The software development industry during the 2010–2020 decade created roles such as Engineering Manager (EM) and Tech Lead (TL). People had already been using one or the other, but during that decade, most companies adopted them. While they vary by company, they share common elements. Note: this article is a translation from the original “Señales de un buen Tech Lead”, in Spanish. However, this post is slightly different and contains a short “Frequently Asked Questions” section. ...

I’ve been thinking more about the Fizzy launch and the reaction around it. To me, the most interesting part is not the Kanban app itself, but the model behind it. A lot of the discussion online jumped straight to the label. “It’s not real open source”. Sure, if your first instinct is to evaluate everything by strict licensing categories, Fizzy will not tick every box. But focusing solely on the label misses the model’s practical value and its intended use. ...

Generative artificial intelligence has amazed the World. Since OpenAI launched ChatGPT 3, its user adoption has been staggering. In 2022, ChatGPT surpassed one million users in just five days. For comparison, Instagram needed 2.5 months back in 2010. And it’s not just OpenAI. Anthropic has Claude, one of the best models for programming tasks. Google is pushing forward with Gemini and VEO, leaders in video. In China, Alibaba Cloud is moving fast with its open-source model Qwen. And rumor has it that Meta is offering millions — with stock packages worth billions — to attract top AI researchers. It’s wild. ...

Every time we talk about promotions and career paths, we listen a lot about how to get there. Today, I want to tell the story of how I failed to get the role… twice! For context: I joined Adevinta as Engineering Manager of the Runtime team in late 2020. We were part of an internal Platform-as-a-Service (PaaS) offering. We provided SCHIP, a Kubernetes container runtime. Fast forward a few years. We onboarded every marketplace of the group. We increased traffic and handled more requests per second in our clusters. We improved how we supported users. We shipped new features. And we helped Adevinta migrate to the Public Cloud on Amazon Web Services (AWS). I was proud of the team. I was proud of the culture we built. I had great feedback from my managers. And I felt I was ready for the next step. ...

As per last week’s rumors, Meta will judge employee performance based on AI skills. Nothing new. Other companies, like Zapier, have even published what they expect “AI fluency” for all new hires: Every time a company announces “AI usage dashboards,” people get mad. My timeline on X gets full of surveillance, micromanagement, and bullshit rankings. I get the reaction. Engineers hate anything that smells like performance theater. But with something as disruptive as AI, betting on “organic adoption” is a fantasy. Habits don’t shift on their own. Teams don’t wake up one day and decide to rewrite workflows. And leaders can’t support what they can’t see. ...

When I first wrote about microservice disasters, I thought we’d eventually “solve” them, with better tooling, frameworks, and operational maturity. We didn’t. We just learned to live with the chaos. Distributed systems will always surprise you: timeouts, retries, and fallacies don’t disappear; they just shift shape. Maybe that’s the real lesson: software engineering isn’t about eliminating uncertainty, but managing it gracefully. Being part of the Runtime team at Adevinta, building an internal Kubernetes-as-a-service for the rest of the company, gave me a perspective on all the things software engineers build atop distributed systems. After all, is a Platform any good if we’re not surprised by what people build? I don’t think so. ...

This week, Dwarkesh Patel interviewed Andrej Karpathy and covered the future trajectory of Artificial Intelligence (AI). For those who don’t know, Karpathy is a leading AI researcher and co-founder of OpenAI. There’s one particular part of the interview that’s getting quoted everywhere: People are interpreting that quote as “don’t write, just build.” But that’s not quite right. Karpathy is absolutely right that building things is the best way to learn. It’s how you internalize concepts, face real trade-offs, and gain deep understanding. And he’s probably addressing the growing crowd of non-technical folks who talk about AI without ever opening a notebook or writing a line of code. ...

A few years ago, I wrote that remote work was here to stay. I still believe that. The nuance I missed: remote is a tool, not a belief system. Some teams will win by being remote-first. Others will win by being together. Both can work. Factorial, a Barcelona-based startup that builds HR software, just made a clear choice: five days in the office, one flex day if your manager agrees. If you follow ITNIG’s podcast (Spanish), you already know where Bernat Farrero and Jordi Romero stood. They’ve always valued in-person energy. Now they’re doubling down. They also recently announced a big office in Barcelona where “everyone can fit, and where we don’t need Zoom”. ...

There’s a massive hype about Large Language Models (LLMs) and Artificial Intelligence (AI), in general. A few days ago, OpenAI and AMD announced a partnership to deploy six gigawatts of AMD GPUs, with an initial rollout scheduled for 2026. I’ve been using ChatGPT since 2022. I did one or two projects with it, even before agentic coding was a thing. Still, I could build a “score guesser” for the Qatar World Cup in under a week. Authentication and social sharing included. I was mind-blown. ...