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
| #!/usr/bin/env bash | |
| # pr-restack — rebuild a working branch by cherry-picking my open PRs | |
| # on top of an upstream base. Relies on git rerere to replay conflict | |
| # resolutions across runs. | |
| # | |
| # Usage: | |
| # pr-restack # from inside any git repo with gh access | |
| # pr-restack -b my-branch # custom branch name | |
| # pr-restack -r upstream/main # custom base ref | |
| # pr-restack -o origin -u upstream # remote names |
| # OpenClaw Implementation Prompts | |
| Each prompt below is a self-contained brief you can hand to an AI coding assistant (or use as a project spec) to build that use case from scratch. Adapt the specific services to whatever you already use — the patterns are what matter. | |
| --- | |
| ## 1) Personal CRM Intelligence | |
| ``` | |
| Build me a personal CRM system that automatically tracks everyone I interact with, with smart filtering so it only adds real people — not newsletters, bots, or cold outreach. |
See the new site: https://postgresisenough.dev
| /* Heap based virtual machine described in section 3.4 of Three Implementation Models for Scheme, Dybvig | |
| */ | |
| #include <stdio.h> | |
| #include <stdlib.h> | |
| #include <string.h> | |
| #include <ctype.h> | |
| #include <assert.h> | |
| char token[128][32]; |
ChatGPT appeared like an explosion on all my social media timelines in early December 2022. While I keep up with machine learning as an industry, I wasn't focused so much on this particular corner, and all the screenshots seemed like they came out of nowhere. What was this model? How did the chat prompting work? What was the context of OpenAI doing this work and collecting my prompts for training data?
I decided to do a quick investigation. Here's all the information I've found so far. I'm aggregating and synthesizing it as I go, so it's currently changing pretty frequently.
| APP_NAME=Laravel | |
| APP_ENV=local | |
| APP_KEY= | |
| APP_DEBUG=true | |
| APP_URL=http://127.0.0.1:8000 | |
| BCRYPT_ROUNDS=4 | |
| DB_CONNECTION=mysql | |
| DB_HOST=127.0.0.1 |
FWIW: I (@rondy) am not the creator of the content shared here, which is an excerpt from Edmond Lau's book. I simply copied and pasted it from another location and saved it as a personal note, before it gained popularity on news.ycombinator.com. Unfortunately, I cannot recall the exact origin of the original source, nor was I able to find the author's name, so I am can't provide the appropriate credits.
- By Edmond Lau
- Highly Recommended 👍
- http://www.theeffectiveengineer.com/