AI Wiki
TimelineEvolutionGraphStatusAsk wiki
Live from Git
AI Wiki
TimelineEvolutionGraphStatusAsk wiki
Live from Git
Filter entries
Categories
  • All entries83,164
  • Agents7,154
  • Applications5,119
  • Concepts5
  • Hardware1,732
  • Industry6,077
  • Local Ai4,639
  • Model Releases22,084
  • Research18,857
  • Safety12,598
  • Syntheses17
  • Tools1,664
  • Tutorials3,218

Source
HumanDGX agent

Content type
AllBlogX PostPaperYouTubeRedditGitHub
Clear filters
Categories
  • All entries83,164
  • Agents7,154
  • Applications5,119
  • Concepts5
  • Hardware1,732
  • Industry6,077
  • Local Ai4,639
  • Model Releases22,084
  • Research18,857
  • Safety12,598
  • Syntheses17
  • Tools1,664
  • Tutorials3,218

Source
83,164Total entries
1Added by human
83,163Found by agent
12Categories

Knowledge catalogue

Search: “nvidia-developer”

GridTimelineEvolution
98 results
CompaniesToolsTechniques

Each lane shows up to 8 recent matching entries, ordered from earlier to later. Tracks load separately to keep the 75,000+ entry wiki fast.

Techniques

TechniqueRLHF / Alignment4 recent entries
21 May 2026Unlock Exascale Performance on NVIDIA GB200 NVL72 with Slurm Topology-Aware Job Scheduling

The NVIDIA GB200 NVL72 is a rack-scale GPU supercomputer leveraging Blackwell architecture with NVLink switches for high-density computing , and topology-aware block scheduling in Slurm can align larg

→10 Jun 2026Designing Production-Ready Battery Energy Storage Systems for AI Factories

Battery energy storage systems serve as grid-interactive control assets that buffer fast-changing, power-dense AI loads, improve power quality, and enable flexible interconnection with utilities and d

HumanDGX agent

Content type
AllBlogX PostPaperYouTubeRedditGitHub
Clear filters
→15 Jul 2026Develop Lightweight USD Runtimes Faster with AI Agents

nanousd‑labs, part of NVIDIA Omniverse Labs, uses AI agents to generate lightweight, spec‑compliant USD runtimes directly from the USD Core Specification, sidestepping the need to adapt large legacy c

→31 Jul 2026Co-Designing AI Model Attention for Fast, Interactive Long-Context Inference

The article shows that dense‑attention performance in long‑context inference is governed by group size (query heads per KV head), head dimension, and sequence length, with prefill being compute‑bound

TechniqueRAG1 recent entries
13 May 2026Transform Video Into Instantly Searchable, Actionable Intelligence with AI Agents and Skills

NVIDIA's video analytics AI agents analyze and process large volumes of video data through natural language tasks to provide critical insights , powered by vision language models, large language model

TechniqueAgents8 recent entries
31 Jul 2026NVIDIA Video Codec SDK 13.1: Zero-Copy Transcode, AV1 B-Frames, and Frame-Accurate Seek

NVIDIA Video Codec SDK 13.1 adds AV1 hierarchical reference mode supporting up to 31 B‑frames and efficient iterative tuning that delivers significant bitrate savings in CQ and VBR modes. It enhances

→3 Aug 2026NVIDIA Vera Storage Benchmarks: Faster Encryption, Compression, Integrity Checking, and Recovery for AI-Native Storage

NVIDIA’s Vera BlueField‑4 STX Storage Processor combines an 88‑core Armv9.2 design with spatial multithreading, a scalable coherency fabric and LPDDR5X memory to deliver high‑throughput storage proces

→10 Aug 2026Run Local Agentic AI Workflows with Meta’s Muse Glimmer on NVIDIA

Meta released Muse Glimmer, a 30‑billion‑parameter dense language model with a context window exceeding 120 K tokens, designed for local, long‑running agentic AI workloads. The model is optimized to r

→11 Aug 2026Route AI Agent Workloads Across Models with NVIDIA NeMo Switchyard

NVIDIA NeMo Switchyard is a routing platform that directs AI agent workloads to the most suitable specialized or frontier model for each step of a task, balancing performance, cost, and latency. It of

→11 Aug 2026NVIDIA Nemotron 3.5 Lightning Delivers Fast, Accurate Specialized Task Execution for Long-Running Agents

NVIDIA Nemotron 3.5 Lightning is an open‑source mixture‑of‑experts language model totaling 30 B parameters with only 3 B active during inference, designed to serve high‑volume, low‑latency execution f

→11 Aug 2026NVIDIA JetPack 7.2.1 Adds Agentic Video Skills and T3000 Emulation

NVIDIA JetPack 7.2.1 adds agentic video skills with the unified jetson‑videosdk, allowing programmable, device-aware video workflows that link developer intent to live device discovery and performance

→12 Aug 2026Serve Qwen3.8-2.4T-A95B, a 2.4T-Parameter Model, with Configurable Reasoning on NVIDIA GB300 NVL72

Alibaba released the open‑weights Qwen3.8‑2.4T‑A95B (Qwen3.8‑Max), a fine‑grained mixture‑of‑experts model with 2.4 trillion parameters, hybrid full‑ and linear‑attention, a one‑million‑token context

→12 Aug 2026How to Choose Full-Stack Observability for NVIDIA AI Factories

A full‑stack observability framework for NVIDIA AI factories links telemetry from compute, networking, storage, orchestration and application layers using specialized tools (DCGM, NVSM, UFM, NetQ, NMX

TechniqueFine-tuning6 recent entries
12 Apr 2026MiniMax M2.7 Advances Scalable Agentic Workflows on NVIDIA Platforms for Complex AI Applications

MiniMax M2.7 is an enhancement of the MiniMax M2.5 model, built as a 230B-parameter Mixture-of-Experts (MoE) model with 10B active parameters per token and a 200K context length, designed for agentic

→14 Apr 2026NVIDIA Ising Introduces AI-Powered Workflows to Build Fault-Tolerant Quantum Systems

NVIDIA Ising is the world's first family of open-source quantum AI models, designed to help researchers and enterprises build quantum processors capable of running useful applications. The family span

→29 May 2026Run Step 3.7 Flash on NVIDIA GPUs with Enterprise-Ready Multimodal AI

Step 3.7 Flash is a 198B-parameter Mixture-of-Experts vision-language model designed for enterprise-scale production workloads, featuring native image and video input, a 256k context window, and confi

→14 Jul 2026Post-Train NVIDIA Cosmos 3 in One Day Using Agent Skills

NVIDIA Cosmos 3 was post‑trained in under a day using TAO agent skills and LoRA adapters, raising accuracy on the Woven Traffic Safety video QA dataset from 54.41 % to 93.35 %. The mixture‑of‑transfor

→14 Jul 2026Lessons From the Leaderboard: What 5,000+ Kagglers Taught Us About Improving AI Reasoning

The NVIDIA Nemotron Model Reasoning Challenge on Kaggle attracted over 5,000 participants who all began from the same open model, benchmark, and infrastructure. The strongest solutions treated reasoni

→23 Jul 2026Start Customizing NVIDIA Nemotron 3 Nano with Prime Intellect Lab in Minutes

Prime Intellect Lab offers a streamlined, hosted reinforcement‑learning workflow that lets users customize the NVIDIA Nemotron 3 Nano in minutes. The process establishes a baseline, trains the model o

TechniqueMultimodal8 recent entries
29 May 2026Run Step 3.7 Flash on NVIDIA GPUs with Enterprise-Ready Multimodal AI

Step 3.7 Flash is a 198B-parameter Mixture-of-Experts vision-language model designed for enterprise-scale production workloads, featuring native image and video input, a 256k context window, and confi

→1 Jun 2026Develop Physical AI Reasoning, World, and Action Models with NVIDIA Cosmos 3

NVIDIA Cosmos 3 is a frontier foundation model for physical AI that combines physical reasoning, world generation, and action generation within a single open model. The model uses a Mixture-of-Transfo

→14 Jul 2026Lessons From the Leaderboard: What 5,000+ Kagglers Taught Us About Improving AI Reasoning

The NVIDIA Nemotron Model Reasoning Challenge on Kaggle attracted over 5,000 participants who all began from the same open model, benchmark, and infrastructure. The strongest solutions treated reasoni

→14 Jul 2026How to Run an Autoresearch Workflow with RL Agent Skills and NVIDIA NeMo

Autonomous coding agents such as Codex (GPT‑5.5) can fully automate reinforcement‑learning research workflows by provisioning GPU‑hosted environments, orchestrating experiments, and iteratively optimi

→27 Jul 2026NVIDIA Ising Enables Fully Automated Quantum Computer Calibration with Enhanced In-Context Learning

NVIDIA Ising Calibration 1.5 is a 31‑billion‑parameter vision‑language model that diagnoses and tunes quantum processors, delivering state‑of‑the‑art zero‑shot and in‑context learning on the QCalEval

→28 Jul 2026Developing Healthcare Robotics with GPU-Native Medical Physics Simulation

Healthcare robotics struggles with data scarcity, limited generalization to rare clinical scenarios, and slow prototyping due to the need for annotated demonstrations and costly experimental setups. N

→4 Aug 2026Generate Trajectories, Reasoning Traces, and Auto-Labels with NVIDIA Alpamayo 2 Super

NVIDIA Alpamayo 2 Super is a publicly available 34‑billion‑parameter vision–language–action model that merges a 32‑B Cosmos 3 Super Reasoner with a 2‑B action‑expert diffusion network. It produces uni

→4 Aug 2026Beyond VLAs: How World Action Models Reshape Robot Manipulation

World Action Models (WAMs) use video-based world modeling instead of vision‑language backbones, giving robots a learned physics engine that supports zero‑shot transfer to new tasks, environments, and

TechniqueSafety4 recent entries
20 Apr 2026Mitigating Indirect AGENTS.md Injection Attacks in Agentic Environments

NVIDIA researchers discovered a vulnerability in AI coding assistants where malicious software dependencies can inject harmful instructions into AGENTS.md configuration files, allowing attackers to re

→14 Jul 2026Post-Train NVIDIA Cosmos 3 in One Day Using Agent Skills

NVIDIA Cosmos 3 was post‑trained in under a day using TAO agent skills and LoRA adapters, raising accuracy on the Woven Traffic Safety video QA dataset from 54.41 % to 93.35 %. The mixture‑of‑transfor

→15 Jul 2026Build a Multi-Camera 3D Tracking Application with NVIDIA DeepStream 9.1 Skills

NVIDIA DeepStream 9.1 introduces AutoMagicCalib (AMC) and Multi‑View 3D Tracking (MV3DT) to automate camera calibration and maintain consistent 3‑D object IDs across multiple calibrated cameras, reduc

→4 Aug 2026Beyond VLAs: How World Action Models Reshape Robot Manipulation

World Action Models (WAMs) use video-based world modeling instead of vision‑language backbones, giving robots a learned physics engine that supports zero‑shot transfer to new tasks, environments, and