From Ad Hoc Prompting to Repeatable AI Workflows with Claude Code Skills
How I turned LLM persona interviews into a repeatable customer research workflow
The post From Ad Hoc Prompting to Repeatable AI Workflows with Claude Code Skills appeared first on Towards Data Science.
OpenAI teams up with Infosys to bring AI tools to more businesses
Infosys said the integration will be used to help its clients modernize software development, automate workflows and deploy AI systems, initially focusing software engineering, legacy modernization, and DevOps.
Design has traditionally required multiple roles working in sequence: a strategist to define the problem, a designer to shape the solution, and a developer to build it. This means coordinating timelines, aligning opinions, and going through rounds of iteration before anything tangible is created. Cl...
Learn faster, build smarter, and unlock the full power of Claude Code through real examples, reusable templates, prompts, workflows, subagents, and system design.
NVIDIA and Google Cloud Collaborate to Advance Agentic and Physical AI
NVIDIA and Google Cloud have collaborated for more than a decade, co‑engineering a full‑stack AI platform that spans every technology layer — from performance‑optimized libraries and frameworks to enterprise‑grade cloud services. This foundation enables developers, startups and enterprises to push ...
The following article originally appeared on the Asimov’s Addendum Substack and is being republished here with the author’s permission. Are LLMs reliable? LLMs have built up a reputation for being unreliable. Small changes in the input can lead to massive changes in the output. The same prompt run t...
AI needs a strong data fabric to deliver business value
Artificial intelligence is moving quickly in the enterprise, from experimentation to everyday use. Organizations are deploying copilots, agents, and predictive systems across finance, supply chains, human resources, and customer operations. By the end of 2025, half of companies used AI in at least t...
Workspace agents in ChatGPT are Codex-powered agents that automate complex workflows, run in the cloud, and help teams scale work across tools securely.
Photon Releases Spectrum: An Open-Source TypeScript Framework that Deploys AI Agents Directly to iMessage, WhatsApp, and Telegram
For all the progress made in AI agent development over the past few years, one fundamental problem has remained largely unsolved: most people never actually interact with agents. They live behind developer dashboards, inside specialized apps that users are asked to download, and within chat interfac...
OpenAI Open-Sources Euphony: A Browser-Based Visualization Tool for Harmony Chat Data and Codex Session Logs
Debugging an AI agent that runs for dozens of steps: reading files, calling APIs, writing code, and revising its own output, is not like debugging a regular function. There is no single stack trace to read. Instead, developers are left staring at hundreds of lines of raw JSON, trying to reconstruct ...
Compile to Compress: Boosting Formal Theorem Provers by Compiler Outputs
arXiv:2604.18587v1 Announce Type: new
Abstract: Large language models (LLMs) have demonstrated significant potential in formal theorem proving, yet state-of-the-art performance often necessitates prohibitive test-time compute via massive roll-outs or extended context windows. In this work, we addre...
Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning
arXiv:2604.18639v1 Announce Type: new
Abstract: Previous LLMs-based RL studies typically follow either supervised learning with high annotation costs, or unsupervised paradigms using voting or entropy-based rewards. However, their performance remains far from satisfactory due to the substantial ann...
FASE : A Fairness-Aware Spatiotemporal Event Graph Framework for Predictive Policing
arXiv:2604.18644v1 Announce Type: new
Abstract: Predictive policing systems that allocate patrol resources based solely on predicted crime risk can unintentionally amplify racial disparities through feedback driven data bias. We present FASE, a Fairness Aware Spatiotemporal Event Graph framework, w...
Curiosity-Critic: Cumulative Prediction Error Improvement as a Tractable Intrinsic Reward for World Model Training
arXiv:2604.18701v1 Announce Type: new
Abstract: Local prediction-error-based curiosity rewards focus on the current transition without considering the world model's cumulative prediction error across all visited transitions. We introduce Curiosity-Critic, which grounds its intrinsic reward in the i...
Beyond One Output: Visualizing and Comparing Distributions of Language Model Generations
arXiv:2604.18724v1 Announce Type: new
Abstract: Users typically interact with and evaluate language models via single outputs, but each output is just one sample from a broad distribution of possible completions. This interaction hides distributional structure such as modes, uncommon edge cases, an...
ARES: Adaptive Red-Teaming and End-to-End Repair of Policy-Reward System
arXiv:2604.18789v1 Announce Type: new
Abstract: Reinforcement Learning from Human Feedback (RLHF) is central to aligning Large Language Models (LLMs), yet it introduces a critical vulnerability: an imperfect Reward Model (RM) can become a single point of failure when it fails to penalize unsafe beh...
AI scientists produce results without reasoning scientifically
arXiv:2604.18805v1 Announce Type: new
Abstract: Large language model (LLM)-based systems are increasingly deployed to conduct scientific research autonomously, yet whether their reasoning adheres to the epistemic norms that make scientific inquiry self-correcting is poorly understood. Here, we eval...
Quantum inspired qubit qutrit neural networks for real time financial forecasting
arXiv:2604.18838v1 Announce Type: new
Abstract: This research investigates the performance and efficacy of machine learning models in stock prediction, comparing Artificial Neural Networks (ANNs), Quantum Qubit-based Neural Networks (QQBNs), and Quantum Qutrit-based Neural Networks (QQTNs). By outl...
Hugging Face Releases ml-intern: An Open-Source AI Agent that Automates the LLM Post-Training Workflow
Hugging Face has released ml-intern, an open-source AI agent designed to automate end-to-end post-training workflows for large language models (LLMs). Built on the company’s smolagents framework, the tool can autonomously perform literature review, dataset discovery, training script execution, and i...