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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 42 044 підписників, посідаючи 3 190 місце в категорії Технології та додатки та 8 789 місце у регіоні Індія.

📊 Показники аудиторії та динаміка

З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 42 044 підписників.

За останніми даними від 03 листопада, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на -161, а за останні 24 години на -8, загальне охоплення залишається високим.

  • Статус верифікації: Не верифікований
  • Рівень залученості (ER): Середній показник залученості аудиторії становить 2.09%. Протягом перших 24 годин після публікації контент зазвичай збирає 0.58% реакцій від загальної кількості підписників.
  • Охоплення публікацій: В середньому кожен допис отримує 877 переглядів. Протягом першої доби публікація в середньому набирає 245 переглядів.
  • Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 2.
  • 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 04 листопада, 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.

42 044
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-197 днів
-16130 днів
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Unpopular opinion: ChatGPT is only as smart as the user; if garbage goes in, garbage comes out.

LinkedIn has become a $15 billion biz
LinkedIn has become a $15 billion biz

We are now on WhatsApp as well Follow for more Artificial Intelligence resources: 👇 https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E

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Snapchat’s new AI chatbot is already raising alarms among teens and parents Less than a few hours after Snapchat rolled out i
Snapchat’s new AI chatbot is already raising alarms among teens and parents Less than a few hours after Snapchat rolled out its My AI chatbot to all users last week, Lyndsi Lee, a mother from East Prairie, Missouri, told her 13-year-old daughter to stay away from the feature. “It’s a temporary solution until I know more about it and can set some healthy boundaries and guidelines,” said Lee, who works at a software company. She worries about how My AI presents itself to young users like her daughter on Snapchat. The feature is powered by the viral AI chatbot tool ChatGPT – and like ChatGPT, it can offer recommendations, answer questions and converse with users. But Snapchat’s version has some key differences: Users can customize the chatbot’s name, design a custom Bitmoji avatar for it, and bring it into conversations with friends. The net effect is that conversing with Snapchat’s chatbot may feel less transactional than visiting ChatGPT’s website. Source-Link: Edition CNN

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In my teen's I thought: • A fancy job title. • A huge salary. • A demanding job. = WINNING In my 20s I realized: • Having control of your time. • Doing work you enjoy. • Seeing family more happy. = WINNING Life Looks Different Every Decade.

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OpenAI New Model-01 Don't let snake-oil salesmen fool you. This new model released by OpenAI today doesn't "think." It just generates an extensive "chain of thought", which is just a discussion of the model with itself that looks like a person talking to themselves. Previous models were trained to be used in one-shot. You ask a question, you get an answer. Because of randomness in next token generation, if you were unlucky, your answer might be wrong. This model was finetuned to generate a long (and hidden from the user by the UI) discussion on how to better solve the problem, what facts are known, what assumptions need to be made, and what constraints should be respected. If you explicitly asked previous models to generate this discussion before answering your question, you would get a better quality result, because the final answer would be conditioned on the information contained in this discussion. They seemingly optimized their model to generate good quality discussions (without the user asking for it) by using reinforcement learning on various problems that have a verifiable solution, so that a reward for finding the right answer could be automatically assigned. For example: Question: 1+1 = ? Discussion: we have 1 and we have 1 more. And we have a plus sign, so it's an addition. What happens if we add 1 and 1? It means 1 is incremented by 1. When we increment 1 by 1, what do we get? Let's count: 1, 2, 3, 4,... Ok, 2 comes after 1, so 1 + 1 must be 2. Answer: 2 Reward: 1 Question: 1+1 = ? Discussion: It's easy. 1+1=11 Answer: 11 Reward: 0 Once the model is trained, what the user sees: Question: 1+1 = ? (Discussion happens behinds the scenes.) Answer: 2 Sure! Here's a more polished version of the statement: Don't get swept away by the hype around AI; Stay grounded and approach it thoughtfully. 💯

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8. Set up the user interface and trigger the main function. • Provides an input field for the user's question • Triggers the
8. Set up the user interface and trigger the main function. • Provides an input field for the user's question • Triggers the main function when the user clicks "Get Answer"

7. Define the main function to run all LLMs and aggregate results. • Runs all reference models asynchronously • Displays indi
7. Define the main function to run all LLMs and aggregate results. • Runs all reference models asynchronously • Displays individual responses in expandable sections • Aggregates responses using the aggregator model • Streams the aggregated response.

6. Implement the LLM call function. • Asynchronously calls the LLM with the user's prompt • Returns the model name and its re
6. Implement the LLM call function. • Asynchronously calls the LLM with the user's prompt • Returns the model name and its response

5. Define the models and aggregator system prompt. • Specifies the LLMs to be used for generating responses • Defines the agg
5. Define the models and aggregator system prompt. • Specifies the LLMs to be used for generating responses • Defines the aggregator model and its system prompt

4. Initialize Together AI clients. • Sets up Together API key as an environment variable • Initializes both synchronous and a
4. Initialize Together AI clients. • Sets up Together API key as an environment variable • Initializes both synchronous and asynchronous Together clients

3. Set up the Streamlit app and API key input. • Creates a title for the app • Adds a secure input field for the Together API
3. Set up the Streamlit app and API key input. • Creates a title for the app • Adds a secure input field for the Together API key

2. Import necessary libraries • Streamlit for the web interface • asyncio for asynchronous operations • Together AI for LLM i
2. Import necessary libraries • Streamlit for the web interface • asyncio for asynchronous operations • Together AI for LLM interactions

1. Install the necessary Python Libraries Run the following commands from your terminal to install the required libraries:
1. Install the necessary Python Libraries Run the following commands from your terminal to install the required libraries: