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Turn any domain into an ai agents’ inbox

Hacker News

Turn any domain into an ai agents’ inbox

Hey HN! I've been working on Inbound (inbound.new) - an email system that lets you programmatically manage email addresses and route them to AI agents, webhooks, or forward them anywhere. The core idea: instead of manually managing email addresses, you create them on-demand via API and route incoming emails to your AI agents or other automated systems. For AI Agents: Create support@yourdomain.com and emails get sent to your AI agent as webhooks. Your agent can reply using our SDK. Perfect for AI customer support or automated responses. For Email Forwarding: Set up hello@yourdomain.com to forward to your Gmail, or create email groups that forward to multiple people with custom rules. For Developers: Full TypeScript SDK with one-line email replies and webhook types. Handles all the AWS SES complexity for you. I built this because existing solutions were either too complex (raw AWS SES setup) or too limited (basic forwarding). I wanted something that made it trivial to connect emails to AI agents with great developer experience. Your AI agent gets clean webhook data with parsed emails, attachments, threading info, etc. It's been running in production for months, handling thousands of emails. The free tier is generous and no credit card required to try it. You can try the email forwarding at inbound.new, check out the SDK at @inboundemail/sdk on npm, or see working examples in the repo. Would love your feedback, especially if you're working on AI agents or email automation!

Visit productView on Hacker NewsJul 21, 2025
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, new · Missing: mac, macos, cursor
97%97% 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
91%91% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
32%32% 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, existing, ide · Missing: https docs, excited, just released
28%28% 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 · Missing: plus, platform, intuitive
22%22% 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
21%21% 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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