For transparency: we also launched GoodSMS on Product Hunt this week as part of our public release.
If you are curious about the screenshots, launch notes, or early user feedback, the PH page is here:
https://www.producthunt.com/products/goodsms
No obligation at all — I mainly want feedback from HN on the technical side, but some people asked for the PH link so adding it here.
I built GoodSMS as a side project, an on-device LLM SMS assistant that drafts fast, context-aware replies to your text messages.
Most “AI messaging” tools today operate as cloud services or full chat apps. We wanted something closer to a digital-twin input method—a thin layer that sits on top of your existing SMS app and quietly helps you respond faster.
What GoodSMS does
• Runs an LLM locally (no cloud round-trip for drafting)
• Reads the incoming SMS text, thread context, and your previous writing style
• Generates 3–5 possible replies instantly
• Lets you accept/edit/paste into your messaging app
• Supports short messages, long-form replies, quick actions, confirmations, and scheduling
• Privacy-first architecture: no message content leaves your device unless you explicitly opt into cloud inference
Why we built it
Most people lose time triaging simple messages: “ok,” “sure,” “on my way,” “what’s the address again,” etc.
Others tend to miss messages or delay replies because the friction is too high.
GoodSMS tries to behave like a personal executive assistant for SMS, especially useful for:
• Busy professionals
• Parents coordinating logistics
• Service providers handling many similar conversations
• Anyone who wants faster, cleaner messaging flow
Technical notes
• Android-first implementation using a custom input-method wrapper
• On-device LLM inference (quantized 3B–8B models)
• Optional cloud-compute escalation for long messages
• Conversation-thread reconstruction
• Lightweight ranking layer for human-like prioritization
• Zero dependency on carrier APIs
Open Questions / Looking for Feedback
I would really appreciate feedback from this community on:
How to improve the on-device inference/latency tradeoff
Whether there is value in adding a plug-in layer (e.g., automate routine replies, reminders, follow-ups)
Ideas for a secure way to integrate with RCS and third-party messaging
Whether a more advanced “agentic” mode would be useful or too risky
I just launched the first public version today.
Happy to answer all technical questions, share architectural details, or discuss edge cases.
No obligation at all — I mainly want feedback from HN on the technical side, but some people asked for the PH link so adding it here.
Most “AI messaging” tools today operate as cloud services or full chat apps. We wanted something closer to a digital-twin input method—a thin layer that sits on top of your existing SMS app and quietly helps you respond faster.
What GoodSMS does
• Runs an LLM locally (no cloud round-trip for drafting) • Reads the incoming SMS text, thread context, and your previous writing style • Generates 3–5 possible replies instantly • Lets you accept/edit/paste into your messaging app • Supports short messages, long-form replies, quick actions, confirmations, and scheduling • Privacy-first architecture: no message content leaves your device unless you explicitly opt into cloud inference
Why we built it
Most people lose time triaging simple messages: “ok,” “sure,” “on my way,” “what’s the address again,” etc. Others tend to miss messages or delay replies because the friction is too high.
GoodSMS tries to behave like a personal executive assistant for SMS, especially useful for: • Busy professionals • Parents coordinating logistics • Service providers handling many similar conversations • Anyone who wants faster, cleaner messaging flow
Technical notes
• Android-first implementation using a custom input-method wrapper • On-device LLM inference (quantized 3B–8B models) • Optional cloud-compute escalation for long messages • Conversation-thread reconstruction • Lightweight ranking layer for human-like prioritization • Zero dependency on carrier APIs
Open Questions / Looking for Feedback
I would really appreciate feedback from this community on:
How to improve the on-device inference/latency tradeoff
Whether there is value in adding a plug-in layer (e.g., automate routine replies, reminders, follow-ups)
Ideas for a secure way to integrate with RCS and third-party messaging
Whether a more advanced “agentic” mode would be useful or too risky
I just launched the first public version today. Happy to answer all technical questions, share architectural details, or discuss edge cases.