DEV Community: Mustafa Baysal

Boosting Your Upwork Journey with Credly Certifications

Mustafa Baysal — Thu, 13 Nov 2025 08:36:16 +0000

This guide is all about how you can become Top-Rated on Upwork — and the small but powerful steps that help you stand out. One of the most important factors is certifications. They may seem simple, but they play a bigger role than most people realize.

Why Certifications Matter

How could you get certifications, and how could you certificate your skills or not work, through Credly.com? And what you should do? Let me talk about it a little bit.

If you're a beginner on Upwork, it's really hard to get jobs. Maybe you check the job postings and think, “Okay, these job postings are quite competitive. There are many proposals. Nobody sees my proposal.” Yeah, that's how it is in the beginning.

But here's the truth: many of those proposals are sent by bots or not‑qualified people. This is a main issue on Upwork. And honestly, I feel sorry for clients, because imagine you're a client looking for a freelancer who will handle your job — and your chance of finding a good freelancer on the platform is too low. It's too low.

So if you know these facts about the market, all of those proposal counts and competitiveness become meaningless. You start thinking: “Okay, these guys are not good enough. I can compete easily. I can beat them.”

When you check other profiles, you'll see it yourself: they don’t have a presentable profile picture, not a professional description, not even a clean, trustworthy profile. So the first thing I would suggest to you is this: if you have any certs compatible with Credly.com, you can easily import your certifications from Credly.

What Credly Gives You

What will it give you? Let's say you are adding your skills on your profile, like Python, databases, etc. By default, you will not be able to see certifications connected to those skills.

But if you really want to show customers that you are qualified for that expertise or specialty, you can literally add your Credly certificate — and with those certificates, your skills become verified.

So customers will see your skills differently. How? When they come to your profile and check your skills, they will see some certificate connected with those skills. And this is really important.

If you can do it, do it now.

If You Don’t Have Credly Yet — No Problem

You can go into Credly.com, it's totally free. You can search for skills related to your expertise. And when you find a certificate, Credly gives you hints about how you can get those certificates.

Some of them are free, some are connected to paid services like Coursera or Meta. You can invest in yourself and get those certificates for your Upwork journey.

And after you add that certification into your profile, related skills with that certificate will be shown as verified. This is really important — and this is something nobody talks about when it comes to Upwork.

These little differences make you stand out against others and improve your profile instantly.

I added mine from Coursera, so this is it. You can do it as well. And it's really a good thing to do for your Upwork journey.

Final Words

I will keep posting about other stuff that you can do to improve your profile on Upwork. Please read more, and you can read other Upwork guides from my profile.

Check it out — and thank you so much.

How I Turned a $25 Upwork Client Into a $1,000+ Active, Recurring Customer

Mustafa Baysal — Tue, 11 Nov 2025 02:45:18 +0000

Hey everyone, I hope you’re all doing great.

Today, I want to share a story—my story—about how I turned a $25 paying client on Upwork into a $1,000+ active, recurring customer. And honestly, this experience changed the way I think about freelancing, opportunity, and how we should treat ourselves as freelancers.

The Struggle Phase

Two or three months ago, I was struggling—really struggling—to get clients. I was desperate, I felt bad, and I was lost in this huge mess of trying to find my first real opportunities.

There I was, scrolling endless job posts while sipping my perfect V60 coffee… and suddenly, I saw an ad:

“$25 — Need a Python script.”

I thought, “Okay, maybe I can do that.”

Funny, right? I was applying to everything at that point.

The client was a lady from the UK. She was new on the platform, which I think played a big role in why she picked my proposal—because I had no reviews, no completed jobs, nothing on my profile.

But somehow… she answered.

The First $25 Job

I won’t lie: in the beginning, $25 made me sad. It felt too low. But I always say:

In this life, everything is a chance. Everything is an opportunity.

We talked, and she was incredibly kind. She also had terrible experiences with previous freelancers, which made her appreciate communication and clarity even more.

The job was easily worth more than $25—so I asked her if she could bump it to $30 since my hourly rate was $45 and it would take about 45 minutes. She agreed, and I delivered the job.

That’s where everything changed.

Unexpected Opportunities

After I delivered, she told me she was “so satisfied” and “so surprised” by the quality and speed. She gave me a lot of compliments.

That’s when I realized:

We had great communication.

And great communication is where bigger deals start.

So I offered an additional idea:

“You have this Python script, but I think you also need another script to automate something else.”

She loved it. Literally said she was thinking about asking me anyway.

That became a $100 job.

Then she brought another—$250.

Then another one next week.

And then she asked:

“Do you also understand WordPress stuff?”

Honestly? I was lost. It had been years since I touched WordPress. But I said:

“Okay, maybe it will be good. Let’s do it.”

That turned into about $600.

And more for sure...

In total?

✅ $1,200 from a customer who started as a $25 job.

Why This Matters

People think $25 means “bad client.”

They think you should skip and run to the next job.

But most customers don’t know what they need when they first post a job.

Sometimes they list $25, sometimes $100, sometimes something random.

What matters is opportunity.

And opportunity usually doesn’t come with a big budget… at least not first.

I even had another story: I turned a $100 client into a $500 client, and now we’re negotiating a $3,000 contract.

Everything starts small.

Then grows if you know how to grow it.

Know Your Place in the Market

This is something many freelancers don’t want to hear.

But you need to know your position.

We are basically products in a marketplace.

You’re not famous.

Nobody knows you yet.

You don’t have a marketing budget.

You’re just getting started.

So when my friend from Turkey created his Upwork profile, he had zero jobs, zero reviews, and he put his rate at $65/hr. I checked the market—top rated and vetted pros from the US were charging $70/hr in the same field.

I literally told him:

“What the fuck are you doing?”

It’s not possible.

Not without proof, not without reviews, not without a reputation.

Customers see those numbers instantly when you apply. And they judge you based on them.

Final Thoughts

So yeah… that was my story.

From a small $25 job to a recurring $1,000+ customer.

From desperation to opportunity.

From nothing to a client who trusts me fully.

And I’m happy you're reading this.

I hope these insights help you approach Upwork—and freelancing in general—with a new perspective.

Opportunities don’t always look big.

But big things often start small.

My 6-Month Upwork Journey — What I Learned Becoming Top-Rated

Mustafa Baysal — Mon, 10 Nov 2025 20:54:26 +0000

Six months ago, I started my Upwork journey. During this time, I learned a lot about consistency, communication, and how the platform really works.

After four months of focused effort, I became a Top‑Rated freelancer on Upwork — even though there were periods when I wasn’t active at all. That inconsistency slowed my progress and taught me how important momentum and responsiveness are.

This article is part of a series where I’ll also talk about:

How important certifications really are on Upwork
How I became Top‑Rated in under 3 months of actual activity
Why and how you should track your proposals properly

How I Became Top‑Rated in 4 Months

Becoming Top‑Rated wasn’t about luck — it was consistency, response time, delivering great work, and keeping clients happy.

Even during the times I disappeared from the platform (a mistake I wouldn’t repeat), the progress I had already made kept pushing me forward.

Here is my profile;

Why Tracking Your Proposals Matters

Every proposal you send costs $1.50–$2.50, so sending blindly doesn’t make sense.

Tracking your proposals helps you understand:

Which types of jobs convert
How many proposals lead to one contract
Your ROI on connects
Patterns in client behavior

I track every proposal manually in a simple sheet:

This one habit alone helped me reduce costs and increase conversions.

Using AI for Writing Cover Letters

AI can save time, especially when you’re applying daily. But if you're a beginner, I still recommend writing some cover letters manually to understand what works.

✅ Manual Cover Letters

These help you learn:

How clients think
How to tailor proposals
What makes you stand out

✅ AI‑Generated Cover Letters

If you're applying frequently (like me), AI speeds things up and keeps your writing consistent.

Here are the tools I use:

⚙️ FreelanceHustle.ai — My Daily Tool

My friend on Upwork recommended this tool. I wasn’t sure at first, but now I use it every day.

✅ What I Like

Voice dictation is a game changer — speak in your native language, and it generates a natural proposal.
Understands chat history + your voice input.
Replies to client messages using AI.
Results don’t look AI‑generated.
You can save and train your best proposals.
Chrome extension works well for Upwork questions.
No usage limits.

❌ What I Don’t Like

Not designed only for Upwork, so sometimes feels slightly off.
Pricing isn’t super clear — they should separate Upwork/Fiverr or use credits.

Price: $30

🔗 https://freelancehustle.ai

⚙️ Pouncer.ai — A Great Secondary Option

✅ What I Like

Large template library (you can create your own too).
Free plan available.
Built‑in profile optimizer.
Helpful community.
Clean, simple UI.

❌ What I Don’t Like

Uses Unicode characters for bold/italic — looks very AI‑styled.
Proposal generation is limited.

Price: $35

🔗 https://pouncer.ai

Note: The both links are affliated you can search from google if you prefer not register from these links.

What Clients Actually See

Most freelancers overestimate how much clients read.

Here’s what clients really see:

Clients only get a preview of:

First 1–2 sentences of your proposal
Your country
Your photo
Your hourly rate
Job Success Score
Total earnings
A small list of skills

This is why improving your profile, portfolio, and appearance matters even more than writing a “perfect” proposal.

Final Lessons & Advice

After hundreds of proposals and months of experimenting, here’s what I learned:

Nobody cares who wrote your proposal — AI or you.
Clients care about your profile, results, and professionalism.
Tracking your proposals saves money and increases conversions.
AI tools can help, but don’t rely on them entirely.
Don’t expect any platform or AI to do the work for you.

I’m just sharing honest experiences here — not advertising anything. I currently use FreelanceHustle.ai daily because it fits my workflow, but you should choose whatever works best for you.

Processing 10 Million Records with AI on a $1,500 Budget

Mustafa Baysal — Mon, 20 Oct 2025 16:20:01 +0000

It was a rainy Sunday morning. I was drinking my home-brewed coffee when my phone rang.

A customer called asking: "Can you do some data extraction job with AI?"

"Sure, why not. How many records and what kind of results do you expect?"

The answer: 10 million records of cryptic product titles that needed to be transformed into structured vehicle compatibility data.

The Challenge

10 million records in Microsoft SQL Database
Minimal product info (just ID + title like: FORD FOCUS 1.5 TDCi GEARBOX 0B5-300-057-PU)
Budget constraint: $1,500 for AI tokens
Need for continuous processing without manual intervention

Basically the challange was this;

The Architecture

I designed a three-tier system:

ClickHouse - For fast data reads
Python async processing - For concurrent AI API calls
MongoDB - For storing structured results
AWS ECS - For containerized deployment

My Approach

I designed this approach to achieve this challange:

It's seems easy but in the software industry, everything go wrong as you guys know.

Step 1: Data Migration to ClickHouse

I moved data from Microsoft SQL to ClickHouse because it's optimized for fast reads and can handle 100,000+ rows per second.

def fetch_records(self, limit=100):
    last_row_id = self.get_last_record_id()

    query = f"""
    SELECT id, title, RowID
    FROM records 
    WHERE RowID > {last_row_id}
    ORDER BY RowID ASC 
    LIMIT {limit}
    """
    return self.client.query(query).result_rows

Key lesson: I initially tried updating records in ClickHouse after processing, but this caused massive memory issues. ClickHouse queues updates, and with hundreds of concurrent updates, the system choked. Solution? Track progress using RowID in a text file instead.

Step 2: AI Processing Pipeline

Choosing the Right Model

I tested three providers:

Provider	Model	Cost per 1M tokens	Speed
OpenAI	GPT-4o-mini	$0.15/$0.60	Fast
Google	Gemini 2.5 Flash	$0.075/$0.30	Fastest
Anthropic	Claude Sonnet	$3.00/$15.00	Medium

Winner: Gemini 2.5 Flash Lite - Best balance of cost and performance.

The Processing Engine

async def process_batch_async(self, batch, batch_number):
    tasks = [self.process_record(record) for record in batch]
    batch_results = await asyncio.gather(*tasks, return_exceptions=True)
    return batch_results

Configuration:

Batch size: 100000 records
Batch delay: 0.5 seconds
Async within batches, sequential between batches

The Prompt Engineering Fix

Initial attempts produced inconsistent results. The breakthrough was using few-shot prompting:

prompt = f"""You are a car parts expert. Extract compatible vehicle models in JSON format.

EXAMPLE: 
[{{"brand": "Ford", "model": "Focus", "engine": "1.5 TDCi", 
   "category": "Gearbox", "part_number": "0B5-300-057-PU"}}]

Product: {product_title}

Respond with only the JSON array."""

This improved accuracy from 70% to 95%.

Step 3: Storing Results

def insert_record(self, record_data):
    mongo_data = record_data.copy()
    mongo_data['inserted_at'] = datetime.now().isoformat()
    result = self.collection.insert_one(mongo_data)
    return result.inserted_id is not None

MongoDB provided flexible schema for varying extraction results and easy JSON export.

Step 4: Deployment

FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
CMD ["python", "run_batch.py"]

Deployed on AWS ECS with Fargate for serverless containers and auto-scaling.

The Problems I Faced

Problem 1: ClickHouse Memory Explosion

Updating processed records caused massive memory usage because ClickHouse queues all updates. With 50+ concurrent records, the system crashed.

Solution: Switched to append-only strategy using RowID tracking in lastRecord.txt.

Problem 2: Inconsistent AI Responses

AI would sometimes return plain text, skip fields, or use different JSON structures.

Solution: Added few-shot prompting with explicit examples and response validation with retry logic for malformed JSON.

Problem 3: Google Cloud Billing Disaster

Google Cloud billed Gemini 2.5 Flash text usage as image processing rates. My budget exploded from $1,500 to $3,400 overnight.

Resolution: Contacted Google Cloud Support. After 48 hours, they confirmed it was a billing system bug and issued a full refund.

Lesson learned: Always set up billing alerts with hard limits.

Problem 4: Failed Records Recovery

Network timeouts and API errors caused some records to fail. I needed reprocessing without starting over.

Solution: Export failed records to JSON, then use targeted retry:

def fetch_records_failed_from_json(self, json_file, start=0, limit=100):
    with open(json_file, 'r') as f:
        failed_data = json.load(f)
    row_ids = [r['row_id'] for r in failed_data['removed_documents']]
    return self.fetch_by_row_ids(row_ids[start:start + limit])

The Results

Final Statistics:

Records processed: 10,000,000
Success rate: 96.2%
Total cost: $1,347 (under budget)
Processing time: 14 days continuous run
Average speed: 740,000 records/day

Cost Breakdown:

Gemini API tokens: $1,234
AWS ECS: $78
ClickHouse hosting: $25
MongoDB Atlas: $10

Key Takeaways

Choose the Right Database: ClickHouse for fast reads, MongoDB for flexible results. Don't force updates where append-only works better.

Async Wisely: Concurrent API calls within batches work great. Sequential batch processing prevents rate limit issues.

Prompt Engineering Matters: A well-crafted prompt with examples improved accuracy by 25%.

Always Have Resume Strategy: Track progress in the simplest way possible. Even a text file works.

Monitor Cloud Bills: Set up alerts and check daily. That $3,400 surprise taught me paranoia pays off.

Cost Optimization is Creative: Batch sizing, delay tuning, and model selection all affect the balance between cost, quality, and speed.

What's Next?

This architecture adapts well for:

Product categorization at scale
Content moderation pipelines
Data enrichment workflows
Entity extraction from unstructured data

The key principles:

Choose the right database for reads vs writes
Use async strategically
Track progress for crash recovery
Monitor costs obsessively
Optimize prompts early

Final Thoughts

Processing 10 million records with AI seemed daunting at first. But by breaking it down into smart database choices, efficient async processing, robust error handling, and careful cost monitoring, it became reliable and cost-effective.

Total investment: $1,347

Total records: 10,000,000

Cost per record: $0.0001347

The rainy Sunday coffee call turned into a 14-day adventure in building a production-grade AI pipeline.

Github Repo: https://github.com/mstfbysl/ai-clickhouse-pipeline