DEV Community: Cx330

How to Learn Prompt Engineering — From Crafting One AI to Leading an AI Team

Cx330 — Tue, 30 Jun 2026 12:12:49 +0000

How to learn Prompt Engineering? Five core techniques — be clear and specific, assign a role, few-shot examples, chain-of-thought (CoT), structured output. Master these in 1–2 weeks. In 2026, Prompt Engineer is already a high-paying role — top performers earn ¥300K–¥800K RMB annually. Many can tune a single prompt to boost their own efficiency; few can orchestrate multi-Agent collaboration to build AI systems. The gap is 5–10x.

Principle 1 — Say It Clearly

The #1 mistake? Vague requests. AI can't read your mind. The more specific you are, the less it drifts off track.

Bad prompt: "Write me an article."

Good prompt: "Write a 1,000-word Python learning roadmap for absolute beginners looking to switch careers. Include: a 3-phase, 6-month timeline, core concepts for each phase, recommended learning resources, and hands-on project suggestions. Use a conversational tone — no academic jargon."

What's the difference? With the first, AI has to guess what you want and will likely deliver something generic and useless. With the second, you've locked in all the key variables — who the reader is, the timeline, what modules to cover, what tone to use — and AI delivers exactly what you asked for. A few tweaks and it's ready to go.

Timeline + scenario + key variables. All three are non-negotiable.

Principle 2 — Give AI an Identity

Assign a role to constrain the output style and depth.

Here's an example: you want AI to draft a trademark infringement warning letter. "Write me a legal letter" — AI gives you a vague, boilerplate template. "You are a senior intellectual property lawyer drafting a trademark infringement cease-and-desist letter for a cross-border e-commerce client. Requirements: cite relevant provisions of the Trademark Law, use formal language but avoid threatening tone, include a 7-day compliance period, and explain the legal consequences of non-compliance. Output in Word document format" — AI delivers a professional letter you can send as-is.

Same request, but with a role vs. without: the output quality gap can be 5–10x. The role determines AI's stance, depth of expertise, and tone — a role isn't decoration, it's a professional filter for AI's output.

Principle 3 — Give Examples, Don't Make It Guess

Want AI to learn your style? Feed it a few examples.

You want AI to translate slang — give it two examples first: "拍马屁 → brown-nose, 放鸽子 → stand someone up" — then ask it to translate "吃醋" and it outputs "jealous." Without those examples? It might return "be jealous in a relationship" — technically correct, but misses the nuance.

That's what few-shot means: provide 2–3 input-output pairs so AI understands your task pattern. The key: examples should be representative, format-consistent, and cover different cases. Two or three is enough — don't pile on too many, or AI will overfit to your examples and lose flexibility.

Principle 4 — Make AI Show Its Thinking Process

Chain-of-Thought (CoT) is the most effective technique for improving complex task quality.

What does it mean? For complex tasks, don't let AI jump straight to the conclusion — make it write out the reasoning process first.

Say you want AI to analyze an investment report. Don't ask "what do you think of this report?" Ask: "How many sections does this report have? What are the core data sources for each section? What layers of logic should the risk hedging strategy consider? Please outline the framework first, then expand on each item."

Without CoT, AI might skip critical steps and serve up a rough conclusion. With CoT, it reasons step by step, covering every angle — and you can see its judgment basis, making it easy to spot errors.

Making AI "write out its thinking process" is 10x more reliable than "giving the answer directly."

Principle 5 — Lock Down the Output Format

Be explicit about the output format — JSON, Markdown tables, bullet points, numbered lists — to prevent AI from freestyling and scattering your data all over the place.

Here's an example: you want AI to generate a competitive analysis report. "Write a competitive analysis" — AI gives you fluff like "Company X has good products, Company Y also has strengths." "Please output in the following format: 1. Executive summary (under 100 words); 2. Competitive matrix table (columns: product name / company / key differentiator / price / pros & cons); 3. Strategic recommendations (3–5 numbered items)" — AI delivers a structured report you can screenshot and present immediately.

Application Priority

These five techniques aren't equal — there's a clear order:

Clarity and specificity are the foundation — must be solid, or the other techniques won't work.

Role assignment comes next — defines AI's stance and depth, highest impact.

Structured output improves readability — makes results ready to use out of the box.

Few-shot and CoT are situational — use few-shot when the task pattern is clear, use CoT when reasoning is complex.

From Crafting One AI to Leading an AI Team

The core shift at the advanced level: it's no longer about tuning a single prompt, it's about orchestrating multiple AI Agents to collaborate.

Traditional Prompt Engineering asks "how do I phrase this so one AI gives better output?" 2026 Prompt Engineering asks "how do I define the roles and collaboration rules for multiple AI Agents so they autonomously complete complex tasks?"

On a low-code Agent platform like SoloEngine, the workflow looks like this: drag in an intent recognition Agent — "your job is to analyze user input and classify it into four types: order inquiry, refund request, logistics consultation, or complaint"; then drag in a customer service response Agent — "your job is to generate professional, humanized replies based on intent type and knowledge base retrieval results."

A user sends "My package from yesterday hasn't arrived yet" — the intent recognition Agent instantly classifies this as "logistics consultation," the customer service Agent automatically queries the logistics API to track the package location, generates a response, and pushes it to the user. The whole process takes 2 seconds. You just assign tasks and review results.

It's no longer about crafting one AI — it's about leading an entire AI team.

Multi-Agent collaboration introduces three new challenges: consistency control — ensuring all Agents share the same style, terminology, and stance, so Agent A doesn't use formal business language while Agent B uses casual internet slang, creating a jarring experience; boundary definition — clearly defining each Agent's scope of responsibility, so two Agents don't fight over the same task or ignore it entirely; handoff protocol — defining how Agents pass information between each other, such as requiring the intent recognition Agent's output to start with a "type:" label so downstream Agents can parse it accurately.

My Recommendation

How do you learn Prompt Engineering?

Entry level (1–2 weeks): Master the five techniques — clarity is the foundation, role assignment has the highest impact. Once you nail these, the quality of your AI conversations jumps to a whole new level.

Advanced direction: Don't keep optimizing single prompts — learn to orchestrate multi-Agent collaboration.

If you're an individual user: Start with ChatGPT or Claude, practice the five techniques, turn bad prompts into good ones, and you'll see noticeable improvement in 1–2 weeks.
If you want to build AI business systems: Use SoloEngine to orchestrate an Agent team — one person doing the work of an entire department.

The ultimate goal isn't "knowing how to write prompts" — it's using Prompt Engineering to build self-running AI productivity systems. Master the first, and you can build a single AI assistant. Master the second, and you can build an entire AI business system. That's the critical step from "skill" to "productivity."

Will AI Replace Programmers?

Cx330 — Tue, 30 Jun 2026 12:11:21 +0000

Will AI replace programmers? Yes — but not all of them. It replaces people who don't know how to use AI.

The question isn't whether you can code — it's how much of your job is repetitive execution versus creative decision-making.

Will AI Actually Replace Programmers?

Three numbers. That's all you need.

In 2026, 41% of new code is already AI-generated. Not auto-completed comments. Full features. Entry-level programmers — the ones writing CRUD, building simple pages, calling API endpoints — have watched their job openings plummet 85%. One AI coding tool can replace 5 to 10 execution-focused developers. Productivity is up 5x to 10x, but the jobs are evaporating.

Here's the counterintuitive part: experienced developers actually got 19% slower after adopting AI. Why? Because they're spending hours reviewing and fixing AI-generated code. AI writes fast — but it doesn't always write right. Experienced devs aren't coding anymore. They're cleaning up AI's mess.

But AI is also creating new roles. AI Orchestration Engineers — programmers who can build and coordinate teams of AI Agents using Agentic AI platforms — command a 56% salary premium. AI Agent-related job postings are up 300% year-over-year. Top Agent engineers earn 800K+ RMB annually. AI isn't killing work — it's killing old work and creating new work.

Corporate attitudes are even more direct. The AWS CEO announced that in 2026, platform engineers will stop traditional programming entirely and pivot to AI-driven development paradigms. Google Cloud research shows over half of enterprises have already deployed AI Agent systems in production. Companies aren't watching from the sidelines — they're overhauling their teams.

Bottom line: AI doesn't eliminate programmers. It eliminates programmers who only know how to follow standard procedures. Coding is table stakes. Orchestrating Agents is where the premium is.

Four Types of Developers, Four Fates

Which one are you? That determines whether you're getting a raise or getting phased out over the next three years.

You're an execution-focused programmer — high risk. Your day is CRUD, simple pages, API calls. Replacement rate: 85%. AI can already generate complete code for straightforward requirements. Every line you write, AI writes faster. There's only one escape: upgrade from "executor" to "orchestrator." Build Agent teams with LangChain / LangGraph. You don't write code anymore — you orchestrate Agents that write code for you.

You're an AI Orchestration Engineer — high demand. Your core skill isn't coding. It's building multi-Agent collaborative systems. You don't write code line by line. You drag Agents onto the SoloEngine canvas, configure their tools, set their collaboration rules, and let the Agent team autonomously complete business requirements. Your deliverable shifts from "code" to "Agent systems." Salary premium: 56%.

You're an architect or system designer — safe. AI can assemble code, but it can't architect from scratch. System architecture requires understanding business fundamentals, weighing design trade-offs, anticipating where the system needs to go — these are AI's "experience blind spots." But architects need to level up too: use AI to rapidly generate multiple architecture options for comparison, use AI to analyze system bottlenecks and suggest optimizations.

You're a domain-expert programmer — safe and premium. AI + finance, AI + healthcare, AI + law, AI + autonomous driving — programmers with deep domain expertise are temporarily out of AI's reach. But you must layer on AI skills: AI safety specialists, AI ethics compliance officers, AI model evaluators — these roles already pay 30%+ above traditional programmer salaries.

From Executor to Orchestrator: How to Save Your Career

There's only one core shift — transform your work mode from "executor" to "orchestrator."

Let's compare a real scenario. The PM says: "We need a user points system live next week — points earning, points redemption, points expiration. Three modules."

How the executor does it — You open your IDE → stare blankly at the requirements doc for 10 minutes → write database schema → write API endpoints → write frontend pages → test it yourself and find the API returns data in the wrong format → dig through docs → fix code → retest → the UI breaks → tweak CSS → submit PR → testing finds bugs → fix bugs → retest → fix again → finally go live after two weeks. You're the bottleneck. You're firefighting the entire process.

How the orchestrator does it — You drag a requirements-analysis Agent onto the SoloEngine canvas → let it break down the three modules of the points system → connect a code-generation Agent → let it generate schema and APIs → connect a testing Agent → auto-run unit and integration tests → connect a code-review Agent → check for security vulnerabilities and coding standards. You press run, and the Agent team handles the entire workflow autonomously. You review key decisions. Two hours to finish what used to take two weeks.

You went from factory-floor worker to factory-floor manager.

How to make the shift? Three steps —

Step one: Let AI write code for you. Use Cursor or Claude Code for development. Let AI generate boilerplate code, unit tests, documentation. Daily development: Cursor (best IDE-enhanced experience). Complex projects: Claude Code (strongest deep codebase understanding). Your focus shifts to architecture design and business logic.

Step two: Let Agents handle repetitive work. Orchestrate Agent teams in SoloEngine. Let Agents replace you on repetitive tasks. No coding required — define Agent roles and tools in natural language, drag and drop on the canvas to build multi-Agent collaborative systems. A copywriting Agent finishes product descriptions and automatically triggers a design Agent to generate images. A customer-service Agent receives an inquiry and automatically pulls answers from the product FAQ. A data-analysis Agent auto-generates weekly operations reports and pushes them to your team chat.

Step three: Use Vibe Coding for rapid prototyping. Describe requirements → AI generates → run and see results → tell it what's wrong → it fixes. Validate a product idea in hours instead of weeks. You don't write code. You don't read code. You just judge whether "it looks right."

My Verdict

Back to the original question — will AI replace programmers?

The answer: AI replaces programmers who don't know how to use AI. Programmers who can orchestrate AI Agents aren't being replaced — their output has become 10x, their pay 1.5x.

My advice:

If you're a junior developer: Start learning Agent orchestration now. Don't wait for the job to disappear before acting — the transition window is still open. Get started with SoloEngine low-code for quick wins, then go deeper into LangChain fundamentals.
If you're a senior developer: Hand repetitive work to AI and Agents. Your value is in architecture design and business judgment. Use AI to generate multiple solution options for comparison, let Agent teams run tests and reviews for you.
If you're a founder: Vibe Coding is the fastest way to validate product ideas. An MVP in one week is no longer a pipe dream. Build competitive moats with Agent orchestration — one person replacing a 3-to-5-person team.
If you're a product manager or designer: Build prototypes yourself. No more waiting in line for engineering resources. Describe requirements and directly generate interactive prototypes. Validate your own ideas.
If you're an architect: AI is your amplifier, not your threat. Use AI to rapidly validate architectural assumptions. Reinvest the time saved into business understanding and long-term planning.
If you're a non-technical manager: Understand this — in 2026, when you hire programmers, the criteria have changed. Coding ability is the baseline. Agent orchestration ability is what separates the good from the great.

AI won't eliminate programmers — it eliminates people who only know how to work by the manual. Those who embrace AI: 10x output, 1.5x pay. Those who ignore AI: their jobs vanish. This isn't a prediction. It's reality in 2026.

From MiMo Code to MiMo SoloEngine: Xiaomi's Dual-Agent Strategy

Cx330 — Thu, 11 Jun 2026 12:32:37 +0000

On June 11, Xiaomi did one thing: it simultaneously launched MiMo Code and open-sourced MiMo SoloEngine.

On the surface, these appear to be two separate products. MiMo Code is a terminal-based programming assistant for developers, while SoloEngine is a low-code Agent platform for everyone.

But if you understand Xiaomi's strategic vision, you'll realize: these are two entry points to the same thing.

I. The Developer's Gateway: MiMo Code

The core breakthrough of MiMo Code lies in its persistent memory system. Traditional AI programming assistants suffer from a "forgetful" problem during long sessions—as the context window fills up, earlier conversations get lost. MiMo Code solves this through a three-layer mechanism: project memory, session checkpoints, and task progress tracking. The main Agent focuses entirely on the work, while a dedicated sub-Agent handles all the recording.

Combined with Compose mode, users simply press Tab, provide a high-level idea, and the system automatically executes the entire pipeline: design → planning → coding → testing → review. It achieves 62% on SWE-Bench Pro, outperforming Claude Code by 5 percentage points.

Developers' problems are solved. They now have an AI programming partner that maintains clarity across hundreds of interaction rounds.

But developers account for less than 1% of the total population.

What about the other 99%?

II. Everyone's Gateway: MiMo SoloEngine

SoloEngine is the first low-code Agentic AI development platform. It tackles the same challenges as MiMo Code—enabling Agentic AI to handle long-horizon tasks, make autonomous decisions, and facilitate multi-agent collaboration—but through an entirely different entry point.

MiMo Code's entry point is the terminal. You need to know how to use the command line, understand programming concepts, and grasp the Harness and Compose modes.

SoloEngine's entry point is a visual canvas in your browser. You drag Agents onto it, connect them with lines, and click run. The backend automatically compiles everything into an executable Agentic AI system.

Two entry points, one underlying capability.

Each Agent in SoloEngine runs the same ReAct loop (think → act → observe → repeat), supports multi-agent collaboration, and employs progressive disclosure to control token consumption. The technical architecture shares the same foundation as MiMo Code—both are genuine Agentic AI, not preset workflows.

Dimension	MiMo Code	MiMo SoloEngine
Target Users	Developers	All professionals
Entry Point	Terminal command line	Browser-based visual canvas
Core Capability	Persistent memory + Compose mode	ReAct + multi-agent collaboration
Programming Required	Yes	No
Agentic AI	✓	✓
Multi-agent Collaboration	✓	✓
Open-source License	MIT	Apache 2.0

III. Why Two Lines Are Necessary

One number explains everything: 87% of enterprises claim to have deployed AI at scale, but only 10% have actually derived value from it.

The root cause of this gap isn't insufficient models or computing power—it's that the ability to build Agents remains locked in developers' hands.

Developers already have Cursor, Claude Code, and MiMo Code—they can work efficiently through Vibe Coding. But a lawyer won't open a terminal, an accountant won't write Python, and an operations manager won't configure a ReAct Agent.

If Xiaomi only built MiMo Code, it would serve only the developer community. If Xiaomi only built SoloEngine, it would abandon the most technically savvy group.

Only by advancing both lines simultaneously can we cover all users.

Developers use MiMo Code for efficient programming via Vibe Coding in the terminal. Professionals from all other industries use SoloEngine to build their own Agent teams through visual operations in the browser.

Both lines share the same model foundation—MiMo. Both lines share the same technical philosophy—genuine Agentic AI. Both lines share the same goal—enabling AI to complete tasks autonomously rather than following preset paths.

IV. SoloEngine's Unique Value: Vertical Deep Dive

MiMo Code is a general-purpose programming assistant—it can write any code and handle any project.

SoloEngine takes a different approach—vertical deep dive.

Manus and the viral "Lobster" earlier this year attempted the general-purpose Agent route—capable of everything but excelling at nothing. General-purpose Agents struggle to meet commercial requirements.

SoloEngine enables experts in every industry to create precise, reusable Agentic AI tools tailored to their specific tasks. A lawyer's Agent handles only legal work, an accountant's Agent focuses solely on financial analysis, and an operations manager's Agent deals exclusively with campaign planning.

Vertical means precision, and precision means usability.

A "Contract Review Agent Team" built by a lawyer using SoloEngine isn't a generic document analysis tool—it's specifically optimized for contract review scenarios, with understanding of legal terminology, familiarity with common risk points, and knowledge of how to cite relevant laws. This vertical depth is unattainable for general-purpose Agents.

SoloEngine's progressive disclosure mechanism makes this vertical deep dive cost-effective. Tools, Skills, and MCP protocols load on demand, reducing token consumption by over 85%. A vertical Agent team operates at a fraction of the cost of a general-purpose Agent.

V. Xiaomi's Agent Ecosystem Closed Loop

From the MiMo foundation model to MiMo Code to MiMo SoloEngine, Xiaomi is constructing a complete Agent ecosystem closed loop:

Brain Layer—MiMo Foundation Model, trillion-parameter scale, global open-source leader. After a 99% price reduction, API costs are extremely competitive.

Programming Agent Layer—MiMo Code, targeting developers, with persistent memory and Compose mode, achieving 62% on SWE-Bench Pro.

General Agent Layer—MiMo SoloEngine, targeting all industries, featuring low-code, visual canvas, and multi-agent collaboration.

Application Layer—miclaw mobile Agent, Agent ecosystem platform, 1 billion IoT devices.

Distribution Layer—SoloEngine's one-click packaging feature enables built Agent teams to be packaged into standalone products for self-deployment or distribution and sales.

From building models to building platforms to building services, Xiaomi's dual-Agent strategy is transforming 60 billion yuan in investment into tangible business value.

MiMo Code is the Agent for developers. MiMo SoloEngine is the Agent for everyone. Two lines, one ecosystem, covering all industries.

MiMo Code Goes Open Source, but Xiaomi's Real Ambition Is MiMo SoloEngine

Cx330 — Thu, 11 Jun 2026 08:53:45 +0000

In the early hours of June 11, Xiaomi officially released and open-sourced MiMo Code. Released under the MIT license, it comes with a free built-in MiMo-V2.5 model and scored 62% on SWE-Bench Pro—5 percentage points higher than Claude Code. The media went wild. Developers were ecstatic.

But if you only noticed MiMo Code, you missed the most critical move in Xiaomi's grand strategy.

MiMo Code is built for developers. Xiaomi's real ambition is MiMo SoloEngine—a low-code platform that enables every industry to build Agentic AI systems.

1. How Powerful Is MiMo Code?

Let's start with MiMo Code, as it is indeed Xiaomi's heavyweight weapon in the Agent space.

Persistent Memory System—A triple mechanism comprising project memory, session checkpoints, and task progress. The main Agent focuses on execution while an independent SubAgent handles all documentation. Context window filling up? The SubAgent automatically compresses a clean briefing, allowing the main Agent to continue seamlessly. Even after hundreds of long-running conversation rounds, no critical information is lost.

Compose Mode—Press the Tab key to switch modes. Provide a simple idea, and the system automatically completes the entire workflow: design → planning → coding → testing → review. Industrial-grade code delivery.

/dream Command—Triggered automatically every 7 days, an independent Agent reads historical sessions and existing memory files, performing merging, deduplication, path validation, and compression to converge scattered memories into a compact current state. MiMo Code doesn't start from scratch each time—it grows continuously with accumulated project understanding.

Voice Input—Powered by MiMo-V2.5-ASR speech recognition, supporting verbal modification instructions and operational commands without ever touching the keyboard.

On SWE-Bench Pro, MiMo Code achieved 62% (Claude Code scored 57%). On Terminal Bench 2, it reached 73% (Claude Code scored 68%). Under identical model conditions, the Agent framework alone accounts for this 5-percentage-point difference.

The developers' problem is solved. But what about everyone else?

2. The Gap Between 87% and 10%

Amazon Web Services revealed a striking statistic at the AIGC2026 Summit: 87% of enterprises claim to have deployed AI at scale, yet only 10% have genuinely derived value from it.

The root cause isn't weak models—MiMo-V2.5-Pro already ranks first globally among open-source models on the Agent Index. Nor is it insufficient compute—after Xiaomi's 99% price reduction, API costs have dropped to electricity-bill-level pricing.

The real issue is this: the ability to build Agents remains locked in the hands of developers.

Currently, there are only two paths to building AI Agents. The first is low-code Workflow platforms like Dify and n8n. Visual canvas, drag-and-drop nodes, rapid assembly. But their core logic relies on "preset paths"—controlling flows with if/else conditions without supporting true autonomous decision-making. When encountering scenarios outside the presets, the workflow stalls.

The second path is code-based development frameworks like LangChain and CrewAI. They support true Agentic AI but require Python programming skills. A lawyer won't use LangChain. An accountant can't configure a ReAct Agent. A marketing specialist doesn't write Python.

Low-code platforms are easy to adopt but don't support genuine autonomous decision-making. Code frameworks enable autonomous decision-making but are only accessible to developers.

MiMo Code fills the gap for developers. MiMo SoloEngine fills the gap for every other industry.

3. SoloEngine: The True Agent Platform for Everyone

SoloEngine is the first low-code Agentic AI development platform. Its mission is clear: enable people in every industry to build their own Agentic AI systems, just as developers use MiMo Code for Vibe Coding.

How does it achieve this?

SoloEngine encapsulates the ReAct architecture, Tool calling, MCP protocol, Skills, and SubAgents entirely in the backend. Users don't need to understand these technical terms—just open a browser, drag Agents onto the canvas, connect their collaboration relationships, configure the required tools, and click Run. The backend automatically compiles everything into a dedicated Agentic AI system.

This system is just as powerful as MiMo Code. Each Agent runs a ReAct loop (Think → Act → Observe → Repeat), making real-time judgments based on current conditions. When encountering unexpected situations, it adjusts its strategy autonomously. When discovering better approaches, it proactively switches paths. The entire process requires no if/else statements and no preset paths.

Progressive Disclosure—Tools, Skills, and MCP protocols load on demand, reducing token consumption by over 85%. Unified Adaptation Layer—covering all major models including OpenAI, Anthropic, Ollama, MiMo, DeepSeek, Tongyi Qianwen (Alibaba), and Zhipu. One-Click Packaging—assembled Agent teams can be packaged into complete products for self-deployment or distribution.

Open-sourced under Apache 2.0, completely free.

4. The OPC Era: SoloEngine's True Battlefield

Nationwide, One-Person Companies (OPCs) have surpassed 16 million, accounting for 27.4% of all enterprises. 2026 has been dubbed the "Year of OPC," with over 20 cities introducing dedicated OPC support policies.

The core demand of these "one-person companies" is to replace traditional teams with AI Agents, achieving "one-person armies." But LangChain requires programming skills, and Dify's Workflows don't support genuine autonomous decision-making.

SoloEngine was built precisely for this era.

A cross-border e-commerce operator builds an Agent team with SoloEngine: a Product Analysis Agent (automatically analyzing competitor data), a Copywriting Agent (generating product descriptions), and a Customer Service Agent (automatically handling customer inquiries). Three Agents collaborating—running 6 online stores single-handedly is no longer a myth.

A lawyer builds a legal affairs Agent team with SoloEngine: a Case File Analysis Agent (automatically extracting key information from case files), a Legal Research Agent (automatically matching relevant regulations), and a Document Generation Agent (automatically drafting legal document templates). The lawyer only needs to review and sign.

An independent developer builds a full-stack development Agent team with SoloEngine: a Requirements Analysis Agent, an Architecture Design Agent, a Code Implementation Agent, and a Testing Agent. One person accomplishing what once required an entire team.

SoloEngine transforms Vibe Coding into Vibe Everything.

5. Xiaomi's True Ambition

Xiaomi's pace of expansion in AI has exceeded most expectations.

In April 2025, MiMo-7B was open-sourced. In March 2026, MiMo-V2-Pro launched, and the miclaw mobile Agent began closed beta the same month. April saw the MiMo-V2.5 series open-sourced and the Agent ecosystem platform enter public beta. In May, MiMo-V2.5-Pro claimed the top spot globally among open-source models, with API prices slashed by 99%. June brought MiMo Code's release and open-sourcing, alongside SoloEngine's simultaneous open-source launch.

From a 7-billion-parameter model to a trillion-parameter flagship, from follower to frontrunner—Xiaomi achieved this in less than a year.

But no matter how powerful large models are, they are essentially just a "brain." What Xiaomi aims to do next is equip this brain with "hands and feet."

MiMo Code provides hands and feet for developers. SoloEngine provides hands and feet for everyone.

MiMo provides the "brain" for Agents, while SoloEngine provides the "building platform" for Agents. Together, they elevate Xiaomi's strategy from "building models" to "building platforms" and "building services."

Xiaomi's ecosystem advantages are further amplified through SoloEngine: MiMo's model capabilities, 99%-reduced API costs, 1 billion IoT devices, the Agent ecosystem platform, and the miclaw mobile Agent—these resources are interconnected by SoloEngine, forming an ecosystem moat that other platforms cannot easily replicate.

MiMo Code is open source, and developers are celebrating. But Xiaomi's true ambition is to give every industry its own "Claude Code."

This ambition has a name: MiMo SoloEngine.

Open Source · Apache 2.0 · github.com/Sh4r1ock/SoloEngine

What Is a One Person Company? — Why 16 Million People Chose It in 2026

Cx330 — Wed, 10 Jun 2026 02:49:47 +0000

What is a One Person Company? One person, one business license, one limited liability firewall — losses are absorbed by the company; profits are yours to keep. By 2026, with the new Company Law in effect, the number of One Person Companies nationwide surpassed 16 million, accounting for 27.4% of all enterprises. In the first half of 2025 alone, 2.86 million new OPCs were registered, a year-on-year increase of 47%.

The essential difference between a One Person Company and a Sole Proprietorship lies in liability — with an OPC, your maximum loss is capped at your subscribed capital (e.g., 100,000 RMB); with a Sole Proprietorship, you're personally liable down to your last penny. How to choose: annual revenue over 300,000 RMB, need to work with corporate clients, want to protect family assets — choose an OPC. Annual revenue under 100,000 RMB, running a small business — a Sole Proprietorship is sufficient.

What Exactly Changed in the 2024 Overhaul?

The new Company Law, implemented in July 2024, brought sweeping changes to One Person Companies.

Under the old law, OPCs were treated as a "special form" with their own dedicated chapter of regulations — a natural person could only establish one OPC, that OPC couldn't in turn create another, and the business license had to be stamped with "solely owned by a natural person," almost like being branded.

The new law scrapped all these restrictions. It no longer uses the term "One Person Limited Liability Company," replacing it with "a company with only one shareholder." There's no longer a limit on how many you can set up. Mandatory labeling is gone. It even allows the establishment of a One Person Joint Stock Company — something previously unimaginable.

So what exactly changed? First, the dedicated chapter on "One Person Limited Liability Companies" was eliminated, and OPCs are no longer treated as a special form. Second, the scope of founding entities was expanded — unincorporated organizations can now establish OPCs. Third, the restriction limiting a natural person to only one OPC was removed.

Attorney Cao Fei of Langke Law Firm summarized it in one sentence: the uniqueness of One Person Companies is diminishing, while their prevalence is increasing.

What does this mean? The government no longer views OPCs as "entities requiring special oversight" but as ordinary, legitimate choices for entrepreneurs.

Three Core Legal Characteristics

One Person Companies have three key legal characteristics. Understand these, and you'll avoid the common pitfalls.

First, independent legal entity. The company is the company; you are you. The company can sign contracts, issue invoices, be sued, and apply for loans — it participates in commercial activities as an independent "person." You sign contracts with the company seal, not your personal signature. You issue VAT special invoices that corporate clients can use for tax deductions. This is something a Sole Proprietorship simply cannot do.

Second, limited liability. This is the most critical value. If the company incurs 1 million RMB in debt, your maximum exposure is limited to your subscribed registered capital (say, 100,000 RMB). The caveat: you must be able to prove that company and personal finances were never commingled — this requires annual audits and complete financial records.

Third, reversed burden of proof. This is the most easily overlooked trap. For ordinary companies, the creditor must prove the shareholder's fault. For OPCs, it's the other way around — the shareholder must prove financial independence. Can't produce an audit report and complete books? The court will "pierce the corporate veil" outright, and your property, savings, and vehicles become fully enforceable assets.

These three characteristics together define the fundamental approach to running an OPC: keep business and personal accounts separate, conduct annual audits, and preserve evidence.

What's the Real Difference Between an OPC and a Sole Proprietorship?

The most fundamental difference isn't the name — it's liability.

Dimension	One Person Company	Sole Proprietorship
Nature of liability	Limited liability (capped at subscribed amount)	Unlimited joint and several liability (personal assets at stake)
When owing 1 million RMB	Maximum loss of 100,000 RMB	Savings, house, car — all enforceable
Invoicing capability	Can issue VAT special invoices (corporate clients can claim deductions)	Can only issue standard invoices (corporate clients reluctant to cooperate)
Suitable annual revenue	Over 300,000 RMB	Under 100,000 RMB
Suitable scenarios	Working with corporate clients, protecting family assets	Small-scale business, no corporate client dealings

There's a real case from 2026: a sole proprietorship renovation studio suffered water damage, resulting in nearly 680,000 RMB in compensation. The court directly enforced the operator's savings and vehicles, nearly seizing their primary residence. Had it been registered as an OPC with 100,000 RMB in subscribed capital, the maximum liability would have been just 100,000 RMB.

Why 2026 Is the Tipping Point

2026 has been dubbed the "Year of the OPC" (One Person Company). In April 2026, Hangzhou released its Several Measures to Support the Innovation and Entrepreneurship Development of One Person Companies (OPCs), providing systemic institutional support through twelve measures across five areas. Beijing, Shenzhen, Shanghai, Chengdu, and other cities have also introduced supportive policies for AI-driven OPCs.

A research report from Far East Credit Rating points out that OPCs in current market practice have already transcended the Company Law definition — they manifest more as an AI-empowered "super-individual" entrepreneurial model. The core formula is "founder + AI agents + flexible collaboration network," requiring no heavy asset investment, enabling efficient decision-making, and keeping costs low.

The driving force behind this transformation is AI.

In the past, starting a company on your own meant hiring people — a designer, a copywriter, customer service staff, an accountant. Now, one person plus an AI Agent team can accomplish the work of five people.

Take SoloEngine as an example. Open your browser, drag a design agent, copywriting agent, and customer service agent onto a canvas, connect their collaboration relationships, and click Run. The backend automatically compiles them into an autonomously collaborating AI team — planning tasks, invoking tools, producing results. All you need to do is review and approve. No coding required, no hiring, no office rental.

This is why 16 million people chose the One Person Company — not because the registration process was simplified (though it was — one-stop online service, 1-3 business days), but because technology has fundamentally shattered the productivity limits of "going it alone."

Trend Analysis

Returning to the original question — what is a One Person Company?

In legal terms, it's an enterprise with a single shareholder bearing limited liability. But in 2026's practical reality, it's one person plus an AI army, equaling a fully functional company. It's not a "cost-saving alternative" — it's a force multiplier for capabilities.

My recommendation: if your expected annual revenue exceeds 300,000 RMB, you need to work with corporate clients, and you want to protect your family's assets — go with an OPC directly. The registration process is already a one-stop online affair, with certification issued in 1-3 business days. The rest is finding your AI army — low-code platforms like SoloEngine make it possible for one person to run an entire company.