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Control Codex via WhatsApp using a Codex plugin

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Control Codex via WhatsApp using a Codex plugin

Codex plugins were released just yesterday, and I wanted to try something I’ve always wished for: controlling Codex from my phone. I put together a small relay that connects WhatsApp to Codex, so I can: - start a session from my phone - send follow-ups while I’m away from my computer - then pick everything back up later on desktop Under the hood it uses the new plugin system (skills + connectors) that was just added to Codex Curious if others are experimenting with similar setups or different interfaces. Code (very rough, but works): https://github.com/abuiles/codex-whatsapp-relay

Visit productView on Hacker NewsMar 27, 2026
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Actual performance

4points
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: codex, computer, new · Missing: mac, agents, macos
92%92% 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
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
54%54% 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
38%38% 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: interface · 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
14%14% 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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