Announced today at SAP Sapphire — where NVIDIA founder and CEO Jensen Huang joined SAP CEO Christian Klein’s keynote by video — SAP and NVIDIA’s expanded collaboration helps enterprises run specialized agents with security and governance controls.
Your First WebAssembly Program and Web App (Written, Tested, and Deployed Entirely in the Web Browser)
Compiling and running C code with Emscripten and GitHub Codespaces — no local installation required.
The post Your First WebAssembly Program and Web App (Written, Tested, and Deployed Entirely in the Web Browser) appeared first on Towards Data Science.
Personal AI doesn’t have to run your life to change it. It just must see you clearly and feed your behavior back to you in a way you can’t dodge. Once you look at AI as feedback loops instead of little butlers, the whole “agent” conversation starts to feel upside down. We’ve overrotated on agents […...
8 ways self-evolving AI agents are about to change how we build software
A new paper out of arXiv this week describes an AI system that builds, improves, and deploys its own specialist agents. Here is what that actually means for engineers and technical teams.
Tilde Research Introduces Aurora: A Leverage-Aware Optimizer That Fixes a Hidden Neuron Death Problem in Muon
Researchers at Tilde Research have released Aurora, a new optimizer for training neural networks that addresses a structural flaw in the widely-used Muon optimizer. The flaw quietly kills off a significant fraction of MLP neurons during training and keeps them permanently dead. Aurora comes with a 1...
A Coding Implementation to Portfolio Optimization with skfolio for Building Testing, Tuning, and Comparing Modern Investment Strategies
In this tutorial, we explore skfolio, a scikit-learn compatible portfolio optimization library that helps us build, compare, and evaluate different investment strategies in a structured Python workflow. We start by loading S&P 500 price data, converting it into returns, and creating a time-based tra...
OpenAI Introduces Daybreak: A Cybersecurity Initiative That Puts Codex Security at the Center of Vulnerability Detection and Patch Validation
OpenAI on just launched Daybreak, a cybersecurity initiative that combines the company’s frontier AI models with Codex Security, its coding-focused agentic system, and a broad network of security partners. The program is aimed at developers, enterprise security teams, researchers, and government-lin...
Thinking Machines wants to build an AI that actually listens while it talks
Right now, every AI model you've ever used works the same way. You talk, it listens. It responds, you listen. Thinking Machines is trying to change that by building a model that processes your input and generates a response at the same time, so it's more like a phone call than a text chain.
Reinforcement learning for inverse structural design and rapid laser cutting of kirigami prototypes
arXiv:2605.08098v1 Announce Type: new
Abstract: Kirigami is an increasingly useful fabrication method to produce shape-programmable metamaterial structures. However, inverse design remains difficult because deployment is nonlinear, and feasible cut layouts must satisfy discrete compatibility rules,...
Path-Based Gradient Boosting for Graph-Level Prediction
arXiv:2605.08102v1 Announce Type: new
Abstract: We propose PathBoost, a gradient tree boosting method for graph-level classification and regression that learns discriminative path-based features directly from the input graph structure. Building on a previous work, which was tailored to a specific c...
Geometry-free prediction of inertial lift forces in microfluidic devices using deep learning
arXiv:2605.08109v1 Announce Type: new
Abstract: Inertial microfluidic devices (IMDs) offer low-cost, high-throughput alternative techniques for many traditional particle- (or cell-) manipulation tasks, but simulating them requires being able to predict particle migration, and thus particle lift for...
BaLoRA: Bayesian Low-Rank Adaptation of Large Scale Models
arXiv:2605.08110v1 Announce Type: new
Abstract: Low-Rank Adaptation (LoRA) has become the standard for fine-tuning large pre-trained models at reduced computational cost. However, its low-rank point-estimate updates limit expressiveness, leave a persistent gap relative to full fine-tuning accuracy,...
Spatial Priming Outperforms Semantic Prompting: A Grid-Based Approach to Improving LLM Accuracy on Chart Data Extraction
arXiv:2605.08220v1 Announce Type: new
Abstract: The automated extraction of data from scientific charts is a critical task for large-scale literature analysis. While multimodal Large Language Models (LLMs) show promise, their accuracy on non-standardized charts remains a challenge. This raises a ke...
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria
arXiv:2605.08354v1 Announce Type: new
Abstract: Aligning multimodal generative models with human preferences demands reward signals that respect the compositional, multi-dimensional structure of human judgment. Prevailing RLHF approaches reduce this structure to scalar or pairwise labels, collapsin...
On Distinguishing Capability Elicitation from Capability Creation in Post-Training: A Free-Energy Perspective
arXiv:2605.08368v1 Announce Type: new
Abstract: Debates about large language model post-training often treat supervised fine-tuning (SFT) as imitation and reinforcement learning (RL) as discovery. But this distinction is too coarse. What matters is whether a training procedure increases the probabi...
What Parameter Golf taught us about AI-assisted research
Parameter Golf brought together 1,000+ participants and 2,000+ submissions to explore AI-assisted machine learning research, coding agents, quantization, and novel model design under strict constraints.
GM just laid off hundreds of IT workers to hire those with stronger AI skills
Some of the positions focus on AI-native development, data engineering and analytics, cloud-based engineering, and agent and model development as well as prompt engineering and new AI workflows.
Modern large language models are no longer trained only on raw internet text. Increasingly, companies are using powerful “teacher” models to help train smaller or more efficient “student” models. This process, broadly known as LLM distillation or model-to-model training, has become a key technique f...
Learning Word Vectors for Sentiment Analysis: A Python Reproduction
How to build sentiment-aware word representations from IMDb reviews using semantic learning, star ratings, and linear SVM classification
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