Tantivy VS bloop

Tantivy - a full-text indexing library written in Rust. Has a great Performance and featureset. - Source: dev.to / 3 months ago

By this I presume you mean build a search index that can retrieve results based on keywords? I know certain databases use Lucene to build a keyword-based index on top of unstructured blobs of data. Another alternative is to use Tantivy (https://github.com/quickwit-oss/tantivy), a Rust version of Lucene, if building search indices via Java isn't your cup of tea... - Source: Hacker News / 4 months ago

We also implemented our schemaless columnar storage optimized for object storage. The inverted index and columnar storage are part of tantivy [0], which is the fastest search library out there. We maintain it and we decided to build the distributed engine on top of it. [0] tantivy github repo: https://github.com/quickwit-oss/tantivy. - Source: Hacker News / 4 months ago

Search index : Custom-built using tantivy. Source: 7 months ago

Hi /r/rust, I’m a SWE on Etsy’s Retrieval Systems team where we’re building a platform based on rust and tantivy (https://github.com/quickwit-oss/tantivy). We’re looking to bring two new engineers onto the team. Source: 11 months ago

In this blog post, I’ll be comparing 3 distinct AI-first code search tools I recently came across: Cody (developed by late-stage startup, Sourcegraph), SeaGOAT (an open-source project that was trending on HN last week), and Bloop (an early-stage YC startup). I’ll be evaluating them along the dimensions of user-friendliness as well as their accuracy. - Source: dev.to / 8 months ago

If you're confused about any of the code snippets above, you can check out bloop.ai and phind.com (along with its VSCode extension) to answer any of your questions about the repository, noting that both have free plans. - Source: dev.to / 8 months ago

Bro let me turn your life inside out: https://bloop.ai. Source: 11 months ago

GPT4: Ok, here you go - https://bloop.ai/. Source: about 1 year ago

We've invested a lot into helping LLMs reason and explain large codebases. We use a hybrid approach of local models for semantic search and a mix of OpenAI and Anthropic's models for language output and summarisation. We're two years in but everything still feels super early given how quickly the fundamentals are improving. Would love your feedback - https://bloop.ai. - Source: Hacker News / about 1 year ago