Analysis
217 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 141 to 160 of 217
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.
Subquadratic's 12 Million Token AI: What It Means for Australian Businesses
A Miami startup just launched an AI model with a 12 million token context window at one-fifth the cost. Here is what that means for your business and why context windows matter more than you think.
AI Agent Managers: Why Your Business Needs Them and How Flowtivity Builds the Agents
Harvard Business Review says companies need agent managers to thrive in the AI era. Here is what that means for your business and how Flowtivity handles the technical side so you can focus on what you do best.
How We Manage AI Agents for Australian Businesses: A Multica Case Study
Real-world case study of using Multica to coordinate multiple AI agents for Australian SMB clients. Learn how Flowtivity reduced agent management overhead by 70% while scaling from 2 to 8 concurrent AI agents across consulting engagements.
The Path to a Super Agent: From ChatGPT Prompts to Autonomous AI Systems
A practical guide to evolving from simple ChatGPT prompts to autonomous AI super agents with sandbox execution. Covers the agent capability hierarchy, multi-model architecture for cost management, and why Australian businesses are uniquely positioned for the super agent transition.
ByteDance DeerFlow Superagent Review: The Open-Source Multi-Agent Framework
ByteDance DeerFlow is an open-source multi-agent framework that orchestrates specialized AI agents for complex tasks. Full review with architecture, benchmarks, and comparison.
Qwen 3.6-27B Is the Model That Makes Autonomous Agent Fleets Affordable
Uber blew its entire 2026 AI budget in 4 months running Claude Code. Qwen 3.6-27B scores 77.2% on SWE-bench, runs on a single RTX 5090, and costs $209/month instead of thousands. Plus: why it beats DeepSeek V4 Flash for agent fleets.
Multica vs Paperclip vs Claude Managed Agents: Which AI Agent Platform Wins in 2026?
Multica, Paperclip, and Claude Managed Agents all manage AI coding agents but serve very different teams. Full feature comparison, pricing, and which one to pick.
Browser Harness: Why Your AI Agent Needs Direct Browser Control (Not Another Framework)
Browser Harness gives AI agents raw CDP access to Chrome with no abstractions, no wrappers, and no rails. The agent writes what missing.
Why Phantom Is the AI Agent Framework Your Business Should Be Watching
Phantom by Ghostwright gives AI its own computer, persistent memory, and self-evolving capabilities. Here's why this open-source agent framework matters for growing businesses.
Autogenesis: The Protocol That Lets AI Agents Evolve Themselves
A deep dive into the Autogenesis Protocol (ICML 2026) — a two-layer architecture that lets AI agents safely evolve their own prompts, tools, and behaviour with full version tracking and rollback.
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