Practical AI news, automation tips, and real-world insights to help your business stay ahead.
We deployed DeepSeek V4 Flash with DSpark speculative decoding on 2x NVIDIA DGX Spark boxes. 49 tok/s, 1M token context, 6-way concurrency, zero API bill. Real numbers, real bugs, real fixes.
Alex Karp went on CNBC and accused AI frontier labs of overselling, overcharging, and extracting enterprise IP. Here's why every Australian business should pay attention.
Nvidia's NVFP4 quantization of Alibaba's Qwen3.6-27B cuts memory by 2.5x with under 1% accuracy loss. The 19.7GB model runs on RTX PRO 6000 and DGX Spark, delivering up to 2,000+ tokens/sec with vLLM and MTP speculative decoding.
Every business sits on mountains of unstructured text. A new generation of LLM-powered extraction frameworks turns documents into databases. This guide covers structured extraction, why hypergraphs beat knowledge graphs, and real use cases for Australian businesses.
A great harness isn't enough. The real engine behind every reliable AI agent is the loop - observe, decide, act, verify. Here's how to engineer loops that actually ship work, the five patterns to choose from, the failure modes to avoid, and how it all connects back to harness engineering. Now updated with peer-reviewed research from PACT 2025 showing 3.54x performance gains from agentic loops in compiler optimization.
Alibaba Qwen team released the first language world model covering 7 agent environments in one model. It beats GPT-5.4 and Claude Opus 4.8 on environment simulation - and makes agents better in the process.
Cua is an MIT-licensed infrastructure framework backed by Y Combinator that lets AI agents control desktop applications across macOS, Linux, and Windows — no APIs required. Here's how it works, how it compares to Browser-Use and OpenCUA, and why it matters for business automation in 2026.
Agentic operations is the next shift in business automation - autonomous AI agents that plan, execute, and learn. Here is how Australian businesses can build them practically.
SkillOpt treats markdown skill documents as trainable parameters, lifting GPT-5.5 accuracy by +23.5 points without fine-tuning. Here is what it is, how it works, and why it matters.