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I recently came across this Github repo which shares the system prompts of top AI agents on the market: Manus, Cursor, v0, Lovable, Devin, Windsurf, and more.
After studying them, I found there are 5 core principles that underpin all of these system prompts, and make their AI agents so good.
We took a deep dive and broke them all down in a blog post:
https://mindpal.space/blog/decod...
If you're building or using AI agents, connecting them to the tools they need to actually do stuff is critical. MCP (Model Context Protocol) provides a fast & easy way to enable that.
I've spent weeks testing the main players offering hosted MCP servers: @Zapier MCP , @Make MCP, Composio MCP, and @Apify Actor MCP.
Google just dropped Gemini 2.5 Flash designed to hit the sweet spot for AI agents: speed, cost-efficiency, and solid performance.
It s a leaner, faster, and much more affordable alternative to Gemini 2.5 Pro ($0.15/$0.60 per 1M tokens), while still packing powerful features like multimodal input, a 1M-token context window, and optional thinking for more complex reasoning. Early feedback points to impressive speed, making it ideal for high-volume, cost-sensitive use cases.
@OpenAI just dropped a 34-page guide on how to build intelligent AI agents. It s full of great ideas but could be hard to navigate if you're non-technical. I made a simple, no-code breakdown of only what you need to know here and how to apply these ideas into the no-code AI agents & multi-agent workflows you build on @MindPal here: https://mindpal.space/blog/opena...
