of mathematics and computer science focused on proving the correctness of systems, and they have long promised a more rigorous foundation for software. However, their complexity has kept them confined to a small community of specialists. That is now changing as agentic AI systems take on increasingly autonomous roles. The question of how to define, enforce, and verify what those agents are allowed to do has become urgent, and automated reasoning is emerging as a critical part of the answer.

Byron Cook is a VP and Distinguished Scientist at AWS, a professor at University College London, and a program manager at DARPA. He founded the Automated Reasoning Group at AWS over a decade ago, where his team built the foundations behind products like IAM Access Analyzer, VPC Reachability Analyzer, and Bedrock Guardrails.

In this episode, Byron joins Sean Falconer to discuss how automated reasoning works and why it scales so well with AI, the rise of neurosymbolic approaches that combine formal logic with large language models, what it means to formally specify agent behavior using temporal logic, and why the convergence of agentic AI and formal methods may represent one of the most significant shifts in how software is built and verified.

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.

Please click here to see the transcript of this episode.

Sponsorship inquiries: [email protected]

The post Formal Methods as Agent Guardrails appeared first on Software Engineering Daily.

Open source software underpins nearly every modern application, including frameworks powering the most popular websites, to the libraries securing financial backend systems. However, while open source drives collaboration and innovation at a global scale, it also faces deep challenges in sustainability, community health, and long-term maintenance. Many of the world’s most critical dependencies are still maintained by just a handful of volunteers.

Abby Cabunoc Mayes leads Open Source Maintainer Programs at GitHub, and Brian Muenzenmeyer is a Principal Engineer, Node.js maintainer, and author of the book, Approachable Open Source. Abby and Brian join Josh Goldberg to talk about what it means to build and sustain healthy open source projects, how maintainers can foster inclusive communities, the evolving role of open source in the workplace, and how AI is reshaping the way we collaborate.

Josh Goldberg is an independent full time open source developer in the TypeScript ecosystem. He works on projects that help developers write better TypeScript more easily, most notably on typescript-eslint: the tooling that enables ESLint and Prettier to run on TypeScript code. Josh regularly contributes to open source projects in the ecosystem such as ESLint and TypeScript. Josh is a Microsoft MVP for developer technologies and the author of the acclaimed Learning TypeScript (O’Reilly), a cherished resource for any developer seeking to learn TypeScript without any prior experience outside of JavaScript. Josh regularly presents talks and workshops at bootcamps, conferences, and meetups to share knowledge on TypeScript, static analysis, open source, and general frontend and web development.

Please click here to see the transcript of this episode.

Sponsorship inquiries: [email protected]

The post Open Source Sustainability appeared first on Software Engineering Daily.

Vector search has risen to become a foundational tool in modern search and retrieval systems, including the RAG pipelines that power many AI applications. However, the demands on retrieval systems are growing more sophisticated, which is revealing the limits of relying on a single vector similarity score.

Vespa is a popular open source search and data serving engine. Central to Vespa’s architecture is tensor-based retrieval, which is an approach that represents data as tensors rather than simple vectors. Tensor-based retrieval enables richer mathematical operations and more flexible ranking functions that can surmount the limitations of a single vector similarity score.

Radu Gheorghe is a software engineer at Vespa with a background spanning nearly 12 years of consulting and training on Elasticsearch and Solr. In this episode, Radu joins Sean Falconer to discuss why vector similarity alone falls short in production, how tensor-based retrieval generalizes to support richer ranking functions, the trade-offs in chunking and multi-stage re-ranking architectures, and where AI search is headed next.

Full Disclosure: This episode is sponsored by Vespa.

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.

Please click here to see the transcript of this episode.

Sponsorship inquiries: [email protected]

The post Vespa AI and Surpassing the Limits of Vector Search 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 Anthropic’s controversial “Mythos” security model and what it means for vulnerability discovery at scale. They also discuss recent layoffs at Snap and Meta, and how AI investment pressures are reshaping hiring, organizational priorities, and the economics of big tech.

Gregor and Sean then zoom out to examine the massive wave of AI infrastructure spending—hundreds of billions in capex across Amazon, Google, Microsoft, and Meta, and what it signals about the future of cloud platforms, model providers, and the engineers who build on top of them. They explore the emerging entanglement between model labs and infrastructure providers, the evolving role of engineers in an AI-native world, and the growing gap between rapid AI adoption and security readiness.

Finally, they highlight standout threads from Hacker News, including creative uses of AI coding tools to revive abandoned side projects, new approaches to training smaller yet highly capable models, surprising demographic data visualizations, and even the mathematics of “cheating” at Tetris.

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.

Please click here to see the transcript of this episode.

Sponsorship inquiries: [email protected]

The post SED News: Anthropic’s Mythos, Supply Chain Hacks, and the AI Spending Surge appeared first on Software Engineering Daily.

AI coding tools have dramatically accelerated the pace of development, and the bottleneck in the software development lifecycle has shifted to code validation and testing. However, the conventional tools and workflows that QA teams have relied on were not designed for a world where a single engineer can generate thousands of lines of code in a day.

SmartBear is a software quality platform spanning test automation, API lifecycle management, and observability. The company recently launched an AI-native QA platform called BearQ, which deploys autonomous agents that explore web applications, learns their structure and behavior, and authors and maintains test cases continuously.

Fitz Nowlan is the VP of AI and Architecture at SmartBear and the co-founder of Reflect, which is a web testing platform acquired by SmartBear in 2024. In this episode, Fitz joins Kevin Ball to discuss why web UI testing is uniquely challenging, how BearQ’s multi-agent architecture coordinates exploration and testing, why test data management becomes a hard distributed systems problem at scale, and what agentic development means for the future of QA.

Full Disclosure: This episode is sponsored by SmartBear.

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.

Please click here to see the transcript of this episode.

Sponsorship inquiries: [email protected]

The post SmartBear and Multi-Agent QA appeared first on Software Engineering Daily.

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.

Please click here to see the transcript of this episode.

Sponsorship inquiries: [email protected]

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.

​

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.

​

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.

Please click here to see the transcript of this episode.

Sponsorship inquiries: [email protected]

The post Open-Weight AI Models appeared first on Software Engineering Daily.

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.

Please click here to see the transcript of this episode.

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.

Please click here to see the transcript of this episode.

Sponsorship inquiries: [email protected]

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