Notes

As local LLMs improve, harnesses are learning to pair them with frontier models. A look at the four collaboration patterns already shipping in open source.

Explains the four-stage modern LLM training pipeline from pre-training through verifiable-reward RL.

Image-focused guide to Shiva as Nataraja, explaining dance posture, symbols, Apasmara, and ring of fire.

Explains Jevons paradox: efficiency gains can increase total resource consumption through rebound effects.

MCP vs plugins: layered distinction (external systems vs assistant-extension packaging); both cross-surface across Claude Code, Cowork, and the Claude apps

Explains Claude adaptive thinking, effort levels, fixed-budget deprecation, and hidden reasoning display.

Summarizes Amodei’s essay on powerful AI risks, safety policy, misuse, and economic disruption.

Explains Nataraja iconography as Shiva’s cosmic dance of time, transformation, ignorance, and liberation.

Overview of Adam Smith’s major works, economic ideas, moral philosophy, and legacy.

Summarizes the six-component agent harness model: execution, tools, context, state, hooks, and evaluation.

Defines atelier as an artist’s workshop or studio and traces its origin from French terms for wood chips.

Explains why ripgrep is often faster than grep via filtering, parallelism, regex literals, and SIMD search.

Defines the Latin phrase ad astra per aspera and its sense of reaching high goals through hardship.

Explains Amdahl’s law as an upper bound on speedup from optimizing or parallelizing part of a system.

Explains TurboQuant, a rotation-based vector quantization method for KV-cache compression and vector search.

Patterns from ARIS for reliable multi-agent research using adversarial review, audits, and persistent memory.

Summary of Brynjolfsson's argument that AI should augment workers rather than mimic and replace them.

Compares TASTE and ACM 3698105 for evaluating AI-generated graphic design, fine art, and aesthetics.

Explains CompactRAG, a multi-hop RAG method using offline atomic QA pairs and fixed two-call inference.

Explains graph-based memory for LLM agents, including taxonomy, GAM consolidation, and hybrid retrieval.