The M4 chip made running serious LLMs on a MacBook genuinely practical. Unified memory means the GPU and CPU share the same pool — no copying data back and forth between VRAM and system RAM — and the memory bandwidth on even the base M4 is fast enough to make 7B and 13B models feel … Read more
Ubuntu 24.04 is one of the best environments for running Ollama — the installation is clean, GPU support via CUDA works reliably, and you can run it headlessly on a server or as a persistent service without any drama. Whether you’re setting it up on a local workstation, a home server, or a cloud VM, … Read more
Running large language models locally on Windows used to be a painful experience — CUDA drivers, Python environments, mysterious errors. Ollama changed that. It wraps everything into a single installer and gives you a clean CLI and API to pull and run models in minutes. This guide walks you through installing Ollama on Windows 11, … Read more
Why Memory Is the Hard Problem in AI Agents LLMs are stateless by default — every request arrives without any memory of previous interactions unless the relevant history is explicitly included in the context. For simple chatbots, this is manageable: include the last N conversation turns and the user’s question feels contextually grounded. For AI … Read more
The Case for Intelligent Routing Most LLM applications use a single model for every request. This is the simplest architecture and the right starting point — but it is almost always suboptimal once you have enough production traffic to analyse. The request distribution of any real application is highly heterogeneous: some requests are simple and … Read more
Why Gemini Flash Matters Google’s Gemini Flash family occupies a specific and valuable position in the LLM market: frontier-adjacent quality at economy pricing, with one of the largest context windows available and native multimodal capabilities included at no additional cost. For developers building cost-sensitive production applications, Gemini Flash is worth serious evaluation alongside the more … Read more
The Startup Advantage in AI Startups have a structural advantage in AI adoption that large enterprises do not: the absence of legacy systems, incumbent processes, and organisational inertia that slow enterprise AI deployment. A ten-person startup can ship an AI-powered product feature in a week that would take a large enterprise six months of approval … Read more
Why AI Governance Has Become Urgent For most of the first decade of enterprise AI, governance was optional. Small AI projects had limited scope, limited impact, and limited visibility — the risk of a poorly governed machine learning model was manageable. That is no longer true. LLMs are now embedded in customer-facing products, internal decision-making … Read more
The System Prompt as Product The system prompt is the most important piece of text in your LLM application. It establishes the model’s persona, defines its scope and constraints, shapes its tone and format, provides essential context, and sets the rules for how it handles the full range of inputs it will encounter. A well-engineered … Read more
The Hallucination Problem in Production Hallucination — LLMs generating confident, fluent text that is factually wrong — is the most discussed quality problem in LLM deployment and, in practice, one of the most manageable once you understand its causes and patterns. The framing of hallucination as a random, unpredictable failure mode is misleading. Hallucination follows … Read more
