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Self-Hosted Social Media Agents with Raspberry Pi

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Self-Hosted Social Media Agents with Raspberry Pi

A while back I posted about a project I was working on for a real-time voice agent that runs ads on Meta to build conviction and prioritize side gig/business ideas ( https://news.ycombinator.com/item?id=48900788 ); you yammer away and it automates market research, problem statements and hypotheses. I've been working with some folks from HN on the app since then. What we've been focused on building is a modern solo-founder GTM tool -- figuring out how to triangulate aspiration, problems only you can solve, and organic distribution in one solution. So, not building apps; but rather helping you figure out what you like and then connect you with the right people that can help you on your journey and find the communities to help you distribute it. For the latter, I was impressed by the recent Grok bot launch; but didn't want Elon accessing all of our accounts to train on -- so I set-up dockerized social media agents that run at home on a Raspberry Pi. They'll do the same job as Grok for this but with your notes, memos, and ad data to inform what your agents do to help you find your audience -- online communities, people who need your solution, people who might be good to talk to about your idea, etc. computer use/openclaw style. Maybe this is lofty, but we're trying to use the internet as a social graph. Microphone is still very much experimental/wip but if you're interested in trying and giving us some feedback, feel free to sign up on the https://www.microphone.computer/ and I'll get you set up on the app and can do zoom calls if you need help setting up your pi (I recommend the new 16gb ram ones but 8gb will work), running a simple curl will connect your account to the app.

Visit productView on Hacker NewsAug 23, 2026
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Launch Intel predictions

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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.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, apps · Missing: mac, macos, cursor
87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: apps, way · Missing: mobile apps, ios, personal
60%60% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
57%57% 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 · Strong signals: host, calls · Missing: plus, platform, intuitive
32%32% 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
20%20% 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.

Incorrect prediction on native model

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