Seconding the recommendation above! Honestly, Cursor is basically the industry standard right now for a reason—it just handles the 'big picture' stuff way better than a standard chat window. But a HUGE warning before you dive in: dont just trust it with massive refactors across your entire React frontend and Node.js backend simultaneously.

I tried doing a global state migration once and it highkey hallucinated some API endpoints that didnt even exist because the context got too cluttered... it was a nightmare to untangle. Basically, it works best if you modularize your files first. I usually compare Cursor with Windsurf for this. While Cursor feels more stable for logic, Windsurf is actually pretty insane at tracking flow across different folders. If youre doing deep architectural stuff, maybe try indexing your codebase first? It helps the reasoning a TON. gl with the SaaS build!

Respectfully, I'd consider another option before letting an AI rewrite your whole backend. Honestly, blindly trusting these tools for large-scale refactoring is super RISKY. I prefer a safety-first approach using specialized linting and strict TypeScript configs instead of just relying on context windows.

* AI often misses edge cases in complex state.
* Manual code reviews are literally non-negotiable for security.
* Automated tests catch things LLMs skip.

Basically, don't let the hype distract you from solid engineering standards. It works well if you're cautious!

Seconding the recommendation above! Honestly, Cursor is basically the industry standard right now for a reason—it just handles the 'big picture' stuff way better than a standard chat window. But a HUGE warning before you dive in: dont just trust it with massive refactors across your entire React frontend and Node.js backend simultaneously.

I tried doing a global state migration once and it highkey hallucinated some API endpoints that didnt even exist because the context got too cluttered... it was a nightmare to untangle. Basically, it works best if you modularize your files first. I usually compare Cursor with Windsurf for this. While Cursor feels more stable for logic, Windsurf is actually pretty insane at tracking flow across different folders. If youre doing deep architectural stuff, maybe try indexing your codebase first? It helps the reasoning a TON. gl with the SaaS build!

Bookmarked, thanks!

Seconding the recommendation above! Honestly, Cursor is basically the industry standard right now for a reason—it just handles the 'big picture' stuff way better than a standard chat window. But a HUGE warning before you dive in: dont just trust it with massive refactors across your entire React frontend and Node.js backend simultaneously.

I tried doing a global state migration once and it highkey hallucinated some API endpoints that didnt even exist because the context got too cluttered... it was a nightmare to untangle. Basically, it works best if you modularize your files first. I usually compare Cursor with Windsurf for this. While Cursor feels more stable for logic, Windsurf is actually pretty insane at tracking flow across different folders. If youre doing deep architectural stuff, maybe try indexing your codebase first? It helps the reasoning a TON. gl with the SaaS build!

Honestly, Cursor is the move, but I think you gotta pair it with Claude 3.5 Sonnet to really handle those complex React/Node architectures... I've heard Windsurf is insane for state management too tho! gl!

Respectfully, I'd consider another option before letting an AI rewrite your whole backend. Honestly, blindly trusting these tools for large-scale refactoring is super RISKY. I prefer a safety-first approach using specialized linting and strict TypeScript configs instead of just relying on context windows.

* AI often misses edge cases in complex state.
* Manual code reviews are literally non-negotiable for security.
* Automated tests catch things LLMs skip.

Basically, don't let the hype distract you from solid engineering standards. It works well if you're cautious!

Respectfully, I'd consider another option before committing to those big names. Everyone's hyped on the specialized IDEs, but I've been really happy just using standard extensions with custom API keys—it's way more cost-effective for a big project. A few questions tho:

* What's your actual budget for monthly AI tokens?
* Are you looking for an "all-in-one" tool or something that plugs into what you already use?

It might save you a ton long-term...

yo, i feel u on hitting that wall. seriously, i started out just like you, hacking together little scripts before trying to scale up to a full saas. i remember when i first tried a huge refactor on my backend and the ai basically started hallucinating functions that didnt even exist... it was a nightmare lol. honestly, i'm pretty happy with my current setup now, but it took some trial and error to get the 'reasoning' right.

before i dive into my diy approach though, i'm curious about two things:

1. are you more focused on finding an ai that can auto-generate the whole file structure at once, or are you looking for something to help you navigate and refactor the code you've already written?
2. also, what's the actual scale of the 'complex logic' you mentioned—like, are we talking heavy third-party api integrations or mostly just complex internal state stuff?

glad to help once i know a bit more!!

Seconding the recommendation above! Honestly, Cursor is basically the industry standard right now for a reason—it just handles the 'big picture' stuff way better than a standard chat window. But a HUGE warning before you dive in: dont just trust it with massive refactors across your entire React frontend and Node.js backend simultaneously.

I tried doing a global state migration once and it highkey hallucinated some API endpoints that didnt even exist because the context got too cluttered... it was a nightmare to untangle. Basically, it works best if you modularize your files first. I usually compare Cursor with Windsurf for this. While Cursor feels more stable for logic, Windsurf is actually pretty insane at tracking flow across different folders. If youre doing deep architectural stuff, maybe try indexing your codebase first? It helps the reasoning a TON. gl with the SaaS build!

+1

I've been going through this same struggle recently trying to get my first full-stack project off the ground. Honestly, I spent way too much money on credits last month just because I was pasting massive blocks of code over and over again... it really adds up when ur trying to fix one tiny bug in a five hundred line file. Here is what I've noticed about the budget side:
- Running things *locally* saves a ton of cash if you have the hardware.
- Breaking things into tiny pieces makes the bills way smaller.
- Sometimes just using the free tiers from the big names is enough if you're patient. Basically, I'd say just go with **OpenAI** or maybe **Google** for their huge context windows, you really can't go wrong with the big brands if you're careful about how much you're sending at once... I'm still trying to figure out the best way to not go broke while learning this stuff though lol. Does anyone else feel like the reasoning gets worse the more you spend?

Honestly, I totally agree with the point about reliability and engineering standards being more important than the specific model choice. I spent several months doing a deep dive into the market landscape before committing to my current setup for a fintech build, and honestly, the technical specs of these tools vary wildly under the hood. I found that most all-in-one brands fail at the architectural level because they rely on simple vector embeddings. When I was trying to refactor a complex state machine for a multi-tenant dashboard, the tool I had at the time completely missed the circular dependencies. It really highlighted the gap between tools that just do RAG and those that actually perform full AST parsing of the codebase. From my research, the most robust builds come from setups that prioritize:

• High-fidelity indexing that understands ur imports and exports
• Low-latency inference for real-time linting
• The ability to toggle between different reasoning engines depending on the task Basically, the market is split between flashy UI and deep logic. I learned the hard way that a pretty interface wont save you when ur backend logic starts hallucinating database schemas. Its all about how the tool manages that specific project metadata rather than just the size of the context window.

Honestly, I am totally with the folks saying engineering standards matter more than the specific model sometimes. If your not careful, AI will just churn out spaghetti that looks like it works until it doesnt. Reliability is the biggest hurdle when you scale past simple scripts. For a reliability-first workflow on a complex SaaS, Ive found a few things help keep the reasoning on track:

• Try using Aider in your terminal. Since it integrates directly with git, you can see every single line it changes and revert instantly if things go south. It makes the refactoring process way more transparent than a black-box chat window.
• Use Continue with a local setup for smaller helper functions. It keeps your architectural patterns consistent without the AI trying to reinvent the wheel every time you hit a token limit.
• Adopt a test-driven prompt style. I usually force the AI to write the Vitest or Jest tests for the Node backend first. If the AI can write a passing test for the logic it just created, the chances of a silent failure in your state management go way down.
• Look into Sourcegraph Cody if you have a massive existing codebase. Its context fetching is a bit more robust for enterprise-scale projects compared to the standard RAG most tools use. Basically, treat the AI like a junior dev that never sleeps. You still gotta be the senior architect and double check the logic or itll eventually wreck your backend logic.