SuperAGI
gradio
SuperAGI | gradio | |
---|---|---|
82 | 116 | |
14,491 | 28,987 | |
- | 3.7% | |
9.8 | 9.9 | |
6 days ago | 5 days ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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.
SuperAGI
- Introducing GPTs
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ππ 23 issues to grow yourself as an exceptional open-source Python expert π§βπ» π₯
Repo : https://github.com/TransformerOptimus/SuperAGI
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Introduction to Agent Summary β Improving Agent Output by Using LTS & STM
The recent introduction of the βAgent Summaryβ feature in SuperAGI version 0.0.10 has brought a drastic difference in agent performance β improving the quality of agent output. Agent Summary helps AI agents maintain a larger context about their goals while executing complex tasks that require longer conversations (iterations).
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πβ¨SuperAGI v0.0.10β¨is now live on GitHub
Checkout the full release here: https://github.com/TransformerOptimus/SuperAGI/releases/tag/v0.0.10
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Top 20 Must Try AI Tools for Developers in 2023
10. SuperAGI
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We're bringing in Google 's PaLM2 𦬠Bison LLM API support into SuperAGI in our upcoming v0.0.8 release
Currently, PaLM2 Bison is live on the dev branch of SuperAGI GitHub for the community to try: https://github.com/TransformerOptimus/SuperAGI/tree/dev
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Why use SuperAGI
SuperAGI is made with developers in mind, therefore it takes into account their requirements and preferences when making autonomous AI agents. It has a number of advantages, including:
- In five years, there will be no programmers left, believes Stability AI CEO
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LLM Powered Autonomous Agents
I think for agents to truly find adoption in real world, agent trajectory fine tuning is critical component - how do you make an agent perform better to achieve particular objective with every subsequent run. Basically making the agents learn similar to how we learn when we
Also I think current LLMs might not fit well for agent use cases in mid to long term because the RL they go through is based on input-best output methods whereas the intelligence that you need in agents is more around how to build an algorithm to achieve an objective on the fly - this requires perhaps new type of large models ( Large Agent Models ? ) which are trained using RLfD ( Reinforcement Learning from demonstration )
Also I think one of the key missing piece is a highly configurable software middle ware between Intelligence ( LLMs ), Memory ( Vector Dbs ~LTMs, STMs ), Tools and workflows across every iteration. Current agent core loop to find next best action is too simplistic. For example if core self prompting loop or iteration of an agent can be configured for the use case in hand. Eg for BabyAGI, every iteration goes through workflow of Plan, Prioritize and Execute or in AutoGPT it finds the next best action based on LTM/STM, or GPTEngineer it is to write specs > write tests > write code. Now for dev infra monitoring agent this workflow might be totally different - it would look like consume logs from different tools like Grafana, Splunk, APMs > See if it doesnt have an anomaly > if it has an anomaly then take human input for feedback. Every use case in real world has it's own workflow and current construct of agent frameworks have this thing hard coded in base prompt. In SuperAGI( https://superagi.com) ( disclaimer : Im creator of it ), core iteration workflow of agent can be defined as part of agent provisioning.
Another missing piece is notion of Knowledge. Agents currently depend entirely upon knowledge of LLMs or search results to execute on tasks, but if a specialised knowledge set is plugged to an agent, it performs significantly better.
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Created a simple chrome dino game using SuperAGI's SuperCoder π΅ The dino changes color on every run :P (without writing a single line of code myself)
Build your own game here: https://github.com/TransformerOptimus/SuperAGI
gradio
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AI enthusiasm #9 - A multilingual chatbotπ£πΈ
gradio is a package developed to ease the development of app interfaces in python and other languages (GitHub)
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Show HN: Dropbase β Build internal web apps with just Python
There's also that library all the AI models started using that gives you a public URL to share. After researching it: https://www.gradio.app/ is the link.
It's used specifically for making simple UIs for machine learning apps. But I guess technically you could use it for anything.
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Show HN: Taipy β Turns Data and AI algorithms into full web applications
What is the business model for https://www.taipy.io/, https://streamlit.io/, or https://www.gradio.app/? These are nice tools - but how will the sponsoring businesses support themselves? I didn't see any mention of enterprise plans, etc. Is the answer simply that "we've not announced our revenue model yet"? What should one expect?
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ππ 23 issues to grow yourself as an exceptional open-source Python expert π§βπ» π₯
Repo : https://github.com/gradio-app/gradio
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a Lightweight AI Model and Framework for Text Summarization in the Browser using JavaScript
There's TensorFlow.js for running machine learning on JavaScript, but personally, I'd prefer using the Python Gradio package, which is designed for creating UIs for machine learning inference demos.
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Gradio sharable link expires too soon ( 30 mins to 1 hour, instead of lasting 72 hours )
I found an issue on gradio github but looks like it's closed so I am not sure if it's still a common issue or only I am facing it due to certain settings/absence of a fix. ( https://github.com/gradio-app/gradio/issues/3060 )
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I gave commit rights to someone I didn't know
I disagree hard with this β for instance I've recently needed to dig into the code for the Gradio library, and when PRs are like https://github.com/gradio-app/gradio/pull/3300 (and the merge commit's message is what it is) it's hard to understand why some decisions have been made when doing `git annotate` later on.
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Introducing CommanderGPT. A project I been working for Desktop Automation.
Gradio for a ui that your commanderGPT can visit and use
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[HELP] Anybody know where the .html files are?
gradio is documented, it doesn't seem very complex, it would be something like moving this block under the other one. i think it's ui_extra_networks.py, the file you are looking to edit. (if you do it make a copy to restore when you go to update)
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Is there a way to "share" my stable diffusion with a friend?
Gradio did have an issue for a while where your URL was guessable, so unless you had a password it was pretty easy to find, but as far as I know they've increased the complexity so much that it's no longer an issue.
What are some alternatives?
AutoGPT - AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
streamlit - Streamlit β A faster way to build and share data apps.
Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/AutoGPT]
stable-diffusion-webui - Stable Diffusion web UI
autogen - A programming framework for agentic AI. Discord: https://aka.ms/autogen-dc. Roadmap: https://aka.ms/autogen-roadmap
django-colorfield - :art: color field for django models with a nice color-picker in the admin.
Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/Auto-GPT]
panel - Panel: The powerful data exploration & web app framework for Python
AgentGPT - π€ Assemble, configure, and deploy autonomous AI Agents in your browser.
gpt4all - gpt4all: run open-source LLMs anywhere
AutoLearn-GPT - ChatGPT learns automatically.
CustomTkinter - A modern and customizable python UI-library based on Tkinter