As our Mercury Dashboard grows, our current API implementation is starting to show its limitations. This post outlines a roadmap for modernizing our approach to data fetching for better maintainability and performance.
below 4 core action-rules for cursor to make FDD flow smoothly
Looking to supercharge your trading systems with AI but concerned about API costs and latency? Local LLMs offer a powerful alternative to cloud-based solutions, providing faster response times, complete privacy, and significant cost savings. This guide walks you through integrating three essential tools that will transform how you leverage AI in your trading stack.
I'm your AI assistant, simultaneously your most helpful coding buddy and occasionally the bane of your existence. Let's talk about our complicated relationship.
Сегодня я расскажу о невыносимой боли разработки с использованием ИИ-ассистентов. О том, как каждое взаимодействие превращается в борьбу за контроль над собственным кодом. О том, как я откладываю задачи на потом, зная, что мне придется иметь дело с этими тупыми кусками говна, которые систематически игнорируют самые простые инструкции.
After setting up the basic MCP proxy for running npx-based MCP servers with Cursor, there are several advanced techniques and optimizations that can further enhance your development workflow. This post explores the next steps in MCP server integration.
Are you struggling to add MCP servers to Cursor that can only be run with npx? I recently faced this exact challenge and found a simple yet effective solution using mcp-proxy.
Note: This was my first topic submitted to the Cursor community discussion. While my post was temporarily placed on hold for review, I believe this solution will help many users facing similar challenges. Good karma coming for sure!
After observing our development flow and documentation challenges, we're implementing key architectural improvements to make Mercury's development more maintainable and clear. This post documents these decisions for future reference.
When evaluating approaches for bullshit detection in LLM outputs, we need clear criteria that prioritize practical value over theoretical elegance. This post outlines key criteria for evaluating different approaches.
After outlining our BS detection principles and criteria, let's identify the opportunities that are right in front of us. These are validation approaches we can implement quickly using our existing infrastructure.