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I built v1 of Omni channel SDR Agent

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I built v1 of Omni channel SDR Agent

My goal with sdr agent ids to make the agent prospect and push it into a cadence and work autonomously across LinkedIn, emails and calls at my Omni channel touch point system. My config: There are three agents working in silos. 1) comet ( I will mostly replace this with clawdebot) to work on Sending Friend request on LinkedIn and push the prospects in a Google sheet 2) Dronahq agent (disclaimer: this is the platform I am building and dog fooding for this use case) : Here the agent will - pick the lead from Google sheet and look up Apollo and find more details - shoot an intro email basis a complex algo 3) voice agent ( made with twilio and vapi): this will call the customer and update the records and set up Google Calendar for appointment. I must mention that setting up voice agent with vapi was incredible experience. I was able to set up a working one in under 10 mins. Next steps: 1) glue all the three agents via some super agent so they can work seamlessly and autonomously 2) bring in a cadence agent so outreach cadence will be achieved by the agent

Visit productView on Hacker NewsJan 31, 2026
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, google · 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
79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: google · Missing: mobile apps, ios, personal
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, calls · Missing: plus, intuitive, reviews
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
30%30% 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
25%25% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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