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📢 CUSTOM SOFTWARE DEVELOPMENT
One Size Doesn't Fit Every Business
Many businesses force their operations to fit generic software.
Custom software reverses that approach.
Instead of changing your processes, technology adapts to your needs.
This improves efficiency, productivity, and scalability.
Technology should serve your business not control it.
What's one repetitive process you'd love to simplify?
#Techyground #SalesFunnels #LeadGeneration #BusinessGrowth #MarketingStrategy #DigitalMarketing #BusinessAutomation
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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AI allows us to write code faster. But that doesn’t necessarily mean we are delivering software faster.
This is one of the most interesting contradictions emerging in software development.
According to GitLab’s 2026 AI Accountability Report, conducted by The Harris Poll among 1,528 developers and technology decision-makers across six countries:
• 78% say developers are writing and committing code faster since adopting AI tools.
• 79% believe individual developer productivity has improved.
• However, 85% agree that the bottleneck has shifted from writing code to reviewing and validating it.
• Additionally, 82% believe AI-generated code could create a new form of technical debt that their organizations are not yet prepared to manage.
GitLab calls this the “AI Paradox”: individual productivity increases, but the overall software delivery process does not accelerate at the same pace.
Research from DORA, Google Cloud’s software delivery performance research program, points in a similar direction.
Its findings show that a 25% increase in AI adoption was associated with a 1.5% decrease in delivery throughput and a 7.2% decrease in delivery stability.
This does not mean that AI always harms software development. These results show an association, not a universal rule or a causal relationship that applies to every team.
The problem is not using AI.
The problem begins when an organization accelerates code generation while leaving the rest of its development process unchanged:
• Slow manual reviews.
• Limited automated testing.
• Ambiguous requirements.
• Oversized pull requests.
• Lack of architectural criteria.
• Poor traceability.
• Unclear ownership of AI-generated code.
If we double the speed of one stage without improving the next ones, we simply move the bottleneck somewhere else.
Manav Khurana, Chief Product and Marketing Officer at GitLab, summarized it clearly:
“Speed without control is a liability, not an advantage.”
As a full-stack developer, I believe AI can genuinely accelerate tasks such as creating components, writing documentation, exploring solutions, refactoring code, or generating an initial set of tests.
But producing more code should never be the main measure of success.
The result should be measured in software that:
• Solves a real need correctly.
• Remains maintainable over time.
• Includes testing and security controls.
• Can be understood by other developers.
• Reaches production without multiplying incidents.
AI does not eliminate the need for architecture, testing, and technical judgment. On the contrary, the faster we can generate code, the more important these elements become.
The next competitive advantage will not be generating more code with AI. It will be verifying it, governing it, and turning it into reliable software.
Has AI actually reduced delivery time for your team, or has it simply increased the amount of code waiting to be reviewed?
And remember: good software starts with good decisions. See you in the next one.
The distinction between writing code faster and delivering software faster is exactly where many teams get stuck. Which non-coding bottleneck do you think AI-assisted teams underestimate most?
Still onboarding clients manually? Here's how I automated the entire process in GoHighLevel and saved hours every week.
Lead fills a form → enters pipeline automatically.
Every step runs without me touching anything.
Automate it once and focus on what actually grows your business.
Want this built for your business? Comment ONBOARDING below and I’ll reach out directly.
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🚀 From writing code to building real products
In today's fast-moving technology world, software development is no longer just about writing code.
A successful product needs:
✅ Strong backend architecture
✅ Scalable APIs
✅ Intelligent AI integration
✅ Great user experience
✅ Reliable deployment
During my journey as a Backend Engineer, I have focused on building practical solutions using:
🐍 Python & Django
⚡ REST APIs
🧠 AI / RAG Systems
🐘 PostgreSQL
🐳 Docker & DevOps
Recently, I have been working on AI-powered systems, SaaS platforms, and business-focused applications where technology solves real-world problems.
My goal is simple:
Build software that is not only technically strong, but also valuable for users and businesses.
I enjoy connecting with founders, developers, and teams who are building the future.
Let's connect and create something meaningful together. 🚀
#AI #Django #Python #SaaS #BackendDevelopment #SoftwareEngineering
Backend architecture and AI integration get overlooked so often in favor of flashy UI, glad to see someone giving that side its own visual identity. What's the biggest mismatch you see between what clients think they need vs what actually gets built?
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