AI

AI chat client that can run on SharePoint and regulated environments

Hacker News

AI chat client that can run on SharePoint and regulated environments

Stina is a lightweight AI chat client specifically designed for environments like government, healthcare, and large corporate settings, where IT policies prohibit the use of web servers, installations of new software, or access to online AI chat bots. Chat with with LLMs with your own API keys or a local Ollama instance after downloading the repo and opening the chat.html file from the file system. Stina keeps all conversation history and data in localstorage on the browser, which is great when your manager asks you about data security considerations of AI chatbots. Stina is a client-side solution that requires no server, no installations, and no build tools. Written in vanilla javascript with all dependencies bundled. This enables editing the code in environments where there is no access to Node.js, NPM or other JS-build tools. Right now it supports Azure AI Foundry deployments, OpenAIs API, Anthropic and Ollama. Stina can be hosted on a SharePoint server, run directly from the filesystem, or accessed through a local web server. It supports multiple language models, custom instructions, and dynamic system prompts. I built this for my own needs at work where we have access to API keys to an Azure AI Foundry sandbox on the company tenant. By renaming chat.html to chat.aspx and putting the repo in a folder on SharePoint, I can run this as a chatbot internally. If you want to contribute, you are very welcome to do so. I would love to extend this with the ability to chat to files, and perhaps even add an in-memory clientside vector database support to allow doing lightweight clientside RAG on documents accessible with the filesystem.

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Actual performance

4points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: filesystem, llama, ide · Missing: https docs, excited, just released
63%63% 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 · 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 · Missing: mobile apps, ios, personal
33%33% 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: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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