AI
Practical guides for developers working with AI tools. Prompt engineering, custom instructions, and integration patterns for ChatGPT, Claude, and other LLMs.
Claude Now Watermarks Everything It Writes, and There Is No Flag to Turn It Off
Anthropic swapped the sampler's randomness for a keyed choice. The mechanism, the length threshold, what survives editing, and why there is no opt-out.
Where AI Actually Went in 2026: Agents, Context, and the Quiet Wins
Past the hype: agents that ship, context engineering, MCP as a standard and multi-agent orchestration - what's real in 2026 and what's still a demo.
The AI Releases That Actually Mattered This June
An opinionated roundup of the June 2026 model releases - GPT-5.5, Gemini 3.5, Claude Sonnet 4.8, Mellum2 and Nemotron 3.5 - and which ones matter.
Six Months with Claude Code: The Workflow Habits That Cut My Token Spend
Six months with AI coding agents: when to delegate, how to manage context, and the habits that separate productive agent use from expensive frustration.
Model Context Protocol, Honestly: What MCP Solves and Where It Still Hurts
What MCP is, how its client-server architecture works, how it compares with function calling, and an honest assessment of where it still hurts.
Eight Prompt Engineering Patterns I Use Daily — and Four That Did Nothing
Structured output, chain-of-thought, few-shot examples, role framing and constraints - the prompt patterns that hold up, and four that did nothing.
AGENTS.md Makes Your AI Coding Agent Worse - and Now There's Research to Prove It
ETH Zurich's research confirms it: bloated AGENTS.md files make AI coding agents slower, pricier and less accurate. What the paper found, and what works.
The 4.5× Token Tax of Bloated AI Custom Instructions (and How to Cut It)
How custom instructions for ChatGPT, Claude and Cursor inflate every prompt, what that costs in tokens, and how a lean instruction set compares.
A Local RAG Chatbot for Your Internal Wiki: Ollama, ChromaDB, Docker, Zero API Keys
A local RAG assistant that indexes your docs with vector embeddings and answers from a local LLM. FastAPI backend, browser UI, full Docker Compose setup.
After a £47 OpenAI Bill on a Side Project, I Moved to Local LLMs. Here's the Stack.
Run Ollama in Docker Compose and build a Python CLI that clusters customer reviews by theme and writes a structured report. No API keys, no monthly bill.
Less Prompt Craft, More Context Engineering
The pattern across everything written here is that the wins came from managing context, not from clever wording. Eight prompt patterns is the practical starting point, and it is deliberately honest about the four that made no measurable difference. Six months with Claude Code covers the working habits - when to delegate a whole task and when to drive it turn by turn.
Custom instructions are where most people lose money without noticing. Every instruction is re-sent on every request, so a bloated set is a permanent tax rather than a one-off cost; the token tax breakdown puts real numbers on it. There is now research pointing the same way, covered in the AGENTS.md study, which found that bigger context files made agents worse rather than better. If you would rather start from something tested than a blank file, the custom instructions templates cover ChatGPT, Claude and Cursor.
Two builds go end to end rather than staying theoretical. The local feedback analyser replaces a per-token API bill with Ollama in Docker Compose and a Python CLI that clusters reviews by theme. The local RAG chatbot indexes your own documents with vector embeddings and answers from them, with a FastAPI backend and no API keys anywhere. Both run entirely on your machine.
For the wider picture, MCP explained covers the tool-integration protocol and where it still hurts, while the 2026 trends piece and the June release roundup sort what shipped from what demoed.