Oliver Ries

I’ve been thinking a lot about accountability—and how misunderstood it often is, especially in fast moving teams. Too often, it gets lumped in with blame and negative consequences after things go wrong. But that’s not how I see it. Real accountability is about clarity and ownership. To hold someone accountable means to be sure there is clarity in expectations and objectives. It’s knowing what success looks like, what’s expected, and why it matters. When managers and engineers have that kind of clarity—and the support to get there—they rise to the challenge. Not because someone’s watching, but because they believe and care. Without clear goals or a shared definition of “done,” and without the right amount of empowerment, accountability isn’t just ineffective—it’s unfair. The best teams I’ve worked with aren’t afraid of being accountable. They want to own things, to ship great work, to know where they stand. But they can only do that when we set them up for success: with the right context, tools, trust, and space to build. As leaders who like to make accountability part of our culture, we must provide all these. Accountability done right isn’t pressure—it’s pride. Would love to hear what you think.

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Recently I went to a local mixer here in Sonoma County and did something radical: I left the AI buzzwords at home and just asked people, “How is technology actually feeling in your business right now?” I heard it all: “Excited, but overwhelmed.” “Curious, but I don’t trust the hype.” “I’d use AI tomorrow… if someone I trust set it up.” Those conversations did more for my roadmap than any online trend report. Face to face, with eye contact, people tell you what they’re really worried about: staff burnout, sloppy data, missed calls, chaos in the back office. That’s where AI actually belongs... quietly fixing the unglamorous problems. If you’re building in AI, go talk to the people on your street, not just your Twitter feed. The answers are usually sitting at the bar table across from you.

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I see a lot of frustration about return-to-office (RTO) policies, often framed as a battle between employees and corporations. But I think there’s a more nuanced conversation to be had. Where and how we work is, at its core, a cultural decision. Some companies thrive in remote environments. Others prioritize in-person collaboration. Neither is inherently right or wrong—just different ways of operating. For me, working from home feels invasive. It blurs the lines between my job and my personal life in a way that I don’t enjoy. I want my home to be my home—separate from work. Before I found Wilmac, I specifically looked for companies that aligned with that preference. Wilmac provides that in-person environment, and I appreciate being part of a culture that values face-to-face collaboration. The key, I think, is choice. If remote work is what you need, there are companies that support it. If in-person work energizes you, those opportunities exist too. Instead of framing RTO as a corporate conspiracy, let’s acknowledge that different people (and companies) work best in different ways.

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The Andrej Karpathy interview is really, really great, I listened in its entirety and plan to listen again. Worth bearing in mind that our industry in general is incredibly optimistic, and the downside of this optimism is that we often can see where things are going long before the tech will be ready, see: self-driving cars in the 2010s, we all thought it was Right Around The Corner, it wasn't. Currently, people are talking about how LLMs are going to obviate entire classes of jobs, but in practice we all see them fail at very basic tasks, while we also underestimate the complexities of most jobs we claim AI will obviate. My own experiences using LLMs for coding assistance, chess coaching, and musical analysis is that they are currently good for small, focused tasks, but often fail at very basic sorts of analysis in completely unintuitive ways. Just this morning, ChatGPT advised a move that would have immediately lost me a queen in a practice game. We've all probably spun our wheels for hours coding when an LLM is going in circles. The whole interview is fantastic and well worth a detailed listen!

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"Don't fix anything that ain't broken." I've seen stable legacy systems get rewritten just to make things look modern, only to introduce bugs, delays, and endless firefighting. Innovation is powerful, but only when it's solving a real, meaningful problem. The real challenge is knowing when to improve and when to let things be. Sure, changes can lead to big wins or big messes. But before jumping in, ask yourself: is the risk really worth it?

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I thought this was a good and very reasonable talk by Eleanor Millman on how to prioritize projects for a platform engineering team: https://lnkd.in/gaYtUHRH A few insights I took from it: - Unlike product features which can be more directly tied to revenue, platform engineering work is a couple steps removed. But that doesn't mean it's unimportant! Far from it, platform engineering projects can make everyone at the company more efficient, able to produce higher quality software, etc. - While "urgency" is always going to be a factor, you don't want to just be putting out fires, you want be able to prioritize work which is "high impact" ... even if that impact isn't felt immediately. - Good rule of thumb: prioritize "highest impact for lowest effort". - The talk has some ideas on what factors to choose for impact, and how to blend them. For instance "speed of development" is one factor, "cloud cost optimization" might be another. The weighting of different impact factors can change over time, depending on the needs of the business. I would say that a lot of companies don't have much methodology here but I can really see the value in codifying it. You can always change the methodology or the weighting if it's spitting out results that don't pass the smell test or you really feel it is leading the company astray. Clarity can give the org more freedom to put resources behind projects that would otherwise never be taken on. When things are unclear, prioritization still happens implicitly, but the decisions tend to be a lot more random and fear-based and the org is worse off as a result.

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