SuperAGI
Wagtail
SuperAGI | Wagtail | |
---|---|---|
82 | 52 | |
14,491 | 17,241 | |
- | 1.0% | |
9.8 | 9.9 | |
6 days ago | 3 days ago | |
Python | Python | |
MIT License | BSD 3-clause "New" or "Revised" 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.
SuperAGI
- Introducing GPTs
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🐍🐍 23 issues to grow yourself as an exceptional open-source Python expert 🧑💻 🥇
Repo : https://github.com/TransformerOptimus/SuperAGI
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Introduction to Agent Summary – Improving Agent Output by Using LTS & STM
The recent introduction of the “Agent Summary” feature in SuperAGI version 0.0.10 has brought a drastic difference in agent performance – 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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🚀✨SuperAGI v0.0.10✨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 🦬 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 😵 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
Wagtail
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Release Radar • February 2024 Edition
If you like Python 🐍 then check out this project. Wagtail is a popular CMS, combining Django’s powerful customization capabilities with a slick user interface. The newest update brings Django 5.0 support, a new searchable and filterable listing UI, the accessibility checker built into the admin interface, and a brand new 10-step tutorial for developers. This release marks Wagtail's 10th birthday 🎂. Happy birthday to the team and all the best for the next ten years and beyond 🥳.
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🐍🐍 23 issues to grow yourself as an exceptional open-source Python expert 🧑💻 🥇
Repo : https://github.com/wagtail/wagtail
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How and why the Wagtail page editor is evolving
- The discussion thread we use to track all public feedback: https://github.com/wagtail/wagtail/discussions/9553. Comments very welcome.
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A Django app that tracks your queries to help optimize them
Not so long ago, I submitted a Pull Request in wagtail to improve the admin performance, especially for non-superusers. Basically, it caches all the user's permissions on first access. However, I was pretty sure that this would load a lot of model fields that we never need but there isn't a tool that gives us that type of report. Therefore, I started building an app that keeps track of all fields accessed so you can easily know which ones haven't been used and apply the only/defer optimisation for Django querysets.
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I want to add unit tests to my Django project but don't know where do i even start
Wagtail would be a good example https://github.com/wagtail/wagtail/tree/main/wagtail/tests
- Build Blog With Wagtail CMS (4.0.0) Released!
- Javascript is still the most used programming language in newly created repositories on GitHub
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On mentoring for an an open-source internship
Paarth moving from no contributions to the 21st most contributions - https://github.com/wagtail/wagtail/graphs/contributors.
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Ten tasty ingredients for a delicious pull request
Over the last few years, I have had the incredible opportunity to be a core team member of the Wagtail project. In that time, I have reviewed many new pull requests, and I’ve also had the chance to submit many of my own across Wagtail and many other projects.
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Still stuck on Wagtail 2.15, how to move forward?
Wagtail 2.15 is the most recent LTS (Long Term Support) release, so it’s not a bad release to stick to at all, at least until February 2023.
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.
django-cms - The easy-to-use and developer-friendly enterprise CMS powered by Django
Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/AutoGPT]
Mezzanine - CMS framework for Django
autogen - A programming framework for agentic AI. Discord: https://aka.ms/autogen-dc. Roadmap: https://aka.ms/autogen-roadmap
Strapi - 🚀 Strapi is the leading open-source headless CMS. It’s 100% JavaScript/TypeScript, fully customizable and developer-first.
Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/Auto-GPT]
WordPress - WordPress, Git-ified. This repository is just a mirror of the WordPress subversion repository. Please do not send pull requests. Submit pull requests to https://github.com/WordPress/wordpress-develop and patches to https://core.trac.wordpress.org/ instead.
AgentGPT - 🤖 Assemble, configure, and deploy autonomous AI Agents in your browser.
Plone - The core of the Plone content management system
AutoLearn-GPT - ChatGPT learns automatically.
FeinCMS - A Django-based CMS with a focus on extensibility and concise code