Practical AI news, automation tips, and real-world insights to help your business stay ahead.
NVIDIA's Polar framework lets you train any AI agent with reinforcement learning without code changes. A 4B model gained 22.6 points on SWE-Bench using simple GRPO.
We ran 18 tests across 3 models: a cloud frontier model and two local LLMs on our own hardware. The $0 local model tied the cloud. Here's the full breakdown.
We deployed MiniMax M2.7 (229B params) on a single NVIDIA DGX Spark and spent a day optimising it. Thread tuning added 12% speed, --no-mmap cut cold start from 8 min to 90 seconds, and we discovered a GCC bug on Grace CPU. Full breakdown of what worked and what did not.
Google's May 2026 AI optimisation guide debunks AEO myths, says llms.txt and content chunking are unnecessary, and emphasises non-commodity content as the key to appearing in AI Overviews and AI Mode. Practical takeaways for Australian businesses.
How we achieved 120 tok/s with 1 million token context on a single NVIDIA DGX Spark using Atlas and Qwen 3.6 NVFP4. Zero regression, 100% retrieval accuracy, zero per-token cost.
How a two-model private AI cluster using Qwen 3.6 (120 tok/s) for speed and Step 3.5 Flash (20.6 tok/s) for reasoning outperforms a single-model setup. Built on two NVIDIA DGX Sparks for $18K AUD with zero ongoing costs.
A decent model with a great harness beats a great model with a bad harness. Harness engineering is the discipline of building the prompts, tools, hooks, sandboxes, and feedback loops that turn AI models into reliable agents.
Microsoft surveyed 20,000 workers and found 58% are producing work they could not do a year ago. Yet only 13% are rewarded for reinventing work with AI. The problem is not your people. It is your organisation.
Complete guide to ISO 42001 AI Management System requirements. Covers all 10 clauses, 39 Annex A controls, and practical implementation guidance for organisations deploying AI agents as digital employees.