DEV Community: Marxon

Token-Efficient Agentic Development — Part 1: What Are You Actually Paying For?

Marxon — Sat, 19 Sep 2026 18:47:40 +0000

AI-assisted development is rapidly moving beyond autocomplete and simple chat interfaces.

We are entering the era of agentic development.

Instead of asking an AI model to generate a function, developers can now give an agent a task such as:

"Find the cause of this bug, inspect the relevant files, implement the fix, run the tests, and verify that everything still works."

The agent may then read dozens of files, search the repository, call tools, execute commands, inspect the results, modify code, encounter an error, retry, and continue until the task is complete.

This is incredibly powerful.

But there is another side to it that is much easier to ignore:

all of those interactions consume tokens.

And as AI becomes a normal part of software development, understanding how those tokens are used will become increasingly important.

This article is the first part of a three-part series about token-efficient agentic development.

Before talking about optimization, monitoring, model selection, or local AI, we first need to understand what we are actually consuming.

What Is a Token?

Large Language Models do not process text exactly the way humans do.

They do not simply see words.

Instead, text is divided into smaller units called tokens.

A token can represent:

a full word,
part of a word,
punctuation,
whitespace,
a number,
or a fragment of source code.

For example, a simple sentence such as:

The user authentication failed.

might be split into several tokens.

Source code is tokenized in the same way.

const user = await getUserById(id);

The model does not necessarily see this as one logical programming statement. It sees a sequence of tokens representing pieces of that statement.

The exact tokenization depends on the model and tokenizer.

This is the first important concept:

Tokens are the basic units of information processed by a language model.

Input Tokens and Output Tokens

At a high level, AI usage can be divided into two categories.

Input tokens

Everything sent to the model.

This may include:

your prompt,
conversation history,
system instructions,
project instructions,
source code,
documentation,
tool results,
terminal output,
error messages,
retrieved files,
previous agent steps.

Output tokens

Everything generated by the model.

For example:

explanations,
generated code,
tool calls,
commands,
plans,
responses.

A simple interaction might therefore look like this:

Input:
2,000 tokens

Output:
800 tokens

Total:
2,800 tokens

For a normal chatbot interaction, this is relatively easy to understand.

Agentic development makes the situation more complicated.

Why Agentic Development Changes Everything

Imagine asking an AI:

Create a TypeScript function that validates an email address.

The model receives a small prompt and produces a relatively small response.

Now compare that with:

Investigate why user registration sometimes fails,
find the relevant frontend and backend code,
fix the issue,
run the tests,
and make sure the solution follows the existing architecture.

An agent handling this task might:

inspect the repository structure,
read several files,
search for registration-related code,
inspect API calls,
inspect backend validation,
read existing tests,
modify code,
execute tests,
receive an error,
inspect the failure,
modify the code again,
rerun the tests,
summarize the solution.

Each step creates additional context.

A simplified workflow could look something like this:

Developer
    ↓
Agent
    ↓
Read files
    ↓
Model
    ↓
Search repository
    ↓
Model
    ↓
Modify code
    ↓
Run tests
    ↓
Model
    ↓
Read errors
    ↓
Modify code again
    ↓
Run tests again

Every interaction between the model and its environment may involve additional tokens.

This creates what we can call an agent loop.

The Agent Loop

A traditional AI interaction is often:

Prompt → Model → Answer

An agentic workflow is closer to:

Task
 ↓
Reason about next action
 ↓
Use tool
 ↓
Receive result
 ↓
Evaluate result
 ↓
Use another tool
 ↓
Receive result
 ↓
Continue...

The important part is that the model often needs context from previous steps to decide what to do next.

That means a task that appears simple from the developer's point of view may involve a surprisingly large amount of model interaction.

The developer might write only:

Fix the login bug.

But the agent could process tens of thousands of tokens before completing the task.

This creates an important distinction:

Prompt length is not the same thing as total AI usage.

In agentic development, the visible prompt may represent only a small fraction of the actual workload.

The Context Window

Another important concept is the context window.

The context window represents how much information a model can consider during an interaction.

The context may contain things such as:

System instructions
Project instructions
Developer prompt
Conversation history
Source files
Documentation
Tool outputs
Terminal logs
Previous agent actions

A larger context window allows the model to work with more information.

That sounds purely beneficial.

But more context is not automatically better.

Consider an agent working on a frontend validation bug.

Ideally, it might need:

Form component
Validation schema
API client
Relevant types
Related tests

Instead, imagine the agent loads:

Entire repository structure
40 unrelated components
Large package lock file
Generated code
Old logs
Documentation
Backend files unrelated to the feature
Thousands of lines of terminal output

The agent now has much more information.

But most of it is irrelevant.

This is context pollution.

And context pollution has two major costs.

First, it consumes more tokens.

Second, it can make it harder for the model to focus on the information that actually matters.

More Context Is Not Always Better Context

One of the easiest mistakes in AI-assisted development is assuming:

"If the model knows everything about the repository, it will perform better."

Sometimes that is true.

Often it is not.

A better principle is:

Give the model enough context to solve the task, but not everything you have.

This is very similar to software design itself.

We rarely want every component to depend on the entire system.

Good software architecture tries to reduce unnecessary dependencies.

Good AI workflows should do something similar with context.

Instead of:

Entire repository
        ↓
      Model

prefer:

Relevant files
Relevant instructions
Relevant documentation
        ↓
      Model

Context should be treated as a resource.

Hidden Token Consumption in Development Workflows

Developers often think about token usage only when they type a prompt.

But modern AI development tools can consume tokens in many other places.

For example:

Repository exploration

An agent may read many files before finding the relevant ones.

Search results

Repository searches can return large amounts of text.

Terminal output

A build failure might produce hundreds or thousands of lines of logs.

Test output

Large test suites can generate significant amounts of context.

Documentation

Agents may automatically load documentation or project instructions.

Repeated context

Information already processed earlier may appear again in later interactions.

Agent retries

An agent can attempt one solution, fail, analyze the failure, and try again.

Subagents

Some systems allow one agent to delegate tasks to additional agents.

Each subagent may have its own context and model usage.

None of these mechanisms are inherently bad.

They are often exactly what makes an agent useful.

The problem starts when we stop thinking about their cost.

A Simple Example

Imagine two agents solving the same problem.

Agent A

Agent A receives:

Fix the validation bug in the registration form.

It reads the entire frontend repository.

Then it reads several backend files.

It runs the full test suite.

The test suite produces a large log.

The agent modifies the wrong component.

Tests fail.

It reads another set of files.

It tries again.

Eventually, the bug is fixed.

Agent B

Agent B receives:

The registration form incorrectly accepts dates in the future.

The form is located in:
src/features/registration/

Validation is handled with Zod.

Find the relevant schema, fix the validation,
and run only the related tests.

The second agent has a better starting point.

It may inspect fewer files, run fewer commands, generate less irrelevant output, and finish in fewer steps.

Both agents solve the same problem.

But their resource usage can be dramatically different.

This is the core idea behind token efficiency.

Token Usage Is Not the Same as Token Efficiency

There is an important distinction here.

The goal should not be:

Use as few tokens as possible.

That can easily become counterproductive.

Imagine a small model uses 20,000 tokens while repeatedly attempting to solve a difficult architectural problem.

A more capable model might solve the same problem using 8,000 tokens.

Even if the stronger model is more expensive per token, it may still be the more efficient choice overall.

That means we should not optimize only for:

Tokens Used

We should think about something closer to:

Useful Work Produced
────────────────────
Resource Consumption

Or, more simply:

Token Efficiency

A useful mental model is:

Token Efficiency =
Useful Output / Token Cost

This is not meant to be a precise mathematical metric.

It is a way of thinking.

A workflow that uses more tokens but reliably solves the problem may be more efficient than one that uses fewer tokens but requires constant human intervention.

Developer Time Matters Too

There is another resource that should not be forgotten:

developer time.

Imagine optimizing an AI workflow so aggressively that developers spend ten minutes preparing the perfect minimal context for a task that the agent could have solved automatically in thirty seconds.

Technically, token usage decreased.

But total productivity may have become worse.

A better optimization target is something closer to:

AI cost
+
Developer time
+
Failure rate
+
Iteration count

Token optimization should therefore support productivity rather than fight against it.

The goal is not to make AI usage artificially cheap.

The goal is to eliminate waste.

Agentic Development Is Becoming an Engineering Problem

When only a few developers occasionally use AI, inefficient token usage may not matter very much.

But imagine a larger engineering organization.

Suppose:

200 developers
×
multiple AI interactions per day
×
agents reading repositories
×
automated tool calls
×
multiple models

Small inefficiencies suddenly become large ones.

An unnecessary repository scan performed once is irrelevant.

Performed thousands of times across an organization, it becomes infrastructure cost.

This is why AI usage will increasingly require the same kind of thinking we already apply to other engineering resources.

We monitor:

CPU usage
Memory usage
Cloud infrastructure
Database queries
Network traffic
API calls

It makes sense to eventually treat:

AI model usage
Context size
Token consumption
Agent iterations
Model selection

with similar discipline.

This is where ideas such as AI FinOps start becoming relevant.

The Most Expensive Model Is Not Always the Best Model

Modern development environments increasingly provide access to multiple AI models.

That creates another important optimization problem.

Different tasks require different levels of capability.

For example:

Rename a variable

and

Redesign the authentication architecture of a distributed system

are very different tasks.

Yet developers sometimes use the same high-capability model for both.

This is similar to running every workload on the largest available cloud machine.

It works.

But it is rarely efficient.

A mature AI development workflow should eventually be able to answer:

What kind of task is this?

How complex is it?

How much context does it require?

Which model is sufficient?

Should this task even use a cloud model?

Could a smaller or local model handle it?

We will explore this in the next part of the series.

Local Models Change the Equation

Token optimization is not only about reducing usage.

Another option is changing where the computation happens.

Open-weight and locally hosted models can make certain workloads independent from traditional per-token API pricing.

For some tasks, organizations might use:

Cloud models
    +
Local models
    +
Specialized smaller models

instead of sending every task to the most capable external model available.

However, local AI does not make computation free.

The cost simply moves.

Instead of paying directly for tokens, organizations may need to think about:

GPU infrastructure
Electricity
Hardware
Deployment
Maintenance
Model serving
Scaling
Monitoring

This creates another engineering tradeoff rather than eliminating the problem.

The Real Goal: More Value per Token

AI coding tools will continue to become more capable.

Agents will read more code, execute more commands, use more tools, and solve increasingly complex tasks.

Trying to prevent them from consuming tokens would defeat much of the purpose.

The better question is:

How much useful engineering work are we getting from the resources we consume?

That leads to a much healthier approach.

Instead of:

Use fewer tokens.

think:

Avoid unnecessary context.

Avoid unnecessary agent loops.

Use the right model for the task.

Provide better instructions.

Monitor usage.

Measure outcomes.

Use expensive models where they create value.

Use cheaper or local models where they are sufficient.

The goal is not minimum token usage.

The goal is maximum useful work per token.

What's Next?

This article focused on the foundations:

what tokens are,
input and output tokens,
context windows,
agent loops,
context pollution,
hidden token consumption,
and the difference between token usage and token efficiency.

In Part 2, we will move from theory to practice.

We will look at how developers and engineering teams can actually reduce unnecessary AI usage through:

token monitoring,
smarter model selection,
context management,
better prompts and project instructions,
limiting unnecessary repository access,
controlling agent loops,
local AI models,
and AI FinOps practices.

Then, in Part 3, we will combine everything into a practical framework for building a token-efficient agentic development workflow.

Because the future of AI-assisted software development is not simply about using more AI.

It is about using AI efficiently.

Thanks for reading — I’m Marxon, a developer and AI specialist exploring how AI reshapes the way we build, manage, and think about technology.

If you enjoyed this year-end special, follow me here on dev.to

and join me on X where I share shorter thoughts, experiments, and behind-the-scenes ideas.

Let’s keep building — thoughtfully. 🚀

The Best Developer AI Tools of 2026 Q1 — What Actually Changed in Real Workflows

Marxon — Fri, 08 May 2026 16:35:16 +0000

2026 did not start with one big AI moment.

It started with something more subtle:

AI tools stopped feeling like “assistants”

and started behaving more like junior teammates.

Not always reliable.

Not always careful.

Not always aware of the bigger picture.

But definitely more capable than before.

In 2025, the question was:

Which AI tools actually help developers?

In Q1 2026, the question changed:

Which AI tools can be trusted with parts of the workflow — without losing control?

This is not a hype list.

No affiliate links.

No “Top 100 tools you need to try.”

This is a practical breakdown of the AI developer tools that actually mattered in Q1 2026 — what worked, what improved, and what still needs human judgment.

1️⃣ ChatGPT — Still the Best Thinking Partner

ChatGPT remains the tool I reach for when the problem is not just “write this code.”

It is still best when I need to think.

Not autocomplete.

Not magic.

Thinking.

Where it worked best

breaking down unclear requirements
comparing architectural options
explaining trade-offs
reviewing refactor ideas
turning messy thoughts into structured plans
preparing technical writing, documentation, and content

The biggest value is not that it gives the perfect answer.

It usually does not.

The value is that it helps me move from:

“I know something is wrong here.”

to:

“Okay, these are the possible causes, and this is the next thing to check.”

That is a real productivity boost.

Where it still fails

it can sound confident while missing project-specific constraints
it may suggest APIs or patterns that do not exist in your exact stack
it can over-engineer simple problems
it does not understand your codebase unless you give it enough context

The rule still stands:

If your prompt is vague, your output will be vague — just better written.

ChatGPT is not a replacement for technical judgment.

But as a thinking partner, it is still one of the strongest tools in the workflow.

2️⃣ GitHub Copilot — From Autocomplete to Workflow Assistant

Copilot is no longer exciting in the old way.

And that is exactly why it is useful.

The best Copilot moments are invisible:

completing repetitive code
suggesting small utilities
helping with test scaffolding
speeding up boring implementation details
reducing context-switching inside the editor

In Q1 2026, the interesting shift is that Copilot is moving beyond simple suggestions.

It is becoming more workflow-aware.

Not just:

“Here is the next line.”

But closer to:

“Here is a possible implementation path.”

That is powerful.

But also dangerous.

Where it worked well

repetitive frontend patterns
component boilerplate
form validation scaffolding
test setup
small refactors
predictable codebase conventions

Where I stay careful

Copilot still amplifies whatever already exists.

If the codebase is clean, it helps you move faster.

If the codebase is messy, it can generate more mess with impressive confidence.

That makes Copilot less of a “code quality tool”

and more of a “pattern acceleration tool.”

The developer is still responsible for the pattern.

3️⃣ Codex — The Agentic Coding Shift Became Real

Codex feels like part of the bigger 2026 shift:

AI tools are no longer just helping inside the editor.

They are starting to take a task, inspect context, make changes, and move toward a result.

That is a different mental model.

Autocomplete helps you write.

Agents help you delegate.

And delegation is much harder than prompting.

Where Codex-like workflows are useful

isolated tasks with clear acceptance criteria
small bug fixes
test generation
documentation improvements
repetitive refactors
codebase exploration before implementation

The important part is the word “clear.”

If the task is vague, the agent will still try to complete it.

That does not mean it understood it.

What I learned

AI agents are useful when the task has boundaries.

They struggle when the task requires deep product judgment, business context, or architectural taste.

So instead of asking:

“Build this feature.”

A better approach is:

“Analyze this area, propose a plan, then only implement the first safe step.”

That keeps the human in control.

And in 2026, that is becoming the real skill.

4️⃣ Cursor — Fast When You Want an AI-First Coding Flow

Cursor is interesting because it does not feel like a normal editor with AI added later.

It feels like an AI-first development environment.

That can be great.

It can also be too much.

Where it shines

moving quickly across multiple files
asking questions directly against the codebase
exploring unfamiliar logic
prototyping UI or workflow changes
generating implementation plans before editing

Cursor is strongest when you want speed.

Especially in early-stage work, prototypes, experiments, or side projects.

It reduces friction in a very noticeable way.

The downside

The same thing that makes it fast can make it risky.

It is easy to accept too much.

It is easy to move faster than your understanding.

And that is where AI-assisted development becomes dangerous.

The tool may generate the change.

But you still need to understand the consequence.

5️⃣ Sourcegraph Cody — Still Underrated for Large Codebases

Cody remains one of the most useful tools when the problem is not writing new code.

The problem is understanding existing code.

That is especially valuable in:

large repositories
legacy systems
enterprise projects
onboarding
hidden dependencies
“why does this exist?” situations

Where Cody is strong

finding where logic is used
explaining relationships between files
navigating unfamiliar services
understanding project-specific patterns
answering questions with repository context

This matters because many real developer problems are not greenfield problems.

They are codebase problems.

You are not asking:

“How do I write this function?”

You are asking:

“Where does this behavior come from, and what breaks if I change it?”

That is where repo-aware AI becomes much more valuable than generic AI.

6️⃣ Claude Code — Strong for Deep Reasoning, But Needs Boundaries

Claude Code became one of the most talked-about developer tools going into 2026.

And I understand why.

It is strong when the task requires reasoning through multiple steps, not just generating code quickly.

Where it fits well

debugging complex issues
explaining unfamiliar code
planning refactors
reasoning through architecture
analyzing edge cases
turning vague implementation ideas into steps

The main benefit is not only code generation.

It is the way it can reason through the work.

That makes it useful when the problem is messy.

But here is the caveat

The better these tools get, the easier it becomes to trust them too much.

That is exactly where developers need to slow down.

A good AI-generated plan is still just a plan.

A good AI-generated diff is still just a diff.

You still need to review, test, and understand it.

Especially when production code is involved.

7️⃣ AI for Documentation — Still a Quiet Win

Documentation is still one of the best use cases for AI.

Not because AI writes perfect docs.

It does not.

But because it removes the blank page problem.

What worked well

first draft of READMEs
explaining setup steps
writing changelog summaries
turning technical notes into clearer language
creating onboarding documentation
summarizing decisions after implementation

The best workflow is still:

AI writes the first draft.

The developer makes it accurate.

That trade-off is worth it.

Because starting is often the hardest part.

8️⃣ AI for Tests — Useful, But Not Automatically Trustworthy

AI-generated tests are better than they used to be.

But they are still not something I trust blindly.

Where they help

generating test structure
covering obvious edge cases
creating mocks
writing repetitive assertions
documenting expected behavior

Where they fail

testing implementation details instead of behavior
missing business rules
creating tests that pass but do not prove much
copying the same assumptions as the original code

AI can help you write tests faster.

But it cannot decide what matters.

That is still a developer responsibility.

A bad test written faster is still a bad test.

9️⃣ Experiments That Still Did Not Fully Stick

Not everything became part of the workflow.

Some AI use cases still feel risky or inefficient.

Things I remain careful with

full feature generation
large automatic refactors
AI-written business logic
automatic architectural decisions
accepting multi-file changes without review
“just build it” prompts

The problem is not that AI cannot do these things.

Sometimes it can.

The problem is validation.

The larger the task, the harder it is to know whether the AI made a good decision or just a convincing one.

And in real projects, convincing is not enough.

🔟 The Biggest Lesson of Q1 2026

The biggest lesson was not about a specific tool.

It was this:

AI is moving from assistance to delegation.

That changes the developer’s job.

In 2025, the skill was writing better prompts.

In 2026, the skill is managing AI work.

That means:

defining smaller tasks
setting clear boundaries
reviewing outputs carefully
understanding trade-offs
knowing when not to use AI
keeping ownership of the final decision

The best developers will not be the ones who let AI do everything.

They will be the ones who know what to delegate — and what to protect.

Final Thoughts

Q1 2026 made one thing clear:

AI developer tools are no longer optional experiments.

They are becoming part of the default workflow.

But that does not mean developers are becoming less important.

Actually, the opposite feels true.

As AI tools become more capable, human judgment becomes more valuable.

The best tools in Q1 2026 were not the loudest ones.

They were the ones that helped me:

think better
move faster
explore safely
understand codebases
reduce hesitation
stay in control

That last part matters most.

Because the future of development is not:

Developer vs AI.

It is:

Developer with AI — but still responsible for the outcome.

And honestly?

That is a much more interesting future.

👋 Thanks for reading — I’m Marxon, a web developer exploring how AI reshapes the way we build, manage, and think about technology.

If you enjoyed this Q1 2026 update, follow me here on dev.to.

I share thoughts about web development, AI tools, developer workflows, and the future of building software.

Let’s keep building — thoughtfully. 🚀

Developers vs AI: Can You Spot When AI Is Wrong?

Marxon — Thu, 30 Apr 2026 22:22:41 +0000

Developers vs AI is back.

And this time, I don’t want to talk about whether AI can write code.

It can.

We all know that by now.

It can generate components, refactor functions, explain errors, write tests, draft documentation, and sometimes even suggest cleaner approaches than the ones we had in mind.

But there’s a much more important question in 2026:

Can you tell when the AI is wrong?

Because that might become one of the most important developer skills of the next few years.

Not prompting.
Not memorizing syntax.
Not knowing every new framework before everyone else.

But judgment.

The ability to look at AI-generated code and say:

“This looks good, but something is off.”

That skill is becoming more valuable every day.

AI-Generated Code Looks Better Than It Is

Here’s the dangerous part about AI-generated code:

It usually looks clean.

The formatting is nice.
The variable names are reasonable.
The structure looks intentional.
The explanation sounds confident.

And because it looks professional, we start trusting it faster than we should.

That’s the trap.

Bad human code often looks messy.
Bad AI code can look beautiful.

And beautiful wrong code is much harder to notice.

AI can produce a solution that:

works only for the happy path,
ignores edge cases,
breaks existing project conventions,
introduces subtle security problems,
creates performance issues,
or solves the wrong problem entirely.

But at first glance?

It looks great.

That’s why AI-generated code should never be treated as finished work.

It should be treated as a draft.

A very fast draft.
A sometimes impressive draft.
But still a draft.

The Developer Role Is Shifting From Writing to Judging

For a long time, developers were mostly measured by what they could build.

Can you implement the feature?
Can you fix the bug?
Can you write the query?
Can you create the component?

Those things still matter.

But AI changes the weight of the job.

When code becomes cheaper to generate, the real value moves somewhere else:

Can you decide whether that code should exist?

That’s a different skill.

It requires understanding the system, not just the syntax.

It requires knowing the business context, not just the framework.

It requires thinking about maintainability, ownership, security, and long-term consequences.

AI can generate five different solutions in seconds.

But someone still has to choose the right one.

And that someone is the developer.

AI Is Like a Junior Developer With Perfect Confidence

I like to think about AI as a junior developer who never gets tired.

It is fast.
It is helpful.
It can surprise you.
It can save you hours.

But it also has one dangerous habit:

It often sounds equally confident when it is right and when it is wrong.

That’s not how experienced developers usually work.

A good senior developer says things like:

“I’m not sure yet.”
“We should check this edge case.”
“This depends on the existing architecture.”
“This might work, but it could be risky later.”

AI often skips that hesitation.

It gives you an answer.

And if you are tired, busy, or under pressure, that confidence feels good.

It feels like progress.

But confidence is not correctness.

That’s why reviewing AI output is not optional.

It is the job.

What AI Often Misses

AI is very good at patterns.

But real software development is not only about patterns.

Real software is full of context.

And context is where AI often fails.

1. Business context

AI does not know why your company made a weird decision three years ago.

It does not know that a strange validation rule exists because of a legal requirement.

It does not know that a “temporary” workaround from 2019 is now somehow business-critical.

To AI, everything looks like code.

To developers, code is only the visible part of a much larger system.

2. Project conventions

AI may write technically valid code that does not fit your project at all.

Maybe your team has a specific folder structure.
Maybe you use a design system.
Maybe you avoid certain dependencies.
Maybe your API layer follows strict patterns.
Maybe your validation schemas live in a specific place.

AI can miss these details unless you give it strong context.

And even then, it can still drift.

3. Edge cases

AI loves the happy path.

The user exists.
The API responds correctly.
The data shape is exactly as expected.
The network is stable.
The permission is valid.
The date format is normal.

Real users are not like that.

Real users click twice, refresh mid-request, paste strange values, lose connection, use old browsers, and somehow find every possible broken state.

If you don’t review for edge cases, AI probably won’t save you.

4. Security

This is one of the most dangerous areas.

AI can generate code that works but is unsafe.

It may forget authorization checks.
It may expose sensitive data.
It may trust user input too much.
It may suggest outdated packages.
It may create logic that looks fine in isolation but becomes risky in production.

Security problems are especially tricky because the code can pass tests and still be wrong.

5. Long-term maintainability

AI is often optimized for the current prompt.

But software lives longer than a prompt.

A solution that looks simple today may become painful in six months.

Will another developer understand it?
Will it scale with the next feature?
Does it fit the architecture?
Does it create hidden coupling?
Does it make future changes harder?

AI can help answer these questions.

But it cannot be the only one asking them.

The New Developer Skill: AI Code Review

In the past, code review mostly meant reviewing another developer’s work.

Now we also review work produced by tools.

And that requires a slightly different mindset.

When reviewing AI-generated code, I try not to ask:

“Does this look correct?”

That question is too weak.

Instead, I ask:

“Would I approve this in a real pull request?”

That changes everything.

Because in a real PR, looking good is not enough.

The code needs to fit the system.
It needs to be readable.
It needs to be testable.
It needs to handle failure.
It needs to respect the project’s standards.
It needs to be something the team can maintain.

AI output should go through the same filter.

Maybe even a stricter one.

Because unlike a teammate, AI cannot explain its real reasoning.

It can generate an explanation.

That is not the same thing.

My Practical AI Review Checklist

When AI gives me code, I try to slow down and check a few things before accepting it.

1. Do I understand every line?

If I can’t explain it, I shouldn’t ship it.

This sounds obvious, but it is easy to ignore when the solution works.

Working code is not enough.

If you don’t understand it, you don’t own it.

2. Does it match the project architecture?

A solution can be correct in isolation and still wrong for your codebase.

I check whether it follows existing patterns, naming conventions, folder structure, state management, API handling, error handling, and component structure.

Consistency matters more than cleverness.

3. What happens when the input is wrong?

AI often assumes clean data.

I try to check empty states, null values, invalid responses, missing permissions, slow requests, failed requests, and unexpected user behavior.

The real bugs usually live outside the happy path.

4. Is this secure?

I ask whether the code trusts the client too much, exposes data, skips validation, ignores authorization, or introduces risky dependencies.

This is especially important when AI touches authentication, permissions, payments, file uploads, user input, or backend logic.

5. Is it still readable without the prompt?

Sometimes AI-generated code only makes sense if you remember what you asked for.

That is a bad sign.

Future developers will not have your prompt.

They will only have the code.

6. Are the tests meaningful?

AI can generate tests that look impressive but test very little.

I check whether the tests actually protect behavior, cover edge cases, and would fail if the implementation broke.

A test that only confirms the mock returns the mocked value is just decoration.

7. Would I defend this decision in a team discussion?

This is my favorite question.

If another developer asked, “Why did you implement it this way?”, could I answer clearly?

If the only answer is “because AI suggested it”, then I’m not done.

The Risk: Developers Becoming Passive Reviewers

There is another danger here.

Reviewing AI output can make us feel like we are still in control, even when we are slowly becoming passive.

You ask.
It answers.
You skim.
You accept.
You move on.

That is not real review.

That is approval by exhaustion.

And I get it.

Deadlines are real.
Context switching is real.
Mental fatigue is real.
Sometimes the AI solution is “good enough” and you just want to close the ticket.

But if that becomes the default, your technical judgment gets weaker.

Not immediately.

Slowly.

You stop questioning trade-offs.
You stop exploring alternatives.
You stop building your own intuition.
You become faster, but less involved.

That is a dangerous trade.

How to Stay Sharp

I don’t think the answer is to stop using AI.

That would be unrealistic.

And honestly, unnecessary.

AI is useful.
Very useful.

But we need better habits around it.

Here are a few that help me.

Try first, then ask

Before asking AI to solve something, spend a few minutes thinking through your own approach.

Even if your solution is worse, the comparison teaches you something.

If you always ask first, you never build the muscle.

Ask AI to criticize, not just create

Instead of only saying:

“Write this function.”

Try:

“Review this approach. What could go wrong?”

or:

“What edge cases am I missing?”

AI becomes much more valuable when it challenges your thinking instead of replacing it.

Keep some practice unplugged

Every now and then, solve something without AI.

A small bug.
A utility function.
A refactor.
A coding challenge.

Not because AI is bad.

But because your own confidence matters.

Use AI to learn the system, not bypass it

The best use of AI is not always generating new code.

Sometimes it is asking:

“Where is this logic coming from?”
“What does this function depend on?”
“Can you explain this module?”
“What are the possible side effects of changing this?”

That kind of usage makes you stronger.

It turns AI into a learning tool instead of a shortcut machine.

The Future Belongs to Developers With Judgment

I don’t think AI will make developers irrelevant.

But I do think it will change which developers stand out.

The value is moving away from simply producing code.

It is moving toward:

understanding systems,
asking better questions,
reviewing outputs critically,
making trade-offs,
protecting quality,
communicating decisions,
and taking responsibility.

AI can generate code.

But it cannot be responsible for it.

You can.

And that responsibility is what separates a developer from a prompt operator.

Final Thoughts

The next big developer skill is not just learning how to use AI.

It is learning how not to be fooled by it.

Because AI-generated code will keep getting better.

It will look cleaner.
It will sound more confident.
It will integrate deeper into our tools.
It will feel more natural to accept.

But the question will remain the same:

Can you spot when it is wrong?

That might be the real skill gap of the AI era.

Not who can generate the most code.

But who can still think clearly when the code is generated for them.

Use AI.
Use it a lot.
But review it like your production system depends on it.

Because eventually, it probably will.

👋 Thanks for reading — I’m Marxon, a web developer exploring how AI reshapes the way we build, manage, and think about technology.

If you enjoyed this post, follow me here on dev.to and connect with me on LinkedIn, where I share shorter thoughts, experiments, and behind-the-scenes ideas.

Let’s keep building — thoughtfully. 🚀

Marxon vs AI #1 - The Developer Who Doesn't Panic

Marxon — Tue, 03 Mar 2026 20:21:47 +0000

A year ago, I was asking the same question everyone else was asking:

Will AI replace developers?

It felt like a legitimate fear. Copilot was writing entire functions.
ChatGPT was explaining frameworks better than most documentation. Tools
were improving weekly. The hype was loud, aggressive, and unavoidable.

But after actually working with AI every single day, the question
changed.

It's no longer about replacement.

It's about leverage.

And leverage is never neutral.

Speed Is Not the Real Battle

Let's get this out of the way: AI is fast.

It generates boilerplate in seconds.\
It refactors legacy code without complaining.\
It drafts documentation instantly.\
It can even scaffold an entire feature before you finish your coffee.

If software development was only about typing speed, this would already
be over.

But it isn't.

Speed amplifies direction. If the direction is wrong, you just get to
the wrong place faster.

AI doesn't understand business trade-offs.\
It doesn't carry the weight of production incidents.\
It doesn't sit in meetings where architectural decisions echo for years.

It generates.\
It predicts.\
It optimizes patterns.

But it does not take responsibility.

And responsibility is where seniority begins.

AI Recognizes Patterns. Humans Choose Direction.

AI is exceptional at pattern recognition.

It has seen millions of repositories. It understands common
architectures, typical pitfalls, idiomatic syntax. In many ways, it has
broader exposure than any individual developer ever will.

But exposure is not judgment.

AI can suggest five possible implementations.

It cannot decide which one aligns with: - the long-term product
vision, - the current team skillset, - the political reality inside the
organization, - the technical debt you're already carrying.

That decision is contextual.

And context is not stored in the model weights.

Context lives in experience.

The Illusion of Intelligence

There's something seductive about AI-generated code.

It looks clean.\
It looks confident.\
It often compiles on the first try.

But confidence is not understanding.

AI does not "know" why a system exists. It does not feel the pain of
previous refactors. It does not remember the time a small shortcut
created months of cascading bugs.

It predicts the most statistically plausible answer.

That's powerful.

But it's not wisdom.

Wisdom comes from consequences.

Where AI Actually Wins

Here's the honest part: AI already changed how I work.

I prototype faster.\
I explore ideas more freely.\
I validate assumptions earlier.\
I write fewer repetitive lines.

Instead of spending mental energy on syntax, I spend it on structure.

Instead of searching documentation for 20 minutes, I test concepts
immediately.

AI didn't replace my thinking.

It removed friction around it.

And friction is expensive.

The Real Risk Is Not Replacement

The real risk is passivity.

If you let AI think for you, your edge erodes.

If you stop understanding the code you ship, you become dependent.\
If you copy without questioning, you lose the ability to evaluate.

AI magnifies competence.

But it also magnifies weakness.

A strong developer becomes stronger.\
A careless one becomes faster at making mistakes.

That's the real shift.

This Is Personal Now

This is no longer "Developer vs AI".

It's personal.

It's about whether I choose to panic or adapt.\
Whether I hide behind fear or build leverage.

AI is not my competitor.

It's my amplifier.

But an amplifier doesn't create music. It just makes it louder.

The signal still comes from the source.

And I'd rather improve the signal than fight the speaker.

Final Thought

AI will not replace developers.

Developers who use AI strategically will replace those who don't.

Not because AI is smarter.

But because leverage always wins.

This isn't Marxon vs AI.

It's Marxon with AI.

And the keyboard is still in my hands.

Thanks for reading — I’m Marxon, a web developer exploring how AI reshapes the way we build, manage, and think about technology.

If you enjoyed this year-end special, follow me here on dev.to

🥊 Developer vs AI – New Season, Same Question: Who’s Really in Control?

Marxon — Sat, 07 Feb 2026 22:01:53 +0000

New year, new hype, same existential crisis.

If you’re a developer in 2026 and not thinking about AI, congratulations — you’ve successfully avoided the internet.

The rest of us? We’re somewhere between “this is amazing” and “is my job still safe?”.

Welcome to the new season of Developer vs AI.

🤖 The AI Is No Longer a Sidekick

Let’s be honest.

AI used to be:

autocomplete on steroids
a fancy Stack Overflow replacement
something you double-checked anyway

Now?

It writes components
It refactors legacy code
It explains your own code better than you can

Sometimes it even asks:

“Are you sure you want to do it this way?”

Rude. But often correct.

👨‍💻 The Developer’s New Role

(Spoiler: Still Needed)

Despite the doom posts on LinkedIn, here’s the uncomfortable truth:

AI didn’t replace developers.

It replaced developers who don’t adapt.

What changed is how we work.

Old mindset

“I write every line of code”
“I know this framework inside out”
“I Google everything myself”

New mindset

“I design systems”
“I review, guide and correct AI output”
“I focus on product impact, not syntax”

You’re less of a code typist

and more of a technical decision-maker.

⚠️ Where AI Still Fails (Daily)

Before we crown our silicon overlords, let’s talk about reality.

AI is still bad at:

understanding business context
dealing with half-broken legacy systems
reading your PM’s emotionally charged Slack message
knowing why something was done “temporarily” in 2019

It gives confident answers.

Not always correct ones.

Which means blind trust is the new junior mistake.

🧠 The Real Skill Gap in 2026

The biggest difference between devs right now isn’t:

language choice
framework wars
tabs vs spaces (relax)

It’s this:

Can you tell when the AI is wrong?

That requires:

fundamentals
architectural thinking
real-world battle scars

Ironically, the better you are as a developer,

the more powerful AI becomes in your hands.

🥊 So… Who’s Winning This Season?

AI wins at:

speed
boilerplate
pattern recognition

Developers win at:

judgment
responsibility
understanding humans (still underrated)

This isn’t a knockout match.

It’s a tag team.

And the developers who survive?

They don’t fight AI.

They orchestrate it.

👋

Thanks for reading — I’m Marxon, a web developer exploring how AI reshapes the way we build, manage, and think about technology.

If you enjoyed this year-end special, follow me here on dev.to

and join me on X where I share shorter thoughts, experiments, and behind-the-scenes ideas.

Let’s keep building — thoughtfully. 🚀

The Best Developer AI Tools of 2025 — What Actually Worked in Real Projects

Marxon — Mon, 29 Dec 2025 08:57:43 +0000

2025 was the year AI tools stopped being “nice to have” and became part of the default developer workflow.

Not because they’re perfect.

Not because they replaced thinking.

But because — when used intentionally — they genuinely save time and mental energy.

This is not a hype list.

No affiliate links.

No “Top 50 tools you’ll never use.”

These are AI tools I actually used in real projects, under real deadlines, with real consequences.

Some helped a lot.

Some surprised me.

Some almost caused problems.

Here’s the honest breakdown.

1️⃣ ChatGPT — Still the Thinking Partner

Yes, it’s obvious.

And yes, it still deserves the top spot.

Where it shines

breaking down unclear problems
exploring architectural options
refactoring ideas
explaining legacy code
writing first drafts of docs or tests

I don’t trust it blindly — but as a thinking partner, it’s unmatched.

Where it fails

confidently hallucinating APIs
missing project-specific constraints
sounding right while being wrong

Rule I learned in 2025:

If you can’t clearly explain the problem, ChatGPT won’t magically fix it for you.

2️⃣ GitHub Copilot — Quiet, Constant Productivity

Copilot isn’t exciting anymore — and that’s a good thing.

It doesn’t try to replace you.

It just removes friction.

Best use cases

repetitive boilerplate
predictable patterns
test scaffolding
small utility functions

It works best when:

you already know what you’re building
the codebase is consistent

Important caveat

Copilot amplifies existing patterns.

If your codebase is messy — it will happily generate more mess.

3️⃣ Sourcegraph Cody — The Underrated Codebase Navigator

This one surprised me.

Cody is especially useful in:

large, unfamiliar codebases
legacy systems
onboarding scenarios

Why it stands out

understands your actual repository
answers questions like:
- “Where is this logic used?”
- “What depends on this service?”
- “Why does this exist?”

This is one of those tools that doesn’t feel flashy —

but quietly saves hours.

4️⃣ AI for Documentation — A Silent Win

AI didn’t make me love it —

but it made it bearable.

What worked well

drafting READMEs
summarizing changes
explaining decisions after the fact

What didn’t

final wording
tone
accuracy

AI writes the first 60%.

You still own the last 40%.

That’s a trade-off I’m happy with.

5️⃣ Hidden Gem: AI as a Debugging Rubber Duck

One unexpected habit I developed this year:

I explain bugs to AI before fixing them.

Not for the solution —

but for the clarity.

By the time I finish explaining the problem clearly,

I often already know what’s wrong.

The AI response is secondary.

The thinking process is the real value.

6️⃣ Experiments That Didn’t Stick

Not everything worked.

Things I tried — and dropped:

full component generation
large-scale refactors via AI
AI-written business logic

Why?

too risky
too context-heavy
too hard to validate

AI is great at assisting decisions.

It’s still bad at owning them.

7️⃣ The Biggest Lesson of 2025

The most valuable insight wasn’t about tools.

It was this:

AI doesn’t make you faster by writing code.

It makes you faster by reducing hesitation.

When used intentionally, AI:

lowers the cost of exploration
shortens feedback loops
helps you move forward with more confidence

But only if you stay in control.

Final Thoughts

AI tools didn’t replace my job in 2025.

They reshaped how I work.

The best ones:

stay quiet
remove friction
respect human judgment

Going into 2026, I’m not looking for “smarter AI”.

I’m looking for tools that make me think better.

👋

Thanks for reading — I’m Marxon, a web developer exploring how AI reshapes the way we build, manage, and think about technology.

If you enjoyed this year-end special, follow me here on dev.to

and join me on X where I share shorter thoughts, experiments, and behind-the-scenes ideas.

Let’s keep building — thoughtfully. 🚀

Developers vs AI: A Holiday Reflection — What Kind of Developer Do You Want to Be?

Marxon — Fri, 19 Dec 2025 17:33:56 +0000

December is a strange month for developers.

Deadlines slow down.

Slack goes quiet.

Pull requests wait a little longer than usual.

And for once, instead of learning the next framework or chasing the next productivity hack, we get something rare:

space to think.

In 2025, AI didn’t just enter our workflow — it became the default.

Not a tool we “try”, but the first thing we reach for.

So before we step into 2026, I want to pause and ask a simpler, more uncomfortable question:

What kind of developer do you want to be in an AI-first world?

2025 Was the Year AI Became Invisible

At the beginning of the year, AI was still something we talked about.

By the end of it, it became infrastructure.

We no longer say:

“Let me try AI for this.”

We say:

“Why wouldn’t I use AI?”

It writes boilerplate.

It explains errors.

It generates tests, docs, configs, even architecture suggestions.

And slowly, quietly, it blended into our thinking process.

That’s not necessarily bad — but it is important.

Because when something becomes invisible, it also becomes unquestioned.

AI Is a Mirror (Not a Brain)

Here’s something I didn’t expect:

The more I used AI, the more it reflected me back at myself.

If I understood the problem clearly → the AI helped a lot
If I was vague → the AI gave vague, shallow answers
If I didn’t know what I wanted → it confidently hallucinated

AI didn’t replace thinking.

It exposed the absence of it.

It became obvious when I was:

asking good questions
or just looking for shortcuts

In that sense, AI isn’t making developers better or worse.

It’s amplifying who we already are.

Two Paths Developers Are Taking Going Into 2026

By the end of 2025, two very different relationships with AI became clear.

1️⃣ The Prompt Consumer

Asks first, thinks later
Copies answers quickly
Trusts output because it “looks right”
Moves fast — but shallow

This developer is productive… until something breaks.

Then everything slows down.

2️⃣ The Thinking Partner User

Thinks first, asks second
Uses AI to challenge ideas, not replace them
Reviews everything
Treats AI like a junior teammate

This developer doesn’t move as fast — but moves with confidence.

The difference isn’t intelligence.

It’s intentionality.

The Quiet Risk We Rarely Talk About

Here’s the uncomfortable part.

When AI always gives you:

the regex
the query
the edge-case handling
the explanation

You slowly stop building technical confidence.

Not skill — confidence.

That inner voice that says:

“I know how this system behaves.”

And without that confidence:

debugging becomes scary
architectural decisions feel risky
leadership feels heavier

AI doesn’t take this away directly.

We give it away by never practicing without it.

Some Questions Worth Asking Before 2026

This isn’t a checklist.

Just a few honest questions to sit with:

When was the last time I solved something without AI?
Do I understand my own codebase — or just maintain it?
Am I faster… or just less involved?
Could I explain my last major decision to another developer?
If AI was gone for a week — would I panic?

No judgment.

Just awareness.

AI Isn’t Stealing the Craft — It’s Raising the Bar

Here’s the good news.

AI didn’t make development less human.

It made the human parts more valuable.

Things like:

systems thinking
judgment
communication
responsibility
understanding trade-offs

Typing code matters less.

Thinking clearly matters more.

And heading into 2026, that distinction will matter even more.

A Holiday Thought to Carry Forward

As we move into 2026, I don’t think the question is:

“Will AI replace developers?”

The better question is:

“What kind of developer am I becoming alongside AI?”

Because AI will follow your lead.

Not the other way around.

🎄

Thanks for reading — I’m Marxon, a web developer exploring how AI reshapes the way we build, manage, and think about technology.

If you enjoyed this reflection, follow me here on dev.to

and join me on X where I share shorter thoughts, experiments, and behind-the-scenes ideas.

Let’s keep building — thoughtfully. 🚀

Developers vs AI: Are We Becoming AI Managers Instead of Coders?

Marxon — Sun, 30 Nov 2025 21:09:43 +0000

AI is no longer a shiny add-on in our workflow, it’s the silent co-worker sitting next to us every day. The one who writes our boilerplate, explains complex errors, generates documentation, and sometimes even finishes entire components before we do.

And that raises a new question for this series:

Are developers slowly becoming AI managers instead of coders?

Not in a dystopian way, but in a real, day-to-day, “I spend more time verifying than writing” kind of way.

This is the third part of my Developers vs AI series.

1. Coding Less, Reviewing More

Developers used to spend most of their time typing. Now we spend more time:

validating AI suggestions,
deciding between multiple AI-generated approaches,
reviewing code we didn’t write.

We're shifting from creators to curators.

This isn’t bad, but it changes the skillset required to stay sharp.

2. Architecture > Implementation

AI can generate clean functions all day. What it still struggles with:

long-term architectural decisions,
domain-specific constraints,
trade-offs that come from real business context.

This makes architecture more valuable than ever.

A developer who understands system design will outperform someone who only relies on prompting, no matter how good the AI becomes.

3. AI as a Team Member (Not a Tool)

The more I use AI, the more it feels like onboarding a junior dev:

It’s fast.
It gets things wrong.
It needs context.
It needs oversight.
It gets better over time.

The difference?

This junior dev never sleeps — and learns from millions of projects.

The role of a developer becomes managing this relationship: giving better instructions, guiding the direction, and maintaining code quality.

4. The New Developer Skill: Teaching AI

We already see it happening.

Developers who think clearly, write clear prompts, and break down problems logically get better AI results. Those who don’t… struggle. Hard.

“Prompting” isn’t a magic trick. It’s just clear thinking turned into text.

In a way, AI is exposing whether we actually understand the problem we’re solving.

5. The Danger: Losing Technical Confidence

Here’s what I’ve noticed, and maybe you feel the same:

The more the AI does for you, the more you start to doubt yourself when you do write code manually.

Not because you’re bad at it.

But because you’ve grown so used to having a second brain.

That’s a problem.

Developers need confidence in their own reasoning — it’s what makes debugging, architectural decisions, and leadership possible.

6. What You Should Focus On Going Into 2025

If AI keeps accelerating, developers who want to stay ahead should double down on:

Architecture & systems thinking
Business understanding (the AI doesn’t know your company)
Debugging intuition
Communication & clarity
Security & data awareness
Thinking in constraints, not just solutions

These are skills AI amplifies, but does not replace.

7. So… Are We Becoming AI Managers?

Honestly?

Yes.

But in the best possible way.

We’re evolving into:

system designers,
decision-makers,
architects,
problem-solvers,
explainers,
quality-gatekeepers.

AI is making development more human, not less.

It automates the typing, not the thinking.

🎄 Holiday Teaser – Two Special Releases Coming Soon

As we move into the holiday season, I'm preparing two special releases for this series and for all developer-focused readers:

1️⃣ Year-End Special: “The Best Developer AI Tools of 2025 — Real-World Tested”

A practical, no-nonsense list featuring:

tools tested in real projects,
workflow optimizations,
hidden gems worth adopting,
and a few surprising experiments that became part of my daily routine.

2️⃣ A Holiday Developers vs AI Extra Episode

A deeper and more provocative follow-up article releasing during the holidays:

“What Happens to the Developer Role When AI Becomes the Default Problem-Solver?”

A personal and analytical look at how the developer profession might evolve as AI becomes the first tool we reach for in 2025.

Both pieces will drop as separate holiday specials. Stay tuned.

👋

Thanks for reading — I’m Marxon, a web developer exploring how AI reshapes the way we build, manage, and think about technology.

If you enjoyed this post, follow me here on dev.to, and join me on X (@Marxolution) where I share shorter thoughts, experiments, and behind-the-scenes ideas.

Let’s keep building — thoughtfully. 🚀

Developers vs AI: How Smart Tools Make Us Dumber Coders

Marxon — Sat, 01 Nov 2025 20:09:19 +0000

A few months ago, I realized I hadn’t written a regex by hand in weeks. Not because I suddenly stopped needing them — but because ChatGPT wrote every single one for me. It worked perfectly. Until it didn’t.

That’s when it hit me: AI wasn’t just helping me — it was quietly dulling my edge.

We love to talk about how AI makes us faster, more productive, and even more creative. But we rarely talk about what it takes away when we stop being intentional about how we use it.

1. Problem Solving → Pattern Matching

AI tools like Copilot or ChatGPT are incredible at recognizing patterns. But they don’t actually think. And when we rely on them too much, neither do we.

You stop debugging. You stop exploring edge cases. You stop asking why something works — because the AI already told you how to fix it.

That’s a dangerous place to be as a developer. The moment you outsource curiosity, you outsource growth.

2. From Learning to Prompting

Remember when learning meant reading docs, experimenting, and breaking things? Now, we just ask the AI and move on.

Sure, you get the answer faster. But you also skip the learning process — the messy, frustrating, but essential part that turns knowledge into intuition.

Prompt-based learning is like using a calculator before learning arithmetic. It gives results, not understanding.

3. Communication & Thought Clarity

AI can write your code comments, your commit messages, even your documentation. Convenient, right?

But if you stop articulating your thoughts, you stop clarifying them. Communication isn’t just for others — it’s how you refine ideas. The less you write and explain, the less you actually understand your own logic.

A developer who can’t explain their reasoning is just a typist with fancy tools.

4. Creativity & Experimentation

AI usually gives you the most probable answer — not the most interesting one.

When you stop tinkering, stop experimenting, and stop trying weird ideas because the AI already gave you the “right” way — you start to lose the playful curiosity that made you a good developer in the first place.

Everyone’s code begins to look the same. Safe. Predictable. Boring.

5. Intuition & Ownership

Ever looked at your codebase and realized you don’t feel connected to it anymore? That’s what happens when too much of it isn’t really yours.

You didn’t design it. You didn’t make the trade-offs. You didn’t debug it at 2AM to understand why it broke.

And because of that, your intuition — that gut feeling about how things should fit together — slowly fades.

6. How to Stay Sharp in an AI-Driven World

AI isn’t the enemy. Mindless use is.

Here’s how to keep your brain (and skills) alive:

Try before you ask. Solve it yourself first, then compare your answer with the AI’s.
Practice unplugged. Code a small project or challenge without any AI for a day. It’ll hurt — in a good way.
Reflect. Track when AI genuinely helps you vs. when it’s just saving you from thinking.
Teach. The best way to ensure you still understand something is to explain it to someone else.

🧠 The Bottom Line

AI won’t replace developers. But developers who forget how to think might replace themselves.

Use AI like a power tool — something that amplifies your skill, not something that replaces it. Because the most valuable thing about being a developer isn’t knowing what to type. It’s knowing why.

👋

Thanks for reading — I’m Marxon, a web developer exploring how AI reshapes the way we build, manage, and think about technology.

If you enjoyed this post, follow me here on dev.to
for more reflections like this — and join me on X
(just started recently!) where I share shorter thoughts, experiments, and behind-the-scenes ideas.

Let’s keep building — thoughtfully. 🚀

AWS Went Down. The Internet Panicked. Here's What It Means for All of Us.

Marxon — Tue, 21 Oct 2025 19:36:30 +0000

Yesterday, the internet had a bad day — and so did millions of users and businesses around the world. On October 20, 2025, Amazon Web Services (AWS) experienced a major outage that disrupted everything from streaming platforms and smart homes to enterprise apps and financial services.

This wasn’t just another technical hiccup. It was a stark reminder of how fragile the modern internet can be when so much of it depends on a handful of companies.

What Happened

The issue began in the US-EAST-1 region — AWS’s most popular and densely used zone. According to Reuters, a problem with internal load balancing and DNS resolution cascaded through multiple AWS services. The result: apps and websites couldn’t connect to the servers they relied on.

Among those affected were McDonald’s, Apple Music, Microsoft 365, Alexa, and countless others. For several hours, essential services ground to a halt until AWS engineers restored connectivity and gradually lifted resource launch restrictions.

Why This Is a Big Deal

1. A Single Point of Failure

Many assume “the cloud” is inherently redundant. But if your redundancy lives entirely within one provider — or worse, one region — it’s not really redundant. When US-EAST-1 sneezes, the internet catches a cold.

2. Market Concentration

A small number of cloud providers — AWS, Microsoft Azure, Google Cloud — host the majority of the internet’s infrastructure. That means a single technical issue or configuration error can have global consequences. The Guardian called this outage a “wake-up call” for over-centralized tech ecosystems.

3. Real-World Consequences

This wasn’t just about broken websites. Smart home devices failed, business operations froze, and customers couldn’t access digital banking tools. The outage exposed how deeply integrated cloud systems are into our daily lives.

4. Transparency and Accountability

When one company’s infrastructure outage affects the global economy, it raises questions: Should these systems be treated as critical infrastructure? Should they face stricter transparency or redundancy regulations?

What Developers and Businesses Can Learn

A. Don’t Put Everything in One Basket

Consider multi-cloud or hybrid cloud strategies. Even a small secondary backup provider can make a huge difference.

B. Think in Regions, Not Just Servers

Deploy across multiple geographic regions. AWS regions can and do fail — sometimes catastrophically.

C. Map Your Dependencies

List all the services your stack relies on: DNS, S3, load balancers, CDNs. Know what happens if each one disappears.

D. Build for Failure

Assume downtime will happen. Automate backups, build health checks, and design your architecture for graceful degradation.

E. Communicate Transparently

If your service depends on AWS (or any third-party provider), have a crisis communication plan. Users appreciate honesty more than silence.

The Bigger Picture

The “cloud” once symbolized infinite scalability and resilience. Yesterday, it reminded us that it’s still made of physical servers, networks, and human mistakes — all controlled by a few companies.

AWS isn’t evil. It’s brilliant, efficient, and the backbone of the internet as we know it. But the more we depend on a single entity, the more we gamble with the internet’s stability.

Maybe the next evolution of cloud computing isn’t just about AI and automation — it’s about decentralization.

👋

Thanks for reading — I’m Marxon, a web developer exploring how AI reshapes the way we build, manage, and think about technology.

Let’s keep building — thoughtfully. 🚀

The Next Developer Skill: Knowing What NOT to Automate

Marxon — Tue, 07 Oct 2025 16:36:53 +0000

We automate to save time — but somehow, we always end up using that time to automate even more.

AI has made development faster, cleaner, and more efficient. It writes boilerplate, refactors logic, even explains bugs better than Stack Overflow ever did.

But in this new era, the question is no longer “Can I automate this?” — it’s “Should I?”

The next generation of developers won’t just master the tools.
They’ll master restraint.

⚙️ Automation Is No Longer Optional

Whether you’re building with Copilot, ChatGPT, or your own scripts, automation is now baked into the development process.

It’s not an edge anymore — it’s the baseline.
If you’re not automating, you’re already behind.

But there’s a trap here: the more we automate, the more we risk detaching from the very systems we’re supposed to understand.
When you no longer write the code, you stop noticing its logic. When you stop debugging, you stop questioning the structure.

Automation gives speed — but sometimes, at the cost of comprehension.

🧩 The Hidden Cost of Over-Automation

AI isn’t dangerous because it’s too powerful.
It’s dangerous because it’s too convenient.

Every time you let a model decide for you — what to name a function, how to structure data, how to handle exceptions — you’re outsourcing a little bit of your judgment.

Do that often enough, and you don’t just lose control of your codebase.
You lose touch with the why behind your decisions.

Over-automation breeds a new kind of developer:
fast, productive, and dangerously shallow.

The hardest bugs aren’t in the code — they’re in our assumptions.

🧭 The Real Skill: Judgment

The best developers will not be those who automate the most.
They’ll be the ones who know what not to automate.

Because not every task that can be automated should be.

Code reviews need human intention. AI can detect style, not subtlety.

Architecture decisions depend on trade-offs, not templates.

Team discussions thrive on empathy, not efficiency.

Learning demands effort, not shortcuts.

Automation should extend you, not replace you.
It should save your energy for the decisions that actually matter.

True skill isn’t about knowing every shortcut — it’s about knowing when not to take one.

🔍 Developers as Curators, Not Just Builders

In the coming years, the best engineers won’t be defined by their syntax knowledge or language mastery.
They’ll be defined by their ability to curate intelligence — human and artificial.

They’ll choose which parts of the workflow to automate, and which to keep human.
They’ll build less from scratch, but think more about systems, ethics, and outcomes.

In the age of AI, creativity will look more like discipline.

✍️ A Quiet Reminder

Don’t automate curiosity.
Don’t outsource thinking.

Automate the process — not the purpose.

👋

Thanks for reading — I’m Marxon, a web developer exploring how AI reshapes the way we build, manage, and think about technology.

Let’s keep building — thoughtfully. 🚀

Developers vs AI: Helpful Partner or Dangerous Shortcut?

Marxon — Wed, 01 Oct 2025 20:29:15 +0000

Artificial Intelligence has exploded into the developer workflow. Tools like ChatGPT, GitHub Copilot, Tabnine, Codeium, Replit Ghostwriter, Amazon CodeWhisperer, and Sourcegraph Cody can now generate code, write documentation, suggest bug fixes, and even spark new ideas when you’re stuck.

As a developer, I use AI almost every day. But here’s the catch: I don’t fully trust it. Too often it produces answers that look correct but are completely wrong. If you blindly accept them, you risk bad code, wasted time, or even security vulnerabilities.

And yet, despite these flaws, I keep coming back to it. Why? Because if used wisely, AI can provide massive advantages.

Why I Use AI as a Developer

Rapid Prototyping and Boilerplates

Spinning up a new project used to take hours. Now AI generates a solid foundation in minutes. It’s rarely perfect, but it gets me 70–80% of the way there — a huge time-saver.

A Thinking Partner

When I’m stuck, AI acts like a “rubber duck that talks back.” It suggests directions I may not have considered, and while I don’t always take its advice, it pushes me forward.

Documentation (the boring part, automated)

Most developers dislike writing docs. AI can draft READMEs, inline comments, or summaries. I still rewrite them, but starting from a draft is so much easier than starting from scratch.

Instant Feedback — a “Mini Mentor”

For junior developers, AI is like a mentor on demand. It reviews code instantly, points out mistakes, and suggests improvements. It doesn’t replace a seasoned colleague, but it accelerates the learning curve.

Why I Don’t Fully Trust AI

Confidently Wrong

AI often hallucinates — inventing functions, APIs, or solutions that don’t exist. The code may look polished but collapses at runtime. This “confidence without correctness” is dangerous.

Over-Reliance

If we outsource too much to AI, we risk losing critical thinking. Coding is more than typing — it’s about architecture, design, and problem-solving. Those skills fade if we let AI do everything.

Data Privacy Concerns

There’s another reason I hesitate: the data.

Every prompt, every code snippet, every question is valuable information. Global AI companies may directly or indirectly use it to train their models.

To be honest, I’m not comfortable with that:

I don’t know how my prompts are stored or analyzed.
I don’t know if they could resurface later in unexpected ways.
I don’t like the idea of contributing free data to make someone else’s product better, especially when it includes my own work or sensitive company code.

For many developers, these data privacy issues are a real dealbreaker — and I share that hesitation.

Ethical and Business Questions

AI-generated code also raises broader concerns:

Who owns the copyright of AI-produced code?
What if sensitive company data leaks into a model?
Can businesses really trust mission-critical logic to a black-box AI?

There are still no clear answers.

How I Choose to Approach AI

I see AI as a partner, not a replacement. It’s like an enthusiastic junior developer: lots of ideas, sometimes brilliant, often off the mark. You’d never deploy a junior’s code without review — and the same goes for AI.

Developers who only “type code” may fall behind. But those who think in systems, ask the right questions, and solve real business problems will thrive with AI in their toolbox.

Final Thoughts

I use AI every day — but I always double-check what it produces. For me, AI is not an enemy but a tool: it makes me faster, more creative, and more productive — as long as I keep my critical eye open.

The key is balance: AI accelerates the work, but it doesn’t replace judgment.

What about you?

Do you trust AI with code generation, or do you always review it?
Is it more of a helpful partner, or an unreliable coworker you constantly have to double-check?

👋

Thanks for reading — I’m Marxon, a web developer exploring how AI reshapes the way we build, manage, and think about technology.

Let’s keep building — thoughtfully. 🚀