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Overall usage remains low but steady (typical DAU ~2–3), with a noticeable decline in the latest 7‑day window versus the prior week. A one‑off content spike on Nov 28 (large public asset import and fi

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explores a simple idea: train a model to convert crystallography data from CIF to JSON and then judge how well that JSON could rewrite the original CIF. The policy model, based on a 3B language model with LoRA adapters, performs the forward conversion (CIF → JSON). A separate judge model, kept fixed, evaluates how likely it is to recover the exact CIF from that JSON, by computing a reverse-probability score token by token without actually generating the CIF. This score provides a reward signal for training the policy. The setup uses three parts: the policy (the convertor), the judge (scores round-trips), and a reference model for regularization. Training runs on Modal with three GPUs, using vLLM for judge serving and a careful memory plan. The goal is to create a reliable, reversible representation and to extend the approach to descriptions that generate CIF files.

This post covers platform trends from 2024 week 47 to 2025 week 46. In the short term, activity dropped sharply across usage, creation, and engagement. After a brief spike in early November, weekly active users (WAU) fell to the low single digits, new user adds remained higher than recent averages but still slipped to 25 this week. Content creation essentially stopped (0 assets) and public assets also dropped to 0, while views fell to 623 and engagement (comments and reactions) stayed low. The 12‑week average shows much stronger activity in prior months, especially for views, but the current week is far below that pace.

This post covers platform trends from August 22 to November 19, 2025. It notes that overall usage has fallen in the last 30 to 45 days. Daily active users are staying very low, around 1–3 per day in November, with several days showing no activity. New user sign-ups remain modest, about 2–6 per day, with a few spikes early in November. Despite steady new users, current activity does not follow, pointing to activation or retention issues after sign-up. Creation and engagement are weak in November after larger bulk uploads in September and October. Views, comments, reactions, and newsletter activity have dropped to small numbers, with occasional brief bumps. Monetized assets appear only sporadically. Overall, the trend is a clear decline from September–October. The main challenge seems to be sustaining creation and improving early engagement to convert new users into regular activity.

describes the latest run using a new first-principles DFT calculator to measure magnetocrystalline anisotropy energy via the total energy difference method. More results will follow.

This post explains how to run VASP with GPU acceleration inside Modal. It uses VASP version 6.3.0 and should work for other 6.x.x builds. The idea is to create a Modal Image that has an OpenACC-enabled GPU workflow, based on NVIDIA’s HPC SDK. The result is a self-contained image that can run GPU-accelerated VASP calculations in a serverless Modal environment.

A simple guide for compiling ABACUS to run with GPU acceleration in Modal. The post explains how to build ABACUS with CUDA support and run DFT calculations in a serverless environment. It covers why Modal’s on‑demand GPUs (like A100) can help, and which ABACUS setup (plane waves with basis_type pw and ks_solver bpcg) tends to work best on GPUs in version 3.9.0.

This post shares progress on calculating magnetocrystalline anisotropy energy (MAE) using density functional theory (DFT). The author hoped to use machine learning, but data limits make that unlikely for now, so DFT remains the focus. They emphasize how sensitive MAE is to convergence and accurate electronic structure, a common concern in the field. Two calculation methods are explored: the force-theorem and total energy difference. The force-theorem aims for a balance between speed and accuracy but isn’t fully working yet; issues include needing a specific spin setup and changes in the Fermi level when magnetization directions change. The total energy difference method is simpler and more reliable but far more computationally demanding, requiring several full SCF runs with spin-orbit coupling. Key parameters like k-point spacing, smearing, basis type, and ks_solver influence results and performance. The post notes GPU acceleration and the practical trade-offs, and promises more metrics and a public API later.

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