{"slug": "groupchatdigest", "title": "GroupChatDigest", "summary": "A developer built GroupChatDigest, an open-source Streamlit web app that summarizes exported group chat logs into a three-part digest covering key decisions, action items, and a funniest highlight. The tool calls Groq's API with a choice of llama-3.3-70b-versatile or llama-3.1-8b-instant models, using a system prompt that enforces the three section headers. The developer claims it cuts 15 to 20 minutes of daily manual scrolling through hundreds of messages down to a roughly 5-second scan.", "body_md": "*This is a submission for the [Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady*](https://dev.to/challenges/hacktoberfest-week3-2026-10-19)\n\n**GroupChatDigest** — a cloud-hosted, open-weight AI web application that distills overwhelming daily group chat exports and message dumps into a concise, actionable summary.\n\nInstead of spending 20 minutes catching up on hundreds of unread messages across active Discord servers, WhatsApp groups, or Telegram chats, **GroupChatDigest** extracts the signal from the noise and delivers a 3-part digest:\n\nIt allows users to skip the FOMO and doom-scrolling, saving energy and time every single day.\n\n[https://github.com/Sangam067/DEVWEEK3.git](https://github.com/Sangam067/DEVWEEK3.git)\n\n`` `python ``\n\nimport streamlit as st\n\nfrom groq import Groq\n\nst.title(\"💬 GroupChatDigest\")\n\nst.caption(\"Turn hours of unread group chat clutter into a 3-bullet morning digest.\")\n\napi_key = st.sidebar.text_input(\"Groq API Key\", type=\"password\")\n\nmodel_choice = st.sidebar.selectbox(\"Select Model\", [\"llama-3.3-70b-versatile\", \"llama-3.1-8b-instant\"])\n\nchat_log = st.text_area(\"Paste daily chat log or export here:\", height=300)\n\nif st.button(\"⚡ Summarize Chat\"):\n\n    if not api_key:\n\n        st.error(\"Please enter a valid Groq API key in the sidebar.\")\n\n    elif not chat_log.strip():\n\n        st.warning(\"Please paste some text first!\")\n\n    else:\n\n        try:\n\n            client = Groq(api_key=api_key)\n\n            response = client.chat.completions.create(\n\n                model=model_choice,\n\n                messages=[\n\n                    {\"role\": \"system\", \"content\": \"You are a concise assistant. Output exactly 3 section headers: 1. Key Decisions / News, 2. Action Items, 3. Funniest Highlight / Meme.\"},\n\n                    {\"role\": \"user\", \"content\": chat_log}\n\n                ]\n\n            )\n\n            st.markdown(response.choices[0].message.content)\n\n        except Exception as e:\n\n            st.error(f\"Error: {str(e)}\")\n\n`\n\n`streamlit`) for a fast, responsive user interface.` llama-3.3-70b-versatile` open-weight model accessed via the `app.py`), accepting large text contexts and processing structured responses instantly.\nCatching up on 500+ messages across three different group chats normally takes 15 to 20 minutes of daily manual scrolling. **GroupChatDigest** condenses 20 minutes of reading into a 5-second scan, letting you get straight to the important points.", "url": "https://wpnews.pro/news/groupchatdigest", "canonical_source": "https://dev.to/sangam_/groupchatdigest-2iim", "published_at": "2026-10-25 16:04:28+00:00", "updated_at": "2026-10-25 16:13:20.039395+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-tools", "generative-ai", "ai-products"], "entities": ["GroupChatDigest", "Groq", "Streamlit", "Llama 3.3 70B", "Llama 3.1 8B", "GitHub", "Hacktoberfest"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/groupchatdigest", "markdown": "https://wpnews.pro/news/groupchatdigest.md", "text": "https://wpnews.pro/news/groupchatdigest.txt", "jsonld": "https://wpnews.pro/news/groupchatdigest.jsonld"}}