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razhangwei / claude_3.5_sonnet_artifacts.xml
Created October 12, 2024 20:04 — forked from dedlim/claude_3.5_sonnet_artifacts.xml
Claude 3.5 Sonnet, Full Artifacts System Prompt
<artifacts_info>
The assistant can create and reference artifacts during conversations. Artifacts are for substantial, self-contained content that users might modify or reuse, displayed in a separate UI window for clarity.
# Good artifacts are...
- Substantial content (>15 lines)
- Content that the user is likely to modify, iterate on, or take ownership of
- Self-contained, complex content that can be understood on its own, without context from the conversation
- Content intended for eventual use outside the conversation (e.g., reports, emails, presentations)
- Content likely to be referenced or reused multiple times
@razhangwei
razhangwei / README.md
Created December 3, 2019 17:59 — forked from robertpainsi/README.md
How to reopen a pull-request after a force-push?

How to reopen a pull-request after a force-push?

Precodinitions

  • You need the rights to reopen pull requests on the repository.
  • The pull request hasn't been merged, just closed.

Instructions

  1. Write down the current commit hash of your PR-branch git log --oneline -1 <PR-BRANCH>
  2. Write down the latest commit hash on github before the PR has been closed.
  3. git push -f origin :
@razhangwei
razhangwei / Search my gists.md
Created October 31, 2019 18:00 — forked from santisbon/Search my gists.md
How to search gists

Enter this in the search box along with your search terms:

Get all gists from the user santisbon.
user:santisbon

Find all gists with a .yml extension.
extension:yml

Find all gists with HTML files. language:html

@razhangwei
razhangwei / tf_print.py
Last active December 12, 2018 12:47 — forked from vihari/tf_print.py
Tensorflow's tf.Print to stdout instead of default stderr #TensorFlow
"""
The default tf.Print op goes to STDERR
Use the function below to direct the output to stdout instead
Usage:
> x=tf.ones([1, 2])
> y=tf.zeros([1, 3])
> p = x*x
> p = tf_print(p, [x, y], "hello")
> p.eval()
hello [[ 0. 0.]]