Practical AI news, automation tips, and real-world insights to help your business stay ahead.
How Australian construction companies are using AI for estimating, project management, drawings, safety, and more. Includes tool comparisons, pricing, and a 30-day adoption roadmap.
A comprehensive guide to AI agents and agentic AI for Australian businesses. Covers the difference between AI agents and agentic AI, real examples, implementation frameworks, platforms, courses, and ROI data.
StepFun released Step 3.7 Flash on May 29, 2026. We deployed it on our NVIDIA DGX Spark within 24 hours. 100% tool call success rate, SWE-Bench PRO 56.3, ClawEval 67.1 (first place). Here is our first-hand review with benchmark comparisons, local deployment guide, and DGX Spark performance data.
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.