I replicated generic AI startups using n8n

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I recently released 20+ AI-focused, source-available, prompt-available, templates for free which I feel could be of great interest to this community. Note: If you don't use n8n, no worries; the ideas, approaches and logic are definitely transferrable to other implementations. You can find the public collection here: https://n8n.io/creators/jimleuk/ Inspiration for many of these templates have been from the flood of AI startups selling simple prompts and/or basic RAG apps. Some of my favourites include: - Automate Your RPF Process with OpenAI Assistants - Actioning Your Meeting Next Steps using Transcripts and AI - Handling Appointment Leads and Follow-up With Twilio, Cal.com and AI - Breakdown Documents into Study Notes using Templating MistralAI and Qdrant - Build Your Own Image Search Using AI Object Detection, CDN and ElasticSearch This is my small contribution to the emerging world of AI product dev - examples to hopefully inspire and towards making the leap! Would love any feedback and to connect with fellow devs working in with AI products.

Visit product View on Hacker NewsJul 23, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, openai, using · Missing: mac, agents, macos

90%90% 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

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: apps · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

15%15% 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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