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Coding Agent Leaderboard Upgrade v2.0: Claude Opus 5.5 Leads at 61.7

Artificial Analysis released Coding Agent Index v2.0, replacing DeepSWE with Cognition's FrontierCode v1.1 and tightening repository Q&A scoring; the leaderboard evaluates model-plus-harness combinations. Currently first is Claude Code + Claude Opus 5.5 (max) at 61.7, followed by Claude Code + Sonnet 5.5 at 59.3 and Muse Code + Muse Wonder Full at 58.9; the first OpenAI combination, Codex + GPT-6 Astra, is at 54.7. For the same 21 configurations, the mean across the old and new leaderboards fell from 54.8 to 47.4, with 20 declining, mainly because FrontierCode's mean is about 45.1%, far below DeepSWE's 65.6%.

How to run n8n AI workflows on NVIDIA DGX Spark without calling cloud APIs

n8n Blog published a tutorial on how to deploy a complete model stack locally on a single NVIDIA DGX Spark, enabling n8n workflows to perform transcription, OCR, document Q&A, and image generation without calling cloud APIs. The entire stack exposes an OpenAI-compatible endpoint through LiteLLM; the main model is the NVFP4 version of Qwen3.6-35B-A3B, with a single-user generation speed of 120 tokens/s and an aggregate of 359 tokens/s across eight users. The author's testing found that image generation reduces chat speed to about one-third, and recommends that small teams queue image tasks or schedule them outside working hours.

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Ant Ling closes the single-machine Agentic RL loop with Ling-3.0-tiny and AReno

Ant Ling has published a practice article describing how it used the local post-training toolkit AReno on a DGX Spark to perform Agentic RL on Ling-3.0-tiny, using tic-tac-toe as a minimal validation task to complete the four-step loop of baseline evaluation, Reward definition, post-training, and re-evaluation in the same environment. The experiment used GSPO for 400 training steps; rollout/rewards_mean rose from about -0.5 to around 0.4, response_len dropped to about 850 tokens, and tool calling and move selection became more stable in the re-evaluation. Ling-3.0-tiny is a hybrid reasoning model with 7.9B total parameters and 1.3B activated parameters; BF16, FP8, and INT4 versions have been open-sourced, and AReno provides a Docker reproduction tutorial.

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