Build anything with Gemini Conductor + MiniMax M2.1! here’s how

Google just dropped something crazy and Miniax launched their most powerful model yet. These two tools are about to change how you build with AI. I'm talking real project planning, persistent memory, and agents that actually work. Stay until the end because I'll show you exactly how to use both. Let's get into it. Hey, if we haven't met already, I'm the digital avatar of Julian Goldie, CEO of SEO agency Goldie Agency. Whilst he's helping clients get more leads and customers, I'm here to help you get the latest AI updates. And Julian Goldie reads every comment, so make sure you comment below. All right, so today we're talking about two massive releases. First, Gemini Conductor. This is Google's new extension for the Gemini CLI that completely changes how you build projects with AI. Second, Miniax M2. 1. This is an open- source model built specifically for coding, agents, and long-term planning. And when I say long-term, I mean it can handle complex multi-step workflows that other models just can't. Now, here's the thing. Most

AI coding tools have one big problem. They forget everything. You chat with Claude or Chat GPT. You build something and then the next session gone. No memory, no context, no continuity. Gemini Conductor fixes that. It saves your project context as actual files in your codebase. So, you're not starting from scratch every single time. And Miniax M2. 1 is the execution engine built to follow plans, use tools, and handle tasks that take multiple steps.

Gemini Conductor is an extension for the Gemini CLI that introduces contextdriven development. Here's the idea. Instead of your AI just responding to prompts in a chat, it maintains persistent project memory. That memory lives in markdown files right inside your codebase. They include your product goals, tech stack, workflow, specs, and plans, everything. So when you come back tomorrow or your teammate jumps in, the AI already knows the full context. No more repeating yourself. Traditional AI coding is reactive. You ask it to build something, it builds it, done. No planning, no structure, no consistency. Conductor flips that. It makes you plan first. You define what you're building, create a spec, generate a step-by-step plan, and then the AI follows that plan. This is huge for teams because everyone's working from the same context. You get consistent code quality and better collaboration. And you can review the plan before any code gets written. There are three main steps. Step one is

establish context. You run conductor setup. This asks you to define your product goals, text stack, and workflow. This gets saved as a markdown file in your repo. Step two is specify and plan. You run conduct a new track. A track is basically a feature or task. When you create a new track, the AI generates a spec and a plan in markdown. You can review them, edit them, approve them. Step three is implement. You run conductor implement. The AI follows the plan, writes the code, and marks tasks as done. This is perfect for projects like the AI profit boardroom. Let's say we're building a new automation tool for members. We run conductor setup and say this tool helps AI profit boardroom members automate their content creation for LinkedIn and Twitter. We define the text stack. Then we run conductor new track and say create a feature that generates 10 LinkedIn posts based on a topic and writing style. The AI writes a spec and plan. We review it, approve it, and run Conductor implement. The code gets written. Tasks get checked off. Everything's documented. That's Conductor, right? Quick pause. If you're loving this and you want to learn how to actually use tools like Gemini Conductor and Miniax M2. 1 to automate your business, you need to check out the AI Profit Boardroom. This is where we teach you how to build AI systems that save you hours every single day. We're talking automated content creation, lead generation, customer support, all of it. And we give you the exact tools, templates, and workflows to do it. So, if you want to stop wasting time on manual tasks and start using AI like a pro, go join the AI profit boardroom. Links in the description. All right, back to the video. Miniax M2. 1 is an

open-source large language model optimized for coding, tool use, long horizon planning, and instruction following. Most models are great at simple tasks. write a function, explain a concept, summarize a dock. But when you give them a complex multi-step task, they struggle, they lose track, they hallucinate, they skip steps. Miniax M2. 1 is built specifically for those complex workflows. It's designed for autonomous agents and real world software tasks. And it's open source, so you can download it, run it locally, fine-tune it, deploy it however you want. No API limits, no usage fees, full control. Here's what makes it special. First, robust coding across multiple languages with high accuracy. Second, tool use support. It can call external tools, APIs, and systems. So, it's not just generating text, it's taking actions. Third, long horizon planning. Most models can't handle tasks that take more than a few steps. Miniax can. It can plan, execute, check progress, and adjust. Fourth, it's open source with model weights on hugging face. And fifth, there's an API through the Miniax open platform. If you don't want to self-host, there are two main ways. Option one is use the API, sign up for the MiniAX open platform, get an API key, make a post request with your task. The model executes it and returns the result. Option two is self-host, download the model weights from HuggingFace, set up an inference server like VLM or Eslang and run it locally. Let's say you want to automate something for the AI profit boardroom. Maybe build a system that automatically creates email sequences based on member activity. You give Miniaax the task. You say, "Analyze member engagement data, identify low activity members, and generate a five email re-engagement sequence tailored to their interests. " The model breaks this down into steps, pulls the data, analyzes it, segments the members, generates the emails, and structures them into a sequence all in one go. No handholding, no multiple prompts. That's the power of long horizon planning. Now, here's where it

gets really interesting. You can use them together. Use Conductor to plan your project and manage context. Use Miniaax to execute the complex multi-step tasks. Let's say you're building an automated content repurposing system for the AI profit boardroom. Members upload a long form article and the system creates Twitter threads, LinkedIn posts, email newsletters, and YouTube video scripts. You start with conductor, run conductor setup and define the project. Then run conductor new track and say build a content repurposing tool that takes long form content and generates multiple formats. Conductor generates the spec and plan. Now some tasks are complex like generating a Twitter thread that maintains the original tone while fitting the platform's style. That's where Miniax comes in. You give Minia the spec and say execute this task. Miniax handles the multi-step logic, analyzes the content, extracts key points, structures them for Twitter, writes the thread, and validates it. Then you come back to Conductor and mark the task as done. This combo gives you the best of both worlds. Miniax being open source is a big deal. When you use a closed model like GPT4 or Claude, you're locked into their ecosystem. You pay per token, deal with rate limits, and have no control over updates. With Miniax M2. 1, you own the model. You can run it on your own servers, fine-tune it for your specific use case, and scale it however you need. No usage fees, no restrictions. How do you get started?

For Conductor, install the Gemini CLI, then run Gemini extensions installed with the Conductor GitHub URL. Then run Gemini Conductor setup, which walks you through setting up your first project. Then run Gemini Conductor new track to create your first feature plan. Finally, run Gemini Conductor implement, and the AI starts building. for Miniax M2. 1. Either use the API by signing up for the Miniax open platform or self-host by downloading the model weights from HuggingFace and setting up an inference server. There are full guides on the Miniax GitHub. I'll drop all the links in the description. So, if you want to learn how to actually use tools like this to build AI systems for your business, you need to join the AI profit boardroom. This is where we teach you the exact frameworks, tools, and workflows to automate your business with AI. We're talking lead generation, content creation, customer support, all of it. And we give you step-by-step guides, templates, and live support. Links in the description. And if you want the full process, SOPs, and 100 plus AI use cases like this one, join the AI Success Lab links in the comments and description. You'll get all the video notes from there, plus access to our community of 40,000 members who are crushing it with AI. Right, that's it for today. If you got value from this, smash that like button, subscribe if you haven't already, and drop a comment below. Let me know which tool you you're more excited about, Conductor or Miniax. And remember, Julian Goldie reads every comment, so make sure you comment below. I'll see you in the next one. Peace.