Excited to share an update on a project I've been building — an AI-powered receptionist for auto dealership showrooms. The problem it solves: dealerships lose leads constantly because no one answers fast enough — after-hours calls go to voicemail, web leads sit overnight, and by the time someone follows up, the customer's already talking to a competitor. What it does: → Answers calls & chat instantly, 24/7 → Qualifies leads (vehicle interest, trade-in, timeline) → Books test drives/appointments straight into the calendar → Checks live inventory availability → Auto-follows up on cold leads so nothing slips through → Hands off warm leads to a real rep with full context I've built it out for a client and it's now live in the testing phase — running real calls, measuring response times and booked appointments before we roll it out further. More updates soon as the pilot data comes in. Happy to answer questions if anyone's exploring something similar in their own niche 👇

Just dropped what I think is the definitive video for high ticket AI agent building and selling. It’s seriously worth checking out if you’re into AI, automation, or building side hustles. 👀 👉 Check it out here: https://youtu.be/L1lswXTpLjw?si=-vuzDzPLDhKH_Kda Would love for all of you in the community to drop your comments and questions under the vid to boost the algo😂 appercaite all you guys

Hey everyone! 👋 Following up on my last update about thread persistence using SqliteSaver, I’ve upgraded the chatbot into a 11-tool agentic engine running on Groq (Llama 3.3 70B) and Streamlit. If you’ve ever tried binding 10+ custom tools to open-weight LLMs, you’ve probably hit APIError: Failed to call a function or malformed argument streams. Here is how I solved those issues and stabilized the loop: 🛠️ The 11-Tool Ecosystem - Live APIs & Web: get_weather (OpenWeatherMap), maps_tool (OpenStreetMap), web_search (DuckDuckGo) - Finance & Computation: get_stock_price, calculator, unit_converter - Productivity: send_email, calendar_event_manager, get_current_datetime - Context & Memory: personal_memory_store, search_conversation_history 💡 Key Technical Takeaways 1. Strict Pydantic Hardening: Instead of relying on raw docstrings or loose dict types, I attached explicit pydantic.BaseModel schemas to every @tool decorator. This forces Llama 3.3 to emit 100% deterministic JSON arguments. 2. Decoupled Execution: Separated function invocation from raw token streaming inside Streamlit to prevent stream-parsing collisions during tool calls. 3. Clean Graph State: Kept SQLite state checkpoints isolated so failed execution attempts don't contaminate thread history. 🧪 Multi-Tool Test Prompt I pushed the agent with a compound request: "Check current time ➔ get Tokyo weather ➔ convert °C to °F ➔ look up map coordinates ➔ store a note in memory." All tools executed sequentially in a single turn with live status blocks in the Streamlit UI! 🔗 Try the Live App: https://saad7420-chatbot-based-on-langgraph-app-utzuat.streamlit.app/ What tool-calling setups are you currently building with LangGraph? Drop your questions or code setup below and I'd be happy to share backend snippets! 👇

I’ve been Dming a ton of marketing agency’s on LinkedIn and instagram but have only booked one meeting out like 100 DMs. I’m just curious how other people are finding marketing agency’s to partner with?

Signed a 5k installation and 350 a month with a contractor in Memphis Tennessee. He wanted an internal quoting agent where the agent has all of their pricing structure and how they quote in its knowledge base and then connected to all of their tools they use