Dev Interrupted

An interview based podcast hosted by Andrew Zigler. Show notes usually have a summary and relevant links.

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Episodes

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How LinearB helps Kraken find hidden bottlenecks across thousands of engineers | Nik Sudan

30 Jun 2026

Challenges in transitioning AI pilots to production include over-reliance on proof-of-concepts, misalignment with scalable systems, and cultural gaps in engineering practices, requiring disciplined strategies like throwaway code, rigorous testing, early user feedback, and systemic data-driven metrics to align AI with organizational goals through collaboration and infrastructure improvements.

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Your developers are the attack surface now and vibe coding as a vulnerability | Tanya Janca

23 Jun 2026

Modern software security grapples with autonomous agent-driven deployment risks, AI-generated code vulnerabilities like "vibe coding," expanded OWASP Top 10 2025 threats, supply chain weaknesses, and the urgent need for collaborative, proactive security practices, cultural shifts, and developer education over fragmented tools and outdated methodologies.

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Your SDLC needs a productivity context engine

16 Jun 2026

Challenges in AI adoption within engineering teams include overwhelmed staff, resource constraints, uneven productivity gains, declining code quality, rework from generated code, and rising costs, necessitating strategic focus on quality assurance, process optimization, AI-native workflows, metrics for ROI, and balancing automation with human oversight.

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How to harness your dragon with Fable, tech leaders turn to model routing, and coping with AI rockstars

12 Jun 2026

AI-driven bots now dominate over half of internet traffic, raising security risks, undermining content quality and traffic metrics, while challenges include AI-generated content diversity loss, rising costs, technical debt, and the need for human oversight, sustainable practices, and skilled engineers to manage AI tools effectively.

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All software is an optimization of tokens and time (and speed is still the moat) | AMDs Anush Elangovan

9 Jun 2026

The evolution of AI from basic orchestration to autonomous, self-improving agentic systems, exemplified by AMD's Rockhamstack platform, highlights open-source collaboration, accelerated software development via multi-agent systems, challenges in intent alignment, and the need for cultural adaptation, abstraction, and portable ecosystems to scale innovation while balancing automation with human oversight.

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Friday Deploy 6/5 Podcast

5 Jun 2026

The text examines AI's disruptive potential on SaaS and job security, weighing its near-term limitations against productivity gains, emphasizing domain expertise's critical role, and highlighting challenges like unverified AI outputs, SDLC inefficiencies, and the need for structured practices to ensure reliability in AI-assisted workflows.

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How to turn your 1000x engineer into a 10x everyone | LinkedIns Karthik Ramgopal

2 Jun 2026

AI leadership enhances team capabilities through upskilling and shared AI technologies like context-aware systems and long-term memory, emphasizing collaborative human-AI workflows, open standards, cultural shifts in learning, and holistic integration beyond coding to adapt to evolving job markets.

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The cost of intelligence will never be this cheap again, the failure of intensive specs, and how bots disguise inefficient workflows

29 May 2026

Evolving AI trends highlight model updates, shifts toward local deployment to cut cloud costs, challenges in AI quality and governance, and the need for strategic model routing, infrastructure improvements, and collaboration between AI and human expertise.

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Observability is your profit center now | Honeycombs Christine Yen

26 May 2026

AI systems' growing complexity demands advanced observability practices to bridge technical-business gaps, foster cross-functional collaboration, and enable adaptive, data-driven decision-making in autonomous, non-deterministic environments.

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What Google didnt announce at I/O, defining dark flow and ignoring your first brain to build your second one

22 May 2026

AI agents are being integrated into real-world systems like communication and blockchain, raising privacy concerns and challenges in handling complex tasks, while critiques focus on the need for specialized tools over generalized AI, alongside advancements in processing scale and emerging concepts like autonomous decision-making and "second brain" frameworks.

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