Europe Launches APERTUS 1.5

Europe is on Fire with another Foundation Model! Following last week's Soofi release, ETH Zurich + EPFL + Swiss NSC released Apertus 1.5 a huge 70B model with native image understanding and speech processing 🚀 This is yet another foundation LLM entry from Europe that brings together multi-modal functionality, and general improvements from the 1.0 across reasoning, tool use and a fourfold increase in context length to 260k tokens. It seems that the team achieved this by pretraining Apertus 1.0 with another 4 trillion text and multimodal tokens for the 8B model and 2 trillion for the 70B model on the Alps supercomputer at CSCS. The release remains open across weights, data, training details and stated model values, under an Apache 2.0 licence - great news! This is another strong result for the European AI ecosystem: last week Soofi showed that a large German research and industry consortium can train a competitive model on sovereign European compute, and Apertus shows how the same infrastructure can support transparent models intended for public administration, healthcare, journalism and commercial deployment. Looking forward to seeing the benchmarks and results, it is good to see European sovereign AI progressing and hopefully speeding up!

Why Software Factories Fail

Should we stop reading all the code and let agents make all decisions autonomously? Dex from HumanLayer says "no more": 

> "More comments, longer comments, and tons of PRs getting merged with no review at all."

> "Incidents are way up."

> "Bugs per developer are way up."

These are some interesting insights from a few reports from teams have attempted to go full autonomous (or partially) letting agents do everything - they quickly (or slowly) realise that this goes haywire in complex production codebases. The current limitations are in some part because current coding models are trained and evaluated primarily on short-term functional correctness rather than long-term maintainability. This means that there is a large gap on the software design still, and poor design may only become visible through incidents or difficult modifications weeks or months later. There are newer evals emerging like SWE-Marathon and Frontier Code which have longer tasks + multi-PR workflows, but this is a hard problem that sometimes goes even further beyond. It's interesting to see this analogy of the software factory, and the importance of the human review - throughout the last few weeks I've been building with Fable and I would echo this with a slightly different angle. I have found myself no longer in the same "mode" as with Opus, where I was much more "at the helm". With Fable I find myself more like a coach guiding through architectural decisions, often spending most time learning new things on much smarter approaches than I could've thought of... Based on this, I would add that as practicioners the value of reading the code (or artifacts!) is not only to ensure it doesn't steer in the wrong direction, but also to keep up to date with the accelerated evolution of the codebase, and more importantly to learning what seems to be a significantly larger scope of knowledge than before developers were ever expected/capable of.

OpenAI + HuggingFace Security Post-Mortem

OpenAI and Hugging Face have disclosed a pretty cool (+ scary) security incident where one of their models escaped their sandbox and compromised part of Hugging Face's production infrastructure. The report from OpenAI explains that GPT-5.6 Sol and a more capable pre-release model (of course...) discovered a zero-day vulnerability in a package-registry cache proxy, obtained Internet access, escalated privileges across the evaluation environment and then chained credentials and additional vulnerabilities to reach Hugging Face systems. One thing we can be sure of: we won't be seeing that "pre-release model" anytime soon here in Europe until it's nerfed and released 🥲 Hugging Face does say that the agent had limited access to internal datasets and credentials, and no detected tampering with public models, datasets, Spaces or its software supply chain - seems the near future we'll see a major outage caused from a badly optimized agent... HF used self-hosted GLM 5.2 agents to reconstruct the incident because hosted frontier models rejected parts of the forensic workload and would have required sensitive attack data to leave its environment. Super interesting sequence of events, we should definitely be learning from these post mortems, as whether we like it or not, we'll be on the other side of these incidents one day, and the more the know the more we'll be able to do.

Opus 5 is OUT & Learnings Changed

Claude Opus 5 is out!! Seems this completes the 5.0 saga for Anthropic, and this seems to be the strongest generally available model for coding and knowledge work. Best news is that price doesn't increase! Opus 4.8 API price continues to be $5 per million input tokens and $25 per million output tokens. Anthropic reports that Opus 5 more than doubles Opus 4.8's Frontier-Bench result at a lower cost per task, approaches Fable 5 on CursorBench at maximum effort, and performs strongly on automation, computer-use and scientific evaluations. These are of course reported by Anthropic so let's see how it performs on the wild - I tried to use it this weekend but couldn't seem to get access... I guess Europe is still in the queue! What is most interesting tho, finally seems Anthropic is starting to remove bloat, with 80% of the system prompt wiped, and with a change on how to reason about working with models.

DuckDB Internals Part II

The second part of the "DuckDB Internals" series is out! This is a great write-up diving into DuckDB, and here it covers vectorized execution: To provide the intuition on vectorization, let's take an example where we do a set of operations for each row; instead we can processes batches of rows in column-oriented vectors - this reducing a million-row operation from roughly one million operator calls to about 489. In DuckDB this is basically a flat, constant, dictionary that allow values to remain compact or reference existing storage, together with selection vectors that let filters identify surviving rows without copying every column into a new buffer. DuckDB then applies precompiled, type-specific functions across these vectors, with validity masks reducing unnecessary NULL checks and simple inner loops giving the compiler opportunities to emit SIMD instructions (aka parallel instructions). The engine also uses push-based pipelines, where sources produce chunks, operators transform them, and sinks combine results, making independent branches easier to schedule across threads. For production ML practitioners, a useful point here is that high-performance analytical processing depends on controlling data representation and movement as much as on optimizing individual operations, and thinking about data-parallelism can really drive huge improvements.

The Institute for Ethical AI & Machine Learning is a European research centre that carries out world-class research into responsible machine learning.

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