A markdown template for taking notes to summarize research papers.

DeepPaperNote is an agent skill for deep-reading a single paper and generating high-quality Obsidian-style research notes. Works with Claude Code, Codex, Cursor, Copilot, Gemini CLI, and more.

Obsidian-first LLM Wiki skill pack with ontology-ready bootstrap, canonical JSONL truth layers, and optional graph projection.

A framework for clustering research notes

An experimental research project developing a foundational theory of frameworks — the structures through which the human mind constructs and engages with reality.

前沿物理仿真与智能感知技术调研资料库 | Frontier physics simulation research notes

Vulnerability Research notes

Website

Personal learning notebook on latent space engineering, representation geometry, and exploratory research notes.

Notes & comparisons on storage options for large LLMs (Work-in-progress)

SDFI emerges specifically under conditions of recursive self-description and sustained high semantic density, not in ordinary task-oriented interaction.This work is intended as a reference for researchers and system designers thinking about neutrality, termination behavior, and control surfaces in future AI systems.

Lab 137 — a place that both exists and does not exist. Talks, posters & research notes.

Notes & experiments on LLMs, open-weight models, multimodal systems, and cloud deployment.

An introduction to actors with working Akka example to demonstrate an actor based approach to concurrency and it's affect of software design on scalability.

Documentation, roadmap, technical notes, and future wiki resources for the Nominal Drift ecosystem.

Personal research notes, exploratory ideas, and early-stage project materials.

Structural analysis of unmeasured cognitive states in brain–machine interfaces, including silence, unresolved intent, and agency constraints for future human–AI cognitive interaction.

Documentation-first research repository analyzing the transfer step in the CCM 2025 zeta spectral triple program.

Research notes on causal machine learning, representation learning, and causal perspectives on language models.