LI

LINQ CLI – an iMessage API you can use from the command line

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LINQ CLI – an iMessage API you can use from the command line

Hey my name is Patrick, I’m a co-founder and CTO of Linq. We’re an API for sending and receiving iMessages (it does RCS/SMS too). It can do everything you can manually in iMessage (typing indicators, reactions, delivery emphasis, FindMy etc.) Our main customers are companies building conversational agents but we’re wanting to make it easier for developers to get started for free. To do that we built a CLI that lets you manage up to 20 contacts and gives you full API access for free. I’d love your feedback so we can keep improving it. Install via npm using: npm install -g @linqapp/cli Recently, I used the CLI to connect my Claude bot to WeWork & iMessage and haven’t had to use the WeWork app in a few weeks to book rooms. Github: https://github.com/linq-team/linq-cli Landing page: https://linqapp.com/cli Three constraints you should know about: 1. The free tier requires inbound-first (ie someone must text you before you text them) and has a limit of 20 contacts. This is to avoid spam. 2. The line is shared. This means a few other people will be using the same phone number as you, none of our paid production lines work this way. If you're testing enterprise grade our sandbox mirrors production, but has a 7 day time limit. The CLI is shared because there is a real infrastructure cost to us and we want to give this away for free. 3. We require an email to sign up. To avoid spam + our infrastructure cost. To be precise about "open source", it's the CLI. The whole client is in that repo, so you can read exactly what leaves your machine. The backend that delivers messages is closed.

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, agent · Missing: macos, cursor, model
88%88% 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: started · Missing: supports, reddit linkedin, podcasting
81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released
60%60% 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: way · Missing: mobile apps, ios, personal
45%45% 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
23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
19%19% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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