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4 Levels of GitHub Actions: A Guide to Data Workflow Automation

GitHub Actions is a CI/CD tool within GitHub that automates development and deployment workflows, including data workflows.
Benefits of GitHub Actions in data workflows include setting up data science environments, streamlining data integration and transformation, and automating machine learning model training.
GitHub Actions is free for public repositories and provides 2,000 free minutes per month for individual accounts with private repositories.
GitHub Actions offers templates, community resources, and support forums for easy implementation.
GitHub Action building blocks include Events, Workflows, Runners, and Runs, allowing for automation directly within repositories.
The article presents 4 levels of GitHub Actions implementation for data workflows, starting from a simple workflow to a secure pipeline workflow.
Level 1 introduces a basic setup with manual triggers and Python script execution.
Level 2 adds environment setup and runs workflows automatically on code pushes to the main branch.
Level 3 involves scheduled jobs and dynamic date handling for periodic data fetching.
Level 4 enhances security and performance through secrets and environment variables management.
GitHub Actions' versatility in building dynamic data pipelines offers a streamlined approach to data solutions and accelerates the development lifecycle.

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Medium

234

Image Credit: Medium

The Soda Pop Universe: A Fractal Feedback Law for Cosmic Expansion, Intelligence, and Thought

The Fractal Flux Bootstrap Time Spiral (FF-BS-TS) framework proposes that expansion, creation, destruction, and thought emerge from a single recursive law.
The framework defines the evolution of radial and angular coordinates of an element and explores the continuum limit at cosmic scales, driving recursive expansion.
The Soda Pop Universe analogy compares carbonated water to dark matter and dark energy, bubbles to galaxies, and syrup to visible matter, visualizing cosmic structure.
Observational predictions include scale-dependent self-similarity in CMB anomalies, explanation of galaxy rotation curves, and gravitational waves as echoes from recursive time spirals.

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Towards Data Science

Agentic AI: Single vs Multi-Agent Systems

Agentic AI involves programming with natural language through large language models (LLMs) for automating tasks, allowing for more dynamic decision-making.
LLMs serve as a communication layer on top of structured systems and data sources, interpreting natural language but not inherently validating facts.
Agentic AI excels in interpreting nuanced language for tasks such as customer service and research but may not be ideal for structured tasks like precise calculations.
LangGraph, Agno, Mastra, and Smolagents are agentic AI frameworks worth exploring, with LangGraph being a popular choice among developers for building workflows.
Single-agent workflows involve one LLM accessing multiple tools to make decisions, while multi-agent workflows distribute tasks among different agents, offering more control and precision.
Single-agent setups are easier to start with but may lack precision for complex tasks, while multi-agent systems require careful architecture design for effective data flow and collaboration.
Using cheaper LLMs for most agents in a multi-agent system and reserving more advanced models for crucial tasks can help optimize costs and performance.
Building multi-agent workflows requires thoughtful architecture and data flow planning, with each agent responsible for specific tasks and interactions among different agents.
Improvements such as parsing user queries into structured formats, ensuring agents use tools effectively, enhancing summarization, handling errors, and implementing long-term memory can enhance workflow efficiency.
State management, particularly isolating short-term memory for each team or agent, is crucial for optimizing performance and cost in agentic systems.
Exploring different agentic workflows, such as single vs. multi-agent setups, can offer insights into the level of control, precision, and complexity achievable in automating tasks with AI.

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Medium

424

Image Credit: Medium

Beyond Reactive Chatbots

The article discusses the trade-off between speed and depth in AI-driven chatbots and explores a solution using a tiered reasoning approach inspired by human cognition.
A practical architectural framework is introduced to create conversational AI systems that think fast, think deep, and evolve over time.
The dual-process theory, involving fast intuition and slow deliberation, serves as the basis for structuring AI processing into distinct layers.
System 1 focuses on fast thinking, providing immediate responses based on prompt information and short-term memory, while System 2 handles deeper, asynchronous processing.
Implementing System 2 involves using tools like Celery for asynchronous task execution to balance responsiveness with deeper analysis.
System 3 operates offline, processing historical data to enhance future interactions and allowing the AI to learn and evolve over time.
The tiered reasoning approach is demonstrated through industry-specific challenges in areas like financial analysis, technical diagnostics, and schedule optimization.
By balancing responsiveness and deep analysis, this architecture creates AI assistants that are both thoughtful and adaptive, inspired by human cognitive processes.
As AI assistants powered by LLMs become more prevalent, the ability to blend immediate engagement with deeper reasoning will differentiate valuable assistants from reactive chatbots.
The article encourages sharing of experiences in implementing tiered reasoning approaches for better conversational AI to advance beyond reactive chatbots.

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Spring

229

Image Credit: Spring

Securing Spring AI MCP servers with OAuth2

Spring AI introduces support for Model Context Protocol (MCP), enabling AI models to interact with external tools efficiently.
Developers can easily create custom MCP Servers with Spring AI and expose functionalities to AI models with minimal code.
The latest version of the MCP specification focuses on enhancing security with OAuth2 framework for enterprise deployments.
In MCP, servers can run locally using STDIO transport or expose standardized HTTP endpoints for external access.
MCP Server acts as both a Resource Server and an Authorization Server, conducting authorization checks based on OAuth2 access tokens.
Implementing OAuth2 involves configuring resources in the application, creating security filters, and enabling authentication for requests.
With OAuth2 integration in Spring Security and Authorization Server, adding authentication to an existing Spring MCP Server is straightforward.
Using OAuth2 client credentials, a secure machine-to-machine scenario can be established for obtaining access tokens.
Requests to the MCP Server must include a valid access token, failing which the server responds with HTTP 401 Unauthorized error.
Future steps include updating MCP Client for OAuth2 authentication, implementing more robust permission control, and exploring external Authorization Servers.

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Dev

132

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useAuth Hook in React

Authentication and authorization are crucial aspects of web applications.
To simplify the logic, a custom useAuth hook can be created to handle authentication and authorization in a reusable way.
The hook allows for centralized management of user state and eliminates the need to repeat authentication logic in multiple components.
It also provides advantages such as simplifying role-based access control and easily protecting routes with a reusable component.

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7 Likes

Dev

132

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How to build an AI Agent for Gmail that can read and send emails (Python + OpenAI + Streamlit)

A developer has shared a project that allows users to interact with Gmail through an AI agent.
The project is built using Python, OpenAI, and Streamlit.
It can read unread emails, send replies, search the inbox, and more.
The developer has open-sourced the code and provided a YouTube tutorial for building the AI agent.

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Dev

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useFetch Hook in React

Fetching data is a common requirement in React applications.
A reusable useFetch hook can be used to simplify the fetching logic.
The useFetch hook can be implemented using useState or useReducer.
By using useFetch, the data fetching logic is separated from the component, making it more modular and reusable.

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5 Likes

Medium

260

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Understanding the Ownership System in Rust

Rust's Ownership Model ensures automatic memory management by freeing memory when no longer in use, preventing common errors.
Key rules of Ownership System are enforced at compile time, preventing runtime memory errors and ensuring memory safety.
Ownership in Rust is compared to a real-world example of owning and lending books, showing concepts of borrowing and moving variables.
Rust allows borrowing references using `&` and distinguishes between mutable and immutable borrowings to prevent conflicts.
Multiple mutable references to a variable are not allowed simultaneously to prevent data races and maintain memory safety.
Rust also supports cloning variables for deep copying, and the Copy trait allows fast copying of simple types like values and booleans.
References in Rust may have lifetimes, represented by single quotes, to ensure data validity and to prevent dangling reference issues.
Lifetime annotations are required to specify how long a reference should live, and Rust infers lifetimes but allows explicit annotations when necessary.
The Ownership System, borrowing, cloning, and lifetimes work together to manage memory effectively in Rust and prevent common programming errors.
Rust emphasizes memory safety and data integrity through its Ownership System, making it a powerful language for writing robust and secure code.

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Dev

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Edge Functions: Deploy from the Dashboard + Deno 2.1

You can now create, test, edit, and deploy Edge Functions directly from the Supabase Dashboard.
The Edge Functions editor in the Dashboard has templates for common use cases and includes syntax highlighting and type-checking for Deno and Supabase-specific APIs.
A built-in tool for testing Edge Functions is introduced in the Dashboard, allowing execution with different request payloads, headers, and query parameters.
Deploying Edge Functions no longer requires Docker and can be done using the Supabase CLI with the --use-api flag.

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3 Likes

Dev

207

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Day 1131 : Chase The Dragon

Professional: Had a productive day with meetings and blog post work.
Personal: Worked on assembling a large 3D printer frame and dealt with a lizard.
Sprayed Cayenne pepper in the shed to repel the lizard.
Planning to complete the printer's enclosure and start coding the website.

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12 Likes

PlanetPython

256

Image Credit: PlanetPython

Test and Code: Python 3.14 won't repeat with pytest-repeat

pytest-repeat is a pytest plugin that allows repeating tests in Python.
Unfortunately, pytest-repeat does not work with Python 3.14.
For more information, refer to the pytest-repeat GitHub page.
The Complete pytest course is now available as a bundle from PythonTest.

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Dev

119

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Why Python Is a Terrible First Language?

Learning Python as a first language can spoil beginners, making them lazy and less disciplined in understanding how things really work.
Python's simplicity and high-level abstraction hide the complexities of memory management and system-level understanding.
The ease of Python can warp the perception of what is considered 'hard' in other languages, leading to bad programming habits.
Despite its drawbacks, Python is still recommended for beginners, but with the advice to explore other languages for a more comprehensive understanding of programming.

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7 Likes

Dev

397

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Postgres Language Server: Initial Release

Postgres Language Server has been released as a LSP implementation for Postgres with tools like autocompletion, syntax error highlighting, type-checking, and a linter.
It is available through VSCode Extension, Neovim, and CLI installation via GitHub Releases and npm.
The project faced challenges with the parser due to Postgres' complex syntax, leading to leveraging the libpg_query library for reliable SQL parsing.
The document lifecycle involves splitting source, identifying and parsing statements, loading schema information, providing diagnostics, and efficiently processing changes and autocompletion.
Responsiveness in autocompletion is highlighted, utilizing tree-sitter for parsing and SchemaCache for suggestions scoring based on the changed node and context.
The goal is to create a comprehensive Postgres tooling ecosystem beyond the Language Server, with plans to enhance reliability, expand accessibility, and invite contributions.
Future improvements include enhancing reliability, extending support to more editors, documenting installation procedures, and welcoming contributions for new features like PL/pgSQL support.
The project invites users to install the Language Server, report issues, suggest features, and contribute code, with upcoming plans for PL/pgSQL support and Wasm build for pglite.
The efforts culminate in the aim to make Postgres LSP more accessible and user-friendly, with installation guides, extensions for various editors, and documentation for prospective contributors.
Community engagement is encouraged through issue reporting, feature suggestions, and code contributions, as the project continues to evolve and improve the Postgres development experience.
The journey of creating the Postgres Language Server involved tackling challenges, optimizing procedures, and reaching milestones, all with the aim of providing a reliable and efficient SQL development tool.

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23 Likes

Dev

331

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Introducing the Supabase UI Library

Introducing the Supabase UI Library, a collection of ready-to-use components built on top of shadcn/ui.
The library is 100% shadcn/ui compatible and is designed to take the time and pain out of building complex functionality in React apps.
Components included in the library are Supabase client initialization, password-based authentication, file upload dropzone, realtime cursor sharing, current user avatar, realtime avatar stack, and realtime chat.
The Supabase UI Library is built on shadcn/ui, offering complete control over customization, styling, and behavior of the components.

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