Experiences with the AI Programming IDE Trae and the Evolving Role of Programmers

On January 20, 2025, ByteDance released its AI programming IDE Trae (https://www.trae.ai/), positioned as a domestic counterpart to Cursor and capable of using Claude‑3.5‑Sonnet and GPT‑4o large language models.

During a three‑hour deep dive, the author built a full front‑end/back‑end separated management console (login/logout, database queries, CORS handling, home page) without writing a single line of code, using Vue for the front end and Go + Beego for the back end.

A major challenge was solving CORS issues; after several failed OPTIONS requests, the problem was traced to an un‑loaded CORS configuration file on the server.

In addition, a 30‑minute effort produced a Java streaming output to simulate a typewriter effect for DeepSeek model calls, highlighting the importance of clear requirement communication with the AI.

After roughly ten dialogue rounds, the AI understood the true intent, and after six more rounds the problematic code sections were highlighted and resolved quickly.

1. Experience and Insights

Clearly stating requirements and providing background information (technology stack, schema, goals) at the start is crucial.

Providing abundant context (using # tags in Trae to reference code, files, directories) yields more accurate answers.

When the AI falls into a dead‑end loop, supplementing it with web‑sourced answers can break the cycle.

Selecting erroneous code and asking the AI to fix it can work without additional description.

Multimodal capabilities allow screenshots to be used for UI‑related problems.

Building the framework first and then implementing features works best with current AI assistance.

For generic functionalities, AI already demonstrates strong capabilities, but whether programmers become fully replaceable depends on the broader project context.

2. Current Responsibilities of Programmers

Developers today focus on three core areas: decision‑making, coordination, and coding.

2.1 Decision‑Making

Decision‑making involves technical selection, architecture design, and risk assessment, influencing team efficiency, product quality, and future direction.

Requirement Analysis: Understanding business needs, setting priorities, and communicating with product managers.

Technology Selection: Choosing front‑end/back‑end frameworks, databases, cloud services, and evaluating feasibility, scalability, and cost.

Architecture Design: Deciding between monolithic or micro‑service architectures, database types, caching, and performance strategies.

Risk Assessment & Management: Identifying performance bottlenecks, technical debt, and preparing contingency plans.

AI Replacement Capability: AI can already suggest suitable technologies and generate preliminary architectural analyses, but final decisions still require deep business understanding.

2.2 Coordination

Coordination turns technical plans into reality by aligning resources across departments.

Requirement Translation: Breaking business goals into executable tasks, defining modules and interfaces.

Team Collaboration: Facilitating communication among front‑end, back‑end, testing, operations, and product teams.

API & Module Design: Defining RESTful or GraphQL contracts, ensuring security and performance.

Testing & Iteration: Planning unit/integration tests and iterating based on feedback.

AI Replacement Capability: AI can generate documentation, API specs, and test cases, and assist in task breakdown, yet nuanced, dynamic coordination remains a human strength.

2.3 Coding

Coding transforms designs into functional software, covering implementation, debugging, optimization, quality assurance, and CI/CD.

Implementation: Developing front‑end UI/interaction and back‑end business logic.

Debugging & Optimization: Fixing bugs, improving performance, handling CORS, concurrency, and other complex issues.

Quality Assurance: Writing unit/integration tests, conducting code reviews, adhering to standards.

CI/CD: Automating builds, deployments, and version control.

AI Replacement Capability: AI can produce high‑quality code snippets, suggest debugging steps, and automate repetitive coding tasks, but complex, innovative development still relies on human expertise.

3. AI Replacement Trends and the Future Value of Programmers

3.1 What AI Will Replace First

Repetitive, template‑driven work such as scripting, CRUD operations, and common bug fixes.

Standardized architecture design and technology selection in data‑driven scenarios.

Automation of documentation, API generation, and test case creation.

3.2 Core Competitive Advantages of Programmers

Deep business understanding combined with technical expertise.

Ability to solve complex, non‑standard problems and coordinate large‑scale systems.

Creativity and innovation beyond existing data patterns.

Interpersonal communication and team collaboration skills.

3.3 What Programmers Should Focus on Going Forward

Enhance abstraction and modeling abilities to shift from coding to high‑level design.

Embrace AI tools (e.g., Trae, Cursor) as assistants to boost productivity.

Deepen industry‑specific knowledge to become domain experts.

Develop soft skills such as communication, teamwork, and project management.

The overall view is illustrated in the following diagram:

In summary, while AI will increasingly automate routine development tasks, programmers will remain indispensable for strategic decision‑making, complex problem solving, and creative innovation.