DEV Community: Aniket Sahu

What are the Top 5 Blockchain Development Companies in Singapore?

Aniket Sahu — Mon, 28 Sep 2026 17:46:21 +0000

Blockchain has moved well beyond cryptocurrency. Today, businesses are exploring blockchain for areas such as digital assets, smart contracts, tokenization, decentralized applications, identity, supply-chain traceability, and Web3 platforms.

But finding the right development partner can be surprisingly difficult. A company may be strong at smart contracts but have limited product-development experience, while another may be better suited to enterprise blockchain or full-stack Web3 development.

If you are looking for blockchain development services in Singapore, here are five companies worth researching, followed by Acelan Technologies as an additional option for businesses that are open to working with an India-based technology partner.

Note: This is not a ranking or endorsement. The companies below are a research shortlist based on publicly available company listings, services, and market presence.

1. ChainUp

ChainUp is a Singapore-headquartered blockchain technology provider offering infrastructure and ecosystem solutions. GoodFirms lists its capabilities across areas including digital-asset exchanges, wallets, NFT trading, liquidity, custody, security-token offerings, and Web3 infrastructure.

For businesses looking beyond a basic blockchain application and considering digital-asset infrastructure, ChainUp is a company worth exploring.

2. 0x Consulting Group

0x Consulting Group is another Singapore-based Web3-focused company. Its positioning is broader than pure software development, with services covering Web3 strategy, research, project launches, business development, and community-related activities. GoodFirms lists the company as Singapore-based and founded in 2019.

This makes it relevant for founders who are still working through the business and Web3 strategy behind a blockchain project.

3. NetSet Software

Blockchain Development Company - NetSet Software is listed in Singapore and focuses on software development, including blockchain-related solutions.

One thing I personally look for when evaluating blockchain vendors is whether they can handle more than the blockchain layer itself. A real product usually needs frontend development, backend systems, APIs, integrations, testing, and ongoing maintenance too.

That broader engineering capability can be important when blockchain is only one part of the product.

4. Hashlabs

Hashlabs is another Singapore-based technology company appearing in local business listings.

For companies comparing blockchain partners, it is worth looking beyond the technology name and asking practical questions: What exactly will be developed? Which blockchain network will be used? Who handles smart-contract testing? How will the application communicate with existing systems?

Those questions often matter more than simply choosing a company because it says “Web3” on its website.

5. SUBX

SUBX is a Singapore-based software company that can also be considered when researching technology partners in the local market.

For startups and businesses, the important thing is to understand the actual project fit before signing a development contract. A blockchain project may require completely different expertise depending on whether you are building a DeFi application, NFT marketplace, tokenization platform, enterprise solution, or blockchain-enabled SaaS product.

An Additional Option: Acelan Technologies

I would also add Acelan Technologies to the research list, particularly for companies that are open to working with an India-based technology partner rather than limiting their search to Singapore-based firms.

Acelan Technologies offers blockchain development covering enterprise blockchain, custom blockchain applications, Web3 development, blockchain integration, tokenization, blockchain gaming, NFT marketplaces, decentralized exchanges, and blockchain consulting. Its stated process covers ideation and research through strategy, design, development, testing, and launch.

What I find interesting about this approach is that blockchain isn't treated as an isolated technology. For many businesses, the actual requirement is a complete digital product where blockchain is one component of the architecture.

For example, a company building a tokenization platform may also need a web application, user authentication, APIs, dashboards, payment or wallet integration, and ongoing support. Having access to broader software-development capabilities can therefore be useful.

Acelan also positions itself around startups and established businesses and lists blockchain alongside its wider software, AI, MVP, app, UI/UX, DevOps, and e-commerce development services.

What Should You Check Before Hiring a Blockchain Development Company?

The company name is only the starting point. Before making a decision, I would check:

1. Relevant experience
Look for projects similar to what you actually want to build.

2. Blockchain expertise
Ask which networks, smart-contract languages, wallets, APIs, and infrastructure the team works with.

3. Security practices
Smart contracts can introduce serious security risks, so testing and auditing should be part of the discussion.

4. Product-development capability
A blockchain application still needs good UX, frontend engineering, backend systems, testing, and deployment.

5. Post-launch support
Blockchain products often require upgrades, monitoring, maintenance, and security improvements after launch.

Final Thoughts

There isn't one blockchain development company that will automatically be the right fit for every project.

The better approach is to start with your business requirement, define the blockchain component clearly, and then compare companies based on relevant experience, technical capabilities, development process, security practices, budget, and ongoing support.

Whether you choose a Singapore-based provider or an international development partner such as Acelan, the most important thing is finding a team that understands both the blockchain technology and the actual product you are trying to build.

Ad Analysis Tool: How to Find Winning Creatives Faster

Aniket Sahu — Sat, 26 Sep 2026 15:20:28 +0000

Running paid ads means you eventually face the same question:

Which creative is actually working, and why?

Finding the ad with the highest ROAS is usually easy. Your ad platform already shows you that.

The harder part is understanding what made that creative work.

Was it the hook? The first three seconds? The offer? The visual? The creator? The CTA? Or was it simply shown to a better audience?

That is where creative analysis becomes useful.

Why Finding Winning Creatives Is Harder Than It Looks

Most ad accounts have plenty of data.

You can see impressions, clicks, CTR, CPC, conversions, CPA and ROAS. But these numbers mostly tell you what happened.

They don't always explain why it happened.

Imagine you have 20 active creatives.

Three are generating most of your conversions. You could simply increase their budget and call them winners.

But that doesn't answer the more important question:

What do these three creatives have in common?

Maybe all three open with a problem statement. Maybe they use UGC. Maybe the product appears within the first few seconds. Maybe the winning factor is actually the offer or messaging.

Without looking deeper, you may copy the entire ad instead of learning from the part that actually worked.

What an Ad Analysis Tool Should Help You Understand

A useful ad analysis process should go beyond a simple leaderboard.

It should help you break a creative into different components.

1. The Hook

The opening seconds are important, especially for video ads.

Look at questions such as:

Does the ad start with a problem?
Is there a clear benefit?
Does the first frame create curiosity?
Does the viewer immediately understand who the ad is for?

If several winning ads use similar hooks, that can become a useful creative pattern.

2. The Visual

Two ads can sell the same product but look completely different.

Look for patterns in:

Product demonstrations
UGC
Lifestyle footage
Talking-head videos
Before-and-after visuals
Product close-ups
Text overlays

The goal isn't to assume that one format is always better.

It's to understand what appears to work for your particular audience and offer.

3. The Message

Sometimes the creative isn't winning because it looks better.

The message is simply clearer.

For example:

"Meet our new skincare formula."

versus:

"Still dealing with dry skin after trying five different products?"

The second message immediately identifies a problem.

Creative analysis can help you identify which problems, benefits, objections and angles repeatedly appear in your stronger ads.

Don't Confuse a Winning Ad With a Winning Idea

This is one of the biggest mistakes teams make.

An ad can perform well because of several factors at once.

Maybe the creative is strong, but the audience is also highly relevant. Maybe the offer is unusually attractive. Maybe the campaign had a lower CPA because of another change in the account.

That's why one ad shouldn't automatically become your next creative strategy.

Instead, look for patterns across multiple winners.

If five successful ads use different visuals but similar hooks, the hook may be worth testing again.

If several winners use different copy but the same product demonstration, the demonstration could be doing more work than the headline.

This is where ad creative analysis tools can be useful. They can help organize large numbers of creatives and identify recurring elements instead of forcing a marketer to inspect every ad manually.

Manual Analysis Still Has a Place

You don't necessarily need an expensive platform to start.

A simple spreadsheet can be enough.

Create columns for:

Creative name
Hook
Format
Angle
Offer
CTA
Creator
CTR
CPA
ROAS
Spend
Conversion rate

Then compare your best and worst creatives.

This simple exercise can reveal patterns that aren't obvious inside Ads Manager.

For example, you might discover that your highest-spending ads aren't necessarily your most efficient ads, or that certain creative formats consistently perform better for a particular audience.

Where AI Can Make the Process Faster

The biggest advantage of AI isn't simply producing more ads.

It can also help analyze the ads you already have.

Modern creative-analysis platforms can examine elements such as hooks, visuals, copy, CTAs, scenes and other creative attributes, then connect those elements with performance data.

That changes the workflow from:

Run ads → check numbers → pick winner

to:

Run ads → analyze creative elements → identify patterns → create new tests → measure again

That second loop is much more useful for creative teams.

The Real Goal Isn't to Find One Winner

A winning creative eventually gets tired.

Audience response changes. Competitors change their messaging. Frequency increases. New offers enter the market.

So the real goal shouldn't be:

"Find our best ad."

It should be:

"Understand why our best ads work so we can create the next generation of them."

That's a much more sustainable approach.

An ad analysis tool is valuable when it helps turn performance data into a creative learning system.

You don't just want to know which ad won.

You want to know what you can learn from the winner, what you should test next, and when the current approach is starting to lose momentum.

That's where creative analysis becomes more than reporting. It becomes part of the creative strategy.

How Can I Make Ad Creatives That Convert?

Aniket Sahu — Tue, 22 Sep 2026 15:43:27 +0000

Creating an ad creative that converts is not just about making a visually attractive image or video. A strong creative needs to grab attention quickly, communicate a clear benefit, and give the audience a reason to take action. Businesses should combine creative strategy, audience insights, testing, and performance data to improve results over time.

A good starting point is using Husnex AdNex to analyze ad creatives, identify performance issues, and understand where an advertisement may be losing potential customers.

1. Start With One Clear Audience

Before designing an ad, decide exactly who it is for. Different audiences respond to different problems, language, visuals, and offers.

Instead of creating a generic message for everyone, define:

Who is the customer?
What problem are they trying to solve?
What motivates them to act?
What objections might stop them?
What benefit matters most to them?

The more specific the audience, the easier it becomes to create relevant creative messaging.

2. Use Ad Creative Analysis Tools

Creative decisions should not rely entirely on guesswork. Ad creative analysis tools can help marketers examine hooks, messaging, visuals, calls to action, and other elements that may influence creative performance.

Analyzing existing ads can reveal patterns such as which hooks attract attention, which messages communicate value clearly, and which creative variations are underperforming. These insights can then be used to develop better versions instead of repeatedly creating ads from scratch.

3. Make the First Few Seconds Count

For video advertising, the opening moment is extremely important. People scroll quickly, so the ad should immediately give them a reason to continue watching.

Effective hooks can include:

A direct question
A common customer problem
A surprising statement
A specific result
A relatable situation
A quick demonstration

For example, instead of beginning with a company introduction, start with the problem your audience already recognizes.

4. Focus on the Customer's Problem

A creative becomes more persuasive when it speaks about the customer's situation rather than simply describing the company.

Instead of saying:

“We provide advanced marketing software.”

A stronger approach could focus on the problem:

“Still spending hours checking underperforming ads manually?”

This immediately connects the creative with a potential pain point.

5. Show the Product in Action

Whenever possible, demonstrate what the product actually does. Screenshots, product walkthroughs, before-and-after examples, dashboards, and short demonstrations can make an advertisement easier to understand.

Visual proof can also reduce uncertainty because viewers can see how the product fits into their workflow.

6. Create Multiple Creative Variations

One creative rarely provides enough information to understand what works. Create variations around different elements, such as:

Hooks
Headlines
Visual formats
Offers
CTAs
Customer pain points
Product benefits

For example, keep the same offer but test three different hooks. This makes it easier to identify which message is attracting attention.

7. Match the Creative to the Platform

An advertisement designed for Instagram may need a different format and presentation than one designed for another advertising platform.

Consider the platform's audience behavior, recommended formats, placement, video length, text visibility, and viewing environment. A creative should feel natural to the platform instead of looking like the same advertisement copied everywhere.

8. Test, Measure, and Improve

The creative process doesn't end when an ad is launched. Monitor performance and compare variations based on relevant metrics such as click-through rate, conversions, cost per result, and return on ad spend.

If an ad receives attention but few conversions, the problem may be the offer, landing page, or message rather than the visual itself. Looking at the complete customer journey helps avoid making incorrect conclusions from one metric.

Conclusion

High-converting ad creatives come from understanding the audience, communicating a clear problem and benefit, using strong hooks, and continuously testing different approaches. Instead of relying only on creative intuition, marketers can combine performance data with creative analysis to make better decisions.

The goal isn't simply to make an ad that looks good. It's to build a creative that gets attention, communicates value, earns trust, and moves the right audience toward action.

Where can I get the AIP-C01 exam dumps to pass the AWS Certified Generative AI Developer Professional certification?

Aniket Sahu — Sat, 19 Sep 2026 18:13:52 +0000

Preparing for an AWS certification can be confusing when there are so many practice resources online. Generative AI Development is also moving quickly, so developers preparing for the AWS Certified Generative AI Developer Professional exam need resources that are current and actually help them understand the technology.

One question that often comes up is: where can I get AIP-C01 exam dumps?

You will find plenty of websites offering AIP-C01 dumps, practice questions, and collections that claim to contain real exam questions. However, I would be careful with these resources. Some may be outdated, inaccurate, or based on unofficial material.

For a developer-focused certification, understanding the concepts is much more useful than memorizing answers.

Start With AWS Official Resources

The first place I would start is the official AWS certification material.

Go through the AIP-C01 exam guide and understand the areas covered in the certification. This gives you a much better idea of what you actually need to learn before spending time on practice tests.

Look at topics related to generative AI applications, foundation models, prompt engineering, application development, security, responsible AI, and AWS services used in generative AI solutions.

The official documentation is also useful when you want to understand how a particular AWS service works rather than just learning a definition.

Practice Questions Are Still Useful

Practice tests can be a good part of your preparation.

The important thing is how you use them.

Don't simply check whether your answer is right or wrong and move on. Try to understand why an answer is correct and why the other options don't fit the situation.

For example, if a question involves choosing an AWS service for a generative AI application, don't memorize the service name. Understand what that service does, when you would use it, and what alternatives are available.

That way, even if the actual exam presents the scenario differently, you can still reason through it.

Build Something Small

This is probably the part I would not skip if you are a developer.

Build a small generative AI project on AWS.

It doesn't need to be complicated. You could experiment with a simple chatbot, document Q&A application, RAG workflow, or another application that uses a foundation model.

While building it, pay attention to things like prompts, model selection, data retrieval, permissions, security, monitoring, latency, and cost.

Hands-on work helps connect the theory with something you can actually see and test.

What About Exam Dumps?

If your main goal is to find “100% real” AIP-C01 questions, you will probably find websites making those claims.

I wouldn't depend on them.

Apart from questions around whether leaked exam content is authorized, there is another practical problem: you don't know how accurate or current the material is.

AWS exams can change, and memorizing a fixed set of questions doesn't necessarily prepare you for scenario-based questions that test the same concept in a different way.

If you want to pass the certification and actually use the knowledge in your work, learning the concepts is a much better investment.

A Simple Preparation Plan

My approach would be:

Start with the official AIP-C01 exam guide.
Break the syllabus into individual topics.
Study AWS documentation for difficult areas.
Build a few small hands-on projects.
Take practice tests.
Review every incorrect answer.
Go back to weak topics and practice again.
Take the exam when you can consistently explain the concepts, not just remember answers.

Final Thoughts

There is nothing wrong with looking for practice questions when preparing for a certification. The problem starts when preparation becomes nothing more than memorizing dumps.

For an AWS generative AI certification, I'd focus on understanding how the services and development patterns work. That knowledge will help you not only with the AIP-C01 exam but also when you're actually building generative AI applications.

In the long run, knowing why something works is much more useful than remembering which option was correct in a practice question.