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Artificial Intelligence & ChatGPT Prompts

Artificial Intelligence & ChatGPT Prompts

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🔓Unlock Your Coding Potential with ChatGPT 🚀 Your Ultimate Guide to Ace Coding Interviews! 💻 Coding tips, practice questions, and expert advice to land your dream tech job. For Promotions: @love_data

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📈 Аналітичний огляд Telegram-каналу Artificial Intelligence & ChatGPT Prompts

Канал Artificial Intelligence & ChatGPT Prompts (@curiousprogrammer) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 42 123 підписників, посідаючи 3 229 місце в категорії Технології та додатки та 9 545 місце у регіоні Індія.

📊 Показники аудиторії та динаміка

З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 42 123 підписників.

За останніми даними від 12 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 175, а за останні 24 години на 12, загальне охоплення залишається високим.

  • Статус верифікації: Не верифікований
  • Рівень залученості (ER): Середній показник залученості аудиторії становить 2.43%. Протягом перших 24 годин після публікації контент зазвичай збирає 0.73% реакцій від загальної кількості підписників.
  • Охоплення публікацій: В середньому кожен допис отримує 1 024 переглядів. Протягом першої доби публікація в середньому набирає 306 переглядів.
  • Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 3.
  • Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як learning, algorithm, detection, llm, pattern.

📝 Опис та контентна політика

Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
🔓Unlock Your Coding Potential with ChatGPT 🚀 Your Ultimate Guide to Ace Coding Interviews! 💻 Coding tips, practice questions, and expert advice to land your dream tech job. For Promotions: @love_data

Завдяки високій частоті оновлень (останні дані отримано 13 червня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Технології та додатки.

42 123
Підписники
+1224 години
+227 днів
+17530 день
Архів дописів
𝟲 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝘁𝗵𝗲 𝗠𝗼𝘀𝘁 𝗜𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀😍 🚀 Want to future-proof
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JOINS Definition Joins in MySQL allow you to retrieve data from two or more tables based on a related column. They are used to combine rows from multiple tables. Types of Joins 1. INNER JOIN: - Returns rows where there is a match in both tables. - Syntax:
            SELECT columns
            FROM table1
            INNER JOIN table2
            ON table1.column = table2.column;
            
- Example:
            SELECT employees.name, departments.name
            FROM employees
            INNER JOIN departments
            ON employees.department_id = departments.id;
            
2. LEFT JOIN (OUTER JOIN): - Returns all rows from the left table and matching rows from the right table. Non-matching rows have NULL. - Example:
            SELECT employees.name, departments.name
            FROM employees
            LEFT JOIN departments
            ON employees.department_id = departments.id;
            
3. RIGHT JOIN (OUTER JOIN): - Returns all rows from the right table and matching rows from the left table. Non-matching rows have NULL. - Example:
            SELECT employees.name, departments.name
            FROM employees
            RIGHT JOIN departments
            ON employees.department_id = departments.id;         
4. FULL OUTER JOIN: - Returns all rows from both tables, matching where possible. Not natively supported in MySQL, but can be simulated using UNION. - Example:
            SELECT employees.name, departments.name
            FROM employees
            LEFT JOIN departments
            ON employees.department_id = departments.id
            
            UNION
            
            SELECT employees.name, departments.name
            FROM employees
            RIGHT JOIN departments
            ON employees.department_id = departments.id;
5. CROSS JOIN: - Returns the Cartesian product of both tables. - Example:
            SELECT employees.name, departments.name
            FROM employees
            CROSS JOIN departments;
Interview Questions 1. What is the difference between INNER JOIN and OUTER JOIN? - INNER JOIN only includes rows with matches in both tables, while OUTER JOIN includes unmatched rows. 2. How can you simulate a FULL OUTER JOIN in MySQL? - Use UNION of LEFT JOIN and RIGHT JOIN. 3. What is a Cartesian product, and when does it occur? - A Cartesian product occurs in a CROSS JOIN or when no ON condition is specified, resulting in all possible row combinations.

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🚀 Coding Projects & Ideas 💻 Inspire your next portfolio project — from beginner to pro! 🏗️ Beginner-Friendly Projects 1️⃣ To-Do List App – Create tasks, mark as done, store in browser. 2️⃣ Weather App – Fetch live weather data using a public API. 3️⃣ Unit Converter – Convert currencies, length, or weight. 4️⃣ Personal Portfolio Website – Showcase skills, projects & resume. 5️⃣ Calculator App – Build a clean UI for basic math operations. ⚙️ Intermediate Projects 6️⃣ Chatbot with AI – Use NLP libraries to answer user queries. 7️⃣ Stock Market Tracker – Real-time graphs & stock performance. 8️⃣ Expense Tracker – Manage budgets & visualize spending. 9️⃣ Image Classifier (ML) – Classify objects using pre-trained models. 🔟 E-Commerce Website – Product catalog, cart, payment gateway. 🚀 Advanced Projects 1️⃣1️⃣ Blockchain Voting System – Decentralized & tamper-proof elections. 1️⃣2️⃣ Social Media Analytics Dashboard – Analyze engagement, reach & sentiment. 1️⃣3️⃣ AI Code Assistant – Suggest code improvements or detect bugs. 1️⃣4️⃣ IoT Smart Home App – Control devices using sensors and Raspberry Pi. 1️⃣5️⃣ AR/VR Simulation – Build immersive learning or game experiences. 💡 Tip: Build in public. Share your process on GitHub, LinkedIn & Twitter. 🔥 React ❤️ for more project ideas!

Repost from Data Analyst Jobs
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Accenture Data Scientist Interview Questions! 1st round- Technical Round - 2 SQl questions based on playing around views and table, which could be solved by both subqueries and window functions. - 2 Pandas questions , testing your knowledge on filtering , concatenation , joins and merge. - 3-4 Machine Learning questions completely based on my Projects, starting from Explaining the problem statements and then discussing the roadblocks of those projects and some cross questions. 2nd round- - Couple of python questions agains on pandas and numpy and some hypothetical data. - Machine Learning projects explanations and cross questions. - Case Study and a quiz question. 3rd and Final round. HR interview Simple Scenerio Based Questions. Data Science Resources 👇👇 https://t.me/datasciencefun Like if you need similar content 😄👍

𝗦𝘁𝗶𝗹𝗹 𝗙𝗮𝗶𝗹𝗶𝗻𝗴 𝗧𝗲𝗰𝗵 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀? 𝗧𝗵𝗲𝘀𝗲 𝟯 𝗙𝗿𝗲𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗖𝗼𝘂𝗹𝗱 𝗙𝗶𝗻𝗮𝗹𝗹𝘆
𝗦𝘁𝗶𝗹𝗹 𝗙𝗮𝗶𝗹𝗶𝗻𝗴 𝗧𝗲𝗰𝗵 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀? 𝗧𝗵𝗲𝘀𝗲 𝟯 𝗙𝗿𝗲𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗖𝗼𝘂𝗹𝗱 𝗙𝗶𝗻𝗮𝗹𝗹𝘆 𝗖𝗵𝗮𝗻𝗴𝗲 𝗧𝗵𝗮𝘁😍 You’ve spent hours solving LeetCode problems. You’ve gone through entire DSA playlists🗣✨️ The internet is filled with confusing roadmaps and endless practice sets. But what you need is clarity, structure, and confidence. That’s exactly what these 3 high-impact, free YouTube videos give you.👨‍💻📌 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4feEnaA This is your new cheat code✅️

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PYTHON SYNTAX
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𝟱 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗧𝗲𝗰𝗵 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲 – 𝗪𝗶𝘁𝗵 𝗙𝘂𝗹𝗹 𝗧𝘂𝘁𝗼𝗿𝗶𝗮𝗹𝘀
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Guide to Building an AI Agent 1️⃣ 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗟𝗟𝗠 Not all LLMs are equal. Pick one that: - Excels in reasoning benchmarks - Supports chain-of-thought (CoT) prompting - Delivers consistent responses 📌 Tip: Experiment with models & fine-tune prompts to enhance reasoning. 2️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗟𝗼𝗴𝗶𝗰 Your agent needs a strategy: - Tool Use: Call tools when needed; otherwise, respond directly. - Basic Reflection: Generate, critique, and refine responses. - ReAct: Plan, execute, observe, and iterate. - Plan-then-Execute: Outline all steps first, then execute. 📌 Choosing the right approach improves reasoning & reliability. 3️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝗖𝗼𝗿𝗲 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 Set operational rules: - How to handle unclear queries? (Ask clarifying questions) - When to use external tools? - Formatting rules? (Markdown, JSON, etc.) - Interaction style? 📌 Clear system prompts shape agent behavior. 4️⃣ 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗮 𝗠𝗲𝗺𝗼𝗿𝘆 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 LLMs forget past interactions. Memory strategies: - Sliding Window: Retain recent turns, discard old ones. - Summarized Memory: Condense key points for recall. - Long-Term Memory: Store user preferences for personalization. 📌 Example: A financial AI recalls risk tolerance from past chats. 5️⃣ 𝗘𝗾𝘂𝗶𝗽 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝗧𝗼𝗼𝗹𝘀 & 𝗔𝗣𝗜𝘀 Extend capabilities with external tools: - Name: Clear, intuitive (e.g., "StockPriceRetriever") - Description: What does it do? - Schemas: Define input/output formats - Error Handling: How to manage failures? 📌 Example: A support AI retrieves order details via CRM API. 6️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗥𝗼𝗹𝗲 & 𝗞𝗲𝘆 𝗧𝗮𝘀𝗸𝘀 Narrowly defined agents perform better. Clarify: - Mission: (e.g., "I analyze datasets for insights.") - Key Tasks: (Summarizing, visualizing, analyzing) - Limitations: ("I don’t offer legal advice.") 📌 Example: A financial AI focuses on finance, not general knowledge. 7️⃣ 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗥𝗮𝘄 𝗟𝗟𝗠 𝗢𝘂𝘁𝗽𝘂𝘁𝘀 Post-process responses for structure & accuracy: - Convert AI output to structured formats (JSON, tables) - Validate correctness before user delivery - Ensure correct tool execution 📌 Example: A financial AI converts extracted data into JSON. 8️⃣ 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝘁𝗼 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 (𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱) For complex workflows: - Info Sharing: What context is passed between agents? - Error Handling: What if one agent fails? - State Management: How to pause/resume tasks? 📌 Example: 1️⃣ One agent fetches data 2️⃣ Another summarizes 3️⃣ A third generates a report Master the fundamentals, experiment, and refine and.. now go build something amazing!

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