Pelayo Arbués

Pelayo Arbués

@pelayoarbues.com

Head of Data Science and AI at idealista.com | Film photographer | Ph.D. in Economics | Sharing my digital garden at pelayoarbues.com | Working full remote from the North of Spain

6.7K followers1.1K following780 posts

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Wow! 2000 followers! Let me reintroduce myself: I’m Head of Data Science at idealista.com, Southern Europe’s leading real estate portal. I earned a Ph.D. and spent 7 years in academia researching transport and spatial econometrics. I’m into AI, management, and film photography.👇👇👇

Abrimos posición en el equipo de datos de idealista/data! Un rol muy de remangarse, hacer nuevas versiones de productos de datos existentes, trabajar con el equipo de negocio, ayudar a migrar a nuevo stack y mucho ELT. www.linkedin.com/jobs/view/44...

linkedin.comidealista hiring Analytics Engineer in Madrid, Community of Madrid, Spain | LinkedInPosted 4:52:15 PM. ¿Quiénes somos?En idealista desarrollamos soluciones de datos de alta calidad que apoyan la toma de…See this and similar jobs on LinkedIn.

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what’s your favorite way to have your data? data base data center data mart data warehouse data lake data stream data pipeline data mesh data fabric data swamp data silo data cube data frame data vault data catalog data platform data store data pipe data table data feed data channel data bus

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Save your data to local Markdown files you own forever, and can access completely offline. Obsidian now helps you migrate from all the major proprietary formats including Notion, Evernote, OneNote, Google Keep, Apple Notes, and more.

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So we know AI helped a negligible number of students. But how many did it harm? I've crunched the raw data provided by the lead researcher, and what I've found is that ~9,500 students using AI studied less than even the *least* studious, non-AI using student. This should shock the conscience.

Benjamin Riley@benjaminjriley.bsky.social

Both the Economist and others suggest this study demonstrates that AI can help students. So I contacted the lead researcher to figure out how many. Answer: Out of ~26,000 kids, a total of 20. And five months after starting to use AI, only four continued to spend 65 minutes on homework. Four.

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Autonomous AI in action. 🤖 Check out how the new Gemma 4 31B model operates as an ADK Agent, exploring, planning, and running experiments on an unfamiliar database to optimize services and maximize revenue. 📈 ✨ Dive into the full session here to explore the complete workflow: goo.gle/4ozP8sK

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When people say 'AI isn't a bubble because the technology isn't going away', it just shows they don't know what a bubble is. The internet didn't go away. Railways didn't go away. Tulips didn't go away. An asset can be overvalued, even if its useful.

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I have been working on Obsidian Reader for over a year. I didn't want to share it until I felt it was good enough. It's finally there. Consistent formatting for any article. Outline, syntax highlighting, nice footnotes, adjustable typography. Runs locally. Just rules, no AI.

When you search for a home, it’s rarely “the whole city.” It’s more like: this neighborhood yes, this one no, that other one too, even if it’s far away. With @idealista.bsky.social new multi-area search, you can mix, exclude and add zones in one map search.

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I am calling for a complete and total boycott of the Mercator projection in all news stories about Greenland until every member of the American public has seen this

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I'm ready to accept a definition of "agent" that I think is widely-enough agreed upon to be useful, at least in the AI engineering space: An LLM agent runs tools in a loop to achieve a goal (I've been dismissing the term as hopelessly ambiguous for years) simonwillison.net/2025/Sep/18/...

simonwillison.netI think “agent” may finally have a widely enough agreed upon definition to be useful jargon nowI’ve noticed something interesting over the past few weeks: I’ve started using the term “agent” in conversations where I don’t feel the need to then define it, roll my eyes …

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The data industry promotes builders. Not leaders. You nailed pipelines. So they gave you politics. Now you're managing people, budgets, and roadmaps. With zero training. You’re not unqualified. You’re just undertrained.

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OpenAI announced Codex (openai.com/index/introd...) on May 16, 2025 for their $200/month Pro users, and Google announced a competing tool called Jules (jules.google) on May 20, 2025 making it available for FREE. There’s no moat in AI—how can a moat disappear in less than a week?

jules.googleJules - An Asynchronous Coding AgentJules is an asynchronous agent that gets out of your way. It lets you focus on the coding you want to do, meawnwhile picking up all the other random tasks that you rather not do.

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Every AI coding tools company doing demos with React + Typescript (and maybe React Native.) Microsoft meanwhile showing off Java and .NET case studies. This doesn’t look like an accident - but a very deliberate strategy. It’s easier to win when you are on a ground with fewer competitors…

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SmolVLM real-time camera is a framework for running computer vision models locally on M3 MacBooks. It combines HTML webcam access with llama.cpp's inference process to enable real-time image capture, understanding, and captioning. www.threads.com/@luokai/post...

threads.comluokai (@luokai) on ThreadsSmolVLM real-time camera is a framework for running computer vision models locally on M3 MacBooks. It combines HTML webcam access with llama.cpp's inference process to enable real-time image capture, ...

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It is a little weird to me countries aren’t more aggressively, formally trying to take advantage of the U.S. science brain drain. Once in a lifetime opportunity to buy low on Non-Dumbass Americans with PhDs who just wanna look into microscopes and quietly cure ass cancer as our country eats shit.

In simple terms, the purpose of data is to give you a causal model of your business in your head. If reality changes, the figures in your business should show it. At which point you get to update your knowledge.

Coaching is no longer a specialty; you cannot be a good manager without being a good coach. The path to success in a fast- moving, highly competitive, technology- driven business world is to form high- performing teams and give them the resources and freedom to do great things.

When leaders take steps to protect their own interests, particularly when those steps are taken at the expense of others, they send a message to everyone else that it is okay to do the same.

To maintain the core of your product there are usually one or two things that have to stay still while everything else spins and changes around them. And that’s a useful constraint. You need some constraints to force you to dig deep and get creative.

If people only comply, we can’t expect people to take responsibility for their actions. The chain of command is for orders, not information. Responsibility is not doing as we are told, that’s obedience. Responsibility is doing what is right.

The push for commercial control of AI capability is dangerous. Naomi Klein, who coined the term “shock doctrine” as “the brutal tactic of using the public’s disorientation following a collective shock… to push through radical pro-corporate measures”

Having two different pipelines to process your data is a common cause for bugs in ML production. One cause for bugs is when the changes in one pipeline aren’t correctly replicated in the other, leading to two pipelines extracting two different sets of features.

Every three to six months, audit your meetings. Are they useful? Are the right people involved? Do they achieve their purpose, and are they worth the time? Could more be accomplished asynchronously? Consider polling participants or having a neutral person do so.

Make sure your candidates know that you’re excited about them. I first encountered this idea reading Rands’s “Wanted” article, 4 and he does an excellent job of covering it there. The remarkable thing is how few companies and teams do this intentionally.

Self-awareness has three components: understanding your underlying value system, identifying your innate preferences—your work style and decision-making tendencies—and being clear about your own skills and capability gaps.

To write something, you have to do things, collect ideas, process them, put words on the page, structure them, edit them, rewrite them, and publish them to your site — and that’s all for one article. Then, there’s the emotional challenges: writer’s block, imposter syndrome (...)