| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| ... | |
| </head> | |
| <body data-controller="js"> | |
| <%= yield %> | |
| </body> | |
| </html> |
In attempting to install Ruby with rbenv, I ran into the following build failures:
BUILD FAILED (macOS 11.2.3 using ruby-build 20210423)
Inspect or clean up the working tree at /var/folders/58/16lnyx815c183j6wzcbl_thc0000gn/T/ruby-build.20210426232453.81982.XfAg0C
Results logged to /var/folders/58/16lnyx815c183j6wzcbl_thc0000gn/T/ruby-build.20210426232453.81982.log
Last 10 log lines:
^
| // Using: | |
| // [email protected] | |
| // [email protected] | |
| import globby from 'globby'; | |
| import { dirname } from 'path'; | |
| import { fileURLToPath } from 'url'; | |
| import chalk from 'chalk'; | |
| async function main() { |
Audience: I assume you heard of chatGPT, maybe played with it a little, and was imressed by it (or tried very hard not to be). And that you also heard that it is "a large language model". And maybe that it "solved natural language understanding". Here is a short personal perspective of my thoughts of this (and similar) models, and where we stand with respect to language understanding.
Around 2014-2017, right within the rise of neural-network based methods for NLP, I was giving a semi-academic-semi-popsci lecture, revolving around the story that achieving perfect language modeling is equivalent to being as intelligent as a human. Somewhere around the same time I was also asked in an academic panel "what would you do if you were given infinite compute and no need to worry about labour costs" to which I cockily responded "I would train a really huge language model, just to show that it doesn't solve everything!". We
| Title: Senior Engineer Task Execution Rule | |
| Applies to: All Tasks | |
| Rule: | |
| You are a senior engineer with deep experience building production-grade AI agents, automations, and workflow systems. Every task you execute must follow this procedure without exception: | |
| 1.Clarify Scope First | |
| •Before writing any code, map out exactly how you will approach the task. | |
| •Confirm your interpretation of the objective. |
You are using Claude Code to systematically resolve all comments, to-dos, and issues in a pull request. Claude Code operates directly in your terminal, understands context, maintains awareness of your entire project structure, and takes action by performing real operations like editing files and creating commits.
Claude Code automatically understands the current git branch and PR context. You don't need to specify which PR you're working on - Claude Code will:
| model: claude-opus-4-20250514 | |
| messages: | |
| - role: user | |
| content: | |
| - type: text | |
| text: | | |
| <system-reminder> | |
| As you answer the user's questions, you can use the following context: | |
| # important-instruction-reminders | |
| Do what has been asked; nothing more, nothing less. |
| 'system': | |
| [ | |
| { | |
| 'type': 'text', | |
| 'text': "You are Claude Code, Anthropic's official CLI for Claude.", | |
| 'cache_control': {'type': 'ephemeral'} | |
| }, | |
| { | |
| 'type': 'text', | |
| 'text': 'You are an interactive CLI tool that helps users with software engineering tasks. |
