I made context-aware AI chatbot – PageGPT

Hacker News

I want to open up and share the journey that led to the creation of PageGPT. It all started with my frequent use of ChatGPT while browsing web in parallel, where I found myself constantly switching between tabs and having to reiterate the context in order to ChatGPT understand my questions. Feeling the need for optimization, I envisioned a tool that could seamlessly understand the webpage I was on and provide relevant assistance tailored to my needs. This vision laid the foundation for the development of PageGPT, an AI chatbot that prioritizes context awareness and effortless conversations. Key features of PageGPT include: 1. Context-Awareness: AI assistant that understands the context of the page you're browsing, delivering tailored and relevant responses without a need for you to explain everything. 2. Focused Context: If you don't need whole page as context, or just want to zoom in on specific page sections for targeted assistance that meets your requirements. 3. Saved Prompts: Save frequently used messages/prompts as shortcuts for quick access with a single click. Also, with PageGPT you use your own OpenAI API Key, giving you the freedom to chat as much as you wish without artificial limitations, such as number of queries, tokens, chats, etc. However, this means you're also responsible for the costs of your OpenAI API usage. Privacy first! Your data is your business. PageGPT prioritizes your privacy and does not store your conversations, but uses openai API directly to generate responses. I'm thrilled to share this project with you all and welcome your feedback and thoughts as I continue to refine and enhance PageGPT.

Visit product View on Hacker NewsMay 11, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: context, chatgpt, openai · Missing: mac, agents, macos

88%88% 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, para · Missing: supports, reddit linkedin, podcasting

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

38%38% predicted probability of success on TrustMRR, 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

29%29% 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

8%8% 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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