More The AI Native Dev episodes
Published 20 Jan 2026
Duration: 1218
The effectiveness of code development is significantly influenced by context, with impact varying from 33% to 80%, and can be improved through the strategic use of coding agents and automation.
Most AI agents fail because you're using them wrong. Heres what actually works in production. In this episode, Simon Maple sits down with Itamar Fried...
The podcast emphasizes the critical role of context in code development, noting that it significantly impacts the effectiveness of coding agents, which can perform anywhere from 33% to 80% effectively. It examines how multi-agent systems can improve productivity and code quality by assigning distinct rolessuch as coding, reviewing, and planningto individual agents, enabling them to work either in parallel or sequentially. Tools like the OpenHands SDK are introduced as platforms for building complex workflows that involve these agents.
The discussion covers various aspects of agent architecture, including the use of diverse vendors to replicate the diversity of human teams and the importance of context management in minimizing errors. It also highlights tasks well-suited for automation, such as dependency management and code upgrades, while underscoring the need for human oversight in areas requiring verification and trust-building. Additionally, the content outlines a structured approach to addressing recurring issues, such as outdated Java versions, through manual investigation, script-based automation, scaling in parallel, and integration with developers.
12 May 2026 "AI Doesn't Stand for Artificial Intelligence" Venkat Subramaniam's Take Will Change How You Think About It
AI in software development requires balancing its speed and automation with human accountability for safety and ethics, emphasizing rigorous quality practices, community-driven adoption, critical thinking, and oversight to ensure AI complements rather than replaces human judgment.
5 May 2026 The Creator of Spring Thinks You Can't Code Serious Software With AI
Integrating AI into enterprises via HTTP calls and existing infrastructure requires balancing language agnosticism, deterministic frameworks like GOAT, Java/Kotlin over Python for reliability, and prioritizing explainability, human oversight, and alignment with business logic over overreliance on AI for simple tasks.
28 Apr 2026 What OpenAI, Stripe & ElevenLabs Devs Do Differently Now | AI Native Dev
The text examines challenges in integrating AI into software workflows, highlights AI-native practices like Stripe's Minions automating code tasks, emphasizes balancing human oversight with automation, and explores future trends in agent-native engineering, specialized models, open-source tools, and ethical considerations in AI-driven development.
21 Apr 2026 Logan Kilpatrick on Who Ships AGI, DeepMind and the Problem With More Software
Software developers must adapt to AI's complexity by bridging tools with real-world applications, mastering context engineering, shifting toward high-level design and system architecture, and navigating fragmented ecosystems as AGI emerges as an integrated system rather than a standalone model.
14 Apr 2026 Everything 100 Episodes Revealed About AI Native Dev
AI in software development shifts from code-centric practices to context- and specification-driven approaches, with humans prioritizing decision-making and oversight while AI handles implementation, but challenges like non-human-readable code, alignment with team practices, and contextual accuracy remain critical.