DEV Community: Alton Zheng

Will AI Replace Developers? Here's What I Think

Alton Zheng — Thu, 02 Jul 2026 00:56:50 +0000

Every few months, a new AI model is released, and the same question comes up:

"Will AI replace software developers?"

My answer is simple: No, but it will change what it means to be a developer.

AI Is Already Changing the Way We Work

Today's AI tools can write code, explain complex functions, generate tests, fix bugs, and even review pull requests. They're incredibly useful, and they've made developers more productive than ever.

But writing code is only one part of software engineering.

Developers still need to:

Understand business requirements
Design scalable systems
Make architectural decisions
Communicate with stakeholders
Review trade-offs
Debug production issues
Think critically about security and performance

These responsibilities require context, judgment, and experience—things AI still struggles with.

Coding Isn't the Hard Part

Many people think programming is mostly about typing code.

In reality, coding is often the easiest part.

The challenging work is figuring out:

What problem should be solved?
What's the best approach?
What are the risks?
How will this scale in the future?
How do different systems work together?

AI can generate solutions, but it doesn't truly understand your business, your users, or your long-term goals.

Developers Who Use AI Will Have an Advantage

The biggest shift isn't developers versus AI.

It's developers who use AI effectively versus those who don't.

The best engineers are already using AI to:

Generate boilerplate code
Create documentation
Write unit tests
Learn unfamiliar frameworks
Prototype ideas quickly
Automate repetitive tasks

This allows them to spend more time solving meaningful problems instead of repetitive ones.

What Skills Will Matter More?

As AI becomes better at generating code, developers will need stronger skills in areas that AI can't easily replace.

These include:

System architecture
Problem-solving
Communication
Product thinking
Cloud infrastructure
Security
Performance optimization
Leadership and mentoring

The value of a developer will increasingly come from making good decisions, not just writing code.

My Prediction

I don't believe AI will replace software developers.

I believe it will replace a lot of repetitive programming work.

The role of developers will evolve from writing every line of code to designing systems, validating AI-generated solutions, and solving complex business problems.

That's an exciting future, not a scary one.

Final Thoughts

Technology has always changed the way developers work.

We moved from assembly language to high-level languages, from manual deployments to CI/CD, and from physical servers to cloud platforms. AI is simply the next evolution.

The developers who stay curious, keep learning, and embrace AI as a tool will continue to thrive.

AI isn't replacing developers. It's redefining what great developers do.

What do you think? Will AI replace developers, or will it simply change the way we build software? I'd love to hear your thoughts in the comments.

From API to AI Agent: How Modern Backend Engineers Should Think About AI Systems

Alton Zheng — Thu, 25 Jun 2026 00:19:26 +0000

Introduction

Most developers today are learning how to “use AI APIs.”

But that’s not enough anymore.

The real shift happening in software engineering is this:

We are moving from building APIs → to building AI-powered systems.

And that requires a completely different mindset.

The Problem with Most AI Tutorials

Most tutorials show this:

Call OpenAI API
Get response
Print output

That’s it.

But in production systems, this approach fails because it ignores:

Context management
State handling
Reliability
Tool integration
System design

In real applications, AI is not a function call — it is an orchestrated system.

What an AI System Actually Looks Like

A production AI system usually includes:

1. Input Layer

Validation
Preprocessing
Safety checks 2. Reasoning Layer (LLM)
Prompt engineering
Context injection
Model selection 3. Tool Layer
APIs
Databases
Search engines
Internal services 4. Memory Layer
Conversation history
Vector DB / embeddings
User context 5. Output Layer
Formatting
Validation
Response filtering

Simple Example: From API Call → AI Agent Thinking

Instead of this:

response = client.chat.completions.create(...)

We design something like this:

class AIAgent:
    def __init__(self, llm, tools):
        self.llm = llm
        self.tools = tools

    def run(self, user_input: str):
        context = self.build_context(user_input)

        response = self.llm.chat.completions.create(
            model="gpt-4o-mini",
            messages=context,
            temperature=0.2
        )

        return self.post_process(response)

Now AI becomes:

✔ structured
✔ extendable
✔ production-ready

Key Shift in Thinking

Old mindset:

“How do I call the model?”

New mindset:

“How do I design the system around the model?”

That’s the difference between:
❌ AI script
✅ AI product system

Why Tools Matter More Than Prompts

Modern AI systems are not just text generators.

They are tool-using systems.

Examples:

Search APIs (RAG systems)
Databases (SQL, NoSQL)
External APIs
Internal business logic

This turns AI from “chatbot” into “agent”

Real-World Use Case

Imagine a student learning platform:

Instead of:

Static video content

We build:

AI tutor that explains concepts
Personalized learning paths
Dynamic Q&A using course material
Context-aware recommendations

That’s exactly where Python + AI becomes powerful.

What Makes a Good AI Engineer Today?

Not just:
❌ knowing prompts
❌ calling APIs

But:
✔ system design thinking
✔ backend engineering skills
✔ API orchestration
✔ data handling
✔ production reliability

Final Thought

AI is not replacing engineers.

But engineers who understand AI systems will replace those who only use APIs.

The real value is not in the model.

It is in how you design the system around it.

Available for Collaboration

Open to discussing and collaborating on:

Python + AI systems
LLM applications
RAG pipelines
AI backend architecture
Production AI engineering

Always happy to exchange ideas or build something real.

Building a Practical AI Assistant with Python: From Prompt to Production Thinking

Alton Zheng — Sun, 21 Jun 2026 01:21:33 +0000

Why Python is still one of the best choices for AI

Python is popular in AI because it has a strong ecosystem, simple syntax, and great support for data processing, APIs, automation, and machine learning.

For AI applications, Python works especially well for:

Building backend AI services
Connecting to LLM APIs
Processing documents and text
Creating automation workflows
Building RAG and chatbot systems
Integrating AI into existing products

But the important point is this:

AI is not just a model. AI is a workflow.

A good AI application usually includes input handling, prompt design, validation, error handling, logging, security, and user feedback.

A simple AI assistant in Python
Here is a basic example of an AI assistant service using Python.

import os
from openai import OpenAI

client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

def ask_ai(user_message: str) -> str:
    if not user_message.strip():
        return "Please provide a valid question."

    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {
                "role": "system",
                "content": "You are a helpful technical assistant. Answer clearly and professionally."
            },
            {
                "role": "user",
                "content": user_message
            }
        ],
        temperature=0.3
    )

    return response.choices[0].message.content

Usage:

question = "Explain REST API in simple terms."
answer = ask_ai(question)

print(answer)

This works, but it is still very basic.
For a real application, we need to think beyond the first response.

Improving the assistant with better structure

class AIAssistant:
    def __init__(self, client):
        self.client = client

    def build_messages(self, user_message: str):
        return [
            {
                "role": "system",
                "content": (
                    "You are a senior software engineering assistant. "
                    "Give practical, clear, and accurate answers."
                )
            },
            {
                "role": "user",
                "content": user_message
            }
        ]

    def ask(self, user_message: str) -> str:
        if not user_message or not user_message.strip():
            raise ValueError("User message cannot be empty.")

        response = self.client.chat.completions.create(
            model="gpt-4o-mini",
            messages=self.build_messages(user_message),
            temperature=0.2
        )

        return response.choices[0].message.content

This makes the code easier to test and extend.

For example, later we can add:

Conversation memory
Document search
User authentication
Logging
Prompt versioning
Rate limiting
Response validation

What makes an AI app production-ready?
Calling an LLM is easy. Building a reliable AI feature is harder.

Here are the main things I focus on:
1. Clear prompts
A vague prompt gives vague answers.

Instead of:

Answer the user.

Use:

You are a technical assistant. Give accurate, concise, and practical answers. 
If the answer is uncertain, say so clearly.

Good prompts reduce random output and make the system more predictable.

2. Lower temperature for serious tasks

For professional or technical systems, I usually prefer a lower temperature.

temperature=0.2

This makes the answer more stable and less creative.

For brainstorming or marketing content, a higher temperature may be useful.

3. Error handling

AI services can fail because of network issues, rate limits, invalid input, or API errors.

def safe_ask_ai(assistant, message: str) -> str:
    try:
        return assistant.ask(message)
    except ValueError as error:
        return f"Input error: {error}"
    except Exception:
        return "Sorry, something went wrong while processing your request."

Never expose raw system errors directly to users in production.

4. Logging and monitoring

If an AI feature is used by real users, you need visibility.

You should track:

Request count
Error rate
Response time
Token usage
Failed prompts
User feedback

This helps you understand whether the AI feature is actually useful.

5. Human feedback loop

The best AI systems improve over time.

Add simple feedback options like:

Was this answer helpful? 👍 👎

That feedback can help identify weak prompts, missing context, or confusing answers.

Simple FastAPI example

Here is how we can expose the assistant as an API.

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from openai import OpenAI
import os

app = FastAPI()

client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
assistant = AIAssistant(client)


class QuestionRequest(BaseModel):
    question: str


class QuestionResponse(BaseModel):
    answer: str


@app.post("/ask", response_model=QuestionResponse)
def ask_question(request: QuestionRequest):
    try:
        answer = assistant.ask(request.question)
        return QuestionResponse(answer=answer)
    except ValueError as error:
        raise HTTPException(status_code=400, detail=str(error))
    except Exception:
        raise HTTPException(status_code=500, detail="AI service failed.")

Now we have a simple AI backend endpoint.

Request:

{
  "question": "What is the difference between REST and GraphQL?"
}

Response:

{
  "answer": "REST uses multiple endpoints for resources, while GraphQL allows clients to request exactly the data they need from a single endpoint..."
}

Final thoughts

Python makes it easy to start building AI applications, but professional AI development requires more than a working demo.

A useful AI system should be:

Clear
Reliable
Secure
Observable
Easy to improve

The biggest lesson I’ve learned is this:

Don’t treat AI as magic. Treat it as part of your software architecture.

The model is only one piece. The real engineering happens around it.

If you design the workflow well, AI can become a powerful feature instead of just a cool experiment.

Memory Leaks in Python and How to Overcome Them

Alton Zheng — Mon, 15 Jun 2026 01:14:30 +0000

Python is known for being simple, readable, and developer-friendly. One of its biggest advantages is automatic memory management, which means developers usually do not need to manually allocate or release memory.

However, this does not mean Python applications are completely safe from memory leaks.

A memory leak happens when a program keeps holding memory that is no longer needed. Over time, this can make the application slower, consume more RAM, and even crash in production.

Why Do Memory Leaks Happen in Python?

Python has a garbage collector that automatically removes unused objects. But memory leaks can still happen when references to objects remain active even though the data is no longer useful.

Common causes include:
1. Global Variables

Global variables stay alive for the lifetime of the program. If large objects are stored globally and never cleared, memory usage can grow continuously.

cache = []

def add_data(data):
    cache.append(data)

This looks simple, but if cache keeps growing without limits, it can become a memory problem.

2. Unbounded Caches

Caching improves performance, but unlimited caching can cause memory leaks.

user_cache = {}

def get_user(user_id, user_data):
    user_cache[user_id] = user_data

Without a cleanup strategy, the cache may keep old data forever.

3. Circular References

Circular references happen when two or more objects reference each other.

class Node:
    def __init__(self):
        self.ref = None

a = Node()
b = Node()

a.ref = b
b.ref = a

Python can handle many circular references, but complex cases involving destructors or external resources may still create problems.

4. Open Resources
Files, database connections, sockets, and network sessions should always be closed properly.

file = open("data.txt")
data = file.read()

If the file is not closed, the program may keep resources longer than necessary.

A better approach:

with open("data.txt") as file:
    data = file.read()

5. Long-Running Processes

Memory leaks are especially dangerous in long-running applications such as APIs, workers, schedulers, and background services. Even a small leak can become serious after days or weeks of continuous execution.

How to Detect Memory Leaks in Python

Use tracemalloc

Python provides a built-in module called tracemalloc to track memory allocation.

import tracemalloc

tracemalloc.start()

# run your application logic here

snapshot = tracemalloc.take_snapshot()
top_stats = snapshot.statistics("lineno")

for stat in top_stats[:10]:
    print(stat)

This helps identify which lines of code are allocating the most memory.

Use Garbage Collector Debugging
Python’s gc module can help inspect objects that are still alive.

import gc

gc.collect()
print(len(gc.get_objects()))

This is useful when checking whether objects are being released correctly.

Monitor Production Metrics

In production, memory should be monitored using tools like Prometheus, Grafana, Datadog, or CloudWatch. Watching memory trends over time helps detect leaks before they become critical.

How to Overcome Memory Leaks

1. Limit Cache Size

Use bounded cache strategies instead of unlimited dictionaries.

from functools import lru_cache

@lru_cache(maxsize=1000)
def get_user_profile(user_id):
    return fetch_user_from_db(user_id)

This prevents the cache from growing forever.

2. Use Context Managers

Always use context managers for files, database connections, and network resources.

with open("report.txt", "w") as file:
    file.write("Report data")

This ensures resources are automatically released.

3. Remove Unused References

When working with large objects, remove references when they are no longer needed.

large_data = load_big_file()

process(large_data)

del large_data

This can help the garbage collector reclaim memory faster.

4. Avoid Unnecessary Global State

Global state makes memory harder to manage. Prefer passing data through functions or using controlled service-level storage.

5. Use Weak References

When an object should not prevent another object from being garbage collected, use weakref.

import weakref

class User:
    pass

user = User()
weak_user = weakref.ref(user)

Weak references are useful for caches and object tracking systems.

6. Restart Long-Running Workers Safely

For background workers, it can be useful to configure safe restarts after a certain number of tasks. This is not a replacement for fixing leaks, but it can protect production systems while investigating the root cause.

Best Practices

To reduce memory leak risks in Python:

Avoid unlimited global data structures
Use bounded caches
Close files, sockets, and database connections properly
Monitor memory usage in production
Use tracemalloc during debugging
Be careful with circular references
Clean up large objects when they are no longer needed
Test long-running processes under realistic load

Final Thoughts

Python’s automatic memory management makes development easier, but it does not remove the need for good engineering practices. Memory leaks often come from hidden references, unlimited caches, open resources, or long-running processes.

The best solution is a combination of clean code, proper resource management, memory profiling, and production monitoring.

A well-optimized Python application is not just about writing working code. It is about writing code that stays reliable, efficient, and stable over time.

Why Writing Pythonic Code Isn’t Just About Syntax

Alton Zheng — Thu, 11 Jun 2026 20:04:06 +0000

As Python developers, we often hear about writing "Pythonic code", but what does that really mean beyond following PEP8 or using list comprehensions? For me, Pythonic code is about clarity, maintainability, and leveraging the language’s philosophy to write code that communicates intent, not just logic.

Some key practices I’ve found invaluable:

Explicit is better than implicit.
Writing code that clearly expresses intent reduces bugs and helps teammates (and your future self!) understand your reasoning.
Use built-in features wisely.
Python has powerful constructs like generators, context managers, and decorators. Using them appropriately can simplify code—but overuse can make it cryptic.
Readability over cleverness.
Just because a one-liner works doesn’t mean it should exist. Sometimes expanding code into readable blocks pays dividends during debugging and scaling.
Test, refactor, repeat.
Python’s dynamic nature is beautiful, but without testing, subtle bugs can slip in. I like to combine unit tests and type hints to catch issues early.

I’m curious how others approach writing Pythonic code in large, complex systems. How do you balance “clean” Python idioms with performance and maintainability?

Let’s share our experiences!
I’d love to hear your strategies and examples.