Operations | Monitoring | ITSM | DevOps | Cloud

Most reliability engineering happens after something breaks. Will Wilson thinks that's the wrong place to be. As co-founder and CEO of Antithesis, the autonomous testing platform that just raised $105M in a Series A led by Jane Street, Will has spent years building the infrastructure to catch failure modes before they ever reach production. His starting point is uncomfortable: the testing practices most teams rely on are structurally incapable of finding the bugs that cause real incidents.

Burnout doesn't announce itself. For Stephen Townsend, SRE team lead and host of the Slight Reliability podcast, it crept in over months of mounting pressure on a massive transformation program, and announced itself overnight with an inability to sleep. In this episode, Stephen shares his personal burnout story with rare honesty: the physical symptoms he dismissed, the org structure that left him without autonomy, and the full year it took to recover.

In this episode, Swizec Teller, author of the bestselling Scaling Fast, makes a bold claim: code is cheap, reliability is not. As AI coding tools accelerate feature development, the real competitive advantage shifts to operating systems reliably in production. We explore the hidden complexity of SRE work, the addictive nature of agentic coding, and why ownership — not automation — remains at the core of modern software engineering.

Many companies don’t invest in incident management until something goes wrong. commonsku took a different path. In this episode of Humans of Reliability, Sylvain sits down with Dileshni Jayasingha, VP of Technology at commonsku, to talk about what it really takes to introduce incident management in a mature, profitable SaaS that had never formalized it. From rolling out observability and incident tooling to practicing internal status updates before going public, Dileshni shares how her team built the right muscles before they were forced to.

Shipping systems powered by LLMs would be hard enough if the models stayed the same. But in reality, they don’t. Models get updated and deprecated at a pace traditional software wouldn’t. All while teams are still expected to hit reliability targets that look a lot like traditional SLAs.

GenAI demos are easy. Production is where everything breaks. In this episode, Eduardo Ordax, Principal GTM GenAI at AWS, breaks down what actually stops companies from shipping reliable AI systems, and why the real blockers have little to do with technology.

A major shift just hit SRE-focused AI. Gemini 3 Pro edged out OpenAI’s models and outperformed them across every single SRE task we tested. In this Rootly AI Labs episode, Sylvain Kalache and Laurence Liang break down.

Learn more: https://rootly.com/blog/the-hidden-costs-of-immature-incident-management

In this episode, Julien Simon, longtime voice in the open-source ML world, reminds us that even in the era of GenAI, reliability fundamentals haven’t changed. Julien breaks down why calling “the same model” from different providers can produce wildly different results, how deployment choices introduce hidden variability, and why reliability teams need to think of LLM systems as distributed systems.