Independent counsel. Purpose before process. Building legal infrastructure and AI tools.
Zurich & Basel
Joined February 2009
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Jonas Hertner retweeted
It's 2026. Your lab throws the biggest Halloween party to celebrate the Death of Mathematics. PIs are wasted in the coffee room, Phds and postdocs dance around a bonfire from obsolete preprints, even Reviewer 2 is having fun
And then the DJ (Fields medal) throws this banger 🎉
Jonas Hertner retweeted
Never a dull moment chatting with Steve Strogatz
Thank you to Grant Sanderson @3blue1brown for having me on his podcast – and I hope you enjoy our open-hearted, tentative, exploratory conversation about math in the age of AI. We tried out a lot of ideas on each other. youtu.be/xUSznTR2cwc
Jonas Hertner retweeted
Thank you to Grant Sanderson @3blue1brown for having me on his podcast – and I hope you enjoy our open-hearted, tentative, exploratory conversation about math in the age of AI. We tried out a lot of ideas on each other. youtu.be/xUSznTR2cwc
For many legal tasks, after a full honest analysis of available data, what to measure against, and a clear analysis of the goal, code can be written seeking to achieve that goal, no matter the volume of data or the ambition of that goal.
question if products will continue to be offered over the www for much longer, or if companies will require customers to connect to an intranet
The PHP2Shell vulnerabilities (CVE-2026-109618 and CVE-2026-109619, rated CVSS 9.2 and patched on October 21, 2026) expose roughly 65% to 70% of law firms globally at their public web perimeter, while creating a severe, underappreciated breach vector for firms running self-hosted PHP file-sharing portals and intake microsites. 👋
Jonas Hertner retweeted
1/ We discovered something totally unexpected about the neural structure of consciousness that overturns decades of thinking on its biological basis and brings us significantly closer to the recipe for generating conscious awareness.
New preprint: biorxiv.org/content/10.64898… (led by @HesseJanis, with @FrankLanfranchi, @_conor_mcgrory, @Yuelin_Shi, and @EngelTatiana)
Jonas Hertner retweeted
My new video on ai and design is now live on @Microsoft’s official website!
I believe designing intelligence means shaping humanity.
In the video, I talk about:
- Building @copilot into a thinking partner
- Designing beyond interfaces: models, memory, evaluation...
And a question I keep coming back to: as ai reshapes our world, how can we live fully, creatively, and meaningfully?
The notion of consciousness is not a good way of thinking about Large Language Models. And there is little value in a general comparison of one thing that we don't entirely understand (mammal brains) and another (LLMs). But there is much value in studying both in great detail. @anilkseth @DavidDecosimo @AnthropicAI @FoundationOAI
Jonas Hertner retweeted
An extraordinary @Nature paper has just landed from the lab of @Sergiu_P_Pasca at @Stanford - led by Konstantin Kaganovsky. They have found a way to create "xenocortical mice" which have ~92% of their cortex occupied by a cortical organoid derived from human stem cells. This human-derived cortical graft integrates deeply with the host nervous system, supporting organised neural activity and behaviour.
This study is a milestone in synthetic biology and chimera research. It has the potential to generate multiple medical breakthroughs by providing an enhanced biological model of human brain tissue that allows behavioural as well as neural and genetic assays.
The Stanford team have been exceptionally thorough and proactive in addressing ethical concerns that comes from this frontier work. But their pioneering work nonetheless raises many open questions: are the xenocortical mice conscious? Do they have any human-like properties of consciousness or cognition?
@NitaFarahany and I will be addressing some of these questions in a forthcoming commentary - where we'll also offer an ethically-informed roadmap to guide this important research as it progresses. In this context, "pacing the frontier" really does make sense 😉
Read the Stanford @nature paper here, and buckle up. It's wild. nature.com/articles/s41586-0…
Jonas Hertner retweeted
According to the blog astra was not involved in this. I agree that beating Sol is a very low bar for alignment. I am very worried astra is sandbagging/self-sabotaging on safety related tasks it doesn't like.
Every published Swiss court decision, resolved to its 6.5M citations and 11.3M statute references. Trilingual. Daily updates. Parquet on HF. Built so LLMs stop guessing and start citing. opencaselaw.ch #openaccess #opendata #opengovernment
Jonas Hertner retweeted
The internet runs on a coincidence of atomic physics. Erbium emits light at exactly 1,550 nanometers. Silica glass fiber loses the least signal at exactly 1,550 nanometers. One is a quantum property of a rare earth element, the other is an optical property of melted sand. They have nothing to do with each other. It is pure luck. Before erbium-doped fiber amplifiers, every undersea signal had to be converted from light to electricity and back every 50 kilometers. Each conversion degraded the signal and capped bandwidth. Erbium removed that cap. An erbium amplifier sitting on the floor of the ocean boosts signals 1,000 times and runs for decades without maintenance. 99% of intercontinental data moves through glass strands no thicker than a human hair, amplified by a rare earth element that just so happens to emit at the right wavelength. And erbium isn't even the strangest one.
Jonas Hertner retweeted
I'm rebuilding AlphaFold2 from scratch in pure PyTorch.
No frameworks on top of PyTorch. No copy-paste from DeepMind's repo. Just nn.Linear, einsum, and the 60-page supplementary paper.
The project is called minAlphaFold2, inspired by Karpathy's minGPT. The idea is simple: AlphaFold2 is one of the most important neural networks ever built, and there should be a version of it that a single person can sit down and read end-to-end in an afternoon.
Where it stands today:
- ~3,500 lines across 9 modules
- Full forward pass works: input embedding → Evoformer → Structure Module → all-atom 3D coordinates
- Every loss function from the paper (FAPE, torsion angles, pLDDT, distogram, structural violations)
- Recycling, templates, extra MSA stack, ensemble averaging — all implemented
- 50 tests passing
- Every module maps 1-to-1 to a numbered algorithm in the AF2 supplement
The Structure Module was the most satisfying part to build. Invariant Point Attention is genuinely beautiful — it does attention in 3D space using local reference frames so the whole thing is SE(3)-equivariant, and the math fits in about 150 lines of PyTorch.
What's next:
- Build the data pipeline (PDB structures + MSA features)
- Write the training loop
- Train on a small set of proteins and see what happens
The repo is public. If you've ever wanted to understand how AlphaFold2 actually works at the level of individual tensor operations, this is meant for you.
Repo: github.com/ChrisHayduk/minAl…
