buzzbyjool

How AI Helped Me Build a Product, Shape a Business Idea, and Write Its Own Code Two years ago, I was running a consulting firm in Luxembourg. After selling my previous telecom company following the tragic loss of both my co-founders — one to COVID, the other to cancer — I found myself managing consultants while trying to rebuild something meaningful. I never expected LinkedIn would become part of that journey. Running a B2B consulting company in 2022 meant one thing: you had to maintain a presence on LinkedIn. Weekly posts. Thought leadership. Showing activity. Sounding smart. All while managing operations and clients. I couldn't keep up. Worse: I didn't want to. It felt like a second job on top of my actual job. So I started looking for shortcuts. Initially, I just scraped article summaries and formatted posts with Make.com. Then I began incorporating analytics, running content through OpenAI to summarize blogs, create hooks, and adjust tone. But it was clunky. Just a patchwork of automation. No interface. All duct tape and digital string. That's when it hit me. If I could automate LinkedIn for myself, maybe others needed this too. Not with generic templates or obviously AI-generated content. I wanted to build something that could: Analyze a company's website or product documentation Capture the brand's authentic voice Create consistent content across multiple platforms Generate complete visuals (banners, logos, CTAs) without Canva hacks Let users preview posts or publish automatically All in one place, no technical expertise required That's how Linkeme.ai was born. I rebuilt everything using Bolt.new for the interface and Make.com for backend automation. Under the hood, we integrated: GPT-4.1 and Claude for content and semantic understanding Internal ranking systems to select the best posts A CTA engine adjusting text length and tone for each platform An LLM-based visual system automatically creating branded illustrations Publishing capabilities for LinkedIn, Twitter, Facebook, and Instagram requiring zero human intervention That was version 1 — when I realized this wasn't just automation, it was a genuine product. The workflows were still cobbled together, but they worked. And people were willing to pay for it. Then came version 2. Today, Linkeme runs entirely on AWS. We've migrated to a serverless architecture using Lambda and Step Functions to handle scaling. The logic now runs through Cline (our internal dev tool using LLM agents in VS Code), with workflows modeled as AI-defined services. We've replaced most of Make.com's business logic and now operate a system processing thousands of posts daily, with complete retry capabilities and audit trails. It wasn't just built with AI. It was built by AI. From conceptualizing the product to prototyping to writing the first infrastructure code — the LLMs weren't assistants. They were partners. That's what amazes me most. AI didn't just generate content for Linkeme. It helped envision the product. It helped build the pipelines. It helped design the workflows. And now, it runs the entire system end-to-end. This wasn't "AI as a feature." This was AI driving the entire development cycle. And the craziest part? It all started because I couldn't find time to write LinkedIn posts.

The “shared context layer” is essentially a lightweight memory and coordination layer that persists project state, intermediate decisions, and validated outputs. It’s not a traditional vector store or RAG setup. Instead, we use: • A Redis-backed scratchpad with typed slots for inputs, constraints, decisions, outputs, and feedback • An MCP (Model Context Protocol) template that defines what agents should expect, expose, and inherit • Each agent works statelessly, but gets a structured payload that includes relevant validated history, filtered to reduce noise

Agents don’t have full access to each other’s output logs (too much context = hallucination risk). Instead, each one produces an “artifact” + optional feedback object. These go into the shared layer, and the orchestrator decides what the next agent should receive and in what form.

We don’t run anything locally (yet). It’s all API-based for now, with orchestration handled in a containerized layer. That will probably evolve if we scale into more sensitive verticals.

Hope that helps clarify. Happy to dig deeper if you want building something similar.

“Leveled up” is subjective, yes. What I meant more precisely is this: the devs who stayed stopped spending time on tasks like writing boilerplate logic or tweaking form validation, and started focusing on higher-order thinking — designing agent workflows, debugging reasoning paths, writing specs that are machine-parsable, and thinking in systems rather than syntax. That shift, in terms of skill depth and adaptability, is something I genuinely view as a level-up. But I agree, it’s through the lens of our environment.

And yes — absolutely fair on the selection bias. When I say “no one wants to go back,” I don’t mean it as proof the approach is universally better. It’s just true for our current team, within the culture and processes we’ve chosen to embrace. Those who didn’t align with this way of working left early — and I don’t hold that against them.

So your comment is a valuable nuance: internal satisfaction is a necessary condition for success, but not a sufficient one. Our team’s enthusiasm is a sign that the model can work — not that it will for everyone.

Thanks for calling it out clearly.

The original post sparked a lot of questions — the most common being:

“Okay, but how did your developers react?”

Here’s a breakdown of what actually happened inside the team — who stayed, who didn’t, and what new skills emerged.

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Some embraced it. Some left. That’s okay.

When we committed to building with agents — not just using LLMs for autocompletion, but making them first-class executors of logic — not everyone was thrilled.

Some engineers were fascinated. They saw the shift coming and wanted to be ahead of it. They became architects of multi-agent workflows, prompt designers, QA strategists, validators.

Others didn’t want to work that way. They liked writing every line, owning every detail, and were (understandably) uncomfortable giving up control to a system that feels less deterministic.

They moved on. We didn’t push them.

Like every evolution in software tooling, this one came with a natural selection effect. Not better or worse. Just different skillsets, different energy.

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This isn’t no-code. It’s new-code.

Some assumed we were just automating CRUD. That’s not what happened.

The tools we use today — Claude 3.7, DeepSeek, bolt.new, role-based agents, memory stacks — aren’t trivial macros. They’re a new level of abstraction. They reason. They refactor. They test. They fail with style.

You don’t “ask the AI to do it.” You engineer constraints, context, fallbacks, tooling, and create robust systems through language.

At Easylab AI, we use context protocols, Redis-based memory layers, and model routing logic based on latency and task weight. It’s not less technical — it’s just built differently.

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Did their skills atrophy?

Actually, the opposite.

Sure, they’re not practicing DSA interview puzzles every day. But they’re building systems that can write tests, simulate failure, and self-correct.

They’re learning new skills you can’t yet Google: • Prompt minimalism • Agent composability • Multi-agent state consistency • Prompt-based debugging

They think more like staff engineers than syntax solvers.

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This is abstraction, not disappearance

The fear that “AI replaces engineering” misses the nuance.

This isn’t magic. It’s not cheating. It’s just abstraction — like every wave before: • Assembly to C • C to Python • Python to Terraform • Terraform to prompt-based execution

As Jensen Huang (NVIDIA CEO) said earlier this year:

“English is now the world’s most popular programming language.”

He’s not wrong. We’re just learning to write instructions that build systems — without the middle step of syntax.

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One more thing

Some developers left. Most who stayed leveled up. And today, no one wants to go back.

That tells me something: It’s not easier work. It’s better work.

Happy to answer more if folks are curious.

At first, there was some skepticism, and even a bit of anxiety. When we said, “We’re going full AI-assisted development,” the natural reaction was: “What does that mean for my skillset?”

But here’s what happened in practice:

Most of the repetitive tasks — CRUD, glue logic, API boilerplate — disappeared. Instead, devs started focusing on system design, agent orchestration, prompt engineering, constraint writing, testing strategy, and overall architecture.

And they’re thriving.

Nobody’s DSA muscles are atrophying — they’re just being used differently. If anything, they’ve gained new skills that aren’t widely available yet: how to design workflows with stochastic tools, how to debug agent behavior, how to build structured memory into LLM stacks. These are things you won’t find in textbooks yet, but they’re very real problems — and deeply technical.

And let’s be real: you don’t forget how to reverse a linked list just because you stopped manually writing route handlers for user creation.

In short: the devs that leaned into it have grown faster, not slower. And the ones who felt it wasn’t for them — they moved on. Which is fine.

Every shift in tooling brings a kind of Darwinian filtering. It’s not about better or worse, just about who’s willing to adapt to a new abstraction layer. And that’s always been part of how tech evolves.

The systems we’re building with LLMs (at Easylab AI) aren’t quick prototypes or business dashboards — they’re fully functioning SaaS platforms with scalable backends, custom business logic, API orchestration, test coverage, and long-term maintainability. The difference is: they’re authored through agents, not typed from scratch.

And to your point about design sensitivity and architecture becoming scarce — I couldn’t agree more.

When LLMs handle 80% of the syntactic work, what’s left is the hard stuff: system thinking, naming, sequencing, interfaces, data flows. That’s exactly where our team shifted: less “builders,” more “designers of builders.” It’s not easier work — it’s just a different level of abstraction.

Thanks for the reply, sincerely. It’s good to talk about this without defaulting to hype or fear.

We’re no longer in the realm of vague completions. Models like DeepSeek or Claude 3.7 aren’t just stochastic parrots — they operate like abstract interpreters, capable of holding internal representations of logic, system design, even refactoring strategies. And when you constrain them properly — through role separation, test feedback, context anchoring — they become extremely reliable. Not perfect, but engineerable.

What you describe as “managing” or “influencing” is, in our case, more like building structured interpreter stacks. We define agent roles, set execution patterns, log every decision, inject type-checked context. It’s messy, yes, but no more magical than compiling C into assembly. Just at a radically higher level of abstraction.

There’s a quote that captures this well. In March 2024, Jensen Huang (NVIDIA CEO) said:

“English is now the world’s most popular programming language.”

That’s not hyperbole. It reflects a shift in interface — not in intent. LLMs let us program systems using natural abstractions, while still exposing deterministic structure when designed that way.

To me, LLMs are not the death of engineering. They’re the beginning of a new kind. I truly believe the next 10 years will make most traditional programming languages obsolete. We’ll go from prompt → code to prompt → compiled binary, bypassing syntax entirely.