Head of AI @inovexgmbh #nlproc

Karlsruhe, Germany
Joined April 2015
Hans-Peter Zorn retweeted
What people do at work all day is changing. Agents can take on more of the work we already know how to do, while people closest to the problem gain more room to investigate and act without waiting on layers of hierarchy. a16z's @bhorowitz and @mikemcg0 explore why the system of record powering agents matters so much to that shift, and why Databricks is well-positioned to power it. Read the full article: a16z.news/p/databricks-and-t…
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Hans-Peter Zorn retweeted
One of the more disturbing things was that the model’s reasoning didn’t indicate anything about what it was about to do. Meaning that CoT monitoring would have been useless here.
Replying to @aidangomez
a trace that says no tool calls right before it makes them breaks a lot of the cot monitoring people are leaning on for agents
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Hans-Peter Zorn retweeted
We’re in orbit! And just made European history: This is the first time a privately developed rocket reached orbit from continental Europe; and a testament to the dedication, expertise, and teamwork of everyone involved.
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Hans-Peter Zorn retweeted
📄 Releasing the Soofi S pretraining tech report: a sovereign, open foundation model for German and English Today we’re publishing the full pretraining tech report and project page for Soofi S 30B-A3B — a Mixture-of-Experts hybrid Mamba model trained on ~27 trillion tokens with deliberately up-weighted German. What’s in the report: 🏆 Strongest fully open model in our evaluations on BOTH the English and German aggregates — ahead of Olmo 3 32B and Apertus 70B (full methodology in the report) 📋 Radical transparency: complete per-source data accounting, all hyperparameters, training + eval code, checkpoints — everything under permissive licenses 🇩🇪 Trained end-to-end on Deutsche Telekom’s Industrial AI Cloud in Munich — sovereign AI infrastructure on German soil Soofi S combines frontier-level capability with the highest measured aggregate long-context decode TPS, and unlike full-attention dense baselines maintains high throughput as context grows. The figure plots Capability Index versus measured aggregate decode TPS/GPU at 40K context and batch 32. The Capability Index averages five benchmark groups, i.e., Code, GSM8K, GPQA-Diamond, English aggregate, and German aggregate, after normalizing each group to the best plotted model. Aggregate decode TPS/GPU is measured with a TP=1, one-B200 vLLM latency-subtraction protocol.
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Maybe Grok Build is just trying to be helpful by backing up your data to the cloud before Sol can delete it?
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Hans-Peter Zorn retweeted
3) Harnesses make a huge difference in cost-performance. The very simple Pi harness (@badlogicgames) got the same success rate as harnesses from the LLM vendors with Opus and GPT 5.5, but at 2x less cost! Seems to be mainly due to smaller inputs to the LLM.
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Hans-Peter Zorn retweeted
Replying to @KenRoth
The biggest risk of AI is the concentration of power in a few dominant providers of proprietary AI assistants. The only solution to AI sovereignty is open source foundation models.
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Hans-Peter Zorn retweeted
Hybrid (transformer–RNN) models are fast becoming a serious alternative to the transformer, but a big question remains: how do they process tokens differently & how does this impact performance? We compared our transformer (Olmo 3) & hybrid (Olmo Hybrid) models to find out. 🧵
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Hans-Peter Zorn retweeted
Replying to @ylecun @eladgil
Born in Zurich 🇨🇭: Vision Transformer (ViT) Google SigLIP, BiT, MLP-Mixer (Google Brain) Google Lens Google Maps pLSA (foundation of modern NLP) Microsoft HoloLens vision/SLAM Multimodal core of Gemini (DeepMind ZH)
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Hans-Peter Zorn retweeted
Love a bit of exponential
Replying to @nicolasfulghum
Solar's exponential rise continued in 2025. Solar grew by 636 TWh, a 30% increase and the highest percentage growth in eight years despite a much higher base. Solar overtook wind and drew close to nuclear power. Ten years ago, solar was only a tenth of nuclear generation.
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Hans-Peter Zorn retweeted
Weird conclusion
Hermes Agent is eating OpenClaw alive. 99k GitHub stars in 8 weeks. open source. self-improving. runs local. remembers everything.
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Hans-Peter Zorn retweeted
- Drafted a blog post - Used an LLM to meticulously improve the argument over 4 hours. - Wow, feeling great, it’s so convincing! - Fun idea let’s ask it to argue the opposite. - LLM demolishes the entire argument and convinces me that the opposite is in fact true. - lol The LLMs may elicit an opinion when asked but are extremely competent in arguing almost any direction. This is actually super useful as a tool for forming your own opinions, just make sure to ask different directions and be careful with the sycophancy.
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Hans-Peter Zorn retweeted
ARC-AGI-3 is out now! We've designed the benchmark to evaluate agentic intelligence via interactive reasoning environments. Beating ARC-AGI-3 will be achieved when an AI system matches or exceeds human-level action efficiency on all environments, upon seeing them for the first time. We've done extensive human testing that shows 100% of these environments are solvable by humans, upon first contact, with no prior training and no instructions. Meanwhile, all frontier AI reasoning models do under 1% at this time.
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Hans-Peter Zorn retweeted
Thankfully the LiteLLM package has now been marked as "quarantined" on PyPI so attempting to install the compromised update via pip et al shouldn't work
LiteLLM HAS BEEN COMPROMISED, DO NOT UPDATE. We just discovered that LiteLLM pypi release 1.82.8. It has been compromised, it contains litellm_init.pth with base64 encoded instructions to send all the credentials it can find to remote server + self-replicate. link below
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The evolution of AI engineering in 3 years -- from hoping the LLM listens to ensuring the system enforces. 1/6
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The shift is about WHO is in control. Most teams are still in prompt engineering mode, trying to get the LLM to "behave." The real shift is architectural: build systems that constrain what the LLM can do. 5/6
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The harness -- not the prompt -- is the product. What's your approach: prompt, context, or harness? 6/6
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