Foundation models are reshaping computational biology. Adapting models to a specific task is nontrivial, so to reduce the difficulty of building these workflows, @nvidia BioNeMo Recipes provide step-by-step training recipes built on familiar PyTorch, Hugging Face, and other patterns. This post walks through two case studies that show how the same parameter-efficient and readable recipe applies across biological modalities. Read the full post:

"Foundation models are now better technically than most people." "OpenAI and Anthropic can subsume any business that only relies on technical talent." "Regulatory moats take years and a lot of trust. There's a lot involved beyond just technology that cannot be vibe-coded." @neerajprasad on why @MoonPay acquired Helio, Iron, Sodot, and Dawn in 18 months: "There are four moats in launching any product today. Technical talent, capital, regulatory licensing, and distribution. At MoonPay we have all four." "We genuinely are playing to win." Neeraj's full thesis on the un-vibe-codeable moats in crypto infrastructure:

Circle Foundation is supporting Pacific Community Ventures with a grant for the Radiant Data Hub, an AI-enabled platform bringing data governance, predictive modeling, and impact tools to community lenders.

Circle Foundation is supporting Accion Opportunity Fund with a grant for Credit Compass 2.0, a tool that helps small business owners better understand loan eligibility and take practical steps toward accessing capital.

Circle Foundation is announcing its inaugural domestic grants to Accion Opportunity Fund and Pacific Community Ventures. Both organizations are using AI-enabled infrastructure to help expand access to capital for underserved small business owners. The grants reflect Circle Foundation’s approach to financial inclusion: investing in systems that help mission-driven lenders reach further, improve decisioning, and better demonstrate impact.

Tabular foundation models have mostly just been predicting a single target variable. A new paradigm learns the relationships across an entire table instead. Title: LimiX-2: A Contextual Mechanism Network Towards General Structured-Data Intelligence URL: 📝 Overview Contextual Mechanism Networks (CMN) learn the full joint distribution p(x, y) across a table, not just the target y, letting a single model handle classification, regression, missing-value imputation, and causal discovery. ❗ Problem it solves Existing PFN-based methods specialize in conditional prediction of the target only, without explicitly modeling the dependency structure between variables. ⚙️ Methodology Cell-level embeddings feed a 24-layer dual-axis transformer, trained via "Context-Conditional Masked Modeling" that masks part of the query rows and jointly predicts both features and targets. It's pretrained at scale on synthetic data generated from structural causal models. 📊 Results On TabArena (51 datasets), it hits an Elo of 1935 vs. 1818 for the baseline; on BCCO (156 datasets), cumulative wins are 2.06x higher. It also took 1st place on F1 score for causal skeleton recovery on all 6 tested datasets, all while using 4x fewer parameters than TabFM. 🔬 Use cases Since the same model handles classification, regression, and causal discovery, it could replace much of a data analysis pipeline with a single foundation model. #TabularData# #MachineLearning#