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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On Github Copilot's memory system and what their engineering wrote fosstodon.org/@pamelafox/1...

The best compliment i can give OpenAI's Codex 5.3 is that it feels way more like Claude Code

Quilicura, Chile, one of the communities I wrote about in EMPIRE OF AI, has launched a brilliant initiative to inspire more responsible AI prompting. Today, don't use AI; ask the townspeople instead: quili.ai. So heartened to see this creative act of resistance.

The future of software engineering isn't less human, but more focused on higher-level problem-solving. Engineers will evolve to master AI tools, leveraging them to build more robust, thoughtful solutions. #FutureOfWork 6/6

This can't be good for Europe.

For all the hate that LangGraph gets, there is a hundred times more people using it in battle-tested grounds (such as gitlab, elastic, klarna, cisco and so on). Also, it's silly to hate a framework.

With the amount of AI-assisted tools for development coming out - thus impacting teams' development practices from the ground up - the concept of cross-functional teams seems ancient. A developer that understands these tools will hardly fit in the same team with one that rejects them vehemently.

Domain driven AI - where domain knowledge drives AI implementations - is the best approach for AI-based products.

Software engineering is, fundamentally, a solved problem. AI Science isn't. The ones who treat AI challenges as software engineering ones are doomed to fail.

This year, I've tried Helium and Waterfox browsers. I found the former to be buggy and there's something about Chromium-based browsers that doesn't bode well with me. And I've always been a Firefox user, since its Firebird days. Waterfox was perfect since the get-go. Both on Mac, Linux and Android.

The Going Dark initiative or ProtectEU is a Chat Control 3.0 attempt View Article | Join the HN Conversation Summary of HN discussion 🧵👇

This was also a year where I have been migrating to European-based services. Went from iCloud to Proton Drive, Gmail to Proton Mail and so on. Migrated from iPhone to Android, since I bought a FairPhone and am very happy with it. FP is much better than I anticipated!

Doing a "year in review" on the technologies I've tried/read about. One thing that comes to mind is the hostility towards MCP, especially by people who didn't try it. And I used to do Javascript, which got a lot of heat back in the day. MCP is just a Protocol, people.

I just read "AI Agents in Action". It's well-supported by code and drawings. Being quite heavy on OpenAI and Microsoft tooling is a drawback. Online reviews are pretty mixed as well. It's not a beginner-level book but needs more depth. Decent read, all in all. www.manning.com/books/ai-age...

📢 Launching EuroLLM-22B - a fully open, EU-made LLM supporting all 24 EU languages (+11 more)! 🤝 Built by a Europe-wide consortium with #HorizonEurope & @eurohpc-ju.bsky.social support. 🔓 Available now on Hugging Face: lnkd.in/e-xgQXTX 🔗 Learn more: lnkd.in/e6FdpYrN

Dolphin-v2 🐬 new document parsing model released by Bytedance huggingface.co/ByteDance/Do... ✨ 3B - MIT license ✨ Works on any document: PDFs, scans, photos ✨ Understands 21 types of content: text, tables, code, formulas, figures & more ✨ Pixel-level precision via absolute coordinate prediction

The influence of domain knowledge is huge when it comes to applied AI. Domain expertise can prevent overengineering and/or unnecessary costs. Working in the legal industry as an AI scientist it became obvious early on how crucial it is to build the bridge towards domain experts. Every day.

A chatbot that interacts with NeurIPS 2025 papers: neurips.zeroentropy.dev Went in to find Information Retrieval related papers and there's a few. Happy reading!

We tested one of the most common prompting techniques: giving the AI a persona to make it more accurate We found that telling the AI "you are a great physicist" doesn't make it significantly more accurate at answering physics questions, nor does "you are a lawyer" make it worse.

We have come full circle.

TITANS & MIRAS: real continual learning MIRAS = a unifying theory of transformers (attention) and state space models (SSM, e.g. Mamba, RNNs) TITANS = an optimal MIRAS implementation that’s “halfway between” SSM & transformer with a CL memory module let’s dive in! research.google/blog/titans-...

I am a fan of SDD but it often gets confused with Vibe Coding, specially in companies where innovation is met with resistance. This articles shares some criticism, yet by someone who actually tried it. I disagree with the article and it seems that HN people in thread do too. Give SDD a try!

I have been trying lefthook and damn this thing is fast. Also, the ability to run scripts is handy if you like to keep the config tidy.

💯 bsky.app/profile/veri...

“Before being technical, science is visionary” Carlo Rovelli, Reality Is Not What It Seems

Business-applied AI development requires a much higher level of experimentation than traditional software development. This is because AI development requires a very tight connection to domain knowledge. And each domain requires its own approach, for the most part.

A short (way too short) yet fantastic reading. Rovelli has the gift of explaining complex things simply. I have quickly moved to reading The Order of Time, another of his books. The way he explains time is just so accessible - thus mind blowing.

It’s reasonably amusing to see the resistance from some traditional software developers to some AI tooling. MCP for example, gets a lot of focus from tech doomers because it’s new. It’s Ok to not use those don’t fit a use case but until they try it it’s just preemptive criticism.

TIL!

The term Agentic Coding is something that resonates quite a lot. But I’ve heard Spec Driven Development and it is spot on. Both these are almost polar opposite to what Vibe Coding is (imo), even though all are AI powered. Also, for prototyping: vibe coding all the way.

An important component of the success of AI-based products is the ability to intertwine data science and (software) engineering. Enforcing a separation of those is a recipe for failure. When it works though, it feels like a dance: there might be toe-stepping but the song is the same.

When people ask what am I doing with so many Pi’s

this seems like a very good idea actually

Phi4 didn’t even try

So Anthropic is now the enterprise AI market leader. After having been playing with Opus 4 for the last month, this is rather unsurprising. Open-source LLMs adoption has stalled. Src: menlovc.com/perspective/...