Ekhos – On-Device AI Transcription App

Hacker News

Hey HN, Tony here, I built an on-device AI transcription assistant for professionals who require full data privacy, such as legal and medical users. Everything is offline and transcripts are encrypted in SQLLite database. The audio files and transcripts never leave your device so it fully cloud-free service. My goal is to provide an intuitive, easy-to-use app with unlimited transcriptions and no caps. I used the following tech stacks to build it: Python (Whisper models), Avalonia UI (frontend), C# (backend), and SQLite (database). Here's some of the key features: Speaker Identification: Automatically labels speakers (SPEAKER 01, SPEAKER 02) and allows you to update them with real names. Whisper Model Transcription: Supports 98 languages and multiple audio/video formats. Real-Time Transcription: Transcribe audio with your device microphone or system speakers which is ideal for meetings, legal depositions, or court proceedings. Bulk Transcription: Queue multiple files for batch processing, useful for podcasts or large audio collections. Proofreading & Editing Tools: Integrated media player and editor for quick corrections or scoping. AI Models: Choose Intermediate, Advanced, or Expert to balance speed and accuracy. GPU Acceleration: Boost transcription performance with NVIDIA GPUs. Export Options: Save transcripts as Word, PDF, or plain text. We just launched version 2.0.0 on Microsoft Store: https://apps.microsoft.com/detail/9nqjkq3l649b Check out demos: https://ekhos.ai/demos Benchmarks & hardware requirements: https://ekhos.ai/help-center/hardware-requirements/ Learn more on our website: https://ekhos.ai What’s next: Additional on-device AI tools (e.g., summarization) and MacOS support Please give it a try. I’d love to hear your feedback and answer any of your questions.

Visit product View on Hacker NewsSep 26, 2025

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

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best fitHighest predicted score across all platforms for this description.

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

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

44%44% 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

41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: apps, video, users · Missing: mobile apps, ios, personal

39%39% 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

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

BetaListMay not resonate with beta-testers · Strong signals: audio · 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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