Pedro Madruga

There was a moment in time but it is premature to say that EU models are done. The EU is cooking.

openjev.com

I believe the same principle can be applied to most of the abstractions out there, especially the ones branded as "build once run everywhere".

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🚨 Announcing Pushin 🚨 Now officially: I'm announcing Pushin.eu, a git hosting platform built and run entirely in Europe. Think GitHub/GitLab but European, better, and faster. Built with #ElixirLang and #RustLang. peterullrich.com/announcing-p...

There has never been a better time to learn software engineering.

Very much worth a read

As a search person, I get a front row seat to AI team problems. Honestly they look like search team mistakes from 10 years ago * Ignoring evals * Thinking retrieval is 'one thing' (in this case RAG) * Thinking in chunks, not metadata softwaredoug.com/blog/2026/08...

I've had the Fairphone 6 for a year now and I'm in general quite satisfied. It took some time to go from iOS to Android, and I still want to try e/OS. www.theguardian.com/technology/2...

When the price per token of proprietary models stops being underpriced, then the edge is sitting on the side of the ones who are running local models.

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pedromadruga.com/blog/domain-... Domain Driven AI

pedromadruga.com/blog/domain-... Domain Driven AI

More on the github topic

🚨I've just released Sentence Transformers v6.0! MultiVectorEncoder joins the family: ColBERT-style late interaction models are now a first-class model type, for training, inference & interpretation, alongside dense, sparse & reranker models. Big thread 🧵

Check out our latest open model data -- which models are mentioned in every arXiv ML paper since ChatGPT. A very fun weekend project with Codex! From idea is full dataset in <72hours. ~500K papers processed 😎 Plot: dashboard.interconnects.ai?axrange=all#...

Just found out about this and it's such an interesting concept to read it together. Following the group closely now and sharing for wider reach. Awesome stuff! 👍

A better way of explaining GitHub's outages. Source: @benjdicken on twitter

AI development should follow domain knowledge. I'm calling it Domain Driven AI and I wrote about it. (No AI was used to write this blogpost which basically took me 4 weeks to write it 🥲) pedromadruga.com/blog/domain-...

Its going to be an era of contradictions. Polls will show everyone hates AI overall but also everyone will secretly use AI all the time for lots of stuff. AI companies will be underwater in public opinion but also many people will feel strongly attached to their own favorite model & care about it.

Great use of Claude

Opencode VS PI VS claude code VS ... reminds me of the coices between Sublime VS vscode VS neovim VS ...

The twitter bubble of silicon valley is saying that Sol has surpassed Fable but I'd really like a better source of information about it.

Opencode looks good so far, although I have only been using it for a couple of days. I chose it over PI because I don't need a barebone option. Most importantly, I can easily switch from different providers and also between cloud and local models easily. Anthropic models seem to be lagging...

Luddites are at it against AI coding, but it feels they're less by the day. I wonder what's going to be their next victim.

For the past year my setup includes a combination of github worktrees, tmux, kitty tabs, with a configuration that syncs all of these together so I can plan, build and review in parallel. Similar to what herdr is, which I have tried now. Recommended.

If we.all agree that a lot of our learnings come from side projects and experiments, why not have teams increase doing this research? This would make people more knowledgeable and a benefit to any organization. Research and experiments are essential in teams work on AI-based products.

Something that I've stayed completely away from when using LLMs is analysing data from a dataset.

Stifling innovation will very rarely be a positive thing.

After SIGIR2025, I selected the best talks (imo) and summarized them using the Feynman Technique. The output was an overview explained in an incredibly straightforward way. I shared them all here github.com/pmadruga/sig... This year, I'm skipping SIGIR2026 but will probably do the same.

"AI is a tool, just like other tools we use. And it's clearly a useful one." Linus 🙏

you 👏🏼 cannot 👏🏼 automatically 👏🏼 detect 👏🏼 plagiarism > the available detection tools are neither accurate nor reliable and have a main bias towards classifying the output as human-written rather than detecting AI-generated text link.springer.com/article/10.1... 1/

Software engineering without innovation is just technical complacency.

MTP is wild. Just a few days after the llamacpp support.

Wait, what? source: www.anthropic.com/engineering/...

So much looking forward to put this on a Raspberry Pi or similar.

Mistral, do your thing.

I need to know the inference speed on a raspberry pi because these sizes look great.

Rust implementation for Speech-to-Text based on Qwen3 models by Michael Yuan * Self-contained binary build — no external dependencies * Uses libtorch on Linux with optional Nvidia GPU support * Uses MLX on MacOS with Apple GPU/NPU support github.com/second-state...

LibreOffice it is. "Danish government agency to ditch Microsoft software in push for digital independence"

Could be interesting, macOS only though but with a small footprint. GitHub - ggml-org/LlamaBarn: A cosy home for your LLMs.

LLM cloud inference dominates usage, but should it? Local models and accelerators have improved massively over recent years. Perfect routing to best local model "reduce energy consumption by 80.4%, compute by 77.3%, and cost by 73.8% versus cloud-only deployment" arxiv.org/pdf/2511.07885

I went from Obsidian after a few years, to Notesnook, to Standard Notes. I am back using Obsidian. I can configure it enough to still keep it minimal while addressing all my use cases. I do notice that the Android version is slower than the iOS one. But manageable.

These are the engineers I like to work with. Boris is (again) on point.

A company's "AI Winter" starts when its AI-based product only has focus on engineering and zero on AI innovation. However, trying to avoid engineering debt creates AI debt. And AI debt has deeper (& longer) consequences due to rapid changes happening in the field. Aim for a balance, always.

The claude code agent teams feature seems great in theory but there's an upper limit on the cognitive load of reviewing every change each agent makes. I'm still reviewing every single change, yes.

Today's reading item

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