Gitanjali Venkatraman does wonderful illustrations of complex subjects (which is why I was so happy to work with her on our Expert Generalists article). She has now published the latest in her series of illustrated guides: tackling the complex topic of <a href="https://www.thoughtworks.com/content/dam/thoughtworks/documents/blog/mainframe_modernisation_illust

If you’re a regular reader of my site, you’ll have noticed that in the last few months I’ve been making a number of “fragments” posts. Such a post is a short post with a bunch of little, unconnected segments. These are usually a reference to something I’ve found on the web, sometimes a small thought of my own. A few years ago, I wouldn’t have covere

Why does AI write like… that (NYT, gift link). Sam Kriss delves into the quiet hum of AI writing. AI’s work is not compelling prose: it’s phantom text, ghostly scribblings, a spectre woven into our communal tapestry. ❄ ❄ ❄ ❄ ❄ <a href="https://coding-is-like-cooking.info/2025/12/test-desidera

Rob Bowley summarizes a study from Carnegie Mellon looking on the impact of AI on a bunch of open-source software projects. Like any such study, we shouldn’t take its results as definitive, but there seems enough there to make it a handy data point. The key point is that the AI code probably reduced the quality of the code base - at least if static code analysis can be trusted to determ

I’ve been on the road in Europe for the last couple of weeks, and while I was there Thoughtworks released volume 33 of our Technology Radar. Again it’s dominated by the AI wave, with lots of blips capturing our explorations of how to use LLMs and similar technology. “Agents” are the big thing these days but we’re also seeing grow

I find most writing on software productivity to be twaddle, but Nicole Forsgren and Abi Noda are notable exceptions. I had a chance to take a look at their new book, published today, and liked it so much I wrote a foreword. <a class = 'more' href = 'https://martinfowler.com/

Unmesh Joshi finds LLMs to be a useful tool, but explains why their help becomes illusory if we use them to shortcut the learning loop that's an essential part of our professional practi

I’m very concerned about the security dangers of LLM-enabled browsers, as it’s just too easy for them to contain the Lethal Trifecta. For up-to-date eyes on these issues, I follow the writings of coiner of that phrase: Simon Willison. Here he examines a post on how OpenAI is thinking about these issues. My takeaways from all

Agentic AI systems are amazing, but introduce equally amazing security risks. Korny Sietsma explains that their core architecture opens up security issues through what Simon Willis

Mathias Verraes writes about the relationship between Domains and Bounded Contexts in Domain-Driven Design. It’s a common myth that there should always be a 1:1 relationship between them, but although it’s sometimes the case, deeper modeling often exposes a more interesting structure. Gary Marcus: (<a href="https://www.nytimes.com/2025/10/16/opinion/ai-specializ

Masked Diffusion Models (MDMs) offer flexible, non-autoregressive generation, but this freedom introduces a challenge: final output quality is highly sensitive to the decoding order. We are the first to formalize this issue, attributing the variability in output quality to the cumulative predictive uncertainty along a generative path. To quantify this uncertainty, we introduce Denoising Entropy, a computable metric that serves as an internal signal for evaluating generative process. Leveraging t

Computational point-of-care (POC) sensors enable rapid, low-cost, and accessible diagnostics in emergency, remote and resource-limited areas that lack access to centralized medical facilities. These systems can utilize neural network-based algorithms to accurately infer a diagnosis from the signals generated by rapid diagnostic tests or sensors. However, neural network-based diagnostic models are subject to hallucinations and can produce erroneous predictions, posing a risk of misdiagnosis and i

Separating signal from noise is central to experimental science. Applying well-established statistical method effectively to LLM evals requires consideration of their unique noise characteristics. We clearly define and measure three types of noise: prediction noise from generating different answers on a given question, data noise from sampling questions, and their combined total noise following the law of total variance. To emphasize relative comparisons and gain statistical power, we propose th

We propose Parallel Token Prediction (PTP), a universal framework for parallel sequence generation in language models. PTP jointly predicts multiple dependent tokens in a single transformer call by incorporating the sampling procedure into the model. This reduces the latency bottleneck of autoregressive decoding, and avoids the restrictive independence assumptions common in existing multi-token prediction methods. We prove that PTP can represent arbitrary autoregressive sequence distributions. P

It has been discovered that latent-Euclidean variational autoencoders (VAEs) admit, in various capacities, Riemannian structure. We adapt these arguments but for complex VAEs with a complex latent stage. We show that complex VAEs reveal to some level Kähler geometric structure. Our methods will be tailored for decoder geometry. We derive the Fisher information metric in the complex case under a latent complex Gaussian with trivial relation matrix. It is well known from statistical information th

Minimizing PDE-residual losses is a common strategy to promote physical consistency in neural operators. However, standard formulations often lack variational correctness, meaning that small residuals do not guarantee small solution errors due to the use of non-compliant norms or ad hoc penalty terms for boundary conditions. This work develops a variationally correct operator learning framework by constructing first-order system least-squares (FOSLS) objectives whose values are provably equivale

The data processing inequality is an information-theoretic principle stating that the information content of a signal cannot be increased by processing the observations. In particular, it suggests that there is no benefit in enhancing the signal or encoding it before addressing a classification problem. This assertion can be proven to be true for the case of the optimal Bayes classifier. However, in practice, it is common to perform "low-level" tasks before "high-level" downstream tasks despite

Solving partial differential equations (PDEs) on shapes underpins many shape analysis and engineering tasks; yet, prevailing PDE solvers operate on polygonal/triangle meshes while modern 3D assets increasingly live as neural representations. This mismatch leaves no suitable method to solve surface PDEs directly within the neural domain, forcing explicit mesh extraction or per-instance residual training, preventing end-to-end workflows. We present a novel, mesh-free formulation that learns a loca

Background: Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) affects ~33% of U.S. adults and is the most common chronic liver disease. Although often asymptomatic, progression can lead to cirrhosis. Early detection is important, as lifestyle interventions can prevent disease progression. We developed a fair, rigorous, and reproducible MASLD prediction model and compared it to prior methods using a large electronic health record database. Methods: We evaluated LASSO logistic reg

The remarkable empirical performance of distributional reinforcement learning (RL) has garnered increasing attention to understanding its theoretical advantages over classical RL. By decomposing the categorical distributional loss commonly employed in distributional RL, we find that the potential superiority of distributional RL can be attributed to a derived distribution-matching entropy regularization. This less-studied entropy regularization aims to capture additional knowledge of return dist