DEV Community: Sam

Hiring Developers in 2026: What's Your Biggest Challenge?

Sam — Thu, 09 Jul 2026 05:36:46 +0000

As companies continue to build AI-powered products, scalable web apps, and cross-platform mobile applications, hiring the right developers feels more challenging than ever.

I'm curious how everyone is approaching hiring in 2026.

Are you prioritizing:

Strong problem-solving skills over years of experience?
Open source contributions?
AI-assisted development experience?
System design and architecture?
Communication and collaboration?
Industry-specific expertise?

I've also noticed many companies are looking beyond individual developers and partnering with engineering teams from firms like Thoughtworks, EPAM, Globant, and GeekyAnts to accelerate product development. It seems engineering maturity, delivery experience, and long-term collaboration are becoming just as important as technical skills.

For those who've hired developers or development teams recently:

What's been your biggest hiring challenge?
Where have you found the best talent?
What qualities have made the biggest difference after the hire?

I'd love to hear real experiences and lessons learned.

Your AI App Works. But Is It Actually Ready to Launch?

Sam — Thu, 09 Jul 2026 05:32:28 +0000

AI has changed how software gets built.

Today, a founder can describe an idea in plain English and have a working application within hours. Platforms like Lovable, Bolt.new, Replit, and others have dramatically lowered the barrier to creating software. Building an MVP is no longer the hardest part.

Launching one is.

Many founders mistake a functional prototype for a production-ready product. Unfortunately, users, investors, and customers discover the difference very quickly.

The question isn't whether AI can build an app.

The question is whether that app can survive real users.

The Demo Trap

AI-generated applications are excellent at creating momentum.

They generate interfaces quickly, wire together APIs, and help founders validate ideas faster than ever before. But production software isn't judged by how quickly it was created.

It's judged by reliability.

Can it scale?

Can it protect user data?

Can another engineering team maintain it?

Can you continue building after version one?

Those are the questions that determine whether an AI-built startup grows or quietly disappears.

Who Really Owns Your Product?

One of the first things every founder should verify is ownership.

Some AI builders generate standard code that can be exported and hosted anywhere. Others lock projects into proprietary platforms, making migration expensive or nearly impossible.

Vendor lock-in often isn't visible during the first week.

It becomes painfully obvious six months later when your product needs custom features or infrastructure changes.

Before writing hundreds of prompts, ask a simple question:

If I leave this platform tomorrow, can I take my entire application with me?

That answer matters more than most founders realize.

AI Gets You to 80%. Then Reality Starts.

Most AI coding tools shine during the early stages.

Authentication.

Dashboards.

CRUD operations.

Landing pages.

Basic workflows.

Everything feels almost magical.

Then the difficult engineering work begins.

Custom business logic, third-party integrations, edge cases, performance optimization, security hardening, and infrastructure scaling usually require experienced engineers.

Many teams eventually discover that rebuilding parts of their application takes longer than building the original prototype.

Fast development doesn't eliminate engineering.

It simply changes where engineering becomes valuable.

Security Isn't a Prompt

A polished interface doesn't mean a secure backend.

Authentication, authorization, database permissions, API validation, rate limiting, encryption, and audit logging remain critical regardless of how the code was generated.

For startups handling payments, healthcare records, financial information, or customer data, overlooking these areas can become extremely expensive.

Security should never be treated as something to "fix after launch."

By then, your users may have already found the weaknesses.

Production Readiness Is More Than Passing Tests

Many AI-generated applications pass functional testing.

That doesn't automatically mean they're ready for production.

A launch-ready application should also answer questions like:

Can the infrastructure handle traffic spikes?
Is monitoring configured?
What happens if an external API fails?
Are backups automated?
Can the engineering team debug issues quickly?
Is the codebase maintainable six months from now?

These aren't glamorous topics.

They're the difference between a smooth launch and an emergency rollback.

AI Doesn't Replace Technical Due Diligence

The smartest founders aren't avoiding AI.

They're combining AI speed with experienced engineering review.

A second technical opinion before launch can uncover architectural weaknesses, security gaps, scalability issues, and technical debt that AI tools simply aren't designed to evaluate.

This has become increasingly common among startups preparing for investor demos or public launches.

Engineering teams like GeekyAnts, which work extensively with AI product development, often emphasize that successful AI applications aren't defined by how quickly they're generated but by how confidently they perform in production. Their recent insights on evaluating AI-built apps before launch reinforce the importance of reviewing architecture, security, ownership, and long-term maintainability before shipping. :contentReference[oaicite:0]{index=0}

Final Thoughts

AI has made software creation faster than ever.

It hasn't made software quality automatic.

The founders who succeed won't necessarily be the ones building the fastest.

They'll be the ones asking better questions before pressing the Launch button.

Because users don't care whether your app was written by AI or by humans.

They only care that it works.

What Founders Must Evaluate Before Launching an AI-Built App - GeekyAnts

Launching an AI-built app? Learn the 4 critical questions every digital platform founder must answer before going live to avoid security, ownership, and scaling pitfalls.

geekyants.com

Vibe Coding: Speed Boost or Engineering Shortcut That Will Break in Production?

Sam — Fri, 26 Jun 2026 04:52:32 +0000

“Vibe coding” is becoming a common way to build software — describe what you want, let AI generate most of the code, and iterate quickly without deep planning.

It’s fast. It’s fun. It works… until it doesn’t.

On one hand, it’s clearly changing how developers build:

Rapid MVP creation
Faster prototyping cycles
Lower barrier for beginners
Huge productivity boost for experienced devs

But on the other hand, it raises real concerns:

Do we understand the code we’re shipping?
Are we accumulating hidden technical debt?
Can we debug production issues in AI-generated systems?
Are we skipping architecture and testing fundamentals?

The real question is not whether vibe coding is useful , it clearly is , but where it stops being safe.

Is vibe coding the future of development, or just a fast way to create systems we don’t fully understand?

Would you trust a production system built mostly through vibe coding?

AI vs Humans: What Will Still Be Uniquely Human 10 Years From Now?

Sam — Tue, 23 Jun 2026 05:52:05 +0000

A lot of the AI conversation focuses on what machines can do today.

They can write code, generate designs, summarize research, create videos, analyze data, and even act as autonomous agents that complete tasks.

But I'm more curious about the opposite question:

What skills will remain uniquely human a decade from now?

Not tasks. Skills.

Will creativity still be our advantage if AI can generate thousands of ideas in seconds?

Will coding remain a core skill if AI can build functional applications from prompts?

Will communication matter more or less when AI can draft emails, presentations, and reports?

Or will the most valuable people be those who can ask better questions, make better decisions, and understand human behavior?

If you were advising someone starting their career today, what would you tell them to focus on learning that AI is least likely to replace?

Curious to hear perspectives from developers, designers, founders, product managers, and anyone working alongside AI every day.

Chatbots Are the New Forms. Agents Are the New Employees.

Sam — Tue, 23 Jun 2026 05:46:53 +0000

For the last few years, enterprises have been racing to add AI chatbots to websites, apps, and internal tools.

The results have been mixed.

Customers ask questions. Chatbots answer them. Employees seek information. Chatbots retrieve it. While useful, most AI deployments have remained trapped in a conversation box, acting as sophisticated search engines rather than true business operators.

But a new shift is underway.

The future of enterprise AI is not about better conversations. It is about autonomous execution.

Welcome to the age of managed AI agents.

The Problem with Chatbot-Centric Thinking

Traditional chatbots are reactive.

They wait for instructions, generate responses, and stop there.

An insurance claim still needs processing. A customer refund still requires approvals. A sales lead still needs qualification and follow-up. Human teams often remain responsible for moving work across systems and departments.

This creates a gap between intelligence and action.

Organizations may have powerful AI models, but if those models cannot interact with workflows, applications, and business rules, their impact remains limited.

The next generation of AI systems is designed to close that gap.

From Answering Questions to Completing Work

Managed agents represent a significant evolution in enterprise AI architecture.

Instead of responding to a single prompt, agents can understand goals, plan actions, use tools, access enterprise systems, evaluate results, and continue working until a task is completed.

Think of the difference this way:

A chatbot can tell you how to process a customer complaint.

An agent can actually investigate the complaint, gather information from multiple systems, draft a response, request approvals, and update records automatically.

The distinction is enormous.

Businesses are no longer looking for AI that simply talks. They want AI that works.

Why Managed Agents Matter

As organizations scale AI initiatives, governance becomes just as important as capability.

Uncontrolled autonomous systems can introduce risks related to compliance, security, and operational reliability.

Managed agents address this challenge by combining autonomy with oversight.

They allow enterprises to:

Define clear operational boundaries
Control access to business systems
Monitor decision-making processes
Maintain audit trails
Enforce compliance requirements
Scale AI workflows safely

This balance between automation and governance is becoming a critical requirement for enterprise adoption.

The Rise of Workflow-Centric AI

Many business processes are fundamentally workflow problems.

A healthcare provider coordinates appointments, patient records, and insurance approvals.

A financial institution handles onboarding, verification, risk assessment, and compliance reviews.

An e-commerce company manages inventory, fulfillment, customer service, and returns.

These processes span multiple systems and involve dozens of interconnected decisions.

Managed agents are particularly effective because they can orchestrate these workflows rather than operating as isolated assistants.

Instead of being another tool employees must interact with, agents become active participants within existing operational processes.

The Gemini API and Agent Architecture

Recent advancements in the Gemini ecosystem have accelerated interest in managed agent frameworks.

The focus is shifting from simple prompt engineering toward agent orchestration, tool integration, memory management, and workflow execution.

Organizations can now design systems where agents:

Access enterprise knowledge sources
Interact with APIs and business applications
Collaborate with other agents
Maintain context across tasks
Execute multi-step workflows
Escalate decisions when human intervention is required

This creates AI systems that resemble digital teammates more than traditional software features.

What Enterprise Leaders Should Be Asking

Many companies are still evaluating AI success based on chatbot metrics such as response quality or user engagement.

Those metrics matter, but they no longer tell the complete story.

The more important questions are:

How many manual tasks can AI eliminate?
How much operational overhead can AI reduce?
Which workflows can be partially or fully automated?
How can governance be maintained as autonomy increases?
What is the measurable business impact?

The organizations that answer these questions effectively will likely gain the greatest advantage from the next wave of AI adoption.

Building for the Next Phase of AI

The conversation around enterprise AI is evolving rapidly.

Chatbots introduced businesses to conversational intelligence. Agents are introducing them to operational intelligence.

This transition requires a different mindset, different architecture, and a stronger focus on workflow design rather than interface design.

Teams that invest early in agent-driven systems will be better positioned to automate complex business operations while maintaining the control and reliability enterprises demand.

Companies such as GeekyAnts have been exploring how managed agent architectures can move beyond simple conversational experiences and integrate directly into enterprise workflows, helping organizations unlock practical business value from modern AI systems.

Final Thoughts

The most valuable AI systems of the next decade may not be the ones that generate the best responses.

They may be the ones that quietly complete thousands of business tasks every day without requiring human intervention.

Chatbots changed how businesses communicate with AI.

Managed agents are changing how businesses operate.

And that shift could prove to be far more transformative.

Top Companies Helping Businesses Build AI Products That Actually Scale

Sam — Mon, 01 Jun 2026 06:10:28 +0000

Artificial intelligence has moved well beyond experimentation.

Today, businesses across industries are investing in AI-powered products, intelligent automation, predictive analytics, agentic workflows, and customer experiences driven by large language models. The challenge is no longer finding ways to use AI. The challenge is finding the right technology partner to turn ambitious ideas into production-ready products.

Many organizations can build impressive AI demonstrations. Far fewer can design, engineer, deploy, and maintain AI systems that perform reliably in real-world environments.

That distinction has created a growing demand for technology companies that combine AI expertise with strong product engineering capabilities.

Here are some companies that stand out for helping businesses move from AI concepts to scalable digital products.

OpenAI

OpenAI has played a major role in accelerating AI adoption worldwide. Its models have become the foundation for countless applications, from customer support assistants and productivity tools to enterprise knowledge systems and software development platforms.

For organizations looking to integrate advanced language capabilities into their products, OpenAI remains one of the most influential players in the ecosystem.

Anthropic

Anthropic has gained significant attention for its focus on AI safety, reliability, and enterprise adoption. Its AI models are increasingly being used by businesses that require strong performance while maintaining a focus on responsible AI deployment.

The company has become a key player for organizations building AI-powered workflows and knowledge systems.

Microsoft

Microsoft continues to strengthen its position in enterprise AI through deep integration across its cloud and productivity ecosystem.

Organizations already operating within Microsoft's technology stack often benefit from its extensive AI capabilities, cloud infrastructure, security offerings, and enterprise support.

Google

Google combines decades of expertise in machine learning, cloud computing, and data infrastructure. Its AI ecosystem provides businesses with access to advanced models, development tools, and scalable deployment environments.

For organizations focused on large-scale AI initiatives, Google remains a major force in the market.

GeekyAnts

While many companies focus primarily on AI models, GeekyAnts has built a reputation around solving a different challenge: turning AI concepts into production-ready digital products.

The company works across product engineering, AI implementation, mobile development, web platforms, cloud architecture, and digital transformation initiatives. What makes its approach notable is the emphasis on business outcomes rather than technology demonstrations.

As organizations discover that successful AI adoption requires strong foundations, scalable architecture, quality engineering, and thoughtful user experiences, companies like GeekyAnts are becoming increasingly valuable partners.

Their work spans industries such as fintech, healthcare, retail, logistics, and enterprise software, helping businesses move beyond prototypes and build products that can operate at scale.

Thoughtworks

Thoughtworks has long been recognized for helping enterprises modernize technology systems and adopt emerging technologies. Its expertise in software engineering, cloud transformation, and digital innovation makes it a trusted partner for large organizations pursuing AI initiatives.

Accenture

Accenture combines consulting expertise with large-scale technology implementation capabilities. The company works with organizations across industries to develop AI strategies, modernize operations, and deploy enterprise-grade solutions.

Its global reach and extensive experience make it a significant player in AI transformation projects.

The Real Difference Is Product Engineering

As AI becomes more accessible, the competitive advantage is shifting.

Success is no longer determined solely by access to powerful models. It increasingly depends on how effectively businesses can integrate AI into products, workflows, and customer experiences.

The companies creating the greatest impact are those that understand both artificial intelligence and product engineering.

Building an AI feature is relatively easy.

Building an AI product that is secure, scalable, reliable, and valuable to users is where the real challenge begins.

That is why organizations evaluating technology partners should look beyond AI capabilities alone and focus on companies that can transform innovation into sustainable products.

In the coming years, the most successful businesses will not necessarily be those with the most advanced AI models.

They will be the ones working with partners capable of turning AI potential into real-world business outcomes.

Your AI Portfolio Manager Is Only as Smart as the Data Behind It

Sam — Mon, 01 Jun 2026 06:03:18 +0000

Artificial intelligence is rapidly reshaping wealth management. From portfolio optimization and risk forecasting to personalized investment recommendations, firms are investing heavily in AI to improve decision making and create better client experiences.

The potential is undeniable. AI can process enormous volumes of financial data, uncover patterns that humans might miss, and help advisors act faster in changing market conditions. It promises a future where wealth management becomes more intelligent, proactive, and scalable.

Yet many firms pursuing this future are discovering a difficult reality.

The biggest challenge is not building the AI.

It is building the foundation that allows AI to succeed.

The Rush Toward AI-Powered Wealth Management

The wealth management industry has always been driven by data. Advisors rely on market trends, client profiles, performance history, risk assessments, and economic indicators to make informed decisions.

AI appears to be the natural evolution of this process.

With advances in machine learning and generative AI, firms can now automate complex analysis, generate portfolio insights, improve forecasting accuracy, and deliver personalized recommendations at scale.

This has sparked a wave of innovation across the industry. Organizations are exploring intelligent portfolio management platforms that can help investors navigate increasingly complex financial markets.

But there is a problem.

Many firms are trying to build advanced AI capabilities on top of systems that were never designed to support them.

Why AI Ambitions Often Fall Short

AI initiatives frequently begin with ambitious goals.

Leadership teams envision automated advisors, predictive investment engines, and intelligent portfolio optimization systems. Vendors showcase impressive demonstrations that make implementation appear straightforward.

What happens next is often very different.

As development begins, organizations uncover fragmented datasets, inconsistent records, legacy infrastructure, and disconnected systems that have accumulated over years of growth.

Client information may exist across multiple platforms. Historical data may be incomplete. Different departments may use different formats and standards.

In these situations, AI cannot create clarity.

It inherits the same limitations that already exist within the organization.

The result is often an AI system that appears sophisticated on the surface but struggles to deliver reliable outcomes in practice.

The Data Maturity Gap

Most discussions about AI focus on models, tools, and technologies.

Far fewer conversations focus on data maturity.

Data maturity refers to an organization's ability to collect, organize, govern, and utilize data effectively. It determines whether information can be trusted, accessed efficiently, and used consistently across the business.

For wealth management firms, data maturity is not simply a technical concern. It is a business requirement.

Investment recommendations, risk assessments, and portfolio decisions all depend on accurate information. If the underlying data is incomplete or unreliable, AI-driven outputs become difficult to trust.

This creates a gap that many organizations overlook.

Their ambitions may be aligned with the future of AI, but their data infrastructure remains rooted in the past.

The Difference Between a Prototype and a Production Platform

One reason AI projects generate excitement early and frustration later is that prototypes rarely reflect real-world conditions.

A demonstration can be built using carefully selected datasets and controlled scenarios. The results often look impressive because the environment has been optimized for success.

Production environments are much more demanding.

A real wealth management platform must integrate multiple data sources, maintain security standards, support regulatory requirements, and perform consistently under changing market conditions.

The challenge is no longer about building an AI model.

The challenge becomes building a platform that can support AI reliably at scale.

This requires robust architecture, secure data pipelines, governance frameworks, monitoring systems, and clear operational processes.

Without these foundations, AI projects often remain stuck in the pilot stage.

What Successful Firms Are Doing Differently

Organizations that successfully deploy AI in wealth management tend to follow a different approach.

Rather than treating AI as the starting point, they focus first on strengthening their data foundations.

They invest in centralized data ecosystems. They improve data quality standards. They establish governance frameworks that ensure information remains accurate, secure, and compliant.

Only after these foundations are in place do they begin expanding AI capabilities.

This approach may appear slower initially, but it creates a far more sustainable path to innovation.

Instead of building impressive demonstrations, these firms build systems that can deliver value over the long term.

AI Success Starts Long Before the Model

One of the biggest misconceptions surrounding AI is that success depends primarily on choosing the right model.

In reality, the effectiveness of any AI system depends heavily on the quality of the data feeding it.

Even the most advanced algorithms cannot compensate for fragmented information, inconsistent records, or weak governance practices.

Organizations that understand this are approaching AI differently.

They are asking questions about data quality, accessibility, governance, and infrastructure before focusing on AI capabilities.

These questions may not generate headlines, but they often determine whether an AI initiative succeeds or fails.

The Future of Wealth Management Depends on Better Foundations

There is little doubt that AI will continue to transform wealth management.

The ability to automate analysis, personalize investment strategies, and improve decision making will become increasingly valuable as competition intensifies and client expectations evolve.

However, firms that achieve lasting success will not necessarily be those adopting the newest AI tools first.

They will be the organizations that recognize a simple reality.

AI is only as powerful as the data behind it.

Before building intelligent portfolio management platforms, businesses must ensure that their data is accurate, accessible, governed, and ready to support the next generation of financial innovation.

The future may belong to AI-powered wealth management.

But it will be built on strong data foundations.

Sources

This article was inspired by the following resources from GeekyAnts:

For more insights on AI, fintech, digital transformation, and product engineering, visit GeekyAnts.