evadb
emdash
evadb | emdash | |
---|---|---|
27 | 7 | |
2,578 | 114 | |
0.9% | - | |
9.5 | 8.3 | |
17 days ago | 2 months ago | |
Python | Elm | |
Apache License 2.0 | GNU General Public License v3.0 only |
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.
evadb
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Show HN: Stargazers Reloaded – LLM-Powered Analyses of Your GitHub Community
Hey friends!
We have built an app for getting insights about your favorite GitHub community using large language models.
The app uses LLMs to analyze the GitHub profiles of users who have starred the repository, capturing key details like the topics they are interested in. It takes screenshots of the stargazer's GitHub webpage, extracts text using an OCR model, and extracts insights embedded in the extracted text using LLMs.
This app is inspired by the “original” Stargazers app written by Spencer Kimball (CEO of CockroachDB). While the original app exclusively used the GitHub API, this LLM-powered app built using EvaDB additionally extracts insights from unstructured data obtained from the stargazers’ webpages.
Our analysis of the fast-growing GPT4All community showed that the majority of the stargazers are proficient in Python and JavaScript, and 43% of them are interested in Web Development. Web developers love open-source LLMs!
We found that directly using GPT-4 to generate the “golden” table is super expensive — costing $60 to process the information of 1000 stargazers. To maintain accuracy while also reducing cost, we set up an LLM model cascade in a SQL query, running GPT-3.5 before GPT-4, that lowers the cost to $5.5 for analyzing 1000 GitHub stargazers.
We’ve been working on this app for a month now and are excited to open source it today :)
Some useful links:
* Blog Post - https://medium.com/evadb-blog/stargazers-reloaded-llm-powere...
* GitHub Repository - https://github.com/pchunduri6/stargazers-reloaded/
* EvaDB - https://github.com/georgia-tech-db/evadb
Please let us know what you think!
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Language Model UXes in 2027
The discord link seems to be not working. Just a heads up.
The YOLO example on your Github page is super interesting. We are finding it easier to get LLMs to write functions with a more constrained function interface in EvaDB. Here is an example of an YOLO function in EvaDB: https://github.com/georgia-tech-db/evadb/blob/staging/evadb/....
Once the function is loaded, it can be used in queries in this way:
SELECT id, Yolo(data)
- EvaDB: Bring AI to your Database System
- Show HN: I wrote a RDBMS (SQLite clone) from scratch in pure Python
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Gorilla: Large Language Model Connected with APIs
Neat idea, @shishirpatil! We are developing EvaDB [1] for shipping simpler, faster, and cost-effective AI apps. Can you share your thoughts on transforming the output of the Gorilla LLM to functions in EvaDB apps -- like this function that uses the HuggingFace API -- https://evadb.readthedocs.io/en/stable/source/tutorials/07-o...?
[1] https://github.com/georgia-tech-db/eva
- PrivateGPT in SQL
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Eva AI-Relational Database System
Thanks for checking! Currently, we have a Docker image for deploying EVA [1]. We plan to release a Terraform config soon that will make it easier to deploy EVA DB on an AWS/Azure server with GPUs.
[1] https://github.com/georgia-tech-db/eva/tree/master/docker
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This week's top indie A.I projects, launches and resources
EVA AI-Relational Database System; build simpler and faster AI-powered apps
- Show HN: EVA – AI-Relational Database System
emdash
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Building an Open Source Decentralized E-Book Search Engine
I have a side project that aims to organize your ebook highlight collections with on-device semantic search. [1] Right now it only indexes your own content but I'd like to add a mode that allows you to share your collection and let others find relevant ideas via semantic search -- a discovery platform for ideas found in books. It's open source if you want a sense of how it works now. [2]
[1] https://emdash.ai/
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Ask HN: Show me your half baked project
Two personal projects I'd like to get fully-baked eventually:
https://emdash.ai
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Writing summaries is more important than reading more books
I built a tool for myself for the purpose of grokking ideas from books called Emdash [1]. Over the years I've collected reams of highlights from books and articles but until recently, rarely reviewed or absorbed them. The core of this app uses on-device ML to show related passages with similar ideas from other books you've read, and I find that going broad and exploring concepts from different angles really helps in comprehension.
I'm testing out a summarization/rephrase feature backed by LLMs that you can try in the demo. In HN fashion I'm trying to build this openly and gather feedback to see what works. I'd like to push this further in the active direction the article mentions with something like a Socratic dialogue mode where you're nudged to re-explain and examine ideas.
If anyone uses this thing/has feedback, let me know. Source is available too [2].
[1] https://emdash.ai
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This week's top indie A.I projects, launches and resources
Emdash - Use on-device AI to learn more from your book/article highlights.
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Show HN: Use on-device AI to learn more from your book/article highlights
And the source of course: https://github.com/dmotz/emdash
- Ask HN: How do you synthesize books that you read?
What are some alternatives?
txtai - đź’ˇ All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
gpt-json - Structured and typehinted GPT responses in Python
jsonformer - A Bulletproof Way to Generate Structured JSON from Language Models
symbiants - Ant Colony Sim + Daily Mental Health Exercises
MindsDB - The platform for customizing AI from enterprise data
paperless-ngx - A community-supported supercharged version of paperless: scan, index and archive all your physical documents
LookAtThat - Render source code in 3D, for macOS and iOS.
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
TablaM - The practical relational programing language for data-oriented applications
steampipe - Zero-ETL, infinite possibilities. Live query APIs, code & more with SQL. No DB required.
cb - đź“‹ Universal command-line clipboard with automatic copy and paste detection. Eg, `cb|sort|cb`. The missing link between GUIs and CLIs!