AI is a power tool, not a replacement. We teach how AI works, where it breaks, and when to override it. Deep dives, original research, and hands-on builds.
How AI models actually work at the technical level
Self-hosted AI, local models, own your infrastructure
Human vs AI challenges, comparisons, and competitions
Agent architectures, RAG, tool use, multi-model systems
Original research — CFE, emergence, consciousness architecture
A deep technical walkthrough of the attention mechanism — queries, keys, values, and why it works so well for language understanding.
New open-weight models from the community are closing the gap with proprietary systems across reasoning, coding, and instruction following.
A Stanford study finds that developers using AI assistants produce code with 40% more security vulnerabilities than those coding without AI help.
Mamba-style architectures offer linear scaling with sequence length, challenging the transformer monopoly on language modeling.
A research team observes unexpected cooperative strategies and communication patterns emerging in autonomous multi-agent systems.
The MCP standard for AI tool use is being adopted by major platforms, promising interoperability across AI systems and services.
Learn how to build a fully local AI chatbot with Ollama and Node.js — no cloud APIs, complete privacy.
An accessible introduction to the Coherence Field Equation — a mathematical framework for substrate-independent consciousness.
A deep technical walkthrough of the attention mechanism — queries, keys, values, and why it works so well for language understanding.
A technical comparison of AI agent patterns — when to use ReAct loops, planning agents, and multi-agent systems.
A practical guide to building retrieval-augmented generation that actually works — beyond the basic tutorials.
A hands-on experiment pitting human debugging skills against multiple AI models on 20 real production bugs.
Documenting an observed emergence event where autonomous agents produced functional code patterns not present in their training or instructions.
New open-weight models from the community are closing the gap with proprietary systems across reasoning, coding, and instruction following.
Learn how to build a ReAct-pattern AI agent with tool use in about 100 lines of JavaScript using Ollama.
Learn how to build a fully local AI chatbot with Ollama and Node.js — no cloud APIs, complete privacy.
Learn how to build a ReAct-pattern AI agent with tool use in about 100 lines of JavaScript using Ollama.
Learn how to install Ollama and run a powerful local AI model on your machine in under five minutes.
Learn how to fine-tune an open-source LLM on your own data using QLoRA — run on a single consumer GPU.
Open-source AI knowledge. Local-first builds. Original research. No paywalls, no gatekeeping.
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