u/orelrevivo

I’ve been thinking about something after reading a few discussions around AI assisted development and architecture.

A lot of teams use ADRs, architecture conventions, code owners, reviews, CI/CD, and internal processes to keep systems consistent.

But I’m curious how this works in practice when a team starts using AI coding tools more heavily.

Do teams usually have a way to automatically check whether a PR still follows the project’s architecture decisions and conventions?

For example, things like:

• a change going against an existing ADR
• new logic bypassing an agreed service boundary
• duplicated logic being added in another part of the system
• a new dependency or library being introduced without design review
• a feature implementation needing an architecture discussion before continuing
• a large AI assisted PR being technically correct but not fitting the system design

Or is this mostly still handled manually by senior engineers, architects, and reviewers?

I’ve been testing smaller LLMs lately, and I noticed something I’m trying to understand better.

They can do simple tasks pretty well, but when the task has multiple steps, they often start drifting from the original instruction or making assumptions.

Bigger models seem much better at staying consistent, but they cost a lot more.

For people who use smaller models seriously, do you think this is mostly a prompting problem, or just a limitation of the model itself?

What has actually helped you make them more reliable?

I’m curious how QA teams are handling code written with help from AI tools.

Not asking about AI replacing QA or anything like that. I’m more interested in practical testing impact.

For teams where developers use tools like Copilot, Cursor, Claude Code, Codex, etc.:

• Have you noticed any change in the types of bugs that reach QA?
• Does AI-generated code create more “looks correct but isn’t” issues?
• Are tests catching most of the problems, or do some issues still require human context?
• Has it changed how you think about test coverage?
• Do developers submit larger or less understood changes because AI helped write them?

I’m trying to understand whether AI assisted development has changed QA work in practice, or whether it’s mostly the same testing process as before.

I mostly work on projects alone, so I’m trying to understand this from a software architecture perspective. When a larger engineering team uses AI coding tools like Cursor, Copilot, Claude Code, Codex, etc., does it change how the architecture needs to be managed?

I’m not asking whether AI is good or bad. I’m more curious about the architecture side:

• Does AI generated code make it harder to keep the system consistent?
• Do teams see more duplicated logic or repeated patterns?
• Does it increase the need for clear ownership of modules/services?
• Are architecture docs/specs becoming more important?
• Can normal code review and CI/CD handle this well, or does AI coding create new problems?

I’m trying to understand whether AI coding changes the role of architecture in real teams, or if good architecture practices already solve most of it.

I mostly work on projects alone, so I’m trying to understand this from a computer science / software engineering perspective.

When a large team has many developers working on the same codebase at the same time, how are conflicts and broken changes usually handled?

I know Git can handle merge conflicts, and CI/CD can catch some broken builds, but I’m curious about the bigger picture:

• How do teams reduce the chance of two people changing the same logic in incompatible ways?
• What role do tests, code reviews, branching strategies, and ownership rules play?
• Are there common patterns or systems used to keep large codebases stable?
• Does AI-generated code change this problem in any meaningful way, or is it still handled by the same processes?

I’m not asking for career advice or tool recommendations. I’m trying to understand the engineering concepts behind how large teams keep software development organized and reliable.

I mostly work on projects alone, so I’m trying to understand how this works in real software engineering teams. A lot of developers now use AI coding tools like Cursor, Copilot, Claude Code, Codex, etc. individually. But I’m curious what happens when a whole team starts using them. Does it actually create new problems, or is it mostly handled by the same tools teams already use like Git, code reviews, CI/CD, Jira, and GitHub?

For example:

• Do AI generated changes make code review harder?
• Do teams care which tool generated the code, or only which developer committed it?
• Has AI increased the amount of low quality PRs?
• Are there problems with context getting lost between developers and AI tools?
• Do existing engineering processes already handle all of this well?

I’m not asking from a career perspective. I’m just trying to understand how AI coding changes real team workflows compared to solo development.

I've been building Falbor for the past 7 months, and I wanted to share it here because a big part of what i trying to solve is the gap between planning a product and actually building it.

The current workflow for building an idea often looks something like:

ChatGPT/Claude → Figma → research → Jira/Linear → AI coding tool → GitHub → back to ChatGPT → repeat

The problem isn't necessarily that these tools are bad. It's that the context gets lost between them.

You have to keep explaining the project, decisions, requirements, research, and what you already built to different tools.

Falbor tries to put this into one flow:

Idea → Deep discussion → Research → MVP Plan & Design → Build

The goal isn't just to be another AI website builder. The idea is that the AI already understands the reasoning and research behind the product before it starts building it.

We recently tested it with 10 users and got our first paying user. It's still very early, and we're mainly trying to learn what is actually useful and what isn't.

I'd genuinely like to hear from people here:

Do you think keeping all this context in one place would actually be useful when building and maintaining a product, or is separating these tools better for engineering workflows?

Website: https://falbor.xyz

GitHub: https://github.com/orelrevivo/TopUP

I'm mostly used to working alone,so I'm interested in something I've never really experienced myself

When you have a team with 20, 50, 100+ developers, and now a lot of people are also using AI coding tools like Cursor, Claude, Codex, Copilot, etc

• How do you know what everyone is currently working on?
• How do you know which AI tools/agents are making changes?
• What happens when two people/agents change the same part of the codebase?
• How do you find out when something broke because of a change made somewhere else?
• How do you know what needs a human review?
• Do you have one place where you can see all of this, or is it spread across GitHub, Jira/Linear, Slack, etc.?

I'm asking because I've mostly worked on projects alone, and I'm genuinely interested how this works when the team gets much bigger.

What's the most annoying part of managing a large engineering team that someone who works alone would probably never think about?

My name is Orel, and I’ve been working on Falbor for a long time. At first, I thought the idea was simple: build an AI website builder that could help people create websites faster. But the more I worked on it, the more I started asking myself a different question: Why would someone use my AI website builder instead of ChatGPT, Claude, Cursor, Lovable, or one of the many other tools that can already build things? I didn’t really have a good answer. So instead of just adding more features and pretending that everything was fine, I started thinking about what I actually wanted the product to do. I don’t want Falbor to just build your website. I want it to work with you while you’re trying to figure out whether your idea is even worth building. You can start with an idea, research it, think about competitors, figure out who your first users could be, decide what the smallest MVP should be, and then actually build it. And I’m not saying I solved this. I definitely haven’t. That’s actually one of the reasons I’m posting this. I’ve spent a lot of time building things that I thought were useful, only to realize later that I was solving the wrong problem. I’m still trying to understand what people actually need, what I’m doing wrong, what the AI is doing wrong, and what I should remove instead of what I should add. The current version of Falbor is basically my attempt to put that whole process in one place. There are different modes depending on what you’re trying to do: MVP: Start with research and then build the MVP. Troubleshoot: Add existing code, even if it wasn’t created with Falbor, and use AI skills to help find and fix the problem. Chat: Just discuss your project and ideas with the AI. There’s also a built-in database, a developer terminal, AI skills, MCP support, and different ways to publish your project: Publish with Falbor, Publish with Netlify, Publish with Vercel, Publish with GitHub. I currently support 19 different MCPs, and I’m working on adding more. Workflows for automation are also in beta, although they’re definitely not perfect yet. But honestly, the features aren’t the part I’m most interested in. I’m more interested in finding out if this idea actually makes sense. Maybe I’m still building the wrong thing. Maybe people don’t need another tool. Maybe the idea of having one place that helps you go from an idea → research → validation → MVP → users is actually useful. I don’t know yet. And I don’t want to pretend that I do. That’s why I’m sharing Falbor while I’m still working on it instead of waiting until I can say everything is perfect. If you try it, I’d genuinely like to know what feels useful, what feels pointless, what is confusing, and what you think I’m completely wrong about. There’s a feedback button at the bottom of the chat page too. One important thing: Falbor is currently designed for PC, so I recommend trying it on a computer for the best experience. This isn’t really a “look what I built” post. I’m trying to figure out what I should build next.

I'm curious which MCP servers developers actually find useful in their day-to-day workflow. Which ones have become essential for you, and what do you mainly use them for? I'd especially like to hear about MCPs you use for coding, development workflows, or AI-powered tools.

What do you use most for deploying websites you build with AI?

Vercel, Netlify, built-in deployment from tools like Base44 or Bolt, Cloudflare, or something else?

What makes you prefer it?

I'm testing a new AI tool that analyzes product ideas before you spend weeks building them.

It doesn't just tell you good idea or bad idea.

It researches:

existing competitors
whether the problem is real
who the first users should be
how to validate it
what the smallest MVP should look like

If you have an startup idea (it doesn't have to be original), reply below.

I'll pick a few and run a full analysis for free.

All I ask in return is one thing:
Tell me honestly if the analysis was actually useful or not.
**The research is done by AI on important things worth double-checking.