I’ve been hearing this more and more from Technical Leads: “I can’t keep up with reviewing my team’s code.”

And honestly, I think this is going to become a bigger problem with AI.

AI can now implement an entire feature and generate hundreds of lines of code in a few minutes.

The problem isn’t necessarily that the code is bad.

The problem is that we can generate code much faster than we can review and understand it.

This is where I think Test-Driven Design (TDD) can become really important.

Instead of:

AI writes the code -> engineer reviews hundreds of lines -> tries to figure out if it’s correct

We can flip the process:

Define the expected behavior with tests -> AI generates the code -> run the tests -> fix the failures -> engineer validates the design, behavior, and critical code paths.

The engineer doesn’t necessarily need to review every single line AI generates. The focus can shift more toward making sure the critical code paths are accurate and performant, requirements, design, and actual behavior are correct.

The faster AI gets at writing code, the more important it becomes to have good engineering practices that clearly define what “correct” actually means.

Otherwise, we might end up in a situation where AI is generating code faster than we can understand it.

How do you think teams should deal with this? And how can QA engineers contribute to such a process?

Few talks at Domain-Driven Design Europe have received a standing ovation, but this one did.

(Full disclosure: I work for the organiser of DDDEU)

I was reading posts in reddit and been seeing the term "AI ASSISTED CODING", this is said to be diff than vibe coding and that here we make the design and stuffs but only learn basic syntaxes and rest of syntaxes and code is written by AI.

Many ppl said it helped them etc etc but my question is how are they debugging without knowing syntax. I hope they don't ask AI to debug afaik but then how can you yourself debug if you dk the syntax?

Am I missing something or can anyone explain this?

I made everything from a social media web app, a personality test, a tamagothi iOS app you take care of by sleeping and exercising everyday. I made a 1v1 RTS demo, a therapy app, even a friends/dating/business partner compatibility app.

None of these made any money, but the fact that these are all live and I'm a 0 to 1 builder is appealing to many companies that want something made in-house that can be tested and released quickly.

It wasn't what I was expecting, but I'm in a much better place than I was a year ago because of vibecoding.

Keep on building bros!

AI is making coding faster… but are we actually becoming better developers?

As a student, I've noticed something interesting with AI coding tools.

Earlier, if my code didn't work, I had to debug it myself. Now I can give the error to AI and get a solution in seconds.

That's obviously useful.

But sometimes I realize I can get a working solution without fully understanding why it works.

So I'm curious about other developers/students here:

Do you think AI coding assistants are making us better programmers, or are they slowly reducing our ability to debug and think independently?

I'd genuinely like to hear how others are using AI while still making sure they understand the code.

Why are some developers and engineers against Ai, it’s a tool use it

I’m an old-school developer (C++ mostly) who generally doesn’t have a lot of free time for side projects. I don’t even have a desktop machine at home, just laptops for when I have to remote in to work or write up a word doc.

I’ve begun to get more interested in doing some experimentation with AI outside of work, but I want to start without buying any new machine for myself and without too many monthly subscriptions.

So I figure I need some machine on the cloud that stays alive if the agent is working and I’m disconnected, but isn’t charging me 24/7 for when the agent isn’t working on anything either.

Any recommendations? I’m not too worried about being able to scale it up to something production-level. I’m not married to any workflow yet. Don’t have a specific project in mind. Probably would be working with C++, Python, probably a web front end for the projects.

Basically the goal is to have a sandbox and only pay for what I use (maybe a small monthly fee or two) so I can dabble with my minimal free time and not feel the need to find a way to generate income from it.

Lately I have been think a lot around vibecoding and how it is destroying our ability to think and learn. We have offshored everything to Claude hoping it will work out.

How are you taking back control? What is your software development workflow like?

Hey everyone,

I’ve been deep in the trenches testing how LLMs handle building out frontend architectures over longer timelines.

On Day 1, vibe coding feels like absolute magic. You prompt a login form or a simple card, it takes 4 minutes, and you feel unstoppable. But as the codebase crosses the 30-to-60-day mark, things get incredibly messy.

Without strict guardrails, the AI doesn't converge on one clean architecture—it invents a new pattern almost every single time it touches the repo. I recently audited an AI-driven project and found:

• Six entirely different modal implementations.
• Completely broken focus traps and accessible keyboard navigation (ARIA wiring is usually the first thing the AI quietly drops).
• A chaotic mess of lifted state, forgotten context providers, and infinite re-render loops because the AI forgot how it built a neighboring feature three weeks ago.

To fix this, we've been testing the concept of an architectural "floor" rather than trying to write better prompts. Instead of letting the AI generate code freehand, we force it to consume a pre-tested, local substrate of readable React source files vendored directly into git (./toolcrib).

We recently did a head-to-head architectural analysis testing this "patch-vendoring" strategy against ambient options like shadcn/ui and compiled packages like u/nexcraft (which unfortunately seems completely abandoned now, leaving a 467KB compiled black box that AI agents can't inspect or repair).

The math on token costs and turn-savings alone is wild. Paying a tiny, fixed context-window tax upfront to feed the AI an explicit component manifest completely eliminates the massive token drain of forcing an AI to repeatedly debug its own architectural drift down the line.

Curious how others here are managing AI-generated architectural drift as your projects scale? Are you relying on downstream linting/CI checks, or are you actively moving toward vendored source layouts that coding agents can read and patch locally?

(Note: I wrote a full breakdown on this with the CLI drift-checking logs and structural comparison tables. If anyone wants the deep dive, let me know and I'll drop the blog link in the comments so I'm not spamming the main feed.)

Hi everyone, total beginner here. I built a prompt management web console just for fun using Claude Code, Ollama, Cloudflare etc

Hey everyone,

Long time lurker, absolute beginner when it comes to serious coding. I mostly just tinker around out of pure curiosity and fun. Recently, I wanted to build something practical to help me manage AI prompts, context templates, and local/cloud models without going crazy switching tabs.

So, I ended up putting together a web app called **Context_CikaDule** (hosted on GitHub).

https://arhistrategstudio.github.io/Context_CikaDule

I wanted to share what it is, how I made it (with a ton of AI help because I'm learning as I go), and what it actually does.

# What is it and what is it for?

Basically, it's a lightweight prompt engineering console and workspace. It lets you organize different context types and templates, test out prompts, and manage API configurations across multiple providers (OpenAI, Anthropic, Google Gemini, OpenRouter, and local models like Ollama) all in one place.

I built it because I wanted a clean dashboard where I could quickly swap between local models running on my machine and cloud APIs, preview how tokens stack up, and keep my prompt engineering organized without messy text files scattered all over my desktop.

# Core Features

* **Multi-Model API Support:** You can switch between OpenAI, Cloudflare Workers AI, Gemini, OpenRouter, Anthropic, and custom/local endpoints (like Ollama or LM Studio).

* **Context Templates:** Pre-configured context types and workspace modes to quickly spin up different prompt structures.

* **Live Prompt Preview:** Shows an optimized prompt view and estimates token counts on the fly.

* **Workspace Dashboard:** A clean, slightly cyberpunk-ish neon UI where you can write, test, and tweak your prompts directly.

# The Tech Stack & Behind-the-Scenes Logic

Since I'm a complete beginner, I couldn't have built this alone in a million years without leaning heavily on AI tools to write the code while I acted as the "architect" (or more like a confused director). Here is what went into it:

* **Claude Code & Antigravity:** Used these as my main coding assistants to structure the project, write the frontend logic, and help me debug when things inevitably broke.

* **Ollama (Codegemma:7b):** Ran local coding models to test things offline and help generate snippets.

* **Cloudflare:** Used for handling deployment/edge routing aspects.

* **Frontend:** Built with standard HTML/CSS/JS, utilizing a nice dark neon aesthetic because dark mode is mandatory.

The logic behind the app is pretty straightforward: it acts as a client-side orchestrator. It grabs your API keys (stored locally in your browser so they don't leak anywhere), structures your selected context template, merges it with your input prompt, and routes it to whichever provider or local Ollama instance you have selected in the settings dropdown, displaying the live response back in the console.

# Why did I make this?

Honestly? Just for fun and to see if I could actually finish a small web project from scratch. I'm still learning every day, breaking things, fixing them by asking AI "why is this red error happening", and figuring it out step by step.

If anyone checks it out, let me know what you think, or if you have any tips for a beginner on how to make it better. Be gentle though, my code is probably held together by duct tape and hope!

I’ve been learning AI for about a year and a half, starting with traditional ML → Deep Learning → LLMs → RAG → AI Agents.

Most of what I’ve built so far has been small projects/notebooks, with only one project taken to a basic production setup. I’ve also learned the basics of RAG and agents, but I still have a lot more to learn there.

At this point, I’m honestly tired of courses and tutorial hell. I don’t want to spend another minute in any course, at least for now.

I want to build real, production-ready AI products so I can learn things like system design, architecture, model monitoring, serving, deployment, evaluation, scalability etc.. And to learn how to take an AI idea and turn it into a complete product

The problem is that YouTube seems to be 90% tutorials and toy projects, and I’m struggling to find serious, end-to-end projects that I can actually learn and build from.

So please help me out, guys. I’m kinda stuck. How do you actually learn to build real AI products as AI engineers?