Rapid-MLX VS MindsDB

💡 💡 Make the fallback cheap to operate. The whole point of using Nano on the supported path is reduced cost. If your fallback is GPT-5.5 at $5/M tokens, you've moved the bill, not deleted it. Two patterns work well: (1) route the fallback to a smaller hosted model (Haiku, Gemini Flash, Mistral Small) that matches Nano's "short summarization" sweet spot; (2) for Mac users specifically, run Rapid-MLX as your /api/llm endpoint — Apple Silicon owners get on-device performance via your server's Mac, not theirs. Same thesis as our DeepClaude guide: the harness is one product, the model is another, and you can swap them.

> Large-context requests auto-route to a cloud LLM (GPT-5, Claude, etc.) when local prefill would be slow. Routing based on new tokens after cache hit. --cloud-model openai/gpt-5 --cloud-threshold 20000

https://github.com/raullenchai/Rapid-MLX

I've verified this end-to-end with structured output (output_type=BaseModel), streaming, multi-turn conversations, and multi-tool workflows. Test suite here.

pip install git+https://github.com/raullenchai/vllm-mlx.git

We’re happy to announce that we’ve integrated MindsDB with Google's open-source project, MCP (Model Context Protocol) Toolbox. This will make your AI applications very, very smart. This enhancement expands the Toolbox's reach, especially for organizations grappling with lots of siloed data.

Just wrapped up my Hacktoberfest project using MindsDB and Streamlit — built a CRM Semantic Search AI app! 😄 If anyone’s into open source + AI, would love feedback on my PR: Hacktoberfest 2025 PR – Add CRM Semantic Search use case (MindsDB)

It’s a mess of tabs, forms, and human fatigue. For Hacktoberfest 2025, our team decided to automate this entire workflow by building AutoBankingCustomerService with MindsDB.

repo: https://github.com/mindsdb/mindsdb

By integrating Gong with MindsDB, enterprises can move beyond static dashboards and unlock real-time, AI-powered insights directly from their customer conversations. With Knowledge Bases, hybrid semantic + metadata search, and natural language agents, teams can explore their Gong data in ways that drive measurable business value—whether it’s improving sales performance, strengthening customer success, or giving executives actionable intelligence at their fingertips.

Customer support is the backbone of any successful business. In today's digital landscape, leveraging artificial intelligence (AI) to automate and enhance support experiences can set your product apart. In this article, we'll explore how to build a customer support application powered by MindsDB, an open-source AI platform that makes it easy to integrate machine learning into your apps.

MindsDB is an AI-powered SQL layer that lets you query machine learning models like databases. Their Knowledge Bases let you embed and semantically search unstructured data—like resumes.

Before going deeper into other details, let's first understand what MindsDB is and how it made it super easy to build the workflow and execute it. MindsDB is an AI Data Solution Platform that makes it easy to connect, unify, and respond (as stated on their site). Simply put, we can understand MindsDB as an abstraction (Hidden Layer) that handles the connection of various data sources and automatically creates pipelines from that data, so you don't need to worry about AI integration development. MindsDB made it easy, like plug and play. Now, you can focus more on business logic rather than other unnecessary stuff like building a chatbot that answers user queries (sadly, but it's the truth!).

Hi, In an era where building AI-powered applications often feels like assembling a spaceship from scratch, I discovered a different path — MindsDB. As a developer working on KbNet, I needed a way to automatically generate summaries of knowledge base articles using AI. Instead of setting up complex machine learning pipelines, I used MindsDB and built a working AI flow in hours — not days.