Galen – a structured layer between humans and AI

Hacker News

I’m a surgeon and have been experimenting with the idea that AI may need a more structured interface than raw natural language. I built an early prototype called GALEN. Instead of sending long-form speech or text directly to an AI model, GALEN converts it into a compact, structured instruction format first: speech/text → structured representation → AI The aim is not only to reduce token usage, but also to improve consistency, determinism and reliability across different AI systems. So far the prototype appears to: - reduce AI input overhead by roughly 30–70% - work across multiple domains (healthcare, legal, finance, travel, etc.) - allow the same structured instruction to be sent to different models Still very early and patent pending. I’m trying to work out whether there is genuinely a useful product here beyond the prototype. Demo: https://galenvoice.com Would really appreciate any thoughts or criticism.

Visit product View on Hacker NewsApr 22, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models · Missing: mac, agents, macos

74%74% 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

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, 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.

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

21%21% 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.

Correct prediction on native model

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