My journey with GPT-4 as a novice programmer has been nothing short of remarkable. I used it to write a game, and despite my limited programming knowledge, I was astonished by the results.
It makes coding suggestions, completes my code, and even identifies bugs, which has been a game-changer for me. It feels like having a co-programmer who anticipates my needs and guides me in the right direction.
When I started using ChatGPT in november of 2022, it was very smart. I used it quite a lot to help me rephrase my emails better, to tidy up my writing, to do some simple math that I'm too lazy to do myself, etc. But recently I noticed that it has become less and less helpful, it does things that I specifically asked it NOT to do, it struggles to rephrase my texts the way I want it to, it makes mistakes in basic math and provides all sorts of incorrect information. Now it annoys me way more than it helps me.
ChatGPT is a powerful, open-source language model AI tool, very fastly response query to users.
Based on our record, ChatGPT seems to be a lot more popular than Weaviate. While we know about 815 links to ChatGPT, we've tracked only 28 mentions of Weaviate. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
By following these steps, developers can train ChatGPT on their own data. This will allow to give personalized, accurate, and domain-specific responses. Keep in mind that this process requires technical skills and can take more time than using no-code platforms. - Source: dev.to / 5 days ago
ChatGPT prompt: Act like a senior software developer mentor. Explain to me in the simplest way possible what javascript Symbols are, making very basic examples that DO NOT use "foo" "bar" words. Make small sentences and ask me often if I am able to understand. Thank you. - Source: dev.to / 5 days ago
AI and LLMs such as ChatGPT are amazing at what they do, but they suffer from the lack of "an opposable thumb", to use an analogy. This implies that as stand alone products, they can't really do much, if anything at all. - Source: dev.to / 10 days ago
Prompt Engineering is the art of instructing an LLM such as ChatGPT to do what you want it to do, using nothing but natural language, logic, and reason. The process is probably easily within reach of 80% of the world's population, while less than 0.3% of the world's population knows how to code. - Source: dev.to / 12 days ago
"ChatGPT" in this case means the frontends https://chat.openai.com and https://chatgpt.com, not the API. - Source: Hacker News / 14 days ago
Weaviate: An open-source, cloud-native vector database built for scalable and fast vector searches. It's particularly effective for semantic search applications, combining full-text search with vector search for AI-powered insights. - Source: dev.to / 4 months ago
Weaviate is an open-source vector search engine with out-of-the-box support for vectorization, classification, and semantic search. It is designed to make vector search accessible and scalable, supporting use cases such as semantic text search, automatic classification, and more. - Source: dev.to / 4 months ago
Congrats to them! What have your experiences with vector databases been? I've been using https://weaviate.io/ which works great, but just for little tech demos, so I'm not really sure how to compare one versus another or even what to look for really. - Source: Hacker News / 5 months ago
A RAG implementation's quality and performance highly depend on the similarity-based search of embeddings. The challenge arises from the fact that embeddings are usually high-dimensional vectors, and the knowledge base may have many documents. It's not surprising that the popularity of LLM catalyzed the development of specialized vector databases like Pinecone and Weaviate. However, SQL databases are also evolving... - Source: dev.to / 6 months ago
To find semantically similar texts we need to calculate the distance between vectors. While we have just a few short texts we can brute-force it: calculate the distance between our query and each text embedding one by one and see which one is the closest. When we deal with thousands or even millions of entries in our database, however, we need a more efficient way of comparing vectors. Just like for any other way... - Source: dev.to / 7 months ago
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Qdrant - Qdrant is a high-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
Writesonic - If you’ve ever been stuck for words or experienced writer’s block when it comes to coming up with copy, you know how frustrating it is.
Milvus - Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.
Jasper.ai - The Future of Writing Meet Jasper, your AI sidekick who creates amazing content fast!
pgvecto.rs - Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database. - tensorchord/pgvecto.rs