When DeepSeek released R1 in January 2025, it did more than establish a new leaderboard position — it opened a completely new paradigm for open-weight AI. For the first time, a model approaching o1-level reasoning was freely available, and the community built an entire ecosystem around it within weeks. What Made R1 Different Previous open models optimized for next-token prediction. R1 optimized for reasoning chains — the model learns to think step by step, generating explicit “thinking” tokens before arriving at an answer. This is achieved through reinforcement learning using Group Relative Policy Optimization (GRPO) , a technique that: ...
The Rise of Multimodal AI: Bridging Text, Vision, and Beyond
The past year has made one thing clear: text-only AI was the warm-up act. The industry has pivoted decisively to multimodality — models that process text, images, audio, and video in a single unified architecture. The State of Play Every major frontier model now ships with vision. OpenAI’s GPT-4o set the pace with native image understanding and generation. Google’s Gemini 2.5 Pro brought million-token context windows to multimodal inputs. Anthropic’s Claude 4 extended the Sonnet and Opus lines with sophisticated visual reasoning. ...
Welcome to AI Showcase
Welcome to AI Showcase — a new space where I explore, experiment with, and write about the rapidly evolving world of open-source AI. Why This Blog Over the past year, I’ve been deep in the open-source AI ecosystem: running models locally, building tools around them, and watching the landscape shift at breakneck speed. From the first time I pulled a quantised Llama model and ran it on my own laptop, I knew this was something worth documenting. The gap between what closed APIs offer and what open models can do locally is closing faster than most people realise. ...
Practical Tips for Setting Up Your Own AI Stack
You Already Have What You Need The most common misconception about running AI locally is that you need expensive hardware. You probably don’t. The vast majority of useful local AI work can be done on hardware you already own — you just need to know which models to run and how to configure things. This guide skips the theory and gets straight to what works, based on setting up stacks on half a dozen machines with different specs. ...
Building Agentic Systems with Local Models
The Agent Shift The LLM space pivoted hard toward agents over the past year. A model that can only chat is useful. A model that can call tools, browse the web, write files, and execute code is transformative. The challenge for the local AI community has been getting all of that capability without sending your orchestration to a cloud provider. The good news: local agent systems are not only possible, they’re increasingly competitive. A properly configured local agent running a Qwen 3.5 32B or Llama 4 8B can handle most of what people use cloud agents for — without the latency, cost, or privacy concerns. ...
Privacy-First AI Workflows — Keeping Your Data Where It Belongs
Why Privacy-First Matters Every time you paste code, a confidential email, or personal notes into a cloud AI chat, that data travels to someone else’s server. The privacy policies vary wildly — some providers train on your inputs, others claim they don’t but change terms regularly, and all of them represent a potential breach surface. For anyone handling sensitive information, this should be unacceptable. The good news: you don’t need to give up AI capabilities to keep your data private. A well-configured local stack can match or exceed the convenience of cloud services while ensuring your data never leaves your control. ...
Open-Weight Model Releases and Comparisons — Spring 2026
The State of Open Weights The open-weight model landscape in 2026 is almost unrecognisable from two years ago. Where we once had a handful of options — Llama 2, Mistral 7B, Falcon — we now have multiple model families that genuinely compete with closed frontier models in real-world tasks. The pace of releases has accelerated to the point where keeping track is a full-time job. Here’s where things stand this spring. ...
Running Local LLMs with Ollama and Custom Toolchains
Why Ollama? If you’ve been curious about running large language models on your own machine but bounced off the complexity of llama.cpp build flags, Python environment hell, or CUDA configuration, Ollama is the answer. It wraps all of that into a clean command-line interface that just works — on macOS, Linux, and Windows. ollama pull llama3.2 ollama run llama3.2 That’s it. You’re now running a reasonably capable 3B parameter model on your laptop. No Docker, no Python, no GPU driver struggle. It’s the brew install moment for local AI. ...