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Welcome to ChatGPT & AI Tutorials! 🤖 Unlock the power of Artificial intelligence with clear and concise guides. From basics to advanced techniques, you'll get free Resources to learn AI. 🚀Artificial Intelligence 🚀Machine Learning 🚀Tech News 🚀ChatGPT
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Канал Artificial Intelligence - ChatGPT & AI Tech News (@aisigma) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 19 485 підписників, посідаючи 6 779 місце в категорії Технології та додатки та 21 631 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 19 485 підписників.
За останніми даними від 12 липня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 42, а за останні 24 години на -3, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
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- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 0.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як learning, openai, phi, capability, llamafile.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Welcome to ChatGPT & AI Tutorials! 🤖 Unlock the power of Artificial intelligence with clear and concise guides. From basics to advanced techniques, you'll get free Resources to learn AI.
🚀Artificial Intelligence
🚀Machine Learning
🚀Tech News
🚀Ch...”
Завдяки високій частоті оновлень (останні дані отримано 13 липня, 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.
19 485
Підписники
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+87 днів
+4230 день
Архів дописів
The only LLM cheat sheet you'll ever need 🚀
Covers the main concepts, architectures, and practical applications.
Basics
- Tokens (tokenization, BPE)
- Embeddings (cosine similarity)
- Attention mechanism (Attention formula, Multi-Head Attention)
Transformer architecture and its variants
- BERT (models with only an encoder)
- GPT (models with only a decoder)
- T5 (models with an encoder and a decoder)
Large language models (LLMs)
- Prompting (context length, Chain-of-Thought)
- Pre-training (SFT, PEFT/LoRA)
- Preference tuning (Reward Model, Reinforcement Learning)
- Optimizations (Mixture of Experts, Distillation, Quantization)
Applications
- LLM-as-a-Judge (LaaJ)
- RAG (Retrieval-Augmented Generation)
- Agents (ReAct)
- Reasoning models (Scaling)
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.
🎯 7 best YouTube channels to learn AI from scratch 👇
1/ DeepLearning AI:
https://www.youtube.com/c/DeepLearningAI
2/ Krish Naik:
https://www.youtube.com/channel/UCNU_lfiiWBdtULKOw6X0Dig
3/ StatQuest with Josh Starmer:
https://www.youtube.com/c/joshstarmer
4/ 3Blue1Brown:
https://www.youtube.com/c/3blue1brown
5/ FreeCodeCamp
https://www.youtube.com/c/Freecodecamp
6/ Yannic Kilcher:
https://www.youtube.com/c/YannicKilcher
7/ IBM Technology:
https://www.youtube.com/c/IBMTechnology
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.
Claude prompts to optimize your GitHub profile 🚀
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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
🚨 Google dropped a 68-page whitepaper on Prompt Engineering — perfect for API users!
It’s packed with advanced tips, techniques, and real examples.
Super useful if you're working with LLM APIs or building AI apps!
PDF below 👇
7 out of 10 businesses are missing the AI automation opportunity.
(𝗜 𝘀𝗲𝗲 𝘁𝗵𝗶𝘀 𝗮𝗹𝗹 𝘁𝗵𝗲 𝘁𝗶𝗺𝗲 𝗶𝗻 𝗺𝘆 𝘀𝗮𝗹𝗲𝘀 𝗰𝗮𝗹𝗹𝘀.)
So many are stuck in manual processes.
Wondering why competitors are suddenly 10x faster.
But this 5-step AI automation process will help you out.
Here’s what business leaders should be implementing:
STEP 1: IDENTIFY AI USE CASES
↳ Content Creation with Jasper, Copy(.)ai
↳ Data Analysis with Julius AI, Code Interpreter
↳ Customer Support with Retell AI, Vectorshift AI
STEP 2: SELECT AI MODEL/TOOL
↳ ChatGPT for conversations & images
↳ Claude for analysis & coding
↳ n8n for workflow automation
STEP 3: DESIGN AGENT WORKFLOW
↳ Map the process with clear inputs/outputs
↳ Define decision logic
↳ Set up error handling
STEP 4: CONNECT APIs & DATA
↳ Zapier for app connections
↳ Make for workflow automation
↳ Langchain for AI frameworks
STEP 5: DEPLOY & MONITOR
↳ Track performance metrics
↳ Optimize based on results
↳ Scale what works
KEY TECH SKILLS YOU NEED:
- API Integration & Webhooks
- Prompt Engineering (ChatGPT, Claude)
- No-Code Platforms (Lovable, n8n)
- Vector Databases
- AI Agent Architecture
7 claude prompts to automate you bussiness ☕️
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Two powerful open-source tools to master Local AI efficiently
1️⃣ LEANN: Extreme Compression for RAG
This open-source repo compresses 60 million text chunks from approximately 201 GB to about 6 GB 🤯 That's about 97% less, while the quality of the retrieval remains very close to standard setups. • No cloud • No GPU • Runs locally on a regular laptop • Full privacy • 100% open source LEANN achieves this by not storing embeddings permanently. Instead, it uses a compact graph and recalculates embeddings only when they are actually needed.GitHub 2️⃣ Transformer Lab: All-in-One set of tools for working with LLMs.
☞ Allows you to train, fine-tune, and communicate with any LLM locally. ☞ One-click model loading, Simple drag-and-drop interface for RAG. ☞ Completely open sourced.GitHub ••••••••••••••••••••••••••••••••• 🤖 Data Science, ML & Big Data with @DataXplore
🌐 AI Frameworks & Their Use Cases 🤖🔬
🔹 TensorFlow ➜ Scalable deep learning for production ML models and distributed training
🔹 PyTorch ➜ Dynamic neural networks for research, prototyping, and flexible AI experiments
🔹 Keras ➜ High-level API for quick neural network building on TensorFlow backend
🔹 Scikit-learn ➜ Classical ML algorithms like classification, regression, and clustering
🔹 Hugging Face Transformers ➜ Pre-trained models for NLP tasks like translation and generation
🔹 XGBoost ➜ Gradient boosting for structured data with high accuracy and speed
🔹 LangChain ➜ Building LLM-powered apps with chaining, memory, and tool integration
🔹 JAX ➜ High-performance numerical computing with auto-differentiation for research
🔹 AutoGen ➜ Multi-agent systems for collaborative AI workflows and automation
🔹 LlamaIndex ➜ RAG pipelines and knowledge bases for context-aware AI apps
🔹 CrewAI ➜ Orchestrating multi-agent teams for complex task decomposition
🔹 Semantic Kernel ➜.NET-based AI orchestration for enterprise plugins and planning
🔹 MLflow ➜ ML lifecycle management with tracking, deployment, and reproducibility
🔹 FastAPI ➜ Building efficient APIs for serving AI models in production
🔹 Apache MXNet ➜ Lightweight deep learning with multi-GPU support for scalability
💬 Tap ❤️ if this helped!
Google and OpenAI employees revolt: "We will not be divided"
The Pentagon's war with Silicon Valley is escalating fast. Over 500 verified employees from Google and OpenAI just dropped a massive open letter titled "We Will Not Be Divided," urging their CEOs to stand with Anthropic and refuse to build AI for mass domestic surveillance or fully autonomous weapons.
The letter accuses the Department of War of trying to play the top AI labs against each other threatening Anthropic with the Defense Production Act while negotiating with Google and OpenAI behind closed doors.
Source.
@aipost 🏴
⚠️ AI Mistakes Developers Make
❌ Trusting output blindly
❌ Skipping code review
❌ Using AI instead of thinking
❌ Shipping untested AI code
❌ Ignoring edge cases
React ❤️ for more like this
#techinfo
