ai-o – Browse the web and chat with AI, side by side

Hacker News

Hi HN I am the developer of "ai-o" - and all-in-one portable chatbot with some useful chat & prompt utilities. I made this from my dislike of context switching between my current task and ChatGPT, alongside the fact that ChatGPT currently is lacking in some core chat utilities such as: conversation search, folders, prompt history etc. - from which have all been added into ai-o. I aim to build a feature-rich chatbot that you can summon on any webpage, any time you have a question. But I need your help I have many things planned in the feature roadmap but as mentioned, I want to build a "utility-focused" chatbot, so I need some steer from you guys towards what kind of features would be most useful for you when using this chatbot. If you would like to give it a try, it's free to use and you get some free requests to chat with GPT-4 Turbo & Vision. Let me know what you like, hate, or find mid Free download here: https://chromewebstore.google.com/u/1/detail/ai-o-gpt-4-turb... I am also launching on Product Hunt ⬆ : https://www.producthunt.com/products/ai-o How to use: - Option (MacOS) / Alt (Windows) + A to activate the sidebar. Features: - Chat with Al whilst browsing the web, side by side. Always-visible sidebar. - Chat with latest OpenAI models: GPT-3.5 Turbo, GPT-4 Turbo, GPT-4 Vision on any website - Create and save custom prompts with support for prompt variables - Folders system to organize your chats - ⬇ Import ChatGPT chat history - Chat history - Advanced search for finding chats and messages - Prompt history - Voice dictation (Speech to text) - Text-to-speech playback - Sync chats & folder across multiple devices - ⌨ Keyboard shortcuts - Dark & light mode support

Visit product View on Hacker NewsJan 21, 2024

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

93%93% 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

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

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

43%43% 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

24%24% 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

16%16% 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.

Correct prediction on native model

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