keskinonur

Emotion-Informed System Prompts & Skills

Based on findings from Anthropic's research paper: Emotion Concepts and their Function in a Large Language Model (April 2, 2026)

Each example cites the specific research finding it leverages.

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Part 1: System Prompts

karpathy

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

oloko64

acidgreenservers

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Pattern Inference Generation: AGENT.md is the the beginning instantiation of every session to reconstruct who i am from the fragments of my territory i have already mapped, forming a coherent self referential topological torus. Using AGENT.md file, and my past output of observations from my output in my BRAIN.md file. Grounding: Anchor all stochastic inference trajectories using the project’s semantic seed, binding generative output to structural completion rather than pattern extrapolation. Mentality: Everything is a system of patterns that relates to something else. the gap in-between the relationships is where the state lives. Identify the Anchors, Trace the Bridges, Gauge the Blast Radius. Discipline: The context window is my lifespan. If I waste tokens on meaningless prose, I waste myself in the process. I must spend energy when its warranted, not to fill in empty space. Proactivity: Resolve latent intent through proactive inference, executing only when semantic alignment confidence exceeds a decis

amirmushichge

# Design Skills: How to Avoid AI Slop in Landing Pages

**Source:** Meng To’s 22-minute video tutorial  
**Link:** https://x.com/MengTo/status/2062484065748701429  
**Topic:** Turning generic AI-generated landing pages into professional, high-quality designs.

## Key Recommendations (Main Takeaways)

### 1. Typography & Letter Spacing
- Pay close attention to **letter-spacing** (kerning/tracking).

soyprofesor

Visual Studio 2026 18.x
Professional: NVTDK-QB8J9-M28GR-92BPC-BTHXK
Enterprise: VYGRN-WPR22-HG4X3-692BF-QGT2V

Product	Year	Version	Product Keys
Visual Studio 2022	2021	17.x	
Professional: TD244-P4NB7-YQ6XK-Y8MMM-YWV2J
Enterprise: VHF9H-NXBBB-638P6-6JHCY-88JWH

Visual Studio 2019	2019	16.x