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The interesting shift is from “train once” toward continuously improving the system around the model.
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The more capable models become, the more important the post-training loop seems to get.
Introducing Naive-N0.5-Flash: Building Frontier AI with AI
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🌐 naive.ai/en/
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SQL is the language behind a lot of AI work.
Before a model is trained, a dashboard is built, or a business question is answered, someone has to turn raw data into something trustworthy.
That takes more than knowing SELECT and WHERE.
A practical SQL learning path looks like this:
→ Start with the basics: filtering, sorting, aliases, and CASE WHEN to shape the result you need.
→ Connect the data: joins and EXISTS help you work across related tables without losing track of which rows belong together.
→ Summarize it: GROUP BY, aggregates, and HAVING turn transactions into useful metrics.
→ Ask deeper questions: CTEs, subqueries, and window functions make it possible to rank records, compare periods, and calculate running totals.
→ Build reliable pipelines: MERGE, transactions, deduplication, incremental loads, and validation help keep datasets current.
→ Make queries efficient: indexes, partitioning, query plans, and execution costs matter as data volume grows.
For data and AI professionals, SQL also reaches into cohort analysis, feature extraction, training datasets, and RAG data preparation.
The goal isn’t to memorize every command on this map. It’s to understand how data moves from source tables to a result someone can trust.
Which part of SQL would you add to this learning path?
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Stop babysitting your AI agents. This new 2-hour course by Andrew Ng shows you exactly how to reach full automation. Saved this instantly! 👇
Andrew Ng just dropped the best 2-hour course on Graph Engineering: from single agent to full automation
9:14 - your first agent
33:11 - loop engineering
1:02:46 - graph engineering
1:30:15 - agents that rewrite themselves
1:49:05 - full graph system
two hours, and it replaces every agent tutorial you bookmarked this year
Prompts → Agents → Loops → Graphs
most people will stop after the first agent and call it automation
he saves the last forty minutes for the graph that runs it without him
same model, same tokens, completely different week
watch it today
the step-by-step guide is below, save it while it is still early ↓
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Did you know? You can run agents in parallel in the GitHub Copilot app.
Learn how to run parallel agents in the GitHub Copilot app, and experience the moment it stops feeling scary and starts feeling powerful.
Did you know? You can run agents in parallel in the GitHub Copilot app.
Each session gets its own Git worktree and context, so you can build, review, and test all at the same time.
Here's how to get started 👇
github.blog/ai-and-ml/github…
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Group Data with Different Languages
Learn how to perform the same grouping and aggregation in SQL, Pandas, and PySpark.
A simple example using product categories and average ratings to compare the syntax.
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Learn Python from scratch and start building real AI applications. This is the fastest, beginner-friendly course Python for AI development.
🚨ULTIMA HORA: Alguien acaba de publicar el curso de 5 horas de Python más completo para IA que existe en internet.
00:00:00 Introducción: Aprende Python para IA
00:01:42 Descripción general del curso
00:03:58 Instalación de Python
00:04:05 Instalar Python en Windows
00:05:10 Instalar Python en Mac
00:06:53 Instalación de VS Code
00:08:34 Configuración de VS Code (Extensiones)
00:12:08 Personalización de VS Code
00:13:31 Crear tu primer proyecto
00:16:18 Crear un espacio de trabajo en VS Code
00:18:02 Tu primer archivo Python (hello.py)
00:20:10 Ejecutar código Python
00:26:23 Ejercicio y resumen
00:29:26 Recursos del curso y comunidad
00:31:07 Entender los entornos de Python
00:33:15 Entender los paquetes de Python y Pip
00:34:00 Crear entornos virtuales (venv)
00:37:34 Nota sobre Anaconda
00:38:32 Instalar paquetes de Python (pip install)
00:42:51 Usar paquetes de Python (Import)
00:44:29 Python interactivo con Jupyter
00:48:30 Resumen completo de configuración y ejercicio
00:51:36 ¿Qué es la programación?
00:55:19 Sintaxis de Python y PEP8
00:58:00 Entender y depurar errores
01:01:33 Variables
01:06:03 Comentarios
01:09:48 Introducción a los tipos de datos
01:10:12 Números (enteros y decimales)
01:13:36 Cadenas de texto (Strings)
01:19:39 Formato de cadenas (F strings)
01:21:49 Métodos de cadenas
01:26:35 Booleanos
01:31:02 Operadores (aritméticos, de comparación y lógicos)
01:39:19 Asignaciones abreviadas (+=)
01:40:24 Introducción al flujo de control
01:41:35 Condicionales (if, elif, else)
01:47:11 Bucles (For y range())
01:52:13 Introducción a las estructuras de datos
01:53:32 Listas
01:59:10 Diccionarios
02:00:23 Tuplas
02:01:37 Conjuntos (Sets)
02:05:51 Funciones (definir y llamar)
02:15:02 Parámetros y argumentos de funciones
02:22:42 Alcance de variables (global vs local)
02:28:50 Retornar valores desde funciones
02:37:37 Herramientas externas (módulos y paquetes)
02:40:48 Importar módulos y funciones integradas
02:47:56 Resumen de métodos de importación
02:48:48 Instalar paquetes y requirements.txt
02:56:04 Trabajar con APIs (ejemplo con Requests)
03:06:20 Trabajar con datos (Pandas y Matplotlib)
03:10:46 Leer y guardar archivos de datos
03:14:50 Introducción a Python práctico
03:16:47 Estructura y organización de proyectos
03:22:02 Entender las rutas de archivos
03:26:37 Trabajar con diferentes tipos de archivos
03:34:05 Organizar el código en módulos
03:39:39 Manejo de errores (Try/Except)
03:45:31 Introducción a las clases (POO)
03:49:09 Crear tu primera clase (__init__, self)
03:57:04 Atributos de clase vs instancias
04:00:10 Métodos de clase
04:05:23 Herencia de clases
04:07:32 Cuándo usar clases vs funciones
04:09:44 Introducción a Git y GitHub
04:12:31 Fundamentos de Git
04:15:41 Instalar Git
04:16:46 Flujo de trabajo básico con Git
04:18:41 Crear cuenta en GitHub y autenticación
04:22:37 Clonar repositorios de GitHub
04:28:12 Crear repositorios y .gitignore
04:36:12 Usar Git con la interfaz de VS Code
04:44:05 Variables de entorno y secretos (.env)
04:52:13 Usar el paquete python-dotenv
04:55:03 Introducción a Ruff (linter y formateador)
04:56:13 Configurar Ruff en VS Code
04:57:23 Ruff en acción
05:01:10 Introducción a Uv (gestor de paquetes moderno)
05:02:07 Instalar Uv
05:02:30 Usar Uv (uv init, add, sync)
05:09:01 Ejercicio completo de flujo de trabajo en Python
05:11:13 Cierre del curso y próximos pasos
Desde cero hasta construir agentes con APIs reales, entornos virtuales, Git, clases y manejo de errores.
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👀 WAIT… LEFT ON SEEN OR THEY REPLIED? 🐍
This looks like a relationship question…
but it’s actually a Python logic trap 😭😂
🧠 What will Python print?
A, B, C or D? 👇
💬 Lock your answer before running the code!
🔥 Let’s see who gets this one right.
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Layers of AI
---
Use this map to assess an AI proposal.
It combines overlapping methods, architectures and capabilities.
Start with the job, the evidence and the control you need.
1. Classical AI
Symbolic AI, expert systems and logic use explicit knowledge and rules.
Knowledge representation helps a system work with that information.
These methods can suit decisions with clear, stable constraints.
Ask your team to write down the rules and the exceptions.
Test what happens when an input falls outside them.
Make ownership of rule changes clear before rollout.
2. Machine learning
Models learn patterns from data for tasks such as classification and regression.
Supervised learning uses labeled examples; unsupervised learning finds structure.
Reinforcement learning uses actions and reward signals.
Check whether your data represents the job the model will do.
Compare performance with a simple baseline on held-out examples.
Choose a metric tied to the business cost of an error.
3. Neural networks
Neural networks are one family of machine learning models.
Perceptrons, hidden layers and activation functions help explain their structure.
Cost functions and backpropagation help explain how many networks train.
Ask which mistakes the training objective penalizes.
Check whether that objective matches the outcome users need.
4. Deep learning
Deep learning uses neural networks with multiple layers.
Transformers, CNNs, RNNs, LSTMs and autoencoders are related architectures.
They support tasks involving language, images, sequences and representations.
Ask why the proposed architecture fits your data and task.
Measure accuracy, latency and cost together.
Retest when inputs or deployment conditions change.
5. Generative AI
LLMs, diffusion models and VAEs can produce new content.
Audio, image and multimodal systems cover different input and output types.
The categories overlap; one system can use several approaches.
Decide what a useful output looks like before choosing a tool.
Test factual accuracy, quality and consistency on your own examples.
Define where a person reviews or approves the result.
6. Agentic AI
Agents combine capabilities such as memory, planning and tool use.
Autonomous execution lets a system take steps toward a goal.
The amount of autonomy varies with the design and permissions.
List the actions it may take and the tools it may access.
Set stop conditions, approval points and a way to recover from errors.
Evaluate completed work, including unintended actions and cost.
Use the layers to make the next vendor conversation concrete.
Ask what the system does, how it is tested and who owns failures.
Which layer needs a clearer success test in your current AI project?
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