Media is too big
VIEW IN TELEGRAM
Jenson: “The AI Industrial Revolution is creating extraordinary demand for skilled craft — plumbers, electricians, technicians, builders of the world’s new AI factories.”
What a crazy time. I never would have thought that the CEO of the most valuable tech company would one day say that we need more skilled craftsmen like plumbers.
What a crazy time. I never would have thought that the CEO of the most valuable tech company would one day say that we need more skilled craftsmen like plumbers.
🚀 AI Journey Contest 2025: Test your AI skills!
Join our international online AI competition. Register now for the contest! Award fund — RUB 6.5 mln!
Choose your track:
· 🤖 Agent-as-Judge — build a universal “judge” to evaluate AI-generated texts.
· 🧠 Human-centered AI Assistant — develop a personalized assistant based on GigaChat that mimics human behavior and anticipates preferences. Participants will receive API tokens and a chance to get an additional 1M tokens.
· 💾 GigaMemory — design a long-term memory mechanism for LLMs so the assistant can remember and use important facts in dialogue.
Why Join
Level up your skills, add a strong line to your resume, tackle pro-level tasks, compete for an award, and get an opportunity to showcase your work at AI Journey, a leading international AI conference.
How to Join
1. Register here.
2. Choose your track.
3. Create your solution and submit it by 30 October 2025.
🚀 Ready for a challenge? Join a global developer community and show your AI skills!
Join our international online AI competition. Register now for the contest! Award fund — RUB 6.5 mln!
Choose your track:
· 🤖 Agent-as-Judge — build a universal “judge” to evaluate AI-generated texts.
· 🧠 Human-centered AI Assistant — develop a personalized assistant based on GigaChat that mimics human behavior and anticipates preferences. Participants will receive API tokens and a chance to get an additional 1M tokens.
· 💾 GigaMemory — design a long-term memory mechanism for LLMs so the assistant can remember and use important facts in dialogue.
Why Join
Level up your skills, add a strong line to your resume, tackle pro-level tasks, compete for an award, and get an opportunity to showcase your work at AI Journey, a leading international AI conference.
How to Join
1. Register here.
2. Choose your track.
3. Create your solution and submit it by 30 October 2025.
🚀 Ready for a challenge? Join a global developer community and show your AI skills!
❤5
🤖 Artificial Intelligence Project Ideas ✅
🟢 Beginner Level
⦁ Spam Email Classifier
⦁ Handwritten Digit Recognition (MNIST)
⦁ Rock-Paper-Scissors AI Game
⦁ Chatbot using Rule-Based Logic
⦁ AI Tic-Tac-Toe Game
🟡 Intermediate Level
⦁ Face Detection & Emotion Recognition
⦁ Voice Assistant with Speech Recognition
⦁ Language Translator (using NLP models)
⦁ AI-Powered Resume Screener
⦁ Smart Virtual Keyboard (predictive typing)
🔴 Advanced Level
⦁ Self-Learning Game Agent (Reinforcement Learning)
⦁ AI Stock Trading Bot
⦁ Deepfake Video Generator (Ethical Use Only)
⦁ Autonomous Car Simulation (OpenCV + RL)
⦁ Medical Diagnosis using Deep Learning (X-ray/CT analysis)
💬 Double Tap ❤️ for more! 💡🧠
🟢 Beginner Level
⦁ Spam Email Classifier
⦁ Handwritten Digit Recognition (MNIST)
⦁ Rock-Paper-Scissors AI Game
⦁ Chatbot using Rule-Based Logic
⦁ AI Tic-Tac-Toe Game
🟡 Intermediate Level
⦁ Face Detection & Emotion Recognition
⦁ Voice Assistant with Speech Recognition
⦁ Language Translator (using NLP models)
⦁ AI-Powered Resume Screener
⦁ Smart Virtual Keyboard (predictive typing)
🔴 Advanced Level
⦁ Self-Learning Game Agent (Reinforcement Learning)
⦁ AI Stock Trading Bot
⦁ Deepfake Video Generator (Ethical Use Only)
⦁ Autonomous Car Simulation (OpenCV + RL)
⦁ Medical Diagnosis using Deep Learning (X-ray/CT analysis)
💬 Double Tap ❤️ for more! 💡🧠
❤6
Prompt:
Minimalist paint-style outline of a [subject], flowing black lines, clean composition, simple yet dramatic pose, fluid movement captured with elegant negative space, expressive and graceful silhouette
Please open Telegram to view this post
VIEW IN TELEGRAM
Please open Telegram to view this post
VIEW IN TELEGRAM
❤7👍2
🧠 Must-Know Concepts for Every Developer 🧰💡
❯ Data Structures & Algorithms
⦁ Arrays, Linked Lists, Stacks, Queues
⦁ Trees, Graphs, Hashmaps
⦁ Sorting & Searching algorithms
⦁ Time & Space Complexity (Big O)
❯ Operating Systems Basics
⦁ Processes vs Threads
⦁ Memory Management
⦁ File Systems
⦁ OS concepts like Deadlock, Scheduling
❯ Networking Essentials
⦁ HTTP / HTTPS
⦁ DNS, IP, TCP/IP
⦁ RESTful APIs
⦁ WebSockets for real-time apps
❯ Security Fundamentals
⦁ Encryption (SSL/TLS)
⦁ Authentication vs Authorization
⦁ OWASP Top 10
⦁ Secure coding practices
❯ System Design
⦁ Scalability & Load Balancing
⦁ Caching (Redis, CDN)
⦁ Database Sharding & Replication
⦁ Message Queues (RabbitMQ, Kafka)
❯ Version Control
⦁ Git basics: clone, commit, push, pull
⦁ Branching strategies
⦁ Merge conflicts & resolutions
❯ Debugging & Logging
⦁ Print debugging & breakpoints
⦁ Logging libraries (log4j, logging)
⦁ Error tracking tools (Sentry, Rollbar)
❯ Code Quality & Maintenance
⦁ Clean code principles
⦁ Design patterns (Singleton, Observer, etc.)
⦁ Code reviews & refactoring
⦁ Writing unit tests
💬 Tap ❤️ for more!
❯ Data Structures & Algorithms
⦁ Arrays, Linked Lists, Stacks, Queues
⦁ Trees, Graphs, Hashmaps
⦁ Sorting & Searching algorithms
⦁ Time & Space Complexity (Big O)
❯ Operating Systems Basics
⦁ Processes vs Threads
⦁ Memory Management
⦁ File Systems
⦁ OS concepts like Deadlock, Scheduling
❯ Networking Essentials
⦁ HTTP / HTTPS
⦁ DNS, IP, TCP/IP
⦁ RESTful APIs
⦁ WebSockets for real-time apps
❯ Security Fundamentals
⦁ Encryption (SSL/TLS)
⦁ Authentication vs Authorization
⦁ OWASP Top 10
⦁ Secure coding practices
❯ System Design
⦁ Scalability & Load Balancing
⦁ Caching (Redis, CDN)
⦁ Database Sharding & Replication
⦁ Message Queues (RabbitMQ, Kafka)
❯ Version Control
⦁ Git basics: clone, commit, push, pull
⦁ Branching strategies
⦁ Merge conflicts & resolutions
❯ Debugging & Logging
⦁ Print debugging & breakpoints
⦁ Logging libraries (log4j, logging)
⦁ Error tracking tools (Sentry, Rollbar)
❯ Code Quality & Maintenance
⦁ Clean code principles
⦁ Design patterns (Singleton, Observer, etc.)
⦁ Code reviews & refactoring
⦁ Writing unit tests
💬 Tap ❤️ for more!
❤2
🚀 7 free AI courses by NVIDIA 👇
1. Generative AI Explained
https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-07+V1
2. LLM with RAG Model
https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-16+V1
3. Building Video AI Apps
https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-IV-02+V2
4. AI on Jetson Nano
https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-RX-02+V2
5. Digital Fingerprinting
https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+T-DS-02+V2
6. Introduction to CUDA
https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+T-AC-01+V1
7. Building RAG Agents with LLMs
https://resources.nvidia.com/en-us-ai-large-language-models/building-rag-agents-with-llms-dli-course
React with ❤️ if you like this :)
1. Generative AI Explained
https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-07+V1
2. LLM with RAG Model
https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-16+V1
3. Building Video AI Apps
https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-IV-02+V2
4. AI on Jetson Nano
https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-RX-02+V2
5. Digital Fingerprinting
https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+T-DS-02+V2
6. Introduction to CUDA
https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+T-AC-01+V1
7. Building RAG Agents with LLMs
https://resources.nvidia.com/en-us-ai-large-language-models/building-rag-agents-with-llms-dli-course
React with ❤️ if you like this :)
❤8
Useful AI Terms You Should Know 🤖✨
1. Bias - AI unfairly prefers some answers due to skewed training data, leading to unfair outcomes like in hiring algorithms.
2. Label - A tag or answer AI learns as correct, essential for supervised training.
3. Model - A program that learns patterns from data to make predictions or generate outputs.
4. Training - Feeding AI examples so it improves at tasks, like teaching it to recognize cats in photos.
5. Chatbot - AI that converses with users, powering tools like customer support bots.
6. Dataset - A collection of data AI trains on—quality matters for accurate results.
7. Algorithm - Step-by-step rules AI follows to process data and solve problems.
8. Token - Small units like words or subwords that AI models like GPT break text into.
9. Overfitting - When AI memorizes training data too well and flops on new, unseen info.
10. AI Agent - Autonomous software that performs tasks independently, like booking meetings.
11. AI Ethics - Guidelines for responsible AI use, focusing on fairness and avoiding harm.
12. Explainability - How well you can understand why AI made a certain decision.
13. Inference - AI applying what it learned to new data, like generating a response.
14. Turing Test - A benchmark to see if AI can mimic human conversation convincingly.
15. Prompt - The input or question you give AI to guide its output.
16. Fine-Tuning - Tweaking a pre-trained model for specific tasks, like customizing for legal docs.
17. Generative AI - AI that creates new content, from text to images (think DALL-E).
18. AI Automation - Using AI to handle repetitive tasks without human input.
19. Neural Network - AI structure mimicking the brain's neurons for pattern recognition.
20. Computer Vision - AI "seeing" and analyzing images or videos, like facial recognition.
21. Transfer Learning - Reusing a model trained on one task for a related new one.
22. Guardrails (in AI) - Safety features to prevent harmful or incorrect outputs.
23. Open Source AI - Freely available AI code anyone can modify and build on.
24. Deep Learning - Advanced neural networks with many layers for complex tasks.
25. Reinforcement Learning - AI improving through trial-and-error rewards, like game-playing bots.
26. Hallucination (in AI) - When AI confidently spits out false info.
27. Zero-shot Learning - AI tackling new tasks without specific training examples.
28. Speech Recognition - AI converting spoken words to text, powering voice assistants.
29. Supervised Learning - AI trained on labeled data to predict outcomes.
30. Model Context Protocol - Standards for how AI handles and shares context in conversations.
31. Machine Learning - AI subset where systems learn from data without explicit programming.
32. Artificial Intelligence (AI) - Tech enabling machines to perform human-like tasks.
33. Unsupervised Learning - AI finding hidden patterns in unlabeled data.
34. LLM (Large Language Model) - Massive AI for understanding and generating human-like text.
35. ASI (Artificial Superintelligence) - Hypothetical AI surpassing human intelligence in all areas.
36. GPU (Graphics Processing Unit) - Hardware accelerating AI training with parallel processing.
37. Natural Language Processing (NLP) - AI handling human language, from translation to sentiment analysis.
38. AGI (Artificial General Intelligence) - AI matching human versatility across any intellectual task.
39. GPT (Generative Pretrained Transformer) - Architecture behind models like ChatGPT for natural text generation.
40. API (Application Programming Interface) - Bridge letting apps access AI features seamlessly.
Double Tap ❤️ if you learned something new!
1. Bias - AI unfairly prefers some answers due to skewed training data, leading to unfair outcomes like in hiring algorithms.
2. Label - A tag or answer AI learns as correct, essential for supervised training.
3. Model - A program that learns patterns from data to make predictions or generate outputs.
4. Training - Feeding AI examples so it improves at tasks, like teaching it to recognize cats in photos.
5. Chatbot - AI that converses with users, powering tools like customer support bots.
6. Dataset - A collection of data AI trains on—quality matters for accurate results.
7. Algorithm - Step-by-step rules AI follows to process data and solve problems.
8. Token - Small units like words or subwords that AI models like GPT break text into.
9. Overfitting - When AI memorizes training data too well and flops on new, unseen info.
10. AI Agent - Autonomous software that performs tasks independently, like booking meetings.
11. AI Ethics - Guidelines for responsible AI use, focusing on fairness and avoiding harm.
12. Explainability - How well you can understand why AI made a certain decision.
13. Inference - AI applying what it learned to new data, like generating a response.
14. Turing Test - A benchmark to see if AI can mimic human conversation convincingly.
15. Prompt - The input or question you give AI to guide its output.
16. Fine-Tuning - Tweaking a pre-trained model for specific tasks, like customizing for legal docs.
17. Generative AI - AI that creates new content, from text to images (think DALL-E).
18. AI Automation - Using AI to handle repetitive tasks without human input.
19. Neural Network - AI structure mimicking the brain's neurons for pattern recognition.
20. Computer Vision - AI "seeing" and analyzing images or videos, like facial recognition.
21. Transfer Learning - Reusing a model trained on one task for a related new one.
22. Guardrails (in AI) - Safety features to prevent harmful or incorrect outputs.
23. Open Source AI - Freely available AI code anyone can modify and build on.
24. Deep Learning - Advanced neural networks with many layers for complex tasks.
25. Reinforcement Learning - AI improving through trial-and-error rewards, like game-playing bots.
26. Hallucination (in AI) - When AI confidently spits out false info.
27. Zero-shot Learning - AI tackling new tasks without specific training examples.
28. Speech Recognition - AI converting spoken words to text, powering voice assistants.
29. Supervised Learning - AI trained on labeled data to predict outcomes.
30. Model Context Protocol - Standards for how AI handles and shares context in conversations.
31. Machine Learning - AI subset where systems learn from data without explicit programming.
32. Artificial Intelligence (AI) - Tech enabling machines to perform human-like tasks.
33. Unsupervised Learning - AI finding hidden patterns in unlabeled data.
34. LLM (Large Language Model) - Massive AI for understanding and generating human-like text.
35. ASI (Artificial Superintelligence) - Hypothetical AI surpassing human intelligence in all areas.
36. GPU (Graphics Processing Unit) - Hardware accelerating AI training with parallel processing.
37. Natural Language Processing (NLP) - AI handling human language, from translation to sentiment analysis.
38. AGI (Artificial General Intelligence) - AI matching human versatility across any intellectual task.
39. GPT (Generative Pretrained Transformer) - Architecture behind models like ChatGPT for natural text generation.
40. API (Application Programming Interface) - Bridge letting apps access AI features seamlessly.
Double Tap ❤️ if you learned something new!
❤2