AI Tools & Frameworks Directory

In this guide, you will learn the process of generating a year's worth of daily temperature readings, mimicking a seasonal curve that looks like real — all together with device-level metadata, and ready to build based on open-source frameworks.

The LLMOps market is projected to grow from <a href="https://www.

Why Agentic BI threatens an entire profession The post Escaping the Valley of Choice in BI appeared first on Towards Data Science.

OpenAI breaks ground on a 1GW data center project in Michigan as part of Stargate, building AI infrastructure to expand access, create jobs, and support communities.

With the rise of agents, many people have been proclaiming that the age of software as a service (SaaS) is over. Who needs to subscribe to a service when you can create your own software with a few English-language prompts and a few dollars spent on tokens? Your own software, most likely a skill tha...

OpenAI frontier models and Codex are now generally available on AWS, giving enterprises a new path to build with OpenAI through the AWS environments, controls, and procurement workflows they already use. Customers can get started with OpenAI on AWS and move faster from evaluation to production.

The NVIDIA AI Cloud ecosystem is accelerating the global buildout of AI factory infrastructure. Partners are expanding capacity to meet growing demand from enterprises, startups, nations, AI labs and developers scaling agentic AI applications.  NVIDIA AI Clouds are a growing ecosystem of purpose-bui...

Taiwan is home to more than 500 NVIDIA ecosystem partners. More than 1 million NVIDIA MGX rack components for NVIDIA Vera Rubin infrastructure come together in Taiwan, from across 25 factory sites. As Vera Rubin ramps into full production to power agentic AI factories worldwide, that ecosystem spans...

Personal agents are exploding in popularity, with open source projects like OpenClaw and Hermes seeing rapid adoption by AI developer communities on GitHub. Built to adapt to individual preferences and workflows, these agents can interact with applications, generate content, automate repetitive proc...

arXiv:2605.30358v1 Announce Type: new Abstract: Quantum computing remains in the Noisy Intermediate-Scale Quantum (NISQ) era, where the performance is highly constrained to noise. Addressing the limitation often requires hardware-facing capabilities beyond gate-sequence circuit specification, inclu...

arXiv:2605.30374v1 Announce Type: new Abstract: Estimating hip muscle forces and joint moments during gait typically relies on musculoskeletal simulation, which is informative but time-consuming and difficult to apply in clinical settings. This study developed a deep learning framework to predict t...

arXiv:2605.30376v1 Announce Type: new Abstract: Modern time series architectures face a fundamental trade-off: channel-independent models scale well with increasing data volume but ignore critical inter-channel dependencies, while channel-dependent models are expressive but remain ``dimension-bound...

arXiv:2605.30512v1 Announce Type: new Abstract: Generating physics diagrams from text requires strict adherence to physical laws. While current generative models produce visually plausible outputs, they systematically hallucinate force vectors, ignore conservation laws, and violate geometric constr...

arXiv:2605.30542v1 Announce Type: new Abstract: World models for embodied AI must be physically viable: constructed to answer intervention queries by representing the physical structure governing action outcomes, rather than merely predicting future observations. Existing observation-predictive wor...

arXiv:2605.30576v1 Announce Type: new Abstract: Exploration in reinforcement learning for autonomous driving is inherently unsafe: agents must experience novel behaviors to learn, yet exploration can lead to collisions or off-road driving. We propose an uncertainty-aware framework that leverages ex...

Two models working together perform worse than one alone, exposing a critical gap in artificial intelligence capabilities.

In this tutorial, we build a governed AI-agent workflow using Microsoft’s Agent Governance Toolkit as the reference point. We create a Colab-ready implementation where agents do not directly execute tools; instead, every action first passes through a governance layer that checks the agent’s identity...

On the latest episode of Equity, we debate whether tech CEOs are "uniquely prone to AI psychosis."

Structure-guided NER optimization for enterprise GraphRAG systems The post Proxy-Pointer RAG: Eliminating Wasteful Entity & Relations Extraction in Knowledge Graphs appeared first on Towards Data Science.

In this tutorial, we implement a practical use case with Loguru, a powerful, flexible, and production-ready logging library for Python. The post A Coding Implementation on Loguru for Designing Robust, Structured, Concurrent, and Production-Ready Python Logging Pipelines appeared first on MarkTechPos...

Sales teams spend hours every day on tasks that should never see a human. Research a prospect, score them against their fit, and put it all into a CRM. These are repeatable, rule based processes AI workflows driven by multi-agent systems can do all three, with speed and consistency that no human tea...

This article is divided into four parts; they are: • The Problem with Static Batching • Code Example of Static Batching • Continuous Batching: Dynamic Scheduling and Ragged Batching • Full Implementation The simplest way to serve multiple requests together is to use static batching, by grouping them...

In this tutorial, we implement a SkillNet use case as a practical framework for discovering, installing, inspecting, evaluating, and organizing reusable AI skills. The post Build Skill-Augmented AI Agents with SkillNet for Search, Evaluation, Graph Analysis, and Task Planning appeared first on MarkT...