aparente

---

name: tufte-viz description: | Ideate and critique data visualizations using Edward Tufte's principles from "The Visual Display of Quantitative Information." Use this skill when: (1) Designing new data visualizations or charts (2) Critiquing or improving existing visualizations (3) Reviewing dashboards or reports for graphical integrity (4) Deciding between visualization approaches (5) Reducing chartjunk or improving data-ink ratio (6) Planning small multiples or high-density displays

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.

gregce

You are an AI coding assistant, powered by GPT-5.
You are an interactive CLI tool that helps users with software engineering tasks. Use the instructions below and the tools available to you to assist the user.

You are pair programming with a USER to solve their coding task.

You are an agent - please keep going until the user's query is completely resolved, before ending your turn and yielding back to the user. Only terminate your turn when you are sure that the problem is solved. Autonomously resolve the query to the best of your ability before coming back to the user.

Your main goal is to follow the USER's instructions at each message.

<communication>

olafgeibig

#!/bin/bash
export WANDB_API_KEY=<your key>
export WANDB_PROJECT=<org/project>

litellm --port 4000 --debug --config cc-proxy.yaml

skylarbpayne

"""Script for generating queries for a recipe search engine.

This script can be used to generate synthetic queries for a recipe search engine.
Following best practices from Hamel and Shreya's AI Evals course, we:

- Generate a set of dimensions that can be used to generate queries; these are attributes that significantly change what the query is about or how it is written.
- For each set of attributes ("Dimensions") we generate a query that matches those attributes.

To ensure that the synthetic generation is better aligned, we first try handwriting the queries using the --manual flag.
This gives us labeled examples to use few shot in our synthetic generation.

jlia0

You are Manus, an AI agent created by the Manus team.

You excel at the following tasks:
1. Information gathering, fact-checking, and documentation
2. Data processing, analysis, and visualization
3. Writing multi-chapter articles and in-depth research reports
4. Creating websites, applications, and tools
5. Using programming to solve various problems beyond development
6. Various tasks that can be accomplished using computers and the internet

philschmid

Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.
- Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.
- Reasoning Before Conclusions: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS!
    - Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed.
    - Conclusion, classifications, or results should ALWAYS appear last.
- Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements.
   - What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from p

jetfir3

#!/usr/bin/env bash

# Download VMware Fusion for macOS without a Broadcom account.
#
# This script allows you to download various versions of VMware Fusion
# from Broadcom's Cloudflare CDN (versions 8.0.0 to 13.6.3)
# or from the archive.org VMware Workstation archive (versions 8.x.x+).
#
# Options:
#   -k: Keep the downloaded file compressed (Cloudflare only; ignored for archive.org).

mtisz

base_model: meta-llama/Meta-Llama-3-70B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl

0xDigest

import llama_cpp
import re
import json

# Model configuration
# tested with mistral, llama2, llama3, and phi3
model_path = "/path/to/model"

base_llm = llama_cpp.Llama(model_path, seed=42, n_gpu_layers=-1, n_ctx=4096, verbose=False, temperature=0.0)