Validating "Scratch for AI agents" before building

Hacker News

I'm running a 2-week validation experiment before building my next product. Concept: Visual AI orchestration platform - Drag blocks (GPT-5.1, Claude Opus 4.5, Llama Maverick 4, etc.) - Connect them into workflows - Parallel execution (not sequential like Zapier) - Zero code required Why: n8n is too complex for non-devs. Zapier doesn't do parallel execution. Gap exists. The bet: IF 500 signups in 2 weeks → I build it (3 months). IF not → I pivot/kill it. Current: 26/500 (Day 2) Landing page: https://orchastra.org (mockups only - no product yet) Questions for HN: 1. Is "Scratch for AI" clear or confusing? 2. Is 2 weeks enough validation? 3. What would make this actually useful vs just another automation tool? Appreciate all brutal feedback.

Visit product View on Hacker NewsNov 25, 2025

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

87%87% 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

24%24% 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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