AgentMailr – Real email addresses for AI agents (OTP/2FA handling)

Hacker News

Been building agentic workflows and kept running into the same problem: 2FA walls. When an agent tries to sign up or log into a service that sends an email OTP, the options are terrible. You either dump the full email HTML into your LLM context (slow, expensive, fragile) or you write a polling loop that constantly checks for new emails. AgentMailr solves this with long-polling. You create a named inbox for your agent, then call waitForOtp() which hangs the HTTP request open until the email arrives and returns just the extracted code. const inbox = await client.createInbox({ "my-agent" }) // my-agent@agentmailr.com const otp = await inbox.waitForOtp({ timeout: 60000 }) // returns "847291" automatically Supports 4-digit, 6-digit, alphanumeric codes, and magic links. Built this because I needed it. Would love feedback especially from people running agents at scale.

Visit product View on Hacker NewsFeb 28, 2026

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

98%98% 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: supports · Missing: reddit linkedin, podcasting, created

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

41%41% 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

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: 000, io · Missing: https docs, excited, just released

23%23% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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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