Agentic AI

Agentic AI AI Agents

Building a VideoAgent-Style Multi-Agent System: Intent Parsing, Graph Planning, and Tool Routing for Video Editing Tasks
ByRicardo July 13, 2026

In this tutorial, we construct a runnable reconstruction of the VideoAgent workflow, specializing in the core agentic pipeline behind video understanding, retrieval, modifying, and remaking. We begin by configuring a light-weight surroundings that works with out API keys. We outline an intent parser, an agent library, a instrument router, a graph planner, and a textual-gradient…

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Agentic AI AI Infrastructure

Stanford Researchers Introduce TRACE: A Capability-Targeted Agentic Training System That Turns Recurrent Agent Failures Into Synthetic RL Environment
ByRicardo July 13, 2026

Agentic LLMs typically fail the identical approach, many times. A Stanford analysis crew traced this to lacking, reusable capabilities. Their system, TRACE, diagnoses these gaps and trains for them immediately. TRACE stands for Turning Recurrent Agent failures into Capability-targeted coaching Environments. It was launched open-source underneath an MIT license. What downside does TRACE resolve? To…

Read More Stanford Researchers Introduce TRACE: A Capability-Targeted Agentic Training System That Turns Recurrent Agent Failures Into Synthetic RL Environment
Agentic AI AI Shorts

Prime Intellect Releases Verifiers v1: Composable Tasksets, Harnesses, and Runtimes for Agentic RL Training and Evaluations
ByRicardo July 13, 2026

Prime Intellect launched verifiers 0.2.0. It previews a rewritten core, shipped beneath the brand new verifiers.v1 namespace. Modern evaluations now run coding brokers with instruments, compaction, and subagents. Accordingly, v1 rebuilds environments to run these agentic workloads at scale. What is verifiers v1? First, contemplate what verifiers is: Prime Intellect’s setting stack for agentic reinforcement…

Read More Prime Intellect Releases Verifiers v1: Composable Tasksets, Harnesses, and Runtimes for Agentic RL Training and Evaluations
Agentic AI AI Infrastructure

Guide to Loop Engineering: How ‘autoresearch’ and ‘Bilevel Autoresearch’ Turn AI Agents Into Autonomous Machine Learning ML Research Loops
ByRicardo July 12, 2026July 12, 2026

Most folks nonetheless use AI like a 2015 search field. You kind, you learn, you kind once more. A more moderen sample replaces that handbook back-and-forth with a loop. This information explains loop engineering utilizing two verified artifacts. The sources are Andrej Karpathy’s autoresearch repository and the Bilevel Autoresearch paper. The framing follows a write-up…

Read More Guide to Loop Engineering: How ‘autoresearch’ and ‘Bilevel Autoresearch’ Turn AI Agents Into Autonomous Machine Learning ML Research Loops
Agentic AI AI Infrastructure

A Coding Guide to NVIDIA’s Tile-Based GPU Programming: From cuTile and Triton Kernels to Flash Attention
ByRicardo July 12, 2026July 12, 2026

In this tutorial, we discover TileGym GPU programming by constructing a sensible Colab workflow that runs throughout completely different {hardware} situations. We start by probing the out there CUDA surroundings, checking whether or not NVIDIA cuTile runs instantly, and falling again to Triton when customary Colab GPUs lack the required cuTile stack. Through this setup,…

Read More A Coding Guide to NVIDIA’s Tile-Based GPU Programming: From cuTile and Triton Kernels to Flash Attention
Agentic AI Applications

How to Build a T4-Friendly Autonomous Data Science Agent with DeepAnalyze-8B, Sandboxed Code Execution, and Iterative Analysis
ByRicardo July 10, 2026

In this tutorial, we construct an autonomous information science agent round DeepAnalyze-8B and run it. We start by getting ready a steady runtime, putting in the required machine-learning dependencies, and loading the DeepAnalyze tokenizer and mannequin in 4-bit mode to preserve the workflow sensible on restricted GPU reminiscence. We then create a sandboxed execution setting…

Read More How to Build a T4-Friendly Autonomous Data Science Agent with DeepAnalyze-8B, Sandboxed Code Execution, and Iterative Analysis
Agentic AI AI Infrastructure

Meet LingBot-World-Infinity: An Open Causal World Model With An Agentic Harness
ByRicardo July 10, 2026July 10, 2026

Robbyant, Ant Group’s embodied-intelligence unit, has launched LingBot-World-Infinity (LingBot-World 2.0). It is a causal video technology mannequin that behaves as an interactive world simulator. It is how the group assaults two failure modes: long-horizon drift and interactive latency. What is LingBot-World-Infinity? An interactive world model generates video frame by frame, conditioned on a stream of…

Read More Meet LingBot-World-Infinity: An Open Causal World Model With An Agentic Harness
Agentic AI AI Agents

OpenAI Releases GPT-5.6 (Sol, Terra, Luna): A Three-Tier Model Family With Programmatic Tool Calling in the Responses API
ByRicardo July 9, 2026

OpenAI simply moved the GPT-5.6 family to common availability, following a restricted preview. The launch ships three fashions moderately than one. Sol is the flagship, Terra is the balanced on a regular basis tier, and Luna is the most cost-efficient. To sum up GPT-5.6 ships three tiers — Sol, Terra, Luna — priced from $1/$6…

Read More OpenAI Releases GPT-5.6 (Sol, Terra, Luna): A Three-Tier Model Family With Programmatic Tool Calling in the Responses API
Agentic AI AI Paper Summary

Meet Nemotron Labs 3 Puzzle 75B A9B: A Compressed Hybrid MoE LLM Delivering 2.03x Server Throughput
ByRicardo July 9, 2026

Large hybrid MoE fashions like Nemotron-3-Super are correct however costly to serve. Their lively parameters, KV cache, and Mamba state cap what number of customers a node can maintain at a given per-user token price. NVIDIA AI staff has launched Nemotron-Labs-3-Puzzle-75B-A9B, a compressed variant of Nemotron-3-Super. The guardian mannequin has 120.7B complete and 12.8B lively…

Read More Meet Nemotron Labs 3 Puzzle 75B A9B: A Compressed Hybrid MoE LLM Delivering 2.03x Server Throughput
Agentic AI AI Paper Summary

NVIDIA Releases Nemotron-Labs-3-Puzzle-75B-A9B: A Compressed Hybrid MoE LLM Delivering 2.03x Server Throughput at Matched User Throughput
ByRicardo July 9, 2026July 9, 2026

Large hybrid MoE fashions like Nemotron-3-Super are correct however costly to serve. Their energetic parameters, KV cache, and Mamba state cap what number of customers a node can maintain at a given per-user token fee. NVIDIA AI group has launched Nemotron-Labs-3-Puzzle-75B-A9B, a compressed variant of Nemotron-3-Super. The guardian mannequin has 120.7B complete and 12.8B energetic…

Read More NVIDIA Releases Nemotron-Labs-3-Puzzle-75B-A9B: A Compressed Hybrid MoE LLM Delivering 2.03x Server Throughput at Matched User Throughput

Agentic AI

Building a VideoAgent-Style Multi-Agent System: Intent Parsing, Graph Planning, and Tool Routing for Video Editing Tasks

Stanford Researchers Introduce TRACE: A Capability-Targeted Agentic Training System That Turns Recurrent Agent Failures Into Synthetic RL Environment

Prime Intellect Releases Verifiers v1: Composable Tasksets, Harnesses, and Runtimes for Agentic RL Training and Evaluations

Guide to Loop Engineering: How ‘autoresearch’ and ‘Bilevel Autoresearch’ Turn AI Agents Into Autonomous Machine Learning ML Research Loops

A Coding Guide to NVIDIA’s Tile-Based GPU Programming: From cuTile and Triton Kernels to Flash Attention

How to Build a T4-Friendly Autonomous Data Science Agent with DeepAnalyze-8B, Sandboxed Code Execution, and Iterative Analysis

Meet LingBot-World-Infinity: An Open Causal World Model With An Agentic Harness

OpenAI Releases GPT-5.6 (Sol, Terra, Luna): A Three-Tier Model Family With Programmatic Tool Calling in the Responses API

Meet Nemotron Labs 3 Puzzle 75B A9B: A Compressed Hybrid MoE LLM Delivering 2.03x Server Throughput

NVIDIA Releases Nemotron-Labs-3-Puzzle-75B-A9B: A Compressed Hybrid MoE LLM Delivering 2.03x Server Throughput at Matched User Throughput

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