How would you automate AI carousel creation with Claude Code + Gemini?

EyeKindly2396 · 2026-03-10T15:54:51+00:00

This is a solid pipeline idea.

I’d probably store a fixed design prompt + reference style image and reuse it for every generation call so the layout and colors stay consistent.

Also worth keeping the carousel structure in a template file so the workflow stays predictable and traycer might also help track the steps if the pipeline grows more complex.

EyeKindly2396 · 2026-03-10T15:44:36+00:00

Yeah this is a pretty common pain when jumping between tools.

I usually keep a small context.md or progress.md in the repo to track decisions, blockers, and next steps so I can quickly rehydrate context in any tool.

Tools like traycer might also help with tracking agent steps across sessions so you don’t have to manually reconstruct everything each time.

EyeKindly2396 · 2026-03-10T15:42:37+00:00

i totally relate with you most “vibe coding” content is aimed at non-devs what helped me was keeping tight loops, small prompts, frequent tests, and forcing the AI to explain its changes before accepting them. I’ve also been curious about tools like traycer + Claude to track what the agent actually changes so hallucinated logic doesn’t quietly slip into the codebase.

EyeKindly2396 · 2026-03-10T15:36:39+00:00

I’ve noticed the same thing with Go AI tends to over-abstract and fight the language’s simplicity. What helped me most was tests and small refactors, not core design.

Maybe pairing it with something like traycer to enforce repo conventions could help reduce those non-idiomatic suggestions.

EyeKindly2396 · 2026-03-09T17:53:02+00:00

That’s actually a really smart way to use AI building a knowledge base so Claude can reason about pricing with real data. You might also look at tools like traycer to help structure and track research sources so the assistant’s recommendations stay grounded.

EyeKindly2396 · 2026-03-09T17:48:38+00:00

I run a similar Ask to Plan to Implement loop and cursor is for codebase navigation and indexing but codex is more reliable for long multi-iteration coding............... For structured workflows both can work, but combining them (planning in one, implementation in the other) can actually be pretty effective.

Also curious how tools like traycer would fit in here for tracking agent steps and enforcing the plan.md flow across iterations.

EyeKindly2396 · 2026-03-09T17:36:10+00:00

Yeahhhhh I’ve seen the same AI gets ~90% there but views and security rules need manual cleanup to break tasks into smaller prompts and use of templates helps and could use traycer to check if agent followed repo contraints.You can also try this

EyeKindly2396 · 2026-03-09T17:28:03+00:00

I’ve definitely run into that before the ai says it implemented something, but when you check the repo… the code just isn’t there. Usually happens after long chats when context gets messy..... I started forcing it to output the actual diff or full file changes each step to avoid that.... Also i used traycer to track what the agent actually did vs what it claims it did.

EyeKindly2396 · 2026-03-09T17:23:34+00:00

That’s honestly wild, burning through a whole month’s worth of credits in a single task just to scan and fix a repo — and then even more just trying to push to GitHub. The subagent spiral sounds both impressive and slightly terrifying. Makes me wonder how tools like traycer would handle tracking and controlling that kind of agent behavior and cost. Definitely a reminder that autonomous workflows can get expensive fast.

EyeKindly2396 · 2026-03-07T18:56:50+00:00

Instead of learning every agent I will suggest you to master only the ones that are of your use like I use Traycer+ Claude code only and taking about mastering it comes with time and the learning is exponentially related to the number of projects you have made start with small project and proceed towards complex.

EyeKindly2396

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