MiniMax Launches General AI Agent Capable of End-to-End Task Execution Across Code, Design, and Media

 

MiniMax Unveils Its General AI Agent: “Code Is Cheap, Show Me the Requirement”

MiniMax, a rising innovator in multimodal AI, has officially introduced MiniMax Agent, a general-purpose AI assistant engineered to tackle long-horizon, complex tasks across code, design, media, and more. Unlike narrow or rule-based tools, this agent flexibly dissects task requirements, builds multi-step plans, and executes subtasks autonomously to deliver complete, end-to-end outputs.

Already used internally for nearly two months, the Agent has become an everyday tool for over 50% of MiniMax’s team, supporting both technical and creative workflows with impressive fluency and reliability.

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🧠 What MiniMax Agent Can Do

• Understand & Summarize Long Documents:
  In seconds, it can produce a 15-minute readable summary of dense content like MiniMax's recently released M1 model.

• Create Multimedia Learning Content:
  From the same prompt, it generates video tutorials with synchronized audio narration—perfect for education or product explainers.

• Design Dynamic Front-End Animations:
  Developers have already used it to test advanced UI elements in production-ready code.

• Build Complete Product Pages Instantly:
  In one demo, it generated an interactive Louvre-style web gallery in under 3 minutes.

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💡 From Narrow Agent to General Intelligence

MiniMax’s journey began six months ago with a focused prototype: “Today’s Personalized News”, a vertical agent tailored to specific data feeds and workflows. However, the team soon realized the potential for a generalized agent—a true software teammate, not just a chatbot or command runner.

They redesigned it with this north star: if you wouldn’t trust it on your team, it wasn’t ready.

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🔧 Key Capabilities

1. Advanced Programming:

• Executes complex logic and branching flows

• Simulates end-to-end user operations, even testing UI output

• Prioritizes visual and UX quality during development

2. Full Multimodal Support:

• Understands and generates text, video, images, and audio

• Rich media workflows from a single natural language prompt

3. Seamless MCP Integration:

• Built natively on MiniMax’s MCP infrastructure

• Connects to GitHub, GitLab, Slack, and Figma—enriching context and creative output

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🔄 Future Plans: Efficiency and Scalability

Currently, MiniMax Agent orchestrates several distinct models to power its multimodal outputs, which introduces some overhead in compute and latency. The team is actively working to unify and optimize the architecture, aiming to make it more efficient, more affordable, and accessible to a broader user base.

The Agent's trajectory aligns with projections by the IMF, which recently stated that AI could boost global GDP by 0.5% annually from 2025 to 2030. MiniMax intends to contribute meaningfully to this economic leap by turning everyday users into orchestrators of intelligent workflows.

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📣 Rethinking Work, Not Just Automation

The blog closes with a twist on a classic developer saying:

“Talk is cheap, show me the code.”
Now, with intelligent agents, MiniMax suggests a new era has arrived:
“Code is cheap. Show me the requirement.”

This shift reframes how we think about productivity, collaboration, and execution in a world where AI can do far more than just respond—it can own, plan, and deliver.

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Final Takeaway:
MiniMax Agent is not just a chatbot or dev tool—it’s a full-spectrum AI teammate capable of reasoning, building, designing, and communicating. Whether summarizing scientific papers, building product pages, or composing tutorials with narration, it's designed to help anyone turn abstract requirements into real-world results.

Andrej Karpathy Declares the Era of Software 3.0: Programming in English, Building for Agents, and Rewriting the Stack

 Andrej Karpathy on the Future of Software: The Rise of Software 3.0 and the Agent Era

At a packed AI event, Andrej Karpathy—former Director of AI at Tesla and founding member of OpenAI—delivered a compelling address outlining a tectonic shift in how we write, interact with, and deploy software. “Software is changing again,” Karpathy declared, positioning today’s shift as more radical than anything the industry has seen in 70 years.

From Software 1.0 to 3.0

Karpathy breaks down the evolution of software into three stages:

• Software 1.0: Traditional code written explicitly by developers in programming languages like Python or C++.

• Software 2.0: Neural networks trained via data and optimized using backpropagation—no explicit code, just learned weights.

• Software 3.0: Large Language Models (LLMs) like GPT-4 and Claude, where natural language prompts become the new form of programming.

“We are now programming computers in English,” Karpathy said, highlighting how the interface between humans and machines is becoming increasingly intuitive and accessible.

GitHub, Hugging Face, and the Rise of LLM Ecosystems

Karpathy draws powerful parallels between historical shifts in tooling: GitHub was the hub for Software 1.0; Hugging Face and similar platforms are now becoming the repositories for Software 2.0 and 3.0. Prompting an LLM is no longer just a trick—it’s a paradigm. And increasingly, tools like Cursor and Perplexity represent what he calls partial autonomy apps, with sliding scales of control for the user.

In these apps, humans perform verification while AIs handle generation, and GUIs become crucial for maintaining speed and safety.

AI as Utilities, Fabs, and Operating Systems

Karpathy introduced a powerful metaphor: LLMs as a new form of operating system. Just as Windows or Linux manage memory and processes, LLMs orchestrate knowledge and tasks. He explains that while LLMs operate with the reliability and ubiquity of utilities (like electricity), they also require the massive capex and infrastructure akin to semiconductor fabs.

But the most accurate analogy, he claims, is that LLMs are emerging operating systems, with multimodal abilities, memory management (context windows), and apps running across multiple providers—just like early days of Linux vs. Windows.

Vibe Coding and Natural Language Development

Vibe coding—the concept of programming through intuition and natural language—has exploded, thanks in part to Karpathy’s now-famous tweet. “I can’t program in Swift,” he said, “but I built an iOS app with an LLM in a day.”

The viral idea is about empowerment: anyone who speaks English can now create software. And this unlocks massive creative and economic potential, especially for young developers and non-programmers.

The Next Frontier: Building for AI Agents

Karpathy argues that today’s digital infrastructure was designed for humans and GUIs—not for autonomous agents. He proposes tools like llm.txt (analogous to robots.txt) to make content agent-readable, and praises platforms like Vercel and Stripe that are transitioning documentation and tooling to be LLM-native.

“You can’t just say ‘click this’ anymore,” he explains. Agents need precise, machine-readable instructions—not vague human UX metaphors.

He also showcases tools like Deep Wiki and Ingest to convert GitHub repos into digestible formats for LLMs. In short, we must rethink developer experience not just for humans, but for machine collaborators.

Iron Man Suits, Not Iron Man Robots

Karpathy closes with a compelling analogy: most AI applications today should act more like Iron Man suits (human-augmented intelligence) rather than fully autonomous Iron Man robots. We need GUIs for oversight, autonomy sliders to control risk, and workflows that let humans verify, adjust, and approve AI suggestions in tight loops.

“It’s not about replacing developers,” he emphasizes. “It’s about rewriting the stack, building intelligent tools, and creating software that collaborates with us.”

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Takeaway:
The future of software isn’t just about writing better code. It’s about redefining what code is, who gets to write it, and how machines will interact with the web. Whether you’re a developer, founder, or student, learning to work with and build for LLMs isn’t optional—it’s the next operating system of the world.