Analysis
127 articles
Analysis and opinion on where AI is going and what it means for a business that has to make decisions now.
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Showing 41 to 60 of 127
Loop Engineering: The Feedback Cycle That Turns AI Agents Into Reliable Workers
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.
Qwen-AgentWorld: The AI That Learns by Simulating Reality
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: The Open-Source Framework Giving AI Agents Full Computer Access
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.
The Interoperability Thesis: How OpenClaw Turned AI Agents From Hype Into Infrastructure
The AI industry bottleneck is not model intelligence - it is interoperability. OpenClaw built the protocol layer that makes agentic operations realistic, with natural reinforcement learning baked in.
Kimi K2.7 Code Review: Open-Source 1T Parameter Model Cuts Reasoning Tokens 30%
Moonshot AI's Kimi K2.7 Code is an open-source 1 trillion parameter coding model that reduces reasoning token usage by 30% while posting double-digit benchmark gains over K2.6.
Google's DiffusionGemma: The Model That Writes Entire Paragraphs at Once
Google just dropped DiffusionGemma, a 26B open model that generates text like an AI image generator, not a typewriter. 1000+ tokens per second, Apache 2.0 license, and it can actually solve Sudoku. Here's what it means for builders.
The Week Open-Source AI Went Nuclear: 25+ Open-Weight Drops That Changed Everything
25+ frontier open-weight AI models dropped in one week across every modality. The full breakdown of the most insane week in open-source AI history.
AI for Childcare: How Australian Early Learning Centres Use AI in 2026
How Australian childcare centres are using AI for observations, EYLF documentation, parent communication, and scheduling. Includes tool adoption data, privacy guidance, and implementation plans.
AI for Construction: How Australian Builders Are Using AI in 2026
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.
AI Agents Deep Dive: What Australian Businesses Need to Know in 2026
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.
We Benchmarked Our AI Agent Against Its Own Local LLM and the Results Blew Us Away
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.
How We Optimised a 229 Billion Parameter AI Model on a Desktop Computer: A 12-Phase Journey
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.
We Hit 120 Tokens Per Second With 1 Million Token Context on a Single Desktop AI Computer
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.
Why We Run Two AI Models on Two Desktop Computers Instead of One Big One
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.
Harness Engineering: Why Your AI Agent Scaffolding Matters More Than the Model
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.
The Transformation Paradox: Why Your AI-Ready Employees Are Being Held Back by Your Organisation
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.
ISO 42001 AI Management System Requirements: What Organisations Building Agentic Employees Need to Know
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.
How to Run DeepSeek V4 Flash Locally: ds4 Engine Runs Frontier AI on Your Laptop
The creator of Redis just built ds4, a custom inference engine that runs DeepSeek V4 Flash (284B parameters) locally on a 128GB MacBook. Here is why this changes everything for businesses that want frontier AI without the cloud.
NemoClaw vs Microsoft Agent Framework 1.0 vs Gemini Enterprise Agent Platform: How to Run Compliant AI Agents in 2026
Three platforms, three philosophies. NVIDIA open-source NemoClaw, Microsoft Agent Framework 1.0 (now production-ready), and Google Gemini Enterprise Agent Platform represent the three ways to run compliant AI agents in 2026.
AI Regulation Is Coming to Australia: What NIST, ISO 42001, and the Privacy Act Mean for Your Business
Three major regulatory frameworks are converging in 2026 that will reshape how Australian businesses deploy AI. Here is what NIST AI RMF, ISO 42001, and the Privacy Act amendments mean for your AI strategy.
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