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timeline

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Here’s a structured development task breakdown for building a Wireshark C plugin that decodes payload data by calling an external API and displays it in the packet detail tree. Project Scope Create a C-based dissector plugin for Wireshark. The plugin will: Detect packets matching a custom condition (e.g., specific IP/Port or protocol signature). Extract payload data from the packet. Send this data to an external REST API for decoding. Parse the API response and display it as hierarchical fields in the Wireshark packet detail tree. Key Technical Considerations Wireshark’s dissector thread is not designed for blocking calls — calling an API directly inside packet dissection would freeze the UI. Solution: Implement an asynchronous model : Option 1: Maintain a local cache/database pre-filled from the API. Option 2: Use a background thread to fetch API data, store in memory, and refresh the view when available. Database integration (optional) — store API results in ...
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Wireshark

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Here’s what I found after checking Wireshark’s official capabilities and development documentation about plugin support—especially regarding integrations with databases: 1. Wireshark Plugin Support Overview Wireshark plugins are primarily used to extend protocol dissection functionality. They come in two flavors: C plugins (compiled dissectors) : Deep integration, high performance, access to Wireshark’s internal APIs and UI. Lua scripts : Easier to write and iterate faster. Great for prototyping, taps (post-dissectors), custom menus, and more . 2. Lua Extension Mechanism & Limitations Wireshark includes an embedded Lua interpreter. Supported Lua versions: Early versions: Lua 5.1 & 5.2 Wireshark 4.4 and later: Lua 5.3 & 5.4 . Lua plugins can register: Dissectors , listener taps , file readers/writers , and custom menus/statistics . Lua does not natively include any database libraries—but you can: Use require() to load shared Lua modules or C-wrappe...
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hacthon

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Got it — you want an ideation PPT for your AI-based document Q&A chatbot idea. I’ll make you a clear, professional slide structure you can directly put into PowerPoint or Google Slides. AI Document Q&A Chatbot – Ideation Deck Slide 1 – Title "AI-Powered Document Intelligence Chatbot" Ask Anything. Get Instant Answers from Your Documents. Image: AI + document icons Tagline: “Your PDF, Excel, Word — Now Speak Your Language” Slide 2 – Problem Statement Current Challenges Searching through long PDF, Excel, Word docs is slow and inefficient. Manual searching leads to missed information. Teams waste hours in finding the right paragraph, cell, or page. Difficult to integrate across multiple file formats. Slide 3 – Opportunity Every business works with large volumes of documents. Faster information retrieval = faster decisions. Knowledge should be instantly available to anyone in the organization. AI can bridge the gap between data storage and dat...
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ok

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from sentence_transformers import SentenceTransformer # Use a better embedding model embed_model = SentenceTransformer("BAAI/bge-base-en-v1.5") # larger, better accuracy embeddings = embed_model.encode(texts, normalize_embeddings=True)
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chat bot

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Got it — you want to create a local AI chatbot that can answer questions based on the content of an Excel file you provide, and you’ll be using VS Code to build it. I’ll walk you through the process step-by-step and then give you a working Python example . Plan 1. Tech Stack Language: Python Editor: VS Code Libraries: pandas → for reading Excel openpyxl → Excel engine for .xlsx langchain (optional) → for question-answering logic ollama / transformers → for local AI model (you mentioned earlier you might use ollama gpt-oss:20b ) 2. Basic Workflow Read Excel File → Load all data into Python using pandas . Convert Data to Text → Transform rows into a searchable text format. Ask Question → Take the user’s query. Search for Relevant Data → Use either: Simple : Keyword search in dataframe Advanced : Embeddings + vector search for semantic meaning Generate Answer → Feed the found data to the AI model to create a natural answer. 3. Example — Simple ...
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case study

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Got it — here’s your Automobile Infotainment AI case study written in a detailed yet concise, human-friendly format , but still keeping the structured slide-style flow so it’s ready for presentation or proposal use. Revolution of AI in Automobile Infotainment Broad Market Trends AI is reshaping how we experience cars. Infotainment systems are evolving from basic media consoles into intelligent, connected hubs that personalize every journey. Personalized Experience – AI tailors playlists, navigation routes, and climate settings to each driver. Voice-First Interfaces – Natural, hands-free voice commands reduce distraction. Predictive Analytics – Anticipates needs like fuel stops, maintenance, or preferred routes. Connected Car Ecosystems – Syncs with IoT devices, smart homes, and cloud services. Driver Monitoring – Detects fatigue or unsafe driving patterns. Augmented Navigation – Real-time hazard detection and AR guidance. Possibilities AI-assisted over-the-air (O...
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excel

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Yes, ✅ you can do this directly in Excel using a formula — no code or external tool required . Here’s how you can achieve the same logic using a formula in Column N of the "Icon Tracker" sheet: 🧠 What You Want: For each row: Check if value in Column M exists in Column G of the "ICON management" sheet. If not found and the value is not "NA" , show: "Icon not present in management Sheet" . If value is "NA" → leave blank . ✅ Excel Formula for Column N (e.g., N2): =IF(OR(M2="NA", ISNUMBER(MATCH(M2, 'ICON management'!G:G, 0))), "", "Icon not present in management Sheet") 💡 Explanation: Part Meaning M2="NA" Checks if current row's value in Column M is "NA" (case-sensitive, use LOWER() for case-insensitive). MATCH(M2, 'ICON management'!G:G, 0) Looks for M2's value in Column G of "ICON management". Returns row number if fo...
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