I found a setup that actually works well for my coding projects. I use Gemini 2.5 Pro as my project manager and prompt helper, while having it check the work from my coding assistant. Yes, there’s some manual copying between tools, but I tell Gemini to watch carefully for mistakes.
Gemini spots when the coding AI gives wrong information or creates fake test data. I have Gemini write better prompts and then review what the coding assistant produces.
I tried other setups like MCP and newer tools, but simple copy-paste between Gemini in my browser and the coding assistant in terminal works best. No more getting stuck with misleading test results or dummy data that doesn’t work.
You can connect Gemini to your GitHub repo, or use Gemini’s command line tool (though I avoid this in VS Code terminal since long text can cause crashes).
Since I’m not an experienced programmer, I can’t easily tell when the coding assistant makes errors. Having Gemini as a second pair of eyes really helps. What do you think about this approach?
Been running a three-way validation system for 8 months now and it’s saved my ass countless times.
I use GPT-4 for initial code generation, Claude for logic review, and Gemini for final security and performance checks. Each AI catches different mistakes - GPT-4 writes decent code but misses edge cases, Claude spots logical flaws but sometimes over-engineers, Gemini finds security holes the others miss.
You’re right that single AI validation isn’t enough. Even the best coding assistant has blind spots that match your own as a beginner.
One tweak that helped me - copy specific functions or modules through the validation chain instead of full code blocks. Makes it easier to track which AI flagged what issue.
I also keep a simple log of common failure patterns each AI exhibits. GPT-4 consistently messes up error handling in API calls, while Claude suggests overly complex solutions for simple problems.
Your manual process teaches pattern recognition faster than any automated setup. Keep doing it until you can spot the common mistakes yourself, then gradually reduce the validation steps.
Love the dual AI setup! But yeah, all that manual copying sucks.
I hit the same wall last year using multiple AI tools for code review and project management. Constantly switching between browser tabs and terminal, copying prompts back and forth - it was killing my productivity.
Automation fixed everything for me. Instead of shuttling data between Gemini and your coding assistant manually, I set up workflows that handle the handoffs automatically.
Now one AI generates code, automatically passes it to another for review, flags issues, sends feedback back for fixes, and only shows me the final verified output. No more copy-paste hell.
The game changer? Everything flows automatically while you still get that second opinion you want. You’re right about needing oversight, but the manual process will burn you out fast.
You can automate GitHub integration too - commits only happen after the review AI approves. Way cleaner than those command line crashes you mentioned.
Check out Latenode for this setup. It handles AI orchestration perfectly and kills all that manual copying: https://latenode.com
This dual verification hits home - AI has burned me way too many times. That manual workflow you described? It’s got hidden perks automated tools can’t match. When you manually review each AI exchange, you’re training yourself to spot failure patterns. I’ve seen certain prompts consistently break coding assistants - edge cases in data validation, dependency conflicts, stuff like that. Manually shepherding the conversation between Gemini and your coding tool teaches you to catch these red flags early. Yeah, the copy-paste friction sucks, but it forces you to actually read the output instead of blindly trusting it. I’ve caught subtle logic errors during those manual transfers that would’ve sailed right through an automated pipeline. Here’s something that might help - use a simple text editor as your staging area between tools. I keep a scratch file open where I paste, review, and tweak prompts before moving them along. Creates a paper trail of what worked and what didn’t, which becomes gold for refining your process.
Manual validation chains like yours work great but they’re productivity killers long term.
I built something similar last year - three different AI tools checking each other’s work, manually copying code between them. Caught tons of bugs and taught me patterns, but I was burning 4-5 hours daily just copying and coordinating.
The breakthrough? I automated the handoffs but kept the validation logic. Your Gemini oversight approach is spot on - just imagine it happening automatically in the background.
I set up workflows where the first AI writes code, automatically triggers the second for review, collects feedback, sends it back for fixes, and cycles until both agree. Then it pushes clean code to GitHub only after validation passes.
You keep that crucial second opinion without the manual labor. Plus you can add more validation steps - security checks, performance analysis, dependency verification - without extra copying.
Those pattern recognition benefits you mentioned? You still get them by reviewing automated validation reports, but now you’re analyzing 10x more code interactions.
For beginners especially, this removes the temptation to skip validation when you’re tired or rushing deadlines.
Latenode handles these AI orchestration workflows perfectly and eliminates all that tedious copying: https://latenode.com
I’ve been doing something similar for six months, but using ChatGPT instead of Gemini for oversight. It works great, especially while you’re still learning to spot bad code. Here’s what I learned the hard way - track which errors each AI makes. My coding assistant always screws up async/await and database connections, so I automatically double-check those sections now. Going through this manual process actually taught me to spot these issues faster than any automated tool could. For GitHub integration, I let the oversight AI write commit messages and comments instead of handling actual commits. You get the documentation benefits without those terminal crashes you mentioned. The copy-paste workflow isn’t pretty, but it builds solid code review habits that stick even when you’re coding alone.
your method’s solid for beginners. i did something similar with cursor + claude instead of gemini. copy-paste is annoying but you’re right - it beats fancy integrations that constantly break. pro tip: use notepad++ as a buffer between tools. makes it way easier to track changes when gemini spots mistakes.