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

intellectronica

Upstash Redis Key-Value Store SKILL

Get upstash-redis-kv.zip

Use this skill to read from and write to Upstash's Redis-compatible key-value store via REST API. Supports the full range of Redis data structures: strings, hashes, lists, sets, and sorted sets—ideal for caching, counters, leaderboards, queues, and persistent storage.

Works with Claude (by extracting it to ~/.claude/skills/) or with any other agent using Skillz.

Setup

lmmx

• comrak
• czkawka
• polars-genson
• powell-opt
• ansi-to-html
• htmd
• polars-scheduler
• fugitrs
• prqrs
• polars-nmf

hamelsmu

Generating Synthetic Data for LLM Evaluation

Summary

1. Use your application extensively to build intuition about failure modes
2. Define 3-4 dimensions based on observed or anticipated failures
3. Create structured tuples covering your priority failure scenarios
4. Generate natural language queries from each tuple using a separate LLM call
5. Scale to more examples across your most important failure hypotheses (we suggest at least ~100)
6. Test and iterate on the most critical failure modes first, and generate more until you reach theoretical saturation

0atman

File Name

{{title|replace(',','_')|replace(':','_')|replace('/','_')|replace('\n','_')}}

Page Metadata

Make sure to change "Resources/Research/Readwise" to where Readwise stores your files!

charlesfrye

See rune2e.sh for info on how to run the experiment.

veekaybee

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

nikvdp

# make sure you have `tac` [1] (if on on macOS) and `atuin` [2] installed, then drop the below in your ~/.zshrc
#
# [1]: https://unix.stackexchange.com/questions/114041/how-can-i-get-the-tac-command-on-os-x
# [2]: https://github.com/ellie/atuin 

atuin-setup() {
  ! hash atuin && return
  bindkey '^E' _atuin_search_widget

  export ATUIN_NOBIND="true"

jinhangjiang

gpu_info = !nvidia-smi
gpu_info = '\n'.join(gpu_info)
if gpu_info.find('failed') >= 0:
  print('Not connected to a GPU')
else:
  print(gpu_info)

mattjj

import torch
import torch.utils.dlpack
import jax
import jax.dlpack

# A generic mechanism for turning a JAX function into a PyTorch function.

def j2t(x_jax):
  x_torch = torch.utils.dlpack.from_dlpack(jax.dlpack.to_dlpack(x_jax))
  return x_torch