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TECH & AI
I Was Cleaning the Same Repeated Words Manually… So I Built This
jackminion 0


The Problem Looked Small at First

I was editing a big chunk of text.

And suddenly I noticed something annoying:

Same words repeated
Duplicate keywords everywhere
Repeated lines inside copied content

At first I thought:

“I’ll just remove them manually.”

Big mistake.

What Happened Next

The more text I checked…

The more duplicates I found.

Same word.Same keyword.Same line.

Again and again.

And after a while:

👉 I wasn’t editing content anymore👉 I was just cleaning repetition

The Most Frustrating Part

You never fully trust manual cleanup.

Because there’s always that feeling:

“I probably missed some duplicates.”

And honestly…

Most of the time, you do.

Why I Built This Tool

So I built something simple:

👉 https://allinonetools.net/duplicate-word-remover/

A tool that can instantly:

Remove duplicate words
Remove duplicate keywords
Clean repeated lines
Process text line by line

No signup.No setup.No complicated options.

Just:

Paste → Remove Duplicates → Done

What I Realized

This problem happens everywhere.

Not just in writing.

People deal with duplicate text while:

Cleaning keyword lists
Organizing copied data
Editing AI-generated text
Formatting SEO content
Managing large text blocks

Why Duplicate Cleanup Matters

Repeated text creates:

Messy content
Poor readability
Harder editing
Confusing keyword lists

Even small repetitions make text feel unclean.

The Problem With Doing It Manually

Manual cleanup sounds easy…

Until:

The text gets large
Keywords repeat hundreds of times
Lines start looking identical

Then it becomes:

Slow, frustrating, and error-prone.

What I Focused On

I wanted the tool to feel instant.

So I kept it:

Fast
Minimal
One-click simple
Easy for large text blocks

Because this isn’t a “complex editing” task.

It’s a:

“Please clean this quickly” problem.

What Surprised Me

After building it:

Many people used it for SEO keyword cleanup
Others used it for AI-generated content cleanup
Some used it just to organize messy copied notes

And the biggest thing?

👉 People loved the “line-by-line” cleanup.

Because it removes duplicates without breaking structure.

The Real Insight

A lot of productivity problems are not difficult.

They’re just:

Repetitive and annoying.

Simple Rule I Follow Now

If users repeat the same cleanup task often…

👉 It should be automated.

Final Thought

You don’t always need powerful software.

Sometimes:

A tiny tool that removes friction is enough.

Be honest — have you ever copied text and later realized:

Half the keywords were repeated?
Or the same lines appeared multiple times?

What do you usually do in that situation? 👇



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TECH & AI
The Hidden 43% — How Teams Are Wasting Almost Half Their LLM API Budget
jackminion 0



You look at your provider dashboard and see one number: the total bill. It’s like getting an electricity bill that just says “$5,000” with no breakdown of whether it was the AC, the fridge, or someone leaving the lights on all month.

tbh, most AI startups are flying blind right now. We recently looked into the cost breakdown for several teams and found something crazy: almost 43% of LLM API spend is completely wasted. It’s not about paying for usage; it’s about paying for bad architecture.

Here’s where the leaks are actually happening:

Retry Storms (34% of waste)Your agent fails to parse a JSON response, so it retries. And retries. Sometimes 5-10 times in a loop. You aren’t just paying for the failure, you are paying for the massive context window sent every single time.
Duplicate Calls (85% of apps have this issue)Multiple users asking the exact same question, or internal systems running the same RAG pipeline on the same document. Without caching at the provider level, you’re paying OpenAI to generate the identical tokens twice.
Context BloatSending the entire 50-page document history when the user just asked “what’s the summary of page 2”. RAG is great, but shoving everything into the prompt “just in case” is burning your runway.
Wrong Model SelectionUsing GPT-4o or Claude 3 Opus for simple classification tasks when Haiku or GPT-3.5-turbo would do it for a fraction of the cost.

You can’t fix what you can’t see. That’s exactly why I built LLMeter (https://llmeter.org?utm_source=devto&utm_medium=article&utm_campaign=hidden-43-percent-llm-waste). It’s an open-source dashboard that gives you per-customer and per-model cost tracking. Stop guessing who or what is draining your API budget.

Fwiw, just setting up basic budget alerts and seeing the breakdown by tenant usually drops a team’s bill by 20% in the first week. Give it a try, it’s open source (AGPL-3.0) and you can self-host or use the free tier.



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TECH & AI
Stop Writing Endpoints. Start Defining Systems.
jackminion 0



For a long time, I thought building APIs meant writing endpoints.

You know the pattern:

Define a route
Validate input
Query the database
Transform the result
Send a response

Do that over and over again.

Different routes. Same structure.

The Illusion of Control

Writing endpoints feels productive.

You’re in control of everything:

The logic
The validation
The data flow

But after a while, something becomes obvious:

You’re not building systems.

You’re repeating patterns.

The Real Problem

Most APIs look like this:

app.get(‘/users/:id’, async (req, res) => {
const id = req.params.id;

if (!id) {
return res.status(400).json({ error: ‘Missing id’ });
}

const user = await db.users.findById(id);

if (!user) {
return res.status(404).json({ error: ‘Not found’ });
}

return res.json(user);
});

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Now multiply that by:

Dozens of endpoints
Multiple resources
Different validation rules
Slight variations in logic

You end up with:

Repeated code
Inconsistent patterns
Hard-to-maintain systems

You’re Not Writing Logic. You’re Rewriting Structure.

Look closer at most endpoints.

They follow the same shape:

Extract input
Validate input
Execute query
Handle errors
Return response

The structure doesn’t change.

Only the details do.

So why are we rewriting the structure every time?

The Shift: Define, Don’t Rewrite

Instead of writing endpoints…

Define them.

What if your API looked like this instead?

get:
user:
GetUserById:
input:
id: number
where:
id: $param.id
response:
id: number
name: string
email: string

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No route handler.

No repeated boilerplate.

Just a definition.

What This Changes

When you define systems instead of writing endpoints:

Structure becomes consistent
Validation becomes automatic
Queries become predictable
Behavior becomes visible

You’re no longer guessing how something works.

You can read it directly.

From Endpoints to Systems

Traditional approach:

Every endpoint is custom
Logic is scattered
Behavior is implicit

System-driven approach:

Endpoints follow a pattern
Logic is structured
Behavior is explicit

You move from “code-first” to “contract-first.”

Where the Code Goes

This doesn’t eliminate code.

It moves it.

Instead of writing endpoint logic repeatedly…

You write:

A compiler that reads definitions
A pipeline that executes them
A system that enforces rules

Code becomes the engine.

Not the repetition.

Example Flow

With a system-driven approach, a request might flow like this:

Request → Parse Definition → Validate → Build Query → Execute → Format Response

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The difference is:

The flow is constant
The behavior is defined in configuration

Why This Matters

Without this approach:

Every developer writes endpoints differently
Bugs are repeated across routes
Refactoring becomes painful

With this approach:

Patterns are enforced
Behavior is predictable
Systems scale cleanly

“Isn’t This Less Flexible?”

Yes.

And that’s the point.

Unlimited flexibility leads to:

Inconsistency
Complexity
Fragile systems

Constraints lead to:

Where This Fits

This kind of system works best when:

You have repeated CRUD patterns
You want consistent APIs
You care about long-term maintainability

It doesn’t replace every use case.

But it replaces most of the boring, repetitive ones.

The Bigger Idea

This isn’t just about APIs.

It’s about how we build software.

Instead of:

Writing everything manually
Repeating patterns
Hoping for consistency

We can:

Define systems
Enforce structure
Let the engine handle execution

Final Thought

Writing endpoints feels like control.

But it’s often just repetition.

Defining systems feels restrictive at first.

But it leads to something better:

Clarity.

Consistency.

Scalability.

That’s why I stopped writing endpoints…

…and started defining systems.



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