Indx.sh – Directory of AI coding rules, MCP servers, and tool

Hacker News

I'm a UX designer turned self-taught developer. Built indx.sh because I got tired of the treasure hunt. Every time I needed a prompt for Cursor, Claude Code, or Windsurf, same loop: searching threads, watching videos, testing, breaking things. The answers existed — just buried across GitHub, Discord, and SEO spam. What it is: - AI coding prompts (Cursor, Claude Code, Windsurf, Copilot) - MCP servers (synced from official registry) - Agent skills (synced from Anthropic repo) - AI tools directory One-click copy, filter by language/framework, no signup. Why: Discovery is broken. Vibe coding is taking off, tools are exploding, but finding good prompts is exhausting. Context: Second project I've built at this level. Hadn't coded in over a decade before this. Also dogfooding a Next.js boilerplate I'm building called Fabrk. V1. Rough edges. Building in public. Looking for: - What's broken - Prompts/MCP servers to add - Honest feedback https://indx.sh Discord: https://discord.gg/cFXGDFqK

Visit product View on Hacker NewsJan 30, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, cursor, claude · Missing: mac, agents, macos

92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, ide · Missing: https docs, excited, just released

34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: video, answers · Missing: mobile apps, ios, personal

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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