ai-cli
sharegpt
ai-cli | sharegpt | |
---|---|---|
11 | 37 | |
1,119 | 1,680 | |
- | - | |
6.4 | 6.9 | |
3 months ago | 6 months ago | |
TypeScript | TypeScript | |
GNU General Public License v3.0 only | 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.
ai-cli
-
ChatGTP tools you may need - Work always in progress)
ai-cli: Get answers for CLI commands from GPT3 right from your terminal
-
What are some of the personal project you had the most fun making ?
and a GPT3 Powered CLI to answer shell commands (Github)
-
Your friendly kubernetes expert built using GPT-3 with extra layer of validation
Nice. I built a CLI powered by GPT3 (github.com/abhagsain/ai-cli and I wanted to add this feature Fine tuning AWS, Docker, kubectl etc but result of fine tuning were not that good and I didn't have much experience with kubectl so didn't continue it.
- My OSS project reached 600 stars ⭐ on Github
- Show HN: Get answers for shell commands from GPT3 from your terminal
-
Hacker News top posts: Nov 18, 2022
Get answers for Shell Commands from GPT3 right from your terminal\ (49 comments)
- Get answers for Shell Commands from GPT3 right from your terminal
-
Ask for Shell Commands from GPT3 right from your terminal
Here's the GitHub repo - https://github.com/abhagsain/ai-cli Installation instructions are in the README.md
sharegpt
-
How Open is Generative AI? Part 2
Vicuna is another instruction-focused LLM rooted in LLaMA, developed by researchers from UC Berkeley, Carnegie Mellon University, Stanford, and UC San Diego. They adapted Alpaca’s training code and incorporated 70,000 examples from ShareGPT, a platform for sharing ChatGPT interactions.
-
create the best coder open-source in the world?
We can say that a 13B model per language is reasonable. Then it means we need to create a democratic way for teaching coding by examples and solutions and algorithms, that we create, curate and use open-source. Much like sharegpt.com but for coding tasks, solutions ways of thinking. We should be wary of 'enforcing' principles rather showing different approaches, as all approaches can have advantages and disadvantages.
-
Thank you ChatGPT
You can see the url in the comment, https://sharegpt.com and if you go there it gives you the option for installing the chrome extension, after that it shouldn’t be hard to use it
- The conversation started as what would AI do if it became self aware and humans tried to shut it down. The we got into interdimensional beings. Most profound GPT conversation I have had.
-
Übersicht aller nützlichen Links für ChatGPT Prompt Engineering
ShareGPT - Share your prompts and your entire conversations
-
(Reverse psychology FTW) Congratulations, you've played yourself.
Or used https://sharegpt.com
-
"Prompt engineering" is easy as shit and anybody who tells you otherwise is a fucking clown.
you can gets lots of ideas here > https://sharegpt.com/ (180,000+ prompts)
-
I built a ChatGPT Mac app in just 20 minutes with no coding experience - thanks ChatGPT!
I would love to read the whole conversation: Check out this cool little GPT sharing extension: https://sharegpt.com - that way the code snippets can be copied easily
-
Teaching ChatGPT to Speak My Son’s Invented Language
> Cool, that’s really the only point I’m making.
To be clear, I'm saying that I don't know if they are, not that we know that it's not the same.
It's not at all clear that humans do much more than "that basic token sequence prediction" for our reasoning itself. There are glaringly obvious auxiliary differences, such as memory, but we just don't know how human reasoning works, so writing off a predictive mechanism like this is just as unjustified as assuming it's the same. It's highly likely there are differences, but whether they are significant remains to be seen.
> Not necessarily scaling limitations fundamental to the architecture as such, but limitations in our ability to develop sufficiently well developed training texts and strategies across so many problem domains.
I think there are several big issues with that thinking. One is that this constraint is an issue now in large part because GPT doesn't have "memory" or an ability to continue learning. Those two need to be overcome to let it truly scale, but once they are, the game fundamentally changes.
The second is that we're already at a stage where using LLMs to generate and validate training data works well for a whole lot of domains, and that will accelerate, especially when coupled with "plugins" and the ability to capture interactions with real-life users [1]
E.g. a large part of human ability to do maths with any kind of efficiency comes down to rote repetition and generating large sets of simple quizzes for such areas is near trivial if you combine an LLM at tools for it to validate its answers. And unlike with humans where we have to do this effort for billions of humans, once you have an ability to let these models continue learning you make this investment in training once (or once per major LLM effort).
A third is that GPT hasn't even scratched the surface in what is available in digital collections alone. E.g. GPT3 was trained on "only" about 200 million Norwegian words (I don't have data for GPT4). Norwegian is a tiny language - this was 0.1% of GPT3's total corpus. But the Norwegian National Library has 8.5m items, which includes something like 10-20 billion words in books alone, and many tens of billions more in newspapers, magazines and other data. That's one tiny language. We're many generations of LLM's away from even approaching exhausting the already available digital collections alone, and that's before we look at having the models trained on that data generate and judge training data.
[1] https://sharegpt.com/
-
Humans in Humans Out: GPT Converging Toward Common Sense in Both Success/Failure
of that conversation. Perhaps something like shareGPT[1] can help?
[1] https://sharegpt.com
What are some alternatives?
askai - Command Line Interface for OpenAi ChatGPT
ChatGPT - Lightweight package for interacting with ChatGPT's API by OpenAI. Uses reverse engineered official API.
aiac - Artificial Intelligence Infrastructure-as-Code Generator.
llm-workflow-engine - Power CLI and Workflow manager for LLMs (core package)
Codex-CLI - CLI tool that uses Codex to turn natural language commands into their Bash/ZShell/PowerShell equivalents
unofficial-chatgpt-api - This repo is unofficial ChatGPT api. It is based on Daniel Gross's WhatsApp GPT
chatgpt-advanced - WebChatGPT: A browser extension that augments your ChatGPT prompts with web results.
openai-python - The official Python library for the OpenAI API
chatgpt-conversation - Have a conversation with ChatGPT using your voice, and have it talk back.
chatgpt-vscode - A VSCode extension that allows you to use ChatGPT
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]