OpenAI has dropped a helpful AI for coders – the new Codex-1 model, which writes code like a top senior with 15 years of experience.
Codex-1 works within the Codex AI agent – it’s like having a whole development team in your browser, writing code and fixing it SIMULTANEOUSLY. Plus, the agent can work on multiple tasks in parallel.
They’re starting the rollout today – check it out in your sidebar.
Codex-1 works within the Codex AI agent – it’s like having a whole development team in your browser, writing code and fixing it SIMULTANEOUSLY. Plus, the agent can work on multiple tasks in parallel.
They’re starting the rollout today – check it out in your sidebar.
👍2
👍3
Roadmap to Building AI Agents
1. Master Python Programming – Build a solid foundation in Python, the primary language for AI development.
2. Understand RESTful APIs – Learn how to send and receive data via APIs, a crucial part of building interactive agents.
3. Dive into Large Language Models (LLMs) – Get a grip on how LLMs work and how they power intelligent behavior.
4. Get Hands-On with the OpenAI API – Familiarize yourself with GPT models and tools like function calling and assistants.
5. Explore Vector Databases – Understand how to store and search high-dimensional data efficiently.
6. Work with Embeddings – Learn how to generate and query embeddings for context-aware responses.
7. Implement Caching and Persistent Memory – Use databases to maintain memory across interactions.
8. Build APIs with Flask or FastAPI – Serve your agents as web services using these Python frameworks.
9. Learn Prompt Engineering – Master techniques to guide and control LLM responses.
10. Study Retrieval-Augmented Generation (RAG) – Learn how to combine external knowledge with LLMs.
11. Explore Agentic Frameworks – Use tools like LangChain and LangGraph to structure your agents.
12. Integrate External Tools – Learn to connect agents to real-world tools and APIs (like using MCP).
13. Deploy with Docker – Containerize your agents for consistent and scalable deployment.
14. Control Agent Behavior – Learn how to set limits and boundaries to ensure reliable outputs.
15. Implement Safety and Guardrails – Build in mechanisms to ensure ethical and safe agent behavior.
React ❤️ for more
1. Master Python Programming – Build a solid foundation in Python, the primary language for AI development.
2. Understand RESTful APIs – Learn how to send and receive data via APIs, a crucial part of building interactive agents.
3. Dive into Large Language Models (LLMs) – Get a grip on how LLMs work and how they power intelligent behavior.
4. Get Hands-On with the OpenAI API – Familiarize yourself with GPT models and tools like function calling and assistants.
5. Explore Vector Databases – Understand how to store and search high-dimensional data efficiently.
6. Work with Embeddings – Learn how to generate and query embeddings for context-aware responses.
7. Implement Caching and Persistent Memory – Use databases to maintain memory across interactions.
8. Build APIs with Flask or FastAPI – Serve your agents as web services using these Python frameworks.
9. Learn Prompt Engineering – Master techniques to guide and control LLM responses.
10. Study Retrieval-Augmented Generation (RAG) – Learn how to combine external knowledge with LLMs.
11. Explore Agentic Frameworks – Use tools like LangChain and LangGraph to structure your agents.
12. Integrate External Tools – Learn to connect agents to real-world tools and APIs (like using MCP).
13. Deploy with Docker – Containerize your agents for consistent and scalable deployment.
14. Control Agent Behavior – Learn how to set limits and boundaries to ensure reliable outputs.
15. Implement Safety and Guardrails – Build in mechanisms to ensure ethical and safe agent behavior.
React ❤️ for more
👍6👎1🔥1
LLM Cheatsheet
Introduction to LLMs
- LLMs (Large Language Models) are AI systems that generate text by predicting the next word.
- Prompts are the instructions or text you give to an LLM.
- Personas allow LLMs to take on specific roles or tones.
- Learning types:
- Zero-shot (no examples given)
- One-shot (one example)
- Few-shot (a few examples)
Transformers
- The core architecture behind LLMs, using self-attention to process input sequences.
- Encoder: Understands input.
- Decoder: Generates output.
- Embeddings: Converts words into vectors.
Types of LLMs
- Encoder-only: Great for understanding (like BERT).
- Decoder-only: Best for generating text (like GPT).
- Encoder-decoder: Useful for tasks like translation and summarization (like T5).
Configuration Settings
- Decoding strategies:
- Greedy: Always picks the most likely next word.
- Beam search: Considers multiple possible sequences.
- Random sampling: Adds creativity by picking among top choices.
- Temperature: Controls randomness (higher value = more creative output).
- Top-k and Top-p: Restrict choices to the most likely words.
LLM Instruction Fine-Tuning & Evaluation
- Instruction fine-tuning: Trains LLMs to follow specific instructions.
- Task-specific fine-tuning: Focuses on a single task.
- Multi-task fine-tuning: Trains on multiple tasks for broader skills.
Model Evaluation
- Evaluating LLMs is hard-metrics like BLEU and ROUGE are common, but human judgment is often needed.
Join our WhatsApp Channel: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
Introduction to LLMs
- LLMs (Large Language Models) are AI systems that generate text by predicting the next word.
- Prompts are the instructions or text you give to an LLM.
- Personas allow LLMs to take on specific roles or tones.
- Learning types:
- Zero-shot (no examples given)
- One-shot (one example)
- Few-shot (a few examples)
Transformers
- The core architecture behind LLMs, using self-attention to process input sequences.
- Encoder: Understands input.
- Decoder: Generates output.
- Embeddings: Converts words into vectors.
Types of LLMs
- Encoder-only: Great for understanding (like BERT).
- Decoder-only: Best for generating text (like GPT).
- Encoder-decoder: Useful for tasks like translation and summarization (like T5).
Configuration Settings
- Decoding strategies:
- Greedy: Always picks the most likely next word.
- Beam search: Considers multiple possible sequences.
- Random sampling: Adds creativity by picking among top choices.
- Temperature: Controls randomness (higher value = more creative output).
- Top-k and Top-p: Restrict choices to the most likely words.
LLM Instruction Fine-Tuning & Evaluation
- Instruction fine-tuning: Trains LLMs to follow specific instructions.
- Task-specific fine-tuning: Focuses on a single task.
- Multi-task fine-tuning: Trains on multiple tasks for broader skills.
Model Evaluation
- Evaluating LLMs is hard-metrics like BLEU and ROUGE are common, but human judgment is often needed.
Join our WhatsApp Channel: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
👍2
9 advanced coding project ideas to level up your skills:
🛒 E-commerce Website — manage products, cart, payments
🧠 AI Chatbot — integrate NLP and machine learning
🗃️ File Organizer — automate file sorting using scripts
📊 Data Dashboard — build interactive charts with real-time data
📚 Blog Platform — full-stack project with user authentication
📍 Location Tracker App — use maps and geolocation APIs
🏦 Budgeting App — analyze income/expenses and generate reports
📝 Markdown Editor — real-time preview and formatting
🔍 Job Tracker — store, filter, and search job applications
Coding Projects: https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
ENJOY LEARNING 👍👍
🛒 E-commerce Website — manage products, cart, payments
🧠 AI Chatbot — integrate NLP and machine learning
🗃️ File Organizer — automate file sorting using scripts
📊 Data Dashboard — build interactive charts with real-time data
📚 Blog Platform — full-stack project with user authentication
📍 Location Tracker App — use maps and geolocation APIs
🏦 Budgeting App — analyze income/expenses and generate reports
📝 Markdown Editor — real-time preview and formatting
🔍 Job Tracker — store, filter, and search job applications
Coding Projects: https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
ENJOY LEARNING 👍👍
👍1
🖥 Large Language Model Course
The popular free LLM course has just been updated.
This is a step-by-step guide with useful resources and notebooks for both beginners and those who already have an ml-base.
The course is divided into 3 parts:
1️⃣ LLM Fundamentals : The block provides fundamental knowledge of mathematics, Python and neural networks.
2️⃣ LLM Scientist : This block focuses on the internal structure of LLMs and their creation using the latest technologies and frameworks.
3️⃣ The LLM Engineer : Here you will learn how to write applications in a hands-on way and how to deploy them.
⭐️ 41.4k stars on Github
📌 https://github.com/mlabonne/llm-course
#llm #course #opensource #ml
The popular free LLM course has just been updated.
This is a step-by-step guide with useful resources and notebooks for both beginners and those who already have an ml-base.
The course is divided into 3 parts:
1️⃣ LLM Fundamentals : The block provides fundamental knowledge of mathematics, Python and neural networks.
2️⃣ LLM Scientist : This block focuses on the internal structure of LLMs and their creation using the latest technologies and frameworks.
3️⃣ The LLM Engineer : Here you will learn how to write applications in a hands-on way and how to deploy them.
⭐️ 41.4k stars on Github
📌 https://github.com/mlabonne/llm-course
#llm #course #opensource #ml
❤3👍1