Reasoning & Memory
How models think, remember, and retrieve information. Reasoning tokens, RAG pipelines, context engineering, and the memory architectures that make agents useful.
Guides and explainers
Detailed guides and practical technical analysis.
Latest analysis
Recent research, benchmark reviews and technical updates.
Practical tools
Templates for budgets and project planning
BoredTools offers practical spreadsheets for budgets, freelance work and small projects.
Context Is The New Prompt
Prompt engineering hit its ceiling. The teams pulling ahead now are engineering context: retrieval, memory, tool access, not tweaking instructions. Context is the new prompt.
The RAG Reliability Gap: Why Retrieval Doesn't Guarantee Truth
RAG is the industry's default answer to hallucination. The research says it's not enough.
The Budget Problem: Why AI Agents Are Learning to Be Cheap
The next generation of agents will not be defined by peak capability but by their ability to match effort to difficulty. Across every subsystem, the field is converging on the same fix: budget-aware routing.
The Prompt Engineering Ceiling: Why Better Instructions Won't Save You
On frontier models, sophisticated prompting underperforms zero-shot queries. The techniques that made mid-tier models usable are now making frontier models worse.
From Answer to Insight: Why Reasoning Tokens Are a Quiet Revolution in AI
OpenAI's o1 jumped from the 11th to the 83rd percentile on competitive programming. The difference wasn't better data or more parameters; it was reasoning tokens, invisible chains of thought that let models think before they answer.
The Goldfish Brain Problem: Why AI Agents Forget and How to Fix It
Stanford deployed 25 agents that planned a party autonomously. But most production agents today can't remember what you told them ten minutes ago. The memory problem isn't a model limitation; it's an architectural one, and new solutions are emerging.