Satyam M.的动态

How to Solve hCaptcha Efficiently with Captcha Solver Introduction This article explores how to efficiently solve hCaptcha challenges using a Captcha Solver, highlighting its key benefits and practical applications. Understanding hCaptcha hCaptcha presents users with a series of images to identify objects, effectively preventing bots but often frustrating frequent users. What is a Captcha Solver? A Captcha Solver uses OCR and machine learning to quickly and accurately automate solving hCaptcha challenges, saving users time and effort. Benefits of Using Captcha Solvers for hCaptcha Time Efficiency: Manually solving hCaptcha can take minutes, but Captcha Solvers can complete these tasks in seconds. Increased Productivity: For businesses and developers facing numerous CAPTCHAs daily, a Captcha Solver can significantly boost productivity by automating these repetitive tasks. Enhanced User Experience: Users can enjoy a smoother and more uninterrupted browsing experience without the constant hassle of manually solving CAPTCHAs. Scalability: Captcha Solvers can handle large volumes of CAPTCHA challenges, making them ideal for businesses of all sizes. How Captcha Solvers Work Captcha Solvers, such as CaptchaAI, employ advanced machine learning algorithms and OCR technology to solve CAPTCHAs. Here’s a step-by-step look at how they work: Image Analysis: The Captcha Solver captures the hCaptcha images presented to the user. Object Recognition: Using machine learning algorithms, the solver identifies the objects within the images as required by the hCaptcha challenge. Response Generation: The solver generates the appropriate responses and submits them on behalf of the user. Continuous Learning: The system continuously learns and adapts to new hCaptcha patterns, improving its accuracy and efficiency over time. CaptchaAI: The Best Solution for hCaptcha CaptchaAI is one of the leading solutions for efficiently solving hCaptcha. Here are some of the key features that make CaptchaAI stand out: High Accuracy: CaptchaAI boasts a remarkable accuracy rate, ensuring that most hCaptcha challenges are solved correctly on the first attempt. Speed: With its advanced OCR technology and machine learning algorithms, CaptchaAI can solve hCaptcha challenges in just a few seconds. User-Friendly: CaptchaAI is easy to integrate and use, making it accessible for both individual users and businesses. CaptchaAI is the best website ever for Captcha solving service Scalability: CaptchaAI can handle large volumes of CAPTCHA challenges, making it suitable for businesses of all sizes. Conclusion Using Captcha Solvers like CaptchaAI can significantly improve efficiency, save time, and enhance productivity when solving hCaptcha challenges. By integrating CaptchaAI into your workflow, you can streamline the process and enjoy a more seamless online experience. https://captchaai.com/

?????????? ?????? | ?????? ?????????????????? ??????????????????: ?????????????????????? ???????? ???????????????????????????? & ????????????????. ?????? & ?????????? ?????????? ???? ?????????????? 3rd part of the series on LLM Analytics Assistant Apps Demonstrating data transformation and analysis on AWS MySQL via an LLM App. The app is deployed on my public website (outside of GPT Store, access-controlled section). Full video of 20 mins is on YouTube. The accompanying video has short snippets. https://lnkd.in/gKcrrmkn I cover 3 areas: ?? ?????? ?????? ???????? ???????? ?????????????????? & ????????????????: prototype customer table and transaction table with a million to 10 million records, creating summaries and merging data into new tables with additional variables... analyzing and creating customer profiles. All instructions in natural language... sometimes fuzzy and unclear... and sometimes with spellos... ?? ?????????? ???????????????????????? Similar to one that I am currently using on a live client project. ?????? ?????? ?????????? ?????? ????: using Flowise AI. Open-source. Allows for rapid deployment. Powerful capabilities. Many other options - e.g. custom build with React/Next.js that can link up to company SSO and authentications. ?????????? ????????????: trade-offs between pricing, speed, response quality, and security/privacy. Premium model vs. open-source on-prem solution. ???????????????????????? ??????????????????????: FastAPI processing server. Separate from the main system, making it reusable with different UI apps and backend databases. ?? ???????? ???????????????????????????? ???????? ??????????????: ran 478 API requests/queries over 10 hours with GPT-3.5, costing around $1... working with the 1 million-10 million dataset referred to above... also discuss optimization strategies... ???????????????? ?????? ????????????: depends on use case. e.g. Multi-LLM option...for difficult tasks, use an expensive model, and for simpler tasks, use a lower cost model.... or On-Prem solution for specific use cases. ???????? ???????? ?????????????????? by the LLM model is not always necessary... can significantly increase costs... potentially increasing by 100 times or more. For many use cases, processing can be done separately, and the LLM only passes SQL queries/Python commands. ?????????? ???????????????? ????????????????: for scenarios requiring full data ingestion, split the workflow into multiple modules. LLM to only ingest the necessary and smallest amount of data directly... process the rest of the data separately. ?? ???????????????? ???????????? ?????? ?????????? Currently preparing detailed tutorials and step-by-step guides covering code, tips, and leveraging GPTs to develop apps. In future videos and posts, I will also cover areas like : processing with on-prem solutions, multiple LLM approaches, segregation of Python processing vs. MySQL processing, machine learning model builds, selective accesses, and more.

?????????? ???? ?????????? ???? ???????????? ??????????????: ?? ??????????-???????????????????? ???? ????????????????? This AI assistant #plutoai utilizes LLMs and Multimodal models to perform multiple tasks, mainly chatting, question-answering, speech recognition, (optical character recognition) OCR, image generation, and many more! Furthermore, it provides a friendly user interface for easier interaction! PlutoAI provides users with cutting-edge capabilities by integrating multiple recent LLMs, e.g., Llama3.1, DeepSeek V3, Genini-1.5-Flash, from which the user can select based on their preference! ? ?????? ????????????????: Speech-to-Text: Powered by OpenAI’s Whisper for seamless voice-based conversations. OCR Support: Extracts text from images and PDFs for versatile content interaction. Image Synthesis: Utilizes Stable Diffusion v1.6 to generate stunning images from prompts. Tool Calling: Enhances precision with models like Llama 3.2 for robust functionality. Conversational Memory: Employs embedding-based memory and FAISS for context retention during chats. ?? ???????????????? The frontend was designed and implemented via TypeScript and Vite. It, however, facilitates the user-interaction by providing a fast and modern interface. ?? ?????????????? The backend was designed and implemented via .NET Core and MSSQL Server. It is, however, the powerhouse of the project that performs multiple tasks, such as managing all session handling, user management, chat storage, etc. Ensuring optimal performance. ?????????????????: User Authentication and Management: - Email/Password Authentication: Supports user registration with email verification. - Google OAuth Integration: Provides a quick, secure login option via Google accounts. Session Management: - Uses HTTP-only cookies for secure session handling, eliminating risks of XSS attacks. - Ensures seamless user experiences by persisting authenticated sessions across interactions. Data Management and Scalability: - Database: MSSQL Server ensures efficient, secure data storage and querying. - User-based Rate Limiting: Applies a user-based rate limiting policy on specific endpoints, such as image generation (which limits the number of generated images per day), aiming at preventing usage abuse. ?? ???????????????????? ?????? ???????????????? ?????? ???????? All the project services were containerized using Docker and hosted on Google Cloud instances. This, however, ensures scalability, reliability, and ease of deployment. To secure the site and protect user data: Nginx was used as a Reverse Proxy. Configured to route traffic securely to backend services. HTTPS Enabled. Integrated Certbot to create SSL certificates, providing encrypted communication for all user interactions. ?? Explore Pluto AI Preview the project: https://lnkd.in/dFmDZwsC Dive right in: https://lnkd.in/dhuSTkHB Feel free to try it out and leave your feedback. I’d love to hear your thoughts

#plutoAI, an #AI assistant system developed by Yousef Shamasneh. It's Supported by various #LLMs and #Multimodal capabilites, it enables the user to use and test open source models such as, Llama3.1, DeepSeek, Gemini, etc. on various tasks such as chatting, QA, Speech Recognition, and image generation. The system is entirely free and easy to you, it's worth trying!

https://lnkd.in/gzzHkBaC The article discusses the burgeoning field of AI agents, highlighting the recent surge in new frameworks and investments that are propelling these systems to potentially replace Retrieval-Augmented Generation (RAG) as a priority in AI implementation. Despite the excitement, significant development is still required before autonomous AI systems can handle tasks like writing emails, booking flights, or data interaction seamlessly. Developers face crucial decisions regarding foundational choices such as models, use cases, architectures, and frameworks, with options like LangGraph, LlamaIndex Workflows, or custom coding. To aid in this decision-making, the author built an agent using various major frameworks to evaluate their technical strengths and weaknesses, with all related code available in a public repository. The tested agent, designed as a chatbot with a simple Gradio interface, includes capabilities like answering questions from a knowledge base, interacting with telemetry data, and analyzing data trends, utilizing skills such as RAG with product documentation, SQL generation on a trace database, and data analysis.

#Technology #DataAnalytics #DataDriven LLM Agents Demystified: Hands-on implementation with LightRAG?library Image source, credits o?Growtika LightRAG library: https://lnkd.in/grJ67Ejp Colab?notebook “An autonomous agent is a system situated within and a part of an environment that senses that environment and acts on it, over time, in pursuit of its own agenda and so as to effect what it senses in the?future.” — Franklin and Graesser?(1997) Alongside the well-known RAGs, agents [1] are another popular family of LLM applications. What makes agents stand out is their ability to reason, plan, and act via accessible tools. When it comes to implementation, LightRAG has simplified it down to a generator that can use tools, taking multiple steps (sequential or parallel) to complete a user?query. What is ReAct?Agent? We will first introduce ReAct [2], a general paradigm for building agents with a sequential of interleaving thought, action, and observation steps. * Thought: The reasoning behind taking an?action. * Action: The action to take from a predefined set of actions. In particular, these are the tools/functional tools we have introduced in?tools. * Observation: The simplest scenario is the execution result of the action in string format. To be more robust, this can be defined in any way that provides the right amount of execution information for the LLM to plan the next?step. Prompt and Data?Models DEFAULT_REACT_AGENT_SYSTEM_PROMPT is the default prompt for React agent’s LLM planner. We can categorize the prompt template into four?parts: * Task description This part is the overall role setup and task description for the?agent.task_desc = r"""You are a helpful assistant. Answer the user's query using the tools provided below with minimal steps and maximum accuracy. Each step you will read the previous Thought, Action, and Observation(execution result of the action) and then provide the next Thought and Action.""" 2. Tools, output format, and?example This part of the template is exactly the same as how we were calling functions in the tools. The output_format_str is generated by FunctionExpression via JsonOutputParser. It includes the actual output format and examples of a list of FunctionExpression instances. We use thought and action fields of the FunctionExpression as the agent’s response.tools = r"""{% if tools %} {% for tool in tools %} {{ loop.index }}. {{tool}} ------------------------ {% endfor %} {% endif %} {{output_format_str}}""" 3. Task specification to teach the planner how to?“think”. We provide more detailed instruction to ensure the agent will always end with ‘finish’ action to complete the task. Additionally, we teach it how to handle simple queries and complex?queries. * For simple queries, we instruct the agent to finish with as few steps as possible. * For complex queries, we… #MachineLearning #ArtificialIntelligence #DataScience

Check out our new blog, ""How AI Powers Web Scraping To Extract High-Quality Data with Deeper Insights,"" and see how AI is reshaping data extraction for better results. Like and Share. #WebScraping #AIWebScraping #WebScrapingServices WebScrapingSolutions #ArtificialIntelligence #AIServices #AISolutions #DataExtraction #WebDataExtraction #WebDataMining #DataMining #DataHarvesting #ScrapingSolutions #WebScrapingCompany

The other day, I used AI again to supercharge a mundane data analysis task, transforming some tedious work into a few seconds of seamless efficiency! As someone who has spent years navigating the intricacies of web development and data analysis, I’ve often been faced with tasks that require diving deep into HTML structures or crafting complex SQL queries to extract meaningful insights. While satisfying in their own way, these tasks can be time-intensive, repetitive, and prone to errors. Here's the situation: I needed to extract an extensive list of data—labels and IDs—that were embedded in a structured HTML document with half of the data visible in the UI and the other half buried in code. Traditionally, I could have approached this in a few different ways: 1. Parsed through the HTML manually or wrote a script from scratch to scrape the data 2. Wrote a SQL query to retrieve the data from a complex data model that was used to create the HTML page 3. Repurpose the C# code that is used to dynamically build the HTML page and interact with the database data Instead, I turned to AI. Within seconds, I had the complete dataset I needed. I uploaded the HTML file, described what I was looking for, and the AI did the rest. It accurately parsed the HTML, extracted the required information, and presented it in a clear, structured format. No time spent coding or digging through data. No debugging. Just results. I then took a screenshot of the results to use in this post and asked AI to black out the ID's in the screenshot. Saving me even more time.. How powerful is this!! Every day, I’m finding new ways that AI enhances my workflow and transforms the way I deliver value to my clients. This wasn’t just about saving time—it was about redefining what’s possible. Whether it's building prototypes faster, analyzing data more effectively, or automating repetitive tasks, AI is becoming an indispensable part of my toolkit. If you’re still relying solely on traditional methods for data extraction, analysis, or problem-solving, I encourage you to explore the capabilities of AI. It’s not just a tool; it’s a game-changer for productivity, innovation, and client satisfaction. Have you had a moment where AI turned a daunting task into something effortless or do you have any tasks you would like to see if AI can help improve your workflow? I’d love to hear your thoughts in the comments! #AI #Productivity #WebDevelopment #Innovation #DataAnalysis

Browse AI: Automate Web Data Extraction with Ease - Save Time and Boost Productivity Learn how Browse AI can help you automate web data extraction effortlessly. Discover its features, benefits, and how to get started. https://lnkd.in/dkTgetJv #Ai #AiTools #Data #DataExtraction #DataScraping #DataCollection #NoCode #NoCodeAutomation #BrowsAi

Artificial Intelligence is ushering in a new era of web scraping capabilities. Web scraping at scale and speed is something that requires a fairly decent scale. Not only do you need to monitor the scripts which are flaky as HTML structure changes, but you might also need to: ?? Configure Proxy networks ?? Manage infrastructure to scrape the data in parallel, which necessitates ?? Maintaining an orchestration tool in code AI has created a RUSH to build LLMs capable of parsing websites *without* the need for intense monitoring and reconfiguration as HTML structures change. The other services are following. Read more here ?? https://lnkd.in/dX_WkVBY #dataengineering #webscraping #ai #genai