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Pi-Foundry – multi user self hosted AI assistant

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Pi-Foundry – multi user self hosted AI assistant

I wanted to solve my wife's work-related pain points revolving around Excel formulas and extracting data from PDFs documents by keeping her data locally. I also wanted to be able to collaborate with her if the need arises. So I've been working on this multi user virtual assistant based on the OpenCode SDK that we host on a Raspberry Pi behind Tailscale. The workflow in short - users upload their work files through the web interface, - add them to a chat session, - invite other users, - they churn the data with the agent, - then download the resulting assets. It has some rough edges especially around handling multi-turn conversations but thought maybe someone could find this useful. :) If you like it, please star and contribute to the project.

Visit productView on Hacker NewsJan 28, 2026
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Actual performance

2points
1comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, user, code · Missing: mac, agents, macos
91%91% 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: wife · Missing: supports, reddit linkedin, podcasting
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: host, interface, users · Missing: plus, platform, intuitive
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, 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
48%48% 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
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat, collaborate · Missing: web3, crypto, cryptocurrency
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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