Author Archives: Qurat Ul Ain

Claude now watermarks its text: here’s what is actually happening under the hood

On 11 August Anthropic announced that every Claude model released after 2 August 2026 will watermark the text it produces. Not just in the chat app: the watermark lives at the model level, so it is there whether the text came through the API, Claude Code, Cowork, or anything else built on top. A follow-up post a few days later explained the mechanism, and the short version is that it’s a version of DeepMind’s SynthID-Text, which in turn descends from a scheme Scott Aaronson sketched out while at OpenAI in 2022.

Predictably it has caused a lot of confusion, especially in the academic circles (“is it zero-width characters?”, “can I strip it with a regex?”, “does this mean Turnitin finally works?”), most of which comes from not knowing how an LLM actually turns a probability distribution into words. So this post starts there, builds the watermark up from the sampler, and then tries to be honest about what it does and doesn’t mean for people who write papers and mark essays for a living.

Nothing here is hidden: the watermark is not metadata, not invisible Unicode, not a hidden token. It is a statistical pattern in which words were chosen, which is exactly why it survives copy-paste and exactly why it fades when you rewrite.

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Analyzing AlphaFold 3’s Diffusion Trajectory

A useful way to understand AlphaFold 3’s sampling behavior is to look not only at the final predicted structure, but at what happens along the reverse diffusion trajectory itself. If we track quantities such as the physical energy of samples, noise scale, and update magnitude over time, a very clear pattern emerges: structures remain physically imperfect for most of sampling, and only take proper global shape in the final low-noise steps.

This behavior is a result of the diffusion procedure implemented in Algorithm 18, Sample Diffusion, which follows an EDM-style sampler with churn. Rather than simply marching monotonically from noise to structure, the sampler repeatedly perturbs the current coordinates, denoises them, and then takes a Euler-like update step. Because of the churn mechanism, AlphaFold 3 deliberately injects additional noise during part of the trajectory, which encourages exploration but also delays local geometric convergence. This mechanism is shown in step 4 -7 of the Sample Diffusion Algorithm from Alphafold3 Supplementary Information.

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Human Learning in the age of Machine Learning

Source: Venus Krier

Oxford University has recently announced that its students will receive free access to a professional-level subscription of ChatGPT Education. This decision is more than just a perk, it’s a signal. One of the world’s leading universities is openly acknowledging that generative AI will be central to the academic experience of its students. But what does this mean for learning? For education? For scholarship itself?

To frame this question, it is worth beginning with a macro view: Mary Meeker’s AI Trends Report (2025) argues that AI is accelerating the transformation of knowledge work, pushing tasks once reserved for experts into more automated or semi-automated regimes. In her framing, AI is less a standalone innovation than a “meta-technology” that amplifies other domains.

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Taming the Trajectory Beast: A Simpler Way to Sample Your MD Simulations

If you’ve ever run a molecular dynamics (MD) simulation, you know the feeling. You spend days, weeks, or even months of precious compute time watching your favourite molecule wiggle and jiggle. The result? A trajectory file bursting with thousands, or even millions, of frames. It’s a treasure trove of data, but it’s also a monster…

Analyzing every single frame is often impossible and, let’s be honest, usually pointless. Many adjacent frames are nearly identical. What we really want are the key representative structures that capture the important shapes, or conformations, your molecule adopted. So, how do we find them?

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Protein Property Prediction Using Graph Neural Networks

Proteins are fundamental biological molecules whose structure and interactions underpin a wide array of biological functions. To better understand and predict protein properties, scientists leverage graph neural networks (GNNs), which are particularly well-suited for modeling the complex relationships between protein structure and sequence. This post will explore how GNNs provide a natural representation of proteins, the incorporation of protein language models (PLLMs) like ESM, and the use of techniques like residual layers to improve training efficiency.

Why Graph Neural Networks are Ideal for Representing Proteins

Graph Neural Networks (GNNs) have emerged as a promising framework to fuse primary and secondary structure representation of proteins. GNNs are uniquely suited to represent proteins by modeling atoms or residues as nodes and their spatial connections as edges. Moreover, GNNs operate hierarchically, propagating information through the graph in multiple layers and learning representations of the protein at different levels of granularity. In the context of protein property prediction, this hierarchical learning can reveal important structural motifs, local interactions, and global patterns that contribute to biochemical properties.

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