Artificial Intelligence - ChatGPT & AI Tech News

GigaChat 3.5 Reasoning is a new open-source LLM designed to reason before generating responses. The model breaks problems into stages, builds execution plans, checks intermediate results, and self-corrects when needed. Built on GigaChat 3.5 Ultra, it was trained on math and coding tasks using multiple step-by-step reasoning paths. An automated verification step reinforces the paths that lead to correct answers, enabling the model to plan multi-step actions, decide when to call external tools, and revise earlier steps independently. The model uses a proprietary linear attention architecture, which improves efficiency on long contexts by retaining key processed points rather than re-matching queries against the entire prior text. On math problems, GigaChat 3.5 Reasoning uses on average 37% fewer tokens than DeepSeek V4 Flash Preview. Benchmark gains over the non-reasoning version: • IFBench: 44 → 77 • Natural Plan: 64 → 80 • LiveCodeBench v6: 56 → 85 The model is open-sourced under the MIT license. Weights are available on Hugging Face:  fp8 | bf16

How to use ChatGPT to improve your critical thinking 🧠🔍 Prompt: I want you to act as my personal critical thinking coach. My goal is to think more clearly, question assumptions, and make better judgments. Help me by: • First, assess my current thinking skills through questions and real-world scenarios • Identify weaknesses in my reasoning and common thinking patterns • Teach me how to distinguish facts, assumptions, opinions, and evidence • Give me realistic problems that require careful reasoning • Ask me to explain my thinking before giving feedback • Challenge my assumptions and present alternative perspectives • Teach me how to recognize common cognitive biases and logical fallacies • Show me how to evaluate the quality of evidence behind a claim • Give me practical exercises to strengthen my reasoning • Gradually increase the difficulty as my critical thinking improves Don't simply tell me whether my answer is right or wrong. Help me understand how to think through the problem better. Start with the first scenario. Double Tap ❤️ For More Useful Prompts ❤️

✅ Top 50 AI Interview Questions 🤖🧠 1. What is Artificial Intelligence? 2. Difference between AI, Machine Learning, and Deep Learning 3. What is supervised vs unsupervised learning? 4. Explain overfitting and underfitting 5. What are classification and regression? 6. What is a confusion matrix? 7. Define precision, recall, F1-score 8. What is the difference between batch and online learning? 9. Explain bias-variance tradeoff 10. What are activation functions in neural networks? 11. What is a perceptron? 12. What is gradient descent? 13. Explain backpropagation 14. What is a convolutional neural network (CNN)? 15. What is a recurrent neural network (RNN)? 16. What is transfer learning? 17. Difference between parametric and non-parametric models 18. What are the different types of AI (ANI, AGI, ASI)? 19. What is reinforcement learning? 20. Explain Markov Decision Process (MDP) 21. What are generative vs discriminative models? 22. Explain PCA (Principal Component Analysis) 23. What is feature selection and why is it important? 24. What is one-hot encoding? 25. What is dimensionality reduction? 26. What is regularization? (L1 vs L2) 27. What is the curse of dimensionality? 28. How does k-means clustering work? 29. Difference between KNN and K-means 30. What is Naive Bayes classifier? 31. Explain Decision Trees and Random Forest 32. What is a Support Vector Machine (SVM)? 33. What is ensemble learning? 34. What is bagging vs boosting? 35. What is cross-validation? 36. Explain ROC curve and AUC 37. What is an autoencoder? 38. What are GANs (Generative Adversarial Networks)? 39. Explain LSTM and GRU 40. What is NLP and its applications? 41. What is tokenization and stemming? 42. Explain BERT and its use cases 43. What is the role of attention in transformers? 44. What is a language model? 45. Explain YOLO in object detection 46. What is Explainable AI (XAI)? 47. What is model interpretability vs explainability? 48. How do you deploy a machine learning model? 49. What are ethical concerns in AI? 50. What is prompt engineering in LLMs? 💬 Tap ❤️ for the detailed answers!

Для @aisigma 🤖 ATOM just announced iXo — an AI that interviews you instead of waiting for a prompt Here's what makes it different from the usual chatbot: instead of typing a question and getting an answer, you just describe what's going on — in a message or a voice note — and iXo asks follow-up questions to actually understand the full situation: work, money, health, relationships, whatever's relevant. Once it has enough context, it connects the pieces you wouldn't lay out side by side yourself, and shows you what's driving what — without deciding anything for you. 📌 It's live now as a web app, one browser tab, nothing to download. Access is limited while it rolls out, with a mobile app on the way.

Google owns one of the most powerful learning tools in the world. It’s free. It’s been available for months. Yet 95% of people still use it the wrong way. Here are 8 NotebookLM use cases that can save you hours of time. 🔖 Bookmark this — you’ll thank yourself later. 1. Private Tutor You have a topic you want to learn but don't know where to start. Upload any documents, videos, or web pages about that topic. Prompt: "As an expert professor, explain this content to me from scratch, provide practical examples, and tell me what I should learn first." 2. Executive Summary You have an 80-page report, and there's a meeting in 2 hours. Upload it to NotebookLM, forget about reading the full version. Prompt: 'Summarize the document into 10 key points. Prioritize what I need to know for decision-making. Under 300 words.' 3. Meeting Preparation Assistant Upload the background for the next meeting: emails, proposals, reports. NotebookLM processes all the content together. Prompt: 'Give me a briefing for the meeting on this topic. Tell me 5 of the most important points and 3 good questions I should ask.' 4. Instant Podcasts This is the most powerful feature that almost no one uses. Upload any document and enable "Audio Overview." NotebookLM generates a podcast where two people discuss the content. Listen while commuting or working out—no need to read. 5. Contradiction Detector You have multiple documents on the same topic and don't know which one to trust. Upload them all at once. Prompt: 'Analyze these documents and tell me where they contradict each other. Which information is more reliable, and why?' 6. Exam Creator Learn better when tested. Upload the material you want to master. Prompt: 'Create 10 exam questions on this content. Mix multiple-choice and open-ended questions. Finally, give me the correct answers.' 7. Terminology Translator You've received a contract, regulation, or technical document filled with professional jargon, and you don't understand it. Upload it. Prompt: 'Explain this document like I'm 12 years old. Highlight the important parts, and tell me if there's anything concerning.' 8. Content Generator You have hours of recordings, PDFs, or scattered notes. Upload them together to NotebookLM. Prompt: 'Use these materials to create a content plan for social media. Give me 10 post ideas and the core message for each.' Double Tap ❤️ For More

AI agents are no longer just a developer toy. OpenAI published new research on how agents are changing work, and the main takeaway is important: AI is moving from short chat interactions to delegated long-horizon tasks. That sounds abstract, but here is the simple version: Old way: ask AI one question, get one answer. New way: give AI a task, let it work for minutes or hours, review the result. This is the shift that matters. According to OpenAI's research, by May 2026, 80.6% of sampled individual Codex users made at least one request estimated to represent more than 30 minutes of human work. 70.2% made at least one request estimated at more than one hour of human work. And 25.6% delegated work estimated to take more than eight hours. The most interesting part: Non-developer adoption is growing fast. That means agents are not only for engineers anymore. They are becoming useful for: - operations - support - finance - recruiting - marketing - research - reporting - personal productivity - small business workflows This is the practical question now: Not "Which AI model is smartest?" But: "What work can I safely delegate to an agent?" The answer is not "everything." The answer is: one clear task, with context, tools, rules, memory, and human review. This is what we will focus on next: how to turn normal work into agent-ready tasks. Not theory. Not hype. Practical AI systems you can actually build and use. Sources: https://openai.com/index/how-agents-are-transforming-work/ https://www.axios.com/2026/06/25/codex-agents-growth-openai

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🏠🤖 Run Your Own LOCAL LLM (Beginner Friendly) LLMs are cool, but running your own local one hits different 😎 No cloud. No API keys. No limits. 🧩 Step 1: Install Ollama Install Ollama on your machine (works on Mac, Windows, Linux). Once installed, open your terminal. 🚀 Step 2: Run a model

ollama run llama3.2

This command: • Downloads the model • Starts it locally • Lets you chat instantly 💬 If you see the prompt, your local LLM is running. ⚙️ Step 3: Do local inference (API style) Ollama runs a local server on your machine.

curl http://127.0.0.1:11434/api/generate \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama3.2",
    "prompt": "Explain overfitting like I am 12",
    "stream": false
  }'

If you get a JSON response with text → ✅ it works. 💡 Why this is powerful • Works offline • Private by default • Perfect for learning, testing, and small apps This is the easiest way to start with LLMs locally.

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Artificial Intelligence isn't easy! It’s the cutting-edge field that enables machines to think, learn, and act like humans. To truly master Artificial Intelligence, focus on these key areas: 0. Understanding AI Fundamentals: Learn the basic concepts of AI, including search algorithms, knowledge representation, and decision trees. 1. Mastering Machine Learning: Since ML is a core part of AI, dive into supervised, unsupervised, and reinforcement learning techniques. 2. Exploring Deep Learning: Learn neural networks, CNNs, RNNs, and GANs to handle tasks like image recognition, NLP, and generative models. 3. Working with Natural Language Processing (NLP): Understand how machines process human language for tasks like sentiment analysis, translation, and chatbots. 4. Learning Reinforcement Learning: Study how agents learn by interacting with environments to maximize rewards (e.g., in gaming or robotics). 5. Building AI Models: Use popular frameworks like TensorFlow, PyTorch, and Keras to build, train, and evaluate your AI models. 6. Ethics and Bias in AI: Understand the ethical considerations and challenges of implementing AI responsibly, including fairness, transparency, and bias. 7. Computer Vision: Master image processing techniques, object detection, and recognition algorithms for AI-powered visual applications. 8. AI for Robotics: Learn how AI helps robots navigate, sense, and interact with the physical world. 9. Staying Updated with AI Research: AI is an ever-evolving field—stay on top of cutting-edge advancements, papers, and new algorithms. Artificial Intelligence is a multidisciplinary field that blends computer science, mathematics, and creativity. 💡 Embrace the journey of learning and building systems that can reason, understand, and adapt. ⏳ With dedication, hands-on practice, and continuous learning, you’ll contribute to shaping the future of intelligent systems! Data Science & Machine Learning Resources: https://topmate.io/coding/914624 Credits: https://t.me/datasciencefun Like if you need similar content 😄👍 Hope this helps you 😊

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Today, we can see AI agents almost everywhere, making our lives easier. Almost every field benefits from it, whether it is your last-minute ticket booking or your coding companion. AI agents have effectively tapped into every market. Everyone wants to build them to optimize their workflows. This post explores the top 8 things that you should keep in mind while building your AI agent.

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Google DeepMind CEO, Demis Hassabis: AGI is now at the edge of the singularity. Cyber is only the first warning shot. Bio and nuclear risks may come within 2 years. "That's just a warning shot for humanity." AGI safety now needs global standards.

AI/ML roadmap Topic: Mathematics - Subtopic: Linear Algebra - Vectors, Matrices, Eigenvalues and Eigenvectors - Subtopic: Calculus - Differentiation, Integration, Partial Derivatives - Subtopic: Probability and Statistics - Probability Theory, Random Variables, Statistical Inference Topic: Programming - Subtopic: Python - Python Basics, Libraries like NumPy, Pandas, Matplotlib Topic: Machine Learning - Subtopic: Supervised Learning - Linear Regression, Logistic Regression, Decision Trees - Subtopic: Unsupervised Learning - Clustering, Dimensionality Reduction[1](https://i.am.ai/roadmap) - Subtopic: Neural Networks and Deep Learning - Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks Topic: Specializations - Subtopic: Natural Language Processing - Text Preprocessing, Topic Modeling, Word Embeddings - Subtopic: Computer Vision - Image Processing, Object Detection, Image Segmentation - Subtopic: Reinforcement Learning - Markov Decision Processes, Q-Learning, Policy Gradients Join for more: https://t.me/machinelearning_deeplearning