Signal Support for NanoClaw

Hacker News

NanoClaw now supports Signal via signal-cli's JSON-RPC interface. Can run alongside WhatsApp or replace it entirely. Signal's E2E encryption pairs well with NanoClaw's container isolation. Voice transcription runs locally too, so nothing leaves the machine unless intended. What's supported: Typing indicators, reactions, quoted replies, polls Signal text styling (bold, italic, strikethrough, spoiler, monospace) with UTF-16 offset handling Image/audio/file attachments (send and receive) Voice message transcription (local, via Whisper) Group metadata (members, admins, description) All existing NanoClaw features (scheduled tasks, per-group isolation, etc.) How it works: signal-cli runs in JSON-RPC mode. Newline-delimited JSON from stdout gets parsed, messages route to the right group queue, a container spawns, and responses go back via stdin RPC calls. The Signal channel is ~800 lines. Setup: run /add-signal in Claude Code, or set SIGNAL_ONLY=true to drop WhatsApp. Needs Java 21+ and signal-cli, no npm dependencies. Upstream skill PR: https://github.com/qwibitai/nanoclaw/pull/490

Visit product View on Hacker NewsFeb 25, 2026

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created

74%74% 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: mac, claude, new · Missing: agents, macos, agent

68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: interface, calls · Missing: plus, platform, intuitive

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

15%15% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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