AI is incredible for generating a quick prototype. You can 'vibe-code' a frontend and a few API r...

AI is incredible for generating a quick prototype. You can 'vibe-code' a frontend and a few API r...AI is incredible for generating a quick prototype. You can 'vibe-code' a frontend and a few API r...

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Paul Abib Camano

• Sep 1

AI is incredible for generating a quick prototype. You can 'vibe-code' a frontend and a few API routes in an afternoon.

But the moment you hit real production traffic, the cracks show. Deadlocks in PostgreSQL, unhandled race conditions, and unstructured data models will silently stall your growth.

Don't throw away your prototype. Let's refactor the data layer, build proper type safety, and containerize your infrastructure that it actually scales. Building an MVP is easy. Scaling a system takes architecture. 🚀

Nice John

• 2d

Your next big business idea might be sitting in your notes app right now.

Or maybe your business is wasting hours every week doing something that could be automated.

You don't always need a huge development team to fix either problem.

Sometimes you just need someone who can understand the problem and build the right solution.

At Optimum Studio, I build:

→ MVPs → SaaS products → AI-powered web apps → Internal business tools → Dashboards → Customer portals → Workflow automations → Custom web applications

I use AI-assisted development to move quickly from idea → prototype → working product, while focusing on usability, clean UI, functionality, and solving the actual problem.

And no, not every business needs a complicated $50k platform.

Sometimes it's a small web app or automation that saves your team hours of repetitive work every week.

If you've been saying:

“I'll build this someday.”

Or:

“There has to be a better way to do this.”

Maybe it's time to build it.

Send me the idea or the process that's slowing you down. Let's see what we can automate or build.

Optimum Studio

aiautomation webdevelopment mvpdevelopment

Abu Bakar

• 1d

Coding is getting faster. Reviewing is becoming the new bottleneck.

AI has changed how quickly we can produce code.

A task that used to take hours can now be drafted in minutes. Boilerplate, API routes, components, tests, refactors, AI can generate all of it surprisingly fast.

But that creates a new problem.

We can now generate code faster than we can properly review it.

And generated code often looks convincing.

It compiles. The structure looks clean. The naming looks professional. The logic looks reasonable at first glance.

But “looks correct” and “is production-ready” are two very different things.

Someone still has to ask:

Does this handle edge cases? Is the authentication actually secure? Could this create duplicate data? What happens when an API fails? Will this architecture still make sense six months from now?

This is why I think the developer skill that becomes more valuable with AI is not simply writing code faster.

It’s developing enough engineering judgment to quickly recognize when generated code is good, when it needs improvement, and when it should be rejected completely.

AI may reduce the time required to produce the first version.

But it can also increase the amount of code we need to understand, verify and maintain.

So perhaps the next productivity problem in software development isn’t:

“How do we generate more code?”

It’s:

“How do we confidently review everything we’re now able to generate?”

The bottleneck is moving.

From coding → to judgment.

What do you think, has AI reduced your workload, or just moved it from writing code to reviewing code?

#SoftwareEngineering #AI #Developers #Coding #AICoding #Engineering #FutureOfDevelopment

Mojtaba Karimiabdar

• 2d

Most AI automations don’t fail at the model. They fail the second time you run them.

The demo always looks great: feed it data, get a clean answer, everyone’s impressed. Then it goes live — and a webhook fires twice, a lead gets messaged twice, a row gets processed twice. Suddenly the “AI product” is an apology to a client.

After building a few of these in production, the lessons that actually mattered had nothing to do with prompts:

🔁 Idempotency first. A re-run should never double-act. Dedupe on a stable key, not on timing. ✅ Validate the model’s output before trusting it. An LLM will confidently return malformed JSON at 2am. Schema-check it, or it breaks downstream silently. 🧯 Guardrails over capability. Read-only tools, rate-limited API calls, and “ask a human before anything irreversible” beat a clever agent with full access. 🧮 Know your cost ceiling. Tokens, vector-DB RAM, and API quotas are the three bills that quietly scale with your success.

The hard part of AI work was never making it smart. It was making it boring and reliable enough that a client forgets it’s running.

❓ For those of you shipping AI into production: what’s the one failure that taught you the most — and would you catch it today?

rag llm AI Automation

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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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Nice John

• 2d

Your next big business idea might be sitting in your notes app right now.

Or maybe your business is wasting hours every week doing something that could be automated.

You don't always need a huge development team to fix either problem.

Sometimes you just need someone who can understand the problem and build the right solution.

At Optimum Studio, I build:

→ MVPs → SaaS products → AI-powered web apps → Internal business tools → Dashboards → Customer portals → Workflow automations → Custom web applications

I use AI-assisted development to move quickly from idea → prototype → working product, while focusing on usability, clean UI, functionality, and solving the actual problem.

And no, not every business needs a complicated $50k platform.

Sometimes it's a small web app or automation that saves your team hours of repetitive work every week.

If you've been saying:

“I'll build this someday.”

Or:

“There has to be a better way to do this.”

Maybe it's time to build it.

Send me the idea or the process that's slowing you down. Let's see what we can automate or build.

Optimum Studio

aiautomation webdevelopment mvpdevelopment

Abu Bakar

• 1d

Coding is getting faster. Reviewing is becoming the new bottleneck.

AI has changed how quickly we can produce code.

A task that used to take hours can now be drafted in minutes. Boilerplate, API routes, components, tests, refactors, AI can generate all of it surprisingly fast.

But that creates a new problem.

We can now generate code faster than we can properly review it.

And generated code often looks convincing.

It compiles. The structure looks clean. The naming looks professional. The logic looks reasonable at first glance.

But “looks correct” and “is production-ready” are two very different things.

Someone still has to ask:

Does this handle edge cases? Is the authentication actually secure? Could this create duplicate data? What happens when an API fails? Will this architecture still make sense six months from now?

This is why I think the developer skill that becomes more valuable with AI is not simply writing code faster.

It’s developing enough engineering judgment to quickly recognize when generated code is good, when it needs improvement, and when it should be rejected completely.

AI may reduce the time required to produce the first version.

But it can also increase the amount of code we need to understand, verify and maintain.

So perhaps the next productivity problem in software development isn’t:

“How do we generate more code?”

It’s:

“How do we confidently review everything we’re now able to generate?”

The bottleneck is moving.

From coding → to judgment.

What do you think, has AI reduced your workload, or just moved it from writing code to reviewing code?

#SoftwareEngineering #AI #Developers #Coding #AICoding #Engineering #FutureOfDevelopment

Mojtaba Karimiabdar

• 2d

Most AI automations don’t fail at the model. They fail the second time you run them.

After building a few of these in production, the lessons that actually mattered had nothing to do with prompts:

The hard part of AI work was never making it smart. It was making it boring and reliable enough that a client forgets it’s running.

❓ For those of you shipping AI into production: what’s the one failure that taught you the most — and would you catch it today?

rag llm AI Automation