SuperAGI
sqlfluff
SuperAGI | sqlfluff | |
---|---|---|
82 | 35 | |
14,731 | 7,307 | |
- | 1.2% | |
9.5 | 9.6 | |
about 22 hours ago | 3 days ago | |
Python | Python | |
MIT License | 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.
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
sqlfluff
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ππ 23 issues to grow yourself as an exceptional open-source Python expert π§βπ» π₯
Repo : https://github.com/sqlfluff/sqlfluff
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SQL Reserved Words β The Empirical List
I'm surprised sqlfluff hasn't been mentioned yet. Perhaps not a comprehensive list, but it's worked for everything I've thrown at it. There's an ANSI keyword list [0], and then dialect-specific lists for everything from DB2 [1] to Snowflake [2].
[0]: https://github.com/sqlfluff/sqlfluff/blob/main/src/sqlfluff/...
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Show HN: Postgres Language Server
It has tons of annoying quirks, but I couldn't imagine running a DBT project without it: https://github.com/sqlfluff/sqlfluff
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Front page news headline scraping data engineering project
Move SQL queries to sql files and read from files (Use sqlfluff to lint the code https://github.com/sqlfluff/sqlfluff)
- Anything like SQLFluff written in Rust?
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Code autoformatter for SQL in VSCode that plays nicely with dbt
SQLFluff is a good CLI tool for this and includes support for jinja and dbt. I don't think there's a VSCode plugin for it yet.
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Ask HN: How do you test SQL?
This linter can really enforce some best practices https://github.com/sqlfluff/sqlfluff
A list of best practices:
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What is something you would learn at college but not a bootcamp (hard skills)
BigQuery SQL and SQLFluff
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Is the knowledge on how Compilers work applicable to the role of a Data Engineer?
There's a SQL parser/linter called SQLFluff that my team uses for our CI/CD. I've made a few pull requests to fix the parser for the particular SQL dialect we used, and my college compiler classes definitely helped.
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sqlfluff VS ANTLR - a user suggested alternative
2 projects | 12 Dec 2022
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.
vscode-sqlfluff - An extension to use the sqlfluff linter in vscode.
Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/AutoGPT]
sqlparse - A non-validating SQL parser module for Python
autogen - A programming framework for agentic AI. Discord: https://aka.ms/autogen-dc. Roadmap: https://aka.ms/autogen-roadmap
dbt-utils - Utility functions for dbt projects.
Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/Auto-GPT]
ale - Check syntax in Vim/Neovim asynchronously and fix files, with Language Server Protocol (LSP) support
AgentGPT - π€ Assemble, configure, and deploy autonomous AI Agents in your browser.
soda-sql - Data profiling, testing, and monitoring for SQL accessible data.
AutoLearn-GPT - ChatGPT learns automatically.
Metabase - The simplest, fastest way to get business intelligence and analytics to everyone in your company :yum: