
CodeClimate
Codacy
SonarQube
ESLint
CodeFactor.io
Coveralls
SensioLabs Insight
Source-Navigator NG
txtai
Weaviate
Vespa.ai
Vectara Neural Search
Staneffect.ai
EVA DB
Typesense
Milvus
CodeClimate
txtaiBased on our record, txtai should be more popular than CodeClimate. It has been mentiond 80 times since March 2021. 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.
Automated analysis tools: SonarQube, CodeClimate, and Codacy detect code-level debt automatically: cyclomatic complexity, code duplication, dependency staleness, and coverage gaps. These tools supplement but don't replace the architectural and business-logic debt that requires human judgment to identify and document. - Source: dev.to / 3 months ago
CodeClimate and Codacy can generate before/after metrics for code quality that make the starting and ending states concrete rather than subjective. - Source: dev.to / 3 months ago
CodeClimate quantifies maintainability so teams canโt hand-wave garbage away. - Source: dev.to / 11 months ago
Code Climate: Link - Automated code review and quality analysis for codebase health. - Source: dev.to / about 1 year ago
Use tools like SonarQube or CodeClimate to spot the high-risk 20%. Then fix one thing at a time not everything at once. This isnโt Dark Souls. - Source: dev.to / over 1 year ago
100% agree on this. I've been working on small local models for years with txtai (https://github.com/neuml/txtai). I've published close to 100 models that can run local for RAG, Agents, Vector Search and more (https://huggingface.co/NeuML/collections). - Source: Hacker News / about 1 month ago
Local models are great for a lot of things past just software development. We need to move towards solving other real world problems vs just building software. I've been focused on that with TxtAI (https://github.com/neuml/txtai) for 6 years now. - Source: Hacker News / about 2 months ago
Context and prompt engineering is the most important of AI, hands down. There are plenty of lightweight retrieval options that don't require a separate vector database (I'm the author of txtai [https://github.com/neuml/txtai], which is one of them). It can be as simple this in Python: you pass an index operation a data generator and save the index to a local folder. Then use that for RAG. - Source: Hacker News / 12 months ago
I built TxtAI with this philosophy in mind: https://github.com/neuml/txtai. - Source: Hacker News / about 1 year ago
Txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows. - Source: dev.to / over 1 year ago
Codacy - Automatically reviews code style, security, duplication, complexity, and coverage on every change while tracking code quality throughout your sprints.
Weaviate - Welcome to Weaviate
SonarQube - SonarQube, a core component of the Sonar solution, is an open source, self-managed tool that systematically helps developers and organizations deliver Clean Code.
Vespa.ai - Store, search, rank and organize big data
ESLint - The fully pluggable JavaScript code quality tool
Vectara Neural Search - Neural search as a service API with breakthrough relevance