EchoStream – A Local AI Agent That Lives on Your iPhone

Hacker News

Hi HN, I’ve been working on a project called EchoStream, and I’d love to share it with you. As an AI startup founder, the first thing I do every morning is open Hacker News. I look for what other founders are building, check out new GitHub projects, and explore new products. But I started noticing that it was taking me the whole morning just to get through it all. I began to wonder: could an AI help me read Hacker News more efficiently? Could it summarize everything so I can quickly scan through, and then dive deeper into what interests me? And more than that—could it store what I’ve seen so I can recall it later when it's relevant? That’s the idea behind EchoStream. EchoStream is a local AI agent that runs entirely on your iPhone. It includes a built-in thinking model to help you do more with less effort—privately and efficiently, without sending your data anywhere. Some of its key features: - Read and understand web pages using AI - OCR for recognizing and processing text in images - Automatically record, transcribe, and summarize audio - An AI-powered news feed with fresh, relevant updates - Memory search that connects new info with what you’ve seen before It’s still early, but I’d really appreciate your feedback or thoughts. I built this for myself, and now I want to share it with all of you. You can have a try here: https://apps.apple.com/us/app/echostream-mind-copilot/id6476... or to the homesite: https://www.echostream-ai.com/ Note: It works well on any iDevice with at least 4GB of RAM.

Visit product View on Hacker NewsJun 19, 2025

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

96%96% 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 · Strong signals: started, efficiently · Missing: supports, reddit linkedin, podcasting

93%93% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: hacker news, ide, io · Missing: https docs, excited, just released

51%51% 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: host, efficient · Missing: plus, platform, intuitive

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

27%27% 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

13%13% 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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