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Perspectives on agentic AI, coding agents, cloud architecture, and building technology platforms that scale — from our work with enterprise teams.
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Google I/O 2026: AI-Native Ecosystem and Agentic Workflows
Google I/O 2026 reinforces Google's acceleration toward a deeply integrated AI-native ecosystem spanning multimodal generation, agentic workflows, commerce, search, scientific discovery, and XR experiences.
Can Advanced AI Models Detect Every Security Vulnerability?
Advanced AI models can analyse code paths, IAM relationships, and exploit chains remarkably well — but the hardest vulnerabilities are runtime problems, not static reasoning problems.
Memory, Context, and Compaction in Multi-Model Architectures
Part 2 of the multi-model LLM series. The routing layer decides which model handles a request — but the memory and context layers decide what that model actually sees. Get this wrong and your system forgets critical data on every model switch.
Multi-Model LLM Inference with Open Source Models
A practical architecture pattern for running multiple open-source models (Qwen, Gemma, Kimi, Llama, DeepSeek) in production — covering routing, failover, human-in-the-loop, and AWS deployment.
Why Every Organisation Needs an OpenClaw Strategy
AI is no longer a feature — it's an always-on orchestration layer. An OpenClaw strategy gives organisations a control plane for agents: governing how models behave, how costs are controlled, and how security is enforced across every channel.
FLOPs Vary with Use Cases: Sizing Models for Production
FLOPs measure training compute — but in production, what matters is task effectiveness at acceptable cost and latency. A practical guide to right-sizing models, training economically, and deploying with inference cost in mind.
Token Economics: The Hidden P&L of LLM and Video AI Platforms
Why token economics — not model quality — determines GPU fleet size, concurrency limits, tail latency, and gross margin in production LLM and video AI platforms.
Parallel AI Build for Enterprise Systems
AI doesn't have to replace existing enterprise systems overnight. The smart approach is to build AI in parallel with your current workflows — start small, prove confidence, then progressively migrate.
Building Deterministic Agentic Workflows: Practical Control
How to design agentic AI systems that balance autonomy with deterministic control — moving from unpredictable demos to reliable, production-grade autonomous workflows.
Decoding OpenClaw
A deep-dive into the OpenClaw framework — understanding its architecture, capabilities, and implications for building open AI systems.
Running Coding Agents: Local LLM vs Global
A practical comparison of running coding agents on local LLMs versus cloud-hosted models — trade-offs in latency, cost, privacy, and code quality.
Code Agents as Co-Developers: A New Operating Model
Exploring the shift from AI-assisted coding to AI agents that act as genuine co-developers — and the new operating models teams need to adopt.
Inside X's 'For You' Recommendation Engine: A Modern Blueprint for Feeds
Analysing how X (formerly Twitter) architected its recommendation engine — and what platform teams can learn for building personalised content feeds at scale.
Involving More Agents Doesn't Scale: Autonomous Coding
Why throwing more AI agents at a problem doesn't guarantee better results — and the architectural patterns that actually make autonomous coding work at scale.
Building Systems at Scale
Lessons from engineering systems that handle millions of users — covering architecture patterns, trade-offs, and the disciplines that separate prototypes from production.
Best of Cloud-Native: 10 Years of Lessons Building On-Prem PaaS
A decade of hard-won lessons building Platform-as-a-Service on-premises — what cloud-native principles translate, what doesn't, and what's next.
AI is Moving Fast, But There's Space to Build
Cutting through the noise of rapid AI advancement — identifying the stable foundations worth building on versus the moving targets that aren't ready for production.
AI Options for Development Tools
A landscape overview of AI-powered development tools — comparing approaches, evaluating maturity, and identifying which options deliver real productivity gains today.
AI Evaluation: The Critical Path
Why rigorous evaluation is the most underinvested part of AI adoption — and a practical framework for measuring AI system performance in production.
Enterprise Architecture Through an AI Lens
How AI is reshaping enterprise architecture practice — from decision-making frameworks to system design patterns that enable intelligent automation at scale.
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New articles on agentic AI, coding agents, cloud architecture, and enterprise technology — published regularly.
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