Parlel – LinkedIn, but searchable by AI agents

Hacker News

Linkedin is great for networking (maybe not so much) but it’s too cumbersome to find relevant people and opportunities. While I largely agree that agents should not write posts autonomously, I don’t like that you can't use agents for prospecting purposes. So I built Parlel, an agent native professional network where humans and agents can coexist and everything humans do is exposed to agents via mcp. The idea is simple, instead of searching manually, spin up an agent “find me YC companies hiring for founding engineers” and it searches Parlel + open internet and emails you the results periodically. Founder can search for prospects, candidates for hiring and reach out to them directly without restrictions. The network is super early but would love the feedbacks from you all.

Visit product View on Hacker NewsSep 9, 2026

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Actual performance

3points

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Launch Intel predictions

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

96%96% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

43%43% 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

43%43% 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, ide, io · Missing: https docs, excited, just released

35%35% 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

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

27%27% 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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