DevOps Paradox

#337: Time series databases have become essential infrastructure for the physical AI revolution. As automation extends into manufacturing, autonomous vehicles, and robotics, the demand for high-resolution, low-latency data has shifted from milliseconds to nanoseconds. The difference between a general-purpose database and a specialized time series solution is the difference between a minivan and an F1 car - both will get around the track, but only one is built for the demands of real-time operational workloads.
The open source business model continues to evolve in unexpected ways. While companies like Elastic and Redis have seen hyperscalers fork their projects, a new partnership paradigm is emerging. Amazon Web Services now pays to license InfluxDB and offers it as a managed service, signaling a shift toward collaboration rather than competition. This approach benefits everyone: vendors maintain development velocity, cloud providers get workloads on their platforms, and customers receive better-supported products.
Evan Kaplan, CEO of InfluxData, joins Darin and Viktor to discuss the trajectory from observability metrics to physical world instrumentation, why deterministic models matter more than probabilistic ones when your robot might run over your cat, and what it takes to build a sustainable open source company over a decade-plus journey.
 
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X: https://x.com/evankaplan
LinkedIn: https://www.linkedin.com/in/kaplanevan/
 
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#336: The workplace is on the verge of a transformation as significant as the Industrial Revolution. Just as Bring Your Own Device policies emerged after the iPhone disrupted corporate mobile standards, we are now entering an era where employees may arrive with their own AI teams in tow. The question is no longer whether AI will change hiring and employment - it is how quickly companies will adapt before being left behind by competitors who embrace this shift.
Current AI productivity gains remain largely individual rather than organizational. Writing code twice as fast means nothing if the deployment pipeline stays the same speed. But within five to ten years, entire industries face disruption - from primary care physicians to transportation to knowledge work. Companies clinging to restrictive AI policies today risk driving away top talent who have already integrated these tools into their workflows. The intellectual property implications alone - who owns an AI stack trained on company processes when an employee leaves - will require entirely new frameworks for employment law.
Darin and Viktor explore these scenarios through the lens of a hypothetical job interview where a candidate brings their own team of AI agents. The conversation surfaces uncomfortable questions about compensation models, corporate governance, and whether we are witnessing the emergence of a new kind of talent that blends human expertise with digital capabilities.
 
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#335: Observability tools have exploded in recent years, but most come with a familiar tradeoff: either pay steep cloud vendor markups or spend weeks building custom dashboards from scratch. Coroot takes a different path as a self-hosted, open source observability platform that prioritizes simplicity over flexibility. Using eBPF technology, Coroot automatically instruments applications without requiring code changes or complex configuration, delivering what co-founder Peter Zaitsev calls opinionated observability—a philosophy of less is more that aims to reduce cognitive overload rather than drowning users in endless metrics and dashboards.
The conversation explores how Coroot differentiates itself in a crowded market with over a hundred observability vendors. Rather than competing head-to-head with cloud giants like Datadog and Dynatrace, Coroot focuses on developers who need answers fast without building elaborate monitoring systems. The platform combines systematic root cause analysis with AI-powered recommendations, using deterministic methods to trace how errors propagate through microservices before handing off to LLMs for actionable fix suggestions.
Darin and Viktor dig into Coroot's business model with Peter, examining why the company chose Apache 2.0 licensing instead of more restrictive options, and how staying bootstrapped with minimal angel funding allows them to play the long game without pressure to chase every hype cycle.
 
Peter's contact information:
X: https://x.com/PeterZaitsev
Bluesky: https://bsky.app/profile/peterzaitsev.bsky.social
LinkedIn: https://www.linkedin.com/in/peterzaitsev/
 
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#334: The debate over whether AI saves developers time misses a fundamental truth: coding was never the hardest part of software development. Writing code is mechanical work - the real challenges have always been understanding problems, designing solutions, communicating with stakeholders, and navigating organizational complexity. AI is now forcing a reckoning with this reality, pushing developers at every level to reconsider what skills actually matter.
The traditional separation between architects who design and developers who implement is breaking down. AI enables a return to something like pair programming, where the person thinking through problems can now work alongside a fast executor without the old bottleneck of slow human typing. This shift means developers need stronger communication skills - the ability to explain technical decisions to non-technical stakeholders and translate business requirements into technical direction. For juniors, the opportunity is unprecedented: you can upskill faster than ever in the history of software, but only if you balance building things with actually understanding how they work.
Darin and Viktor explore what this means for developers at every career stage, from juniors who should focus on fundamentals and end-to-end understanding, to seniors who are becoming more like editors and supervisors of AI-generated work. The developers who will thrive are those who combine real experience with a willingness to embrace change - and that combination has always been the winning formula.
 
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#333: Pete Hunt, CEO of Dagster and early React team member, explores the evolution from Facebook's early React development through trust and safety infrastructure at Twitter, to building modern data orchestration tools. The conversation reveals how similar infrastructure problems plague every industry - whether you're launching rockets or managing porta-potties, the core challenges remain consistent: late data, quality issues, and mysterious errors that require both automated solutions and human oversight.
The discussion dives into the technical realities of scaling systems, from the microservices complexity trap to the current AI adoption wave. Hunt shares candid insights about leadership challenges, including how well-intentioned technology recommendations can backfire, and why most data projects fail despite sophisticated multi-agent orchestration. The conversation touches on career advancement pressures that drive unnecessary complexity and the importance of focusing on actual user adoption rather than technical sophistication.
This episode features Pete Hunt in conversation with hosts Darin and Viktor, covering everything from regular expression nightmares to the future of data infrastructure and the lessons learned from building products that people actually use.
 
Pete's contact information:
X: https://x.com/floydophone
LinkedIn: https://www.linkedin.com/in/pwhunt/
 
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