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Artificial Intelligence - ChatGPT & AI Tech News

Artificial Intelligence - ChatGPT & AI Tech News

前往频道在 Telegram

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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📈 Telegram 频道 Artificial Intelligence - ChatGPT & AI Tech News 的分析概览

频道 Artificial Intelligence - ChatGPT & AI Tech News (@aisigma) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 19 485 名订阅者,在 技术与应用 类别中位列第 6 779,并在 印度 地区排名第 21 631

📊 受众指标与增长动态

невідомо 创建以来,项目保持高速增长,吸引了 19 485 名订阅者。

根据 12 七月, 2026 的最新数据,频道保持稳定运转。过去 30 天订阅人数变化为 42,过去 24 小时变化为 -3,整体触达仍然可观。

  • 认证状态: 未认证
  • 互动率 (ER): 平均受众互动率为 3.07%。内容发布后 24 小时内通常能获得 0.66% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 598 次浏览,首日通常累积 129 次浏览。
  • 互动与反馈: 受众积极参与,单帖平均反应数为 0
  • 主题关注点: 内容集中在 learning, openai, phi, capability, llamafile 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台:
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...

凭借高频更新(最新数据采集于 13 七月, 2026),频道始终保持新鲜度与高覆盖。分析显示受众积极互动,使其成为 技术与应用 类别中的关键影响点。

19 485
订阅者
-324 小时
+87
+4230
帖子存档
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 yo
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.

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.

50 AI/Dev Projects 🚀 React ❤️ For More

Claude prompts to optimize your GitHub profile 🚀 React ❤️ For More

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

🧠 How to Build an AI Agent
🧠 How to Build an AI Agent

Google Prompt Engineering.pdf8.30 MB

🚨 Google dropped a 68-page whitepaper on Prompt Engineering — perfect for API users! It’s packed with advanced tips, techniq
🚨 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 𝘀𝗮𝗹
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 ☕️ React 🩷 For More

🤖 9 Free Al Testing Platforms
🤖 9 Free Al Testing Platforms

Two powerful open-source tools to master Local AI efficiently 1️⃣ LEANN: Extreme Compression for RAG This open-source repo co
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
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

🤖 DeepSeek Al Prompt Hacks
🤖 DeepSeek Al Prompt Hacks

AI & ML DIGITAL NOTES 📝 REACT ❤️ For More ✌️