MinimalChat Is a Full-Featured and Self-Contained Chat Application

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I've been building MinimalChat for a while now, and based on the feedback I've received, it's in a pretty decent place for general use. I figured I'd share it here for anyone who might be interested! I previewed an earlier version a while ago, but this is major overhaul of the previous version that was shown. # Quick Features Overview: 1. Mobile PWA Support: Install the site like a normal app on any device. 2. Any OpenAI formatted API support: Works with LM Studio, OpenRouter, etc. 3. Local Storage: All data is stored locally in the browser with minimal setup. Just enter a port and go in Docker. 4. Experimental Conversational Mode (GPT Models for now) 5. Basic File Upload and Storage Support: Files are stored locally in the browser. 6. Vision Support with Maintained Context 7. Regen/Edit Previous User Messages 8. Swap Models Anytime: Maintain conversational context while switching models. 9. Set/Save System Prompts: Set the system prompt. Prompts will also be saved to a list so they can be switched between easily. The idea is to make it essentially foolproof to deploy or set up while being generally full-featured and aesthetically pleasing. No additional databases or servers are needed, everything is contained inside the web app itself. It's another chat client in a sea of clients but it is unique in its own ways in my opinion. Enjoy! Feedback is always appreciated! Docker instructions are in the readme. Mobile Layout - https://imgur.com/a/XrAy6VD Public Site - https://minimalchat.app ) (For anyone who wants to try it out before installing locally

Visit product View on Hacker NewsJun 14, 2024

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

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

52%52% 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

50%50% 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: way · Missing: mobile apps, ios, personal

31%31% 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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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