The GDELT Project
gcdatlas
The real universe, drawn entirely in ASCII. Zoom from Earth to the edge of the observable universe.
Essays
Personal website of Gwern Branwen (writer, self-experimenter, and programmer): topics: psychology, statistics, technology, deep learning, anime. This index page is a categorized list of Gwern.net pages.
GRN · German Reproducibility Network
Working together for trustworthy and useful research
Geomys, a blueprint for a sustainable open source maintenance firm
Announcing Geomys, a small firm of professional maintainers with a portfolio of critical Go projects.
$\texttt{BilbyFlow}$: user-friendly neural posterior estimation for gravitational-wave astronomy
Bayesian inference plays a central role in the new field of gravitational-wave astronomy. However, traditional Bayesian inference with stochastic samplers is computationally expensive, taking hours to days per event. Transformative changes are therefore required to enable the science of next-generation observatories whose event rates and signal-to-noise ratios will increase significantly over the current generation. Recent work has shown that neural posterior estimation (NPE) is a promising path forward. A neural net is trained to approximate the posterior distribution of gravitational-wave parameters, allowing generation of posterior samples in a fraction of the time required by stochastic samplers. In this work, we introduce $\texttt{BilbyFlow}$, which harnesses the power of NPE in the popular $\texttt{Bilby}$ code suite. We use $\texttt{BilbyFlow}$ to analyze a subset of 38 high-mass events from the third LIGO-Virgo-KAGRA Gravitational-Wave Transient Catalog (GWTC-3). For 29 events (76\%), we obtained an importance-sampling efficiency $>$1%, allowing us to produce reliable posterior distributions within 3 min - 1.5 hours. For the other events, with importance-sampling efficiency $\ll$1%, the run time can be as long as 35 hours. We achieve a median importance-sampling efficiency of 7%, which is roughly comparable to the $\texttt{DINGO}$ package. We aim to significantly improve this efficiency with further development to make the runtime more reliably $O(\text{min})$. $\texttt{BilbyFlow}$ is open source and $\texttt{pip}$-installable.
Hyperproblems: New Ways of Doing and Communicating Science - Hyperproblems
Hyperproblems: Hyperproblems are scientific challenges whose scale, complexity, novelty and interdependence overwhelm traditional research models, requiring…
What happened to Science Goodreads and how do we rebuild it? A 65 million dollar question (at least) - CAIROS Blog
The story of the rise and fall of Mendeley
Rainbows End: what lessons can we take from future worlds? - Boris Mann Tech Blog
Vernor Vinge's sci-fi novel Rainbows End and what it can teach us about b2b software
GLM-OCR Technical Report
GLM-OCR is an efficient 0.9B-parameter compact multimodal model designed for real-world document understanding. It combines a 0.4B-parameter CogViT visual encoder with a 0.5B-parameter GLM...
The Scientific Contribution Graph: Automated Literature-based Technological Roadmapping at Scale
Sir Isaac Newton famously wrote, “If I have seen further, it is by standing on the shoulders of giants”. Scientific contributions are rarely developed in isolation, but build upon prior contributions, such as problem framings, experimental methods, and empirical findings. Understanding these prerequisite relationships is important for studying scientific progress, and for automated scientific discovery systems that must reason about which existing capabilities can be used to develop new ones (e.g. Lu et al., 2024; Jansen et al., 2025b; Baek et al., 2025).
Something is afoot in the land of Qwen
I’m behind on writing about Qwen 3.5, a truly remarkable family of open weight models released by Alibaba’s Qwen team over the past few weeks. I’m hoping that the 3.5 …
ATmosphereConf 2026 - Sooraj's Scribbles
My thoughts and experiences as I volunteered at ATmosphereConf 2026!
