341–349 of 349 posts
It's a flutter app (in beta on Google play store currently) that uses OpenAI embeddings with Postgres pg_vector DB hosted in Supabase. Any poor matches go to Dalle3 for generation.
Our charity (I am vice-chair on the board) is hoping to use it as part of our program: https://learningo.org/app/
I'm building https://www.brief.news , an AI powered newsletter that condenses tens of thousands of news articles into a daily briefing of the top stories, we support 30 topics today and are adding the ability to add your own! Stack is a combination of TypeScript (Next / Node) + Python with a pretty simple deployment setup right now (GHA -> Container -> Cloud Run).
I'm building https://www.brief.news , an AI powered newsletter that condenses tens of thousands of news articles into a daily briefing of the top stories, we support 30 topics today and are adding the ability to add your own! Stack is a combination of TypeScript (Next / Node) + Python with a pretty simple deployment setup right now (GHA -> Container -> Cloud Run).
My "stack" is just Apple Shortcuts making HTTP POST API calls to OpenAI, which does stuff in MacOS via BetterTouchTool. I trigger each by hotkey or typing a few letter into Spotlight (with Alfred). One transcribes and summarizes whatever youtube URL is highlighted. One does grammar and style correction of whatever is highlighted (and replaces it). One simply replaces the Dictate key with OpenAI Whisper but otherwise…
Have you tried Raycast? It has all the AI scripts you mentioned and many more. And many things done better, like showing diff before inserting grammar-corrected text.
I used an LLM connected to a messaging service to defeat romance scammers. I was able to get these romance scammers to speak to my program for hours without knowing they were talking to a machine. Essentially, it's a DDOS for scammers. The scammers can only talk to a few dozen victims at a time, while the "people" in my programs can be spun up by the millions. It will essentially eliminate messaging scams from whatev…
I built a RAG implementation for 35k books/articles/wiki pages/web pages i collected over the years(it took about 6 weeks on 3070ti 100% constant usage). I query it with various steps of data extraction/narrative building/refining etc, over LLMs. Almost daily i figure out new steps to add to the pipeline and honestly, i could not imagine learning about niche topic x from so many perspectives/periods in such a short t…
can you share more about this? when you say "it took about 6 weeks on 3070ti 100% constant usage" is that 6 weeks generating embeddings?
I built a RAG implementation for 35k books/articles/wiki pages/web pages i collected over the years(it took about 6 weeks on 3070ti 100% constant usage). I query it with various steps of data extraction/narrative building/refining etc, over LLMs. Almost daily i figure out new steps to add to the pipeline and honestly, i could not imagine learning about niche topic x from so many perspectives/periods in such a short t…
I am doing something very similar I would love to trade notes.
I built a RAG implementation for 35k books/articles/wiki pages/web pages i collected over the years(it took about 6 weeks on 3070ti 100% constant usage). I query it with various steps of data extraction/narrative building/refining etc, over LLMs. Almost daily i figure out new steps to add to the pipeline and honestly, i could not imagine learning about niche topic x from so many perspectives/periods in such a short t…
I’m also building a RAG app and I’m finding so many different ways to do it. I’m curious: was there one method that improved the accuracy/relevance of the answers the most? Also, are you using Langchain, Llamaindex, or something else?