Vibe Coding Revolutionizes Software Development

Unpopular opinion: Traditional coding, as we know it, is on borrowed time. We're used to writing line after line of code to tell a machine *how* to do something. But Agentic AI flips this on its head, focusing on *what* you want to achieve. 🤔 Imagine telling an AI, "Build me a web app that tracks my daily habits," and it handles the database, front-end, and deployment without you touching a single line of Python or JavaScript. This isn't just about AI writing snippets; it's about AI owning the entire task execution, from planning to debugging. It's a fundamental shift from instructing to delegating. This means the role of a developer isn't disappearing, but evolving. You become a strategist, an architect of intent, guiding intelligent agents rather than meticulously crafting every function. So, instead of mastering the next framework, perhaps it's time to master prompt engineering, system design, and the art of defining clear objectives for autonomous agents. The value shifts from execution to vision. What's your take on this shift? Are you ready to delegate the code? #AgenticAI #FutureOfCoding #SoftwareDevelopment #AIDevelopment

Coding has evolved…a lot. From flipping 0s and 1s, to writing in assembly, to low-level languages, to high-level abstractions… And now? We’re instructing computers in natural language. Think about that for a second. For the first time, we’re not just writing code for machines—we’re communicating intent with them. Tools powered by AI can now interpret what we mean, not just what we type. That changes everything. It shifts the role of a developer from just “writing code” to thinking clearly, designing systems, and asking the right questions. But like every major shift in tech, not everyone will adapt. Some will hold on to “how things used to be.” Others will lean in, learn, and evolve. And history has shown us how that ends. This doesn’t mean fundamentals don’t matter—they matter even more now. Because the better you understand systems, the better you can guide these tools. We didn’t just improve how we code. We changed how we communicate with machines. The question is—are you evolving with it?

The surprising full-circle moment in programming (and why your OOP mindset might be your biggest advantage in the AI era) We started with procedural code — step-by-step scripts telling the computer exactly what to do. Then came Object-Oriented Programming (OOP): encapsulation, inheritance, polymorphism, design patterns. Suddenly we were modeling real-world systems with clean, reusable, interacting objects. It felt like an art form. Along came Python. Simple, readable, batteries-included. A lot of us ditched heavy class hierarchies for quick scripts, list comprehensions, and powerful libraries. “Why over-engineer when I can ship in 20 lines?” OOP started feeling like a lost art for many. Then AI hit. Suddenly you didn’t even need to write much code at all. Prompt, generate, vibe-check the output. The “art” of programming seemed to be disappearing fast. But here’s the beautiful irony we’re living right now in 2026: We’re moving full-speed into agentic AI — autonomous agents that plan, use tools, reflect, and collaborate in teams. Building these systems isn’t about grinding out syntax anymore. It’s about: - Defining clear roles and responsibilities (sounds familiar?) - Designing modular components that interact cleanly - Managing state, memory, and orchestration flows - Handling composition of agents into reliable workflows (hierarchies, graphs, supervisor patterns) It’s OOP principles all over again — except the “objects” are now intelligent agents running on probabilistic LLMs, and the runtime is a framework like LangGraph, CrewAI, or AutoGen. The low-level coding is increasingly handled by AI. What matters more than ever is system design, orchestration, boundaries, and architectural thinking. If you only learned Python as a scripting tool and never developed that object-oriented/systems mindset… you might actually be at a disadvantage. The winners won’t be the fastest prompt engineers. They’ll be the ones who can think like architects: modeling complex interactions, ensuring reliability, and conducting symphonies of agents. The art didn’t die. It just leveled up. #AI #AgenticAI #SoftwareEngineering #OOP #Python

Programming vs. Coding? I was always wondering, how a system that does not #understand neither the prompt nor any #programming approaches or theoretical frameworks can program? Well, it looks like I am a member of a tiny group <2% (?) who program to solve new approaches (not found on GitHub), while vast majority of "programmers" just wire together APIs and Excel access functions? ... and then it hit me. AI replaces #coding while I always think about #programming (see https://lnkd.in/eGmBz-fp). "... coding is the step of writing the actual instructions in a programming language" can be also understood as implementing the resulting structure in the code (which can be also copied from somewhere). We used to call the places from where we copied "libraries" before stealing the code from others became SOTA! 🔥 #Coding is something that #AI can replace. We have seen also previous implementations of this in, e.g., Matlab-Simulink with graphical #programming (should be coding, the difference is that you did not speak but drew). Hey, higher programming #languages killed assembler coding, now AI abstracted it to "functional blocks" wired through the prompt instead of some C++ file. You still need a programmer. The examples presented in press are very simple processings requiring just 1 processing and 1 output module (visualization). ‼️ AI community, learn to understand keywords, before you kill professions that are still needed. Programmers are more than coders! I talked about this already years ago (https://lnkd.in/eyiJWUUJ). For example, navigation "accuracy" is confused in DL with statistical accuracy. DL community [in its infinite wisdom] switched accuracy to percent, which says nothing without the reference definition. https://lnkd.in/eTttdQ4w

I didn't know until recently that all the top coding agents edit code differently. Claude Code finds and replaces exact chunks of text, Codex CLI generates diffs, Cursor rewrites the entire file. We tested 7 approaches across 4 models on a suite including some very large files and complex edits. The best approach isn't currently used by any major tool. It's called AST edit. Instead of making the model reproduce chunks of old code to show where the edit goes, you just let it say “replace this function” or “add a method to this class” and a parser figures out the rest. Three out of four models score 100% with AST edit. Nothing else comes close except rewriting the entire file, which takes 18x more tokens and 12x more time. The other formats all break in the same way: the model has to copy text perfectly from a file it saw once, and on big files it just can't. This work was done for Geometric. We use evolutionary algorithms to optimise CUDA kernels in complicated DSLs. AST edit means models can edit kernels without having to copy hundreds of lines of unfamiliar syntax. Full results and code here: https://lnkd.in/gNUgMgqR

Yep. Best way to refactor is by using AST transforms that are deterministic. eslint fixers and jscodeshift. Roslyn analyzers. etc. I added a nice diff tool to my toolbox earlier this year, difft (Difftastic) uses semantic diff (via treesitter I believe). Much nicer having that as well on the cli with side by side diffs too

It's this kind of post that leaves me stunned that: a) AI coding tools don't make more use of LSP servers - CC supports it, but I've never seen it seriously reach for it rather than grep. b) LSP servers haven't started making edits much safer/more targeted. Y'all need a LISP. (h/t Peter (PEZ) Strömberg for the link)

Been using Cursor AI for my backend work recently, and honestly—it’s changed how I approach coding. Not in a “it writes everything for me” way. But in a “I don’t waste time on dumb friction anymore” way. A few things that actually stood out: It understands the context of the codebase, not just single files Refactoring feels less risky because you can ask before you break things Debugging is faster—you can literally walk through logic with AI Writing boilerplate is almost effortless now What I liked the most is how it fits into real workflows. Not just toy examples, but actual backend services, APIs, and messy production code. It’s not perfect. You still need to know what you’re doing. But if you do, it acts like a strong pair programmer who doesn’t get tired. For anyone working in backend or dealing with large codebases—this is worth trying. Curious to know—what AI tools are you all using in your dev workflow? #BackendDevelopment #AI #DeveloperTools #Productivity #SoftwareEngineering #time #GoLang #Python #Terraform #CICD #GraphQL #Dataengineer #DataEngineer #DataEngineering #DataEngineers

“Vibe coding” is a distraction. And honestly? It’s becoming the new form of procrastination. People are celebrating AI generating a landing page or a Python script… while completely missing the real shift happening underneath. This isn’t about coding faster. It’s about replacing the need to code at all in early-stage execution. We didn’t just get better tools. We got leverage. Right now, the gap isn’t between technical and non-technical people. It’s between: Those who still think AI is a tool… and those who use it as a team. Because here’s the uncomfortable truth: If you’re still using AI to: – write emails – generate snippets – “assist” your workflow You’re operating at 5% of what’s now possible. The new game is different. You don’t prompt. You orchestrate. You don’t build step by step. You define outcomes and let systems execute. And yes — it’s messy. Yes — it breaks. Yes — it still needs oversight. But so did every early technology that created massive asymmetry. This is one of them. The rise of multi-agent systems doesn’t just lower the barrier. It removes it. Which means: The advantage is no longer technical skill. It’s clarity of thinking. Because in a world where execution is cheap… Bad ideas scale faster too. So no — the question isn’t: “Can AI code?” The real question is: When execution is no longer your bottleneck… What exactly are you building?

AI can write code in seconds…  but most of us are still trapped in endless "vibe coding" loops — prompt, fix, re-prompt, repeat. There’s a better way. Spec-Driven Development (SDD) lets you stay in full control: - You write one clear specification  - AI implements it reliably  - You iterate by simply updating the spec (never fighting the code) Hence, I am announcing this new course https://lnkd.in/dg5z3B4F on SDD! In this practical, hands-on course you’ll go from: → Building CLI tools with precision  → Creating real web apps  → All the way to full-stack projects and production workflows -------- without coding!! ---------- 32 hours • Project-based • Python-focused (Yes!, we need you to review the AI generated code) If you’re a Python developer (2+ years exp) tired of babysitting AI tools, this course will change how you build software. DM me for more details about the course. 👉 https://lnkd.in/dg5z3B4F #SpecDrivenDevelopment #AIForDevelopers #FutureOfCoding #SDDMastery #VibeCoding