AI Skills

🔗 RAG, AI Agents and Generative AI with Python and OpenAI 2025 🌟 4.6 - 553 votes 💰 Original Price: $54.99

📖 Mastering Retrieval-Augmented Generation (RAG), Generative AI (Gen AI), AI Agents, Agentic RAG, OpenAI API with Python

🔊 Taught By: Diogo Alves de Resende 📤 Download All Courses

👆 Round sizes for software startups over the past 12 months. ✔️Powered by The Startups VC

😎The AI that will 100% simplify your life! 🛠 Smithery AI – An AI platform for automating everyday tasks, compatible with various services. 🔰 The platform integrates 4,000 apps that will handle all your routine tasks: 🔹Connect the apps you want to give the AI assistant access to: code editors, GitHub, Slack 🔹 Ask the AI to automate any task 🔹 The Toolbox instantly directs the agent to the right tool, and voilà—task solved! 🔗 Links: https://smithery.ai

🧠 Hugging Face introduced SmolLM-3B — a compact and powerful open-source LLM with 3 billion parameters that runs *right on your laptop*. 📦 Features: • Trained on 1T tokens (RefinedWeb + books + code + academic texts) • Outperforms Mistral-7B and LLaMA-3 8B on many tasks • Runs in GGUF, supported by LM Studio, Ollama, LM Deploy, and others. 💡 Why is this needed? SmolLM is not about SOTA, but about local scenarios: quick startup, privacy, low hardware requirements. 📁 Repository and demo: https://huggingface.co/blog/smollm3

This diagram explains how Reinforcement Learning (RL) works in Machine Learning. It starts with raw input data. An agent interacts with an environment by selecting actions. The environment gives feedback in the form of rewards and new states. The agent learns which actions give the best rewards and improves over time. The result is an optimized output, based on trial, error, and learning from feedback.

🔅 Introduction to Artificial Intelligence 🌐 Author: Doug Rose 🔰 Level: Beginner ⏰ Duration: 2h 26m

🌀 Get an overview of some of the latest tools and techniques in predictive and generative artificial intelligence (AI).

📗 Topics: Artificial Intelligence 📤 Join Artificial intelligence for more courses

📂 Full description Computer scientists are just a small slice of people working in artificial intelligence (AI). Most people working with AI are just like you. Theyre professionals, teachers, and students who want to use AI to enhance their products, creativity, and career. AI has been around for over half a century. Despite huge advancements in predictive and generative AI, the core concepts of artificial intelligence are still accessible.This course is designed for project managers, product managers, directors, executives, and students starting a career in AI. First, learn what it means for a system to display “intelligence.” Then, explore the difference between classic predictive AI and newer generative AI. Next, youll get an overview of machine learning algorithms, artificial neural networks, foundation models, and deep learning. From the AI curious to the AI careerist, this course will help you get started with intelligent systems.This course is part of a Professional Certificate from Microsoft.This course is part of a Professional Certificate from Microsoft.

🤖 AI agents are now a real business — not just a demo What started as experimental tools is turning into serious revenue. Five AI agent startups have crossed $100M in ARR: Anysphere, Glean, Mercor, Replit, and Lovable. The agent market is exploding — from $5B to $13B in just a year. 🖱 42% of AI agent startups now operate at commercial scale 🖱 Revenue per employee is catching up with Big Tech 🖱 Valuations go as high as 127x revenue 🖱 Nearly half of the top players were founded in the last 3 years 🖱 Winners solve tasks with clear ROI — coding, support, ops

Next up: dominance will go to those with tight workflow integration, unique data, and high switching costs.

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⚡️What's under the hood of Cursor? A good article explaining the basic principles of how AI ideas work (and agent systems in general) tl;dr: The basic minimum for an agent to be able to write code: 1. Good model (claude 3.7, gpt 4.1, ...) 2. Functions for working with files (read_file, write_file) - so that the model can interact with the external (relative to it) world, i.e. our code base 3.1. Code embeddings in a vector base - to enable semantic search by code (find places where API service X is called). 3.2. Alternative: teach the agent to iteratively search using regular code search - similar to how people act when diving into a new code base (this is how Claude Code does it) 4. High-quality promts - for example, inside Cursor this promt is used And the picture shows how these components interact with each other. --- And some useful points about writing rules for code agents: 1. No need to write something like "you are an experienced backend Java developer" - it will be strange for the agent, because he already has a built-in prompt telling who he is 2. It is better to write rules in an encyclopedia format (rather than in a clear algorithm format) and with links to code. This allows the agent to more easily find the context needed to execute the user's request and reuse the same rule in different situations. 3. It is worth investing in high-quality descriptions of the rules. According to the description, the model will better match whether a specific rule is applicable in a specific situation. The next bells and whistles are connecting various MCPs that allow the agent to go to external systems: task tracker, internal wiki, or just google something. Give a thumbs up if you'd be interested in a post about this. Follow @Truth

🚨 OpenAI raises $8.3B at $300B valuation According to the New York Times, OpenAI has raised $8.3 billion at a jaw-dropping $300 billion valuation — months ahead of schedule. The deal is part of its plan to secure $40B in total funding this year. 🖱 First reported by DealBook, this is OpenAI’s largest round to date 🖱 SoftBank has already committed $30B for the year 🖱 The funding underscores the escalating race for AI dominance 🖱 Wall Street attention has shifted from tech giants to frontier labs like OpenAI

The compute arms race is in full swing — and OpenAI just pulled far ahead.

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📣 Why Sam Altman Says You Shouldn’t Track User Growth Early On In the early stages of a startup, it’s not about how many users you have — it’s about how much they love your product. Altman explains why obsession with absolute growth is a mistake, and what you should focus on instead: 🔸 A small group of obsessed users is better than a wide pool of casual ones 🔸 Deep engagement signals product-market fit more than raw numbers 🔸 Retention and frequency are your best early metrics 🔸 Word-of-mouth is the ultimate validation

Almost all great companies start with a product that a few people love.

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📣 Why Sam Altman Says You Shouldn’t Track User Growth Early On In the early stages of a startup, it’s not about how many users you have — it’s about how much they love your product. Altman explains why obsession with absolute growth is a mistake, and what you should focus on instead: 🔸 A small group of obsessed users is better than a wide pool of casual ones 🔸 Deep engagement signals product-market fit more than raw numbers 🔸 Retention and frequency are your best early metrics 🔸 Word-of-mouth is the ultimate validation

Almost all great companies start with a product that a few people love.

📊 Powered by Crypto Insider

📣 Why Sam Altman Says You Shouldn’t Track User Growth Early On In the early stages of a startup, it’s not about how many users you have — it’s about how much they love your product. Altman explains why obsession with absolute growth is a mistake, and what you should focus on instead: 🔸 A small group of obsessed users is better than a wide pool of casual ones 🔸 Deep engagement signals product-market fit more than raw numbers 🔸 Retention and frequency are your best early metrics 🔸 Word-of-mouth is the ultimate validation

Almost all great companies start with a product that a few people love.

📊 Powered by Crypto Insider