commitgpt
faqtory
commitgpt | faqtory | |
---|---|---|
14 | 4 | |
1,532 | 197 | |
- | - | |
3.4 | 4.4 | |
5 months ago | 3 months ago | |
TypeScript | Python | |
- | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
commitgpt
-
[Git] Générer automatiquement des messages de validation à l'aide de chatppt
[https://github.com/romanhotsiy/commitgpt
-
Imagine your PR getting rejected because of this reason
Not if you use ChatGPT to generate commit messages
-
Show HN: Generate commit messages using GPT-3
There is a similar tool: https://github.com/RomanHotsiy/commitgpt
- Automatically generate commit messages using ChatGPT
- GPT based tool that writes the commit message for you
- Dec 12, 2022 FLiP Stack Weekly
faqtory
-
This Week In Python 🎅
faqtory – A tool to generate GitHub flavoured FAQ.md documents
- Dec 12, 2022 FLiP Stack Weekly
- FAQtory - a tool to compile a FAQ.md for your repository and suggest answers to GitHub issues
What are some alternatives?
add-gpt-summarizer - Summarize your PRs with the power of GPT-3
pulsar-chatgptgenerated-functions - Functions Generated by ChatGPT
gpt-commit-summarizer
schemata - Schema modelling framework for decentralised domain-driven ownership of data.
micronaut-pulsar - Integration between Micronaut and Pulsar
trebuchet-client - A friendly siege weapon to get 2-way communication through tough firewalls and bad mobile networks
create-nifi-pulsar-flink-apps - How to create a real-time scalable streaming app using Apache NiFi, Apache Pulsar and Apache Flink SQL
modern-errors - Handle errors in a simple, stable, consistent way
pulsar-csp-ce - Cloudera CSP CE Plus Apache Pulsar
wine
dreamshard - [NeurIPS 2022] DreamShard: Generalizable Embedding Table Placement for Recommender Systems