r/AskVibecoders

I vibecode with Claude Code. Supabase, Clerk, Shadcn, Stripe, Next.js, Vercel AI SDK, Mastra, Uploadthing. Most of the time Claude handles it fine.

But every few sessions the code just doesn't work. Looks clean, no errors, nothing happens. After hours I'd find out the API call doesn't exist anymore. Something changed in the docs and Claude didn't know.

THIS WAS MY SINGLE BIGGEST PROBLEM FOR OVER A YEAR. I almost quit projects over it. When you don't know how to code you can't tell if you messed up or if the docs changed. You just sit there thinking you suck.

Here's what most people don't get. This isn't a model problem. It's a data problem. APIs ship breaking changes every day. The model's training data is months behind. So it writes perfect looking code against endpoints that don't exist anymore.

The fix is simple. There's a CLI called nia-docs. One command and Claude can read any documentation site before writing code. Like actually read the real current docs, not guess from memory.

npx nia-docs setup | claude

CLAUDE.md is the first thing to set up. It loads every session automatically. Stack, naming conventions, rules you'd otherwise re-explain constantly. Write it once.

Context degrades past 90% capacity. Claude Code gets measurably worse, not just slower. Between tasks, run /clear. That's it.

Escape Escape opens a checkpoint menu that lets you rewind both code and conversation to any earlier state. If a session goes sideways, you're not stuck with it.

Worktrees keep main clean. claude -w feature-branch spins up an isolated git worktree. Claude Code commits and opens pull requests there. Your main branch stays untouched.

The -p flag makes Claude Code composable. Pipe logs, diffs, files directly in: git diff main | claude -p "review". Non-interactive, scriptable, works in any pipeline.

Effort level is not maxed by default. There are four: low, medium, high, max. On hard problems, pass --effort high explicitly.

Auto memory is on by default. Claude Code saves build commands, project quirks, and your preferences across sessions without you doing anything.

Set a spend cap for automated runs. --max-budget-usd 5.00 combined with --max-turns 3 prevents runaway costs in continuous integration and continuous delivery setups.

Path-scoped rules keep things tidy. Rules in .claude/rules/api.md only load when you're in API files. Test rules stay in test files. No bleedover between contexts.

Named sessions make parallel work manageable. claude -n "auth-refactor" names the session. claude -r "auth-refactor" picks it back up later. Multiple workstreams, no context collisions.

I used to copy entire documentation pages into my context window just so Claude wouldn't hallucinate API calls. Worked okay but ate up a ton of context and felt dumb.

Started using this tool called nia-docs that lets agents browse documentation through bash commands. You point it at any doc URL and it turns the site into something your agent can navigate with tree, cat, grep. One npx command, nothing to install.

The thing that got me is how simple it is. npx nia-docs setup | claude and you're done. Works with Claude Code, Cursor, Codex, pretty much anything.

Honestly surprised this isn't a built-in feature yet. Are you guys still doing the copy-paste thing or have you found better ways to handle this?

https://agentsearch.sh

Multi-Model Skill Sync: A Vibe-Coding Workflow Worth Sharing

One of the most frustrating parts of AI-directed development is unpredictability. You set an agent on a task, walk away, and come back to find it deleted something, rewrote something you didn't ask it to touch, or just went sideways in a way you didn't anticipate. And that costs real money both the tokens burned doing the wrong thing and the tokens burned fixing it.

You can't always know upfront how many tokens a task will consume, which makes runaway sessions even more painful.

Something I've been doing that's helped significantly — not sure how widely this is practiced — is a cross-model skill sync approach:

1. Per-project, per-model skill generation. For any active project, I go to each LLM I'm working with (Claude, GPT, Gemini, etc.) and have it generate a skill and agent definition for that project.
2. Centralize and review. Those skills come back to my primary model, which reviews and reconciles them. The repo becomes the single source of truth the skill tells each AI how to interact with it correctly, and agents handle the actual execution work.
3. Sync on significant changes. Whenever the project evolves meaningfully, I update the skill and reinstall it across every model in my stack.

The payoff: far less time spent re-explaining context, far fewer unintended changes, and dramatically fewer correction loops. Your models stay aligned to your project state rather than operating from stale or incomplete assumptions.

Hey everyone, I've been deep in the vibe coding flow lately (mostly using Cursor + Claude Code), and I wanted to ask what you guys are using for testing and debugging that isn't just more prompting? I'm looking for specific tools

Hey guys :-)

I've been active in Vibe Coding groups for months, scrolling through your projects/questions every day. At some point I realized — I'm losing track. Cool projects get buried, interesting stories disappear in the feed, and I'm jumping between Reddit, Facebook and X just to stay up to date.

Then I thought — why is there no platform just for us vibe coders?

So I built one. 😅

https://preview.redd.it/jth6aev840tg1.png?width=320&format=png&auto=webp&s=dd7fb57f54c8852d22703c2553783664980bfcfa

checkmyvibecode.com — a community platform where you can share your projects with the full story behind them. How long did you build? What did it cost? Which AI tools did you use? What was the idea behind it?

No hidden algorithms deciding what gets seen. Just builders supporting builders.

I'm still at the very beginning and looking for the first people to submit their projects. If you've built something with AI — no matter how small or unfinished it is — I'd love to see it on the platform.

From a Vibe Coder, for the Vibe Coders. :-)

Hey guys,


Solo founder here. I've been building Krafl-IO for some time now. 

It's an AI tool that writes LinkedIn posts in YOUR writing voice.


**The problem I was solving:**
 Every AI writing tool I tried produced the same generic LinkedIn voice. Posts read like ChatGPT wrote them. My connections could tell immediately.


**What I built:**


A 3-agent AI system:
1. **Voice Agent** — analyzes your past posts through a 3-step pipeline (not single-shot). Maps sentence structure, vocabulary choices, rhetorical patterns.

2. **Quality Agent** — scores every generated post 0-100 for authenticity. Below threshold = rejected and rewritten automatically.

3. **Generation Agent** — takes voice profile + quality requirements and produces posts that match your style.


**Key features:**
- 7 input types (paste a URL, upload a PDF, send a voice note, share a YouTube link, upload an image, paste text, or describe what you want)
- 1,252 hooks library for when you have writer's block
- 20 templates across 5 categories
- Telegram bot — I generate most of my posts from my phone
- Carousel creator for visual content
- Starts at $18/mo (competitors charge $40-60)


**Tech stack:**
 React 19 (SPA), Cloudflare Workers (edge-deployed), Supabase (auth + DB), Claude Haiku + GPT-4o (AI backbone), Razorpay (payments)


**What I learned building it:**


1. 
**Voice cloning is harder than it sounds.**
 Single-shot analysis (one prompt analyzing your posts) gives about 60% voice accuracy. A 3-step pipeline where each step builds on the previous gets to 90%+. The difference is dramatic.


2. 
**Telegram as a delivery channel is a massive differentiator.**
 Nobody else in the LinkedIn AI space offers this. And it turns out people love generating posts from their phone while commuting.


3. 
**Pre-rendering an SPA for SEO is non-trivial.**
 I use a Playwright post-build script, not SSR. It works surprisingly well for a React SPA — comparison pages are ranking.


4. 
**Pricing matters more than features.**
 When I tested at $29/mo, signups were okay. At $18/mo, the conversion rate jumped significantly. The LinkedIn AI tool market is price-sensitive.


5. 
**Building alone is lonely but fast.**
 No meetings, no consensus-building, no PRDs. Just build, ship, test, iterate. The tradeoff is you have nobody to catch your blind spots.


Live at kraflio.com. Also launched on Product Hunt today.


Free 7-day trial, no credit card required.


Would genuinely love feedback from this community — what would make this more useful for you and What am I missing?