Digital Sixsha

In late 2024, a solo indie developer quietly pushed a 47-line Python script to GitHub.

No fancy launch campaign. No YC backing. No elaborate startup infrastructure.

Just 47 lines of code.

Six months later, that same snippet was powering a SaaS product generating nearly $2 million in annualized revenue.

This isn’t some fairy-tale cooked up for Twitter likes. It’s a sharp reminder that we are now in an era where simplicity, when paired with AI’s explosive capabilities, can outperform teams of engineers and tens of thousands of lines of code.

So how did it happen? Let’s break down the story, the idea, the execution — and what this means for every developer today.

The Problem It Solved

Every SaaS founder knows the pain: customer support tickets.

They pile up in Zendesk, Intercom, Freshdesk, or email inboxes.

Support teams have to manually triage them: “Is this a bug? A feature request? A billing problem?”

This grunt work delays responses, frustrates customers, and drags down CSAT (Customer Satisfaction) scores.

The indie dev saw an opportunity hiding in plain sight.

What if AI could:

Summarize each incoming ticket so the support agent didn’t have to read walls of text.

Classify it automatically into one of three categories:

Bug

Feature Request

Billing Issue

This was not a new idea conceptually. But the key was execution. GPT-based APIs had become cheap and reliable enough that a single script could outperform existing, bloated enterprise “AI support automation” platforms.

The Original 47 Lines of Code

Here’s a simplified version of the original code that started it all:

import openai import json def classify_and_summarize(ticket_text): prompt = f""" Summarize the following support ticket in 2 sentences. Then classify it into one of these categories: - Bug - Feature Request - Billing Issue Ticket: {ticket_text} """ response = openai.ChatCompletion.create( model="gpt-4o-mini", messages=[{"role": "user", "content": prompt}], max_tokens=150 ) result = response['choices'][0]['message']['content'] return result # Example usage ticket = "I tried to pay my subscription but the payment keeps failing. Can you fix it?" print(classify_and_summarize(ticket))

That was it. A wrapper around the OpenAI API, packaged into a simple callable function.

47 lines.

No complex architecture. No databases. No machine learning pipelines. Just a clean bridge between support tickets → GPT → structured output.

From Script to Product

Of course, a GitHub repo doesn’t generate millions on its own. The genius was in how the developer turned those 47 lines into a productized service.

Here’s the step-by-step journey:

1. Packaging as a SaaS API

The developer wrapped the function in a lightweight FastAPI service and hosted it on a $5/month VPS.

Instead of requiring teams to copy-paste tickets, they could send support tickets programmatically and receive structured JSON summaries back.

2. Zapier/Slack Integrations

The breakthrough came when he built plug-and-play integrations with the tools support teams already used:

Zapier workflows

Slack notifications

Direct Zendesk/Intercom connectors

Suddenly, support managers didn’t need to be technical. They could just install the integration and watch their ticket triage vanish overnight.

3. Landing Page & Positioning

The landing page wasn’t fancy — just a clean headline:

“Automated Support Ticket Summaries & Classification – Powered by AI.”

Underneath, a demo GIF showed tickets being ingested and spit out as neat summaries with categories attached.

The pricing? $49/month per team, with higher tiers for volume.

4. Word-of-Mouth Growth

Support leaders started tweeting about how it cut their triage time by 80%. Indie hackers shared it on Product Hunt.

Within weeks, dozens of SaaS teams subscribed. Within months, hundreds.

The Numbers Behind the Growth

By month three: $40,000 MRR (monthly recurring revenue). By month six: $160,000 MRR (~$2M annualized run rate).

Here’s why the growth was so explosive:

Painkiller, not vitamin: Support ticket triage is a real, burning pain.

Immediate ROI: Saving hours of manual labor directly improved customer satisfaction scores.

Simple pricing: Flat monthly fee, no complex usage tiers initially.

Ridiculously lean: The “company” was literally one developer and a few server bills.

Why This Worked in 2024 (and Beyond)

The timing mattered. Here’s why those 47 lines worked in 2024 when they might not have in 2020:

AI Reliability GPT-4-level models were accurate enough that companies could trust them for real business workflows.

Cheap Infrastructure Cloud hosting and serverless APIs meant the entire backend cost less than $100/month at launch.

Market Maturity By 2024, SaaS founders were comfortable plugging AI into their workflows. The “trust barrier” had dropped.

Solo Dev Advantage A solo indie hacker could move faster than bloated incumbents building “AI support suites” with 20 engineers.

Technical Lessons from the 47 Lines

For developers eyeing similar opportunities, here are some takeaways:

Don’t over-engineer early. The first version was a single function. Get something working, then iterate.

Leverage APIs instead of reinventing. The magic wasn’t in building a custom LLM — it was in how the GPT API was applied.

Focus on integration. The “product” wasn’t the model. It was the ease of plugging it into Zendesk and Slack.

Measure business value, not just technical novelty. The script solved a painful, costly problem. That’s what made people pay.

The Human Side: Indie Hacker Mindset

What makes this story remarkable isn’t just the code — it’s the mindset.

Most developers, when they think of AI, dream of building the next ChatGPT. Huge visions, massive engineering projects.

But this indie hacker took the opposite approach: find a small, boring, painful problem and apply AI in the simplest possible way.

The result wasn’t glamorous. It wasn’t revolutionary. But it was valuable. And in business, value wins.

From Side Project to Serious Business

Six months in, the indie dev faced a new challenge: scale.

Server Scaling: Moving from a $5 VPS to autoscaling cloud infrastructure.

Team Growth: Hiring contractors to handle support and integration requests.

Compliance: Adding SOC-2 and GDPR compliance to win enterprise deals.

But here’s the kicker: even as the business grew, the core logic was still just those original 47 lines of Python. Everything else was scaffolding.

What This Means for Developers Today

So, what’s the lesson for you, reading this in 2025?

Small Code Can Have Big Impact You don’t need 10,000 lines of microservices to create value. Sometimes, 47 lines is enough.

AI is a Force Multiplier APIs like GPT mean you can focus on workflow and integration, not training models.

Execution > Ideas A hundred people thought of “AI support ticket triage.” One person executed simply, quickly, and effectively.

The Indie Hacker Era is Back The barrier to building profitable SaaS has never been lower. One laptop + one idea = potentially millions.

Final Thoughts

The story of the “47-line, $2M script” is more than just a feel-good indie hacker tale. It’s a snapshot of where we are today:

AI has democratized leverage.

Solo developers can rival startups with dozens of engineers.

Simplicity often beats complexity.

And maybe most importantly: the best AI businesses aren’t always about building new models. They’re about applying existing ones to real problems, in the leanest way possible.

If you’ve ever sat in a university lecture hall, you know there are certain moments that feel straight out of a movie. The professor strides in, assignment paper in hand, and announces:

“Class, you have three days to complete this. And let me warn you — no plagiarism, no copy-paste, and absolutely no AI-generated nonsense. Don’t think I won’t know. I’ll know.”

Now, in that moment, everyone pretends to scribble notes, but really, our brains are running wild: “Wait, can he actually tell? What if I just tweak it? What if I… never mind, I’ll Google it later.”

This was exactly the scene in one of my recent classes. The lecturer, early thirties, tech-savvy, the type who casually references TikTok trends and probably knows how to code in Python, delivered that very warning. And here’s the thing — when he said he could tell if you used ChatGPT, I believed him.

Not because he’s some AI-detecting superhero, but because he has that confidence of a man who has spent years reading through bland, recycled essays. He doesn’t need a detector — his eyes are trained like a hawk.

So, I decided to play it safe. Forget ChatGPT, forget shortcuts, I was going to do this assignment the old-fashioned way: research, caffeine, and maybe a breakdown or two.

But not my friend. Oh no. He had a masterplan.

The Friend With the “Foolproof” Hack

Let’s call him Mr. Slick. The minute class ended, he leaned over and whispered his secret strategy like he had just uncovered a lost chapter of Sun Tzu’s Art of War.

“Bro, don’t worry. I’ll just use ChatGPT and then ask it to rewrite everything in a more human tone. He’ll never know.”

At that moment, I swear I could hear Tony Stark’s famous eye roll echoing across the multiverse.

Like… really? That was the grand plan? To tell ChatGPT to not be ChatGPT? That’s the equivalent of trying to outsmart a metal detector by stuffing your pockets full of aluminum foil.

You see, what students often forget is that lecturers are not dumb. They know the tricks. Some of them even use the same tools we do. And if you think you can slip past by simply saying, “Rewrite this to sound human,” you’re basically serving the same soup in a slightly different bowl.

Spoiler alert: the taste is still the same.

Why Lecturers Can “Tell”

Now, let’s break down the psychology here. How exactly do lecturers know when you’ve used ChatGPT? Are they running your essays through secret FBI-level AI detectors? Sometimes yes, but often — no.

Overly Formal Sentences ChatGPT has this habit of making every essay sound like it’s being submitted to the United Nations. You ask it about climate change, and it writes: “In conclusion, the multifaceted challenges posed by environmental degradation necessitate a collaborative, multidimensional framework of global cooperation.” Bruh, normal students do not talk like that.

Lack of Personal Voice Real students slip up. They rant. They throw in awkward phrasing. Sometimes they even contradict themselves. AI, on the other hand, is clean, polished, and consistent. Ironically, that’s what makes it suspicious.

Too Much Breadth, Not Enough Depth ChatGPT can give you a bird’s-eye view of any topic, but it often fails to dig into the nitty-gritty details — like page numbers from readings, lecture notes, or inside jokes the professor made in class.

The “Detectable Glow” Some lecturers really do use AI detection software like Turnitin’s AI checker or GPTZero. And while these tools aren’t perfect, they’re often accurate enough to raise a red flag.

So when my lecturer said, “I’ll instantly know if you used ChatGPT,” he wasn’t bluffing. He had a combination of experience, intuition, and maybe a tool or two in his back pocket.

Why the “Make It More Human” Trick Fails

This is where my friend’s logic collapsed faster than a Jenga tower on shaky hands. He believed that simply asking ChatGPT to “sound more human” was the ultimate disguise.

But here’s the problem:

It’s still AI. Asking ChatGPT to pretend to be human is like asking Siri to develop sarcasm — it can try, but the uncanny valley vibes always remain.

Over-correction happens. Instead of sounding natural, the text swings to “forced casual.” You’ll get lines like: “Okay, so basically climate change is kinda bad, right? Like, no cap, the earth is heating up.” And unless your assignment was literally a TikTok script, you’re busted.

Detectors are evolving. AI detection tools are being updated constantly. They’re trained to catch these very attempts at masking.

In short: my friend’s plan was about as effective as hiding a Lamborghini under a blanket.

The Smarter (and Safer) Alternatives

Now, don’t get me wrong. I’m not here to preach the “never use AI” gospel. AI is a tool, and like any tool, it depends on how you use it. The real issue is when students use it as a crutch instead of a guide.

Here’s what actually works:

Use AI for Brainstorming, Not Copying Ask ChatGPT for an outline, key points, or references. Then do the writing yourself. This way, the voice is authentically yours.

Mix in Class-Specific Details Mention readings, lectures, or examples your professor gave. AI won’t know these, but you will — and that’s what makes your essay stand out.

Edit Heavily If you must use AI-generated drafts, rewrite them line by line in your own style. Think of it as clay — you shape it, don’t just submit the lump.

Keep Your Voice Don’t be afraid of imperfections. A slightly awkward sentence is more believable than a flawless, soulless paragraph.

Final Thoughts: The Myth of Being “Too Slick”

My lecturer’s warning wasn’t just about catching AI users. It was about discipline, honesty, and — let’s be real — a bit of pride. Professors don’t want to be outsmarted by a chatbot. And honestly, they usually aren’t.

So when my friend confidently announced his genius plan to “out-human” ChatGPT, all I could think was: this man is going to get roasted alive in the grading room.

At the end of the day, the real hack isn’t about tricking AI detectors. It’s about actually doing the work — or at least being smart enough to blend tools with your own effort.

What Docker Got Right

Docker revolutionized how we approached infrastructure. It swapped out traditional VMs for lightweight containers — portable, repeatable, and incredibly fast (at the time).

Between 2013 and 2018, Docker’s impact was undeniable:

Developers could Dockerize their apps and easily ship them.

CI/CD pipelines became simpler and more efficient.

Kubernetes adopted Docker as its default container runtime.

Nearly every developer had a Dockerfile in their project.

It worked — until it didn’t.

What Went Wrong?

1. The Docker Daemon Dilemma

Docker relies on a single, long-running process known as the Docker Daemon. This comes with a few big downsides:

Single Point of Failure: If the Daemon crashes, everything crashes.

Security Risks: Running the Daemon as root creates a major security vulnerability.

Debugging Nightmares: Killing the Daemon takes everything down, making debugging a pain.

Alternatives like containerd don’t have this central Daemon. They’re smaller, faster, and don’t require “God-mode” to operate.

Docker Run Exampledocker run nginx

With containerdctr run --rm docker.io/library/nginx:latest nginx /bin/sh

2. The Docker Desktop Paywall

Docker’s move to charge for Docker Desktop in enterprise environments was a major misstep. Teams started looking for alternatives like Podman, Rancher Desktop, and Colima. Developers don’t like surprises in their toolchain, and this was a big one.

3. Kubernetes Dropped Docker

Let’s clarify: Kubernetes didn’t abandon containers. It dropped Docker as a runtime, opting instead for containerd and CRI-O.

Why? Kubernetes prefers runtimes that natively implement the Container Runtime Interface (CRI). Docker doesn’t. The additional shim layers Docker needed to function with Kubernetes added unnecessary complexity.

UML Diagram: Container Lifecycle in Kubernetes Pre and Post Docker Deprecation

+------------------+ +------------------+ | kubelet | | kubelet | +--------+---------+ +--------+---------+ | | +-------v--------+ +---------v--------+ | Docker Daemon | | containerd / CRI-O| +-------+--------+ +---------+--------+ | | +----v-----+ +---v-----+ | container | | container | +----------+ +-----------+

4. Podman > Docker (for Most Use Cases)

Enter Podman — a more streamlined, feature-packed alternative to Docker:

Daemonless: No single long-running process.

Rootless: More secure.

Docker Command-Compatible: Just alias docker to podman, and you won’t even know the difference.

Docker Commanddocker build -t my-app .

Podman (same)podman build -t my-app .

You can even alias it for seamless integration:alias docker=podman

The result? Podman is faster, more secure, and just as easy to use.

What Should You Use Instead?

For Local Development: Try Podman, Colima, or NerdCTL. They’re lightweight, fast, and don’t require Docker Desktop.

For CI/CD Pipelines: Go with containerd. It’s more efficient, integrates better with Kubernetes, and avoids relying on a Daemon.

For Kubernetes Runtime: Choose CRI-O or containerd. They’re Kubernetes-native and don’t need extra layers.

For Desktop Users: Use Rancher Desktop (GUI) or Podman (CLI) as great alternatives.

Docker Hub? Yes, Docker Hub is still a major player in the world of image hosting. You can pull images from Docker Hub using tools like skopeo or ctr.

Example:skopeo copy docker://nginx:latest dir:/tmp/nginx

A Real-World Migration

We used Docker and Docker Compose for local development with multiple microservices. But we hit a few issues:

Inconsistent dev environments.

CI builds timing out due to Docker’s caching quirks.

Docker Desktop licensing problems.

What We Did:

Switched from Docker Compose to podman-compose.

Replaced Docker with nerdctl + containerd in CI.

Introduced Rancher Desktop for team members who needed a GUI.

Outcome?

CI builds were 30% faster.

No more licensing headaches.

The team adopted Podman with zero friction.

Conclusion

Docker isn’t dead, but it’s no longer the go-to solution for everything. Like jQuery — it was the best tool for a problem that’s now better solved by alternatives.

Introduction: How I Forgot How to Code

You ever sit in front of your screen and suddenly blank out on something as basic as a for-loop? Same. The moment I realized it, I was shocked. I was setting up a new machine, Copilot wasn’t logged in, and suddenly I was staring at my empty editor wondering: “Wait… how do I even write this loop again?”

That’s when it hit me. AI tools like GitHub Copilot, ChatGPT, Cursor, and CodeWhisperer have rewired how we code. We’re shipping faster than ever, but we’re also thinking less. Our brains are slowly outsourcing fundamentals to autocomplete. We’re becoming developers who can prompt, but can’t explain why the code actually works.

This post isn’t anti-AI. I use these tools daily. They save time, reduce boilerplate, and even help with tricky edge cases. But let’s be honest: there’s a hidden cost. We traded depth for speed, mastery for convenience.

The good news? We can rebuild. And this is how.

My Muscle Memory Is Dying, and AI Is the Knife

Coding used to be a craft. You repeated the same loops and functions until they lived in your fingers. Syntax flowed naturally. You could debug without Googling.

AI killed that repetition. And repetition is how mastery is built.

Today, Copilot finishes functions for us. ChatGPT explains design patterns instantly. Sounds great — until you’re offline, debugging a legacy system, or asked to whiteboard a solution from scratch. That’s when you realize your coding brain has atrophied.

Without struggle, there is no skill. And AI has stolen most of the struggle.

Faster Isn’t Better — It’s Just Faster

AI-assisted coding feels like magic. You type half a function name, and an entire working block appears. But here’s the trap:

We confuse shipping fast with understanding deeply.

We confuse syntax suggestions with system design skills.

We confuse short-term productivity with long-term growth.

Yes, you ship features quicker. But every “just works” line of code is a future bug waiting for maintenance. And when things break — and they will — you need understanding, not speed.

Debugging Is Where AI Breaks (And You Break With It)

Debugging is the crucible of software development. It’s where real skills are forged. AI can help, but it doesn’t see context the way a human does.

We’ve all seen Copilot hallucinations:

Inventing methods that don’t exist

Suggesting broken logic with confidence

Copying old Stack Overflow snippets with bugs intact

When AI fails, you fall back on your own mental model of how systems work. But if you never built that foundation, debugging becomes a nightmare.

You can’t debug what you never understood.

The Fall of Stack Overflow and the Rise of Shallow Answers

Remember when you had to fight your way through Stack Overflow threads? Painful, yes, but educational. You didn’t just get an answer — you got five different explanations, tradeoffs, and sometimes an entire argument in the comments.

Today? AI gives us instant snippets without discussion. We lose the “why” behind the code. We skip the community wisdom. And we stop developing the ability to ask better questions.

Stack Overflow made you think. AI makes you accept.

Speed Scales Flat, Understanding Compounds

At first, AI-powered devs look like superheroes. They ship features in hours. Meanwhile, old-school devs slog through documentation.

But over time, something interesting happens:

The AI-heavy dev plateaus.

The fundamentals-heavy dev compounds.

Why? Because understanding scales. Every bug you solve deepens your knowledge graph. Every system you debug makes the next one easier. Speed is a short-term advantage. Understanding is a long-term moat.

Rebuilding My Coding Brain: Practical Steps

Here’s how I’m retraining my brain to code like a developer again, not just a prompt engineer.

1. Do “AI-Free” Builds

Once a week, I force myself to build something without AI. No Copilot. No ChatGPT. Just me, docs, and Google. Painful? Absolutely. But the friction rebuilds muscle memory.

2. Rebuild From Scratch

Pick core systems and implement them manually.

Write a router in raw JavaScript

Build a toy authentication system

Parse JSON manually It won’t be pretty, but it will teach you why frameworks exist — and why AI scaffolds generate what they do.

3. Ask “Why” in Every Code Review

Stop treating reviews like checklist approvals. Ask why an approach was chosen, what tradeoffs exist, and how it might break. Treat every PR as a mini-mentorship.

4. Debug Without AI First

When something breaks, resist the urge to paste the error into ChatGPT. Spend 30 minutes debugging manually. Train your brain to trace, not just query.

5. Join Learning Communities

AI can’t replace human mentorship. Engage in Discord servers, open-source discussions, or niche dev forums where you’re forced to defend your ideas. Growth happens in community.

AI Isn’t the Villain — But It’s a Tempting Crutch

Let’s be clear: AI isn’t bad. It’s powerful. But it’s too easy to lean on. Used incorrectly, it’s like watching someone else lift weights and wondering why you’re not stronger.

The trick is to use AI as an accelerator, not a wheelchair. Let it scaffold, but revisit the code. Let it suggest, but interrogate the suggestions. Treat AI like a junior dev you don’t fully trust.

The Real Dev Flex? Depth, Not Speed

In 2025, AI can build landing pages, solve LeetCode, and scaffold microservices. So what’s left for us?

Depth.

The real flex isn’t how fast you ship. It’s how deeply you understand. The dev who can:

Design systems from first principles

Debug chaos no AI can untangle

Explain tradeoffs clearly to humans, not just compilers

That’s the dev who wins.

Conclusion: Don’t Become Human Autocomplete

We’re in a golden age of tools. But tools don’t make mastery. AI can autocomplete your code, but it can’t autocomplete your understanding.

If you want to future-proof your skills:

Build things from scratch

Struggle with debugging

Ask “why” until you annoy your teammates

Use AI — but never let it think for you

Because when the AI fails, and it will, the developers who can still think in code will be the ones everyone turns to.