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GitHub Copilot adds agent mode, MCP support in latest release

GitHub Copilot has released a new version with agent mode and MCP support.
Agent mode allows Copilot to iterate across a project, suggesting terminal commands and analyzing errors.
MCP support enables Copilot to use different tools for different tasks, based on an open standard for connecting data sources to AI tools.
Premium requests to AI models other than Copilot's base model will now be subject to limits.

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Dev

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Faster Loads with Smaller Bundles: The Power of Dynamic Imports in Next.js

Users expect pages to load instantly and a large JavaScript bundle can lead to slower performance and poor user experience.
Dynamic imports in Next.js allow components to be imported only when they are needed, resulting in smaller bundles and faster loads.
By using dynamic imports, components that are not immediately used are excluded from the initial bundle, leading to faster page load times.
Dynamic imports improve performance, speed, and scalability of Next.js apps, making them more efficient and responsive.

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Medium

148

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Laravel 12 Dev Hack: Generate CRUD with One Simple Command

Laravel 12 has a tip to save time in generating basic CRUD operations.
By creating an Artisan command, all the necessary components like Model, Migration, Controller, Form Request, and route can be auto-generated.
By implementing this command, developers can avoid repetitive copy-pasting and switching between multiple terminal commands.
This feature in Laravel 12 improves the developer workflow and saves time, allowing them to focus on building features rather than boilerplate code.

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Dev

433

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What tests to write for React

The article discusses testing strategies for web applications, influenced by Igor Luchenkov's talk at React Advanced London and industry experts.
The classic Test Pyramid model suggested unit tests at the bottom, integration tests in the middle, and end-to-end tests on top.
Modern testing tools have evolved, challenging the assumptions of the Test Pyramid and leading to the development of the Testing Trophy.
The Testing Trophy model places integration tests as a key component, along with static tests, unit tests, and end-to-end tests.
Static tests check code without execution, while unit tests verify individual functions and integration tests check component interactions.
End-to-end tests simulate real user behavior but are slower and complex to maintain compared to integration tests.
Integration tests offer a balance of confidence and cost, capturing real issues users may encounter and mirroring software usage.
The article provides examples of static tests, unit tests using Vitest, integration tests with React Testing Library, and end-to-end tests using Playwright.
Recommended frameworks for each test type include TypeScript, ESLint, Vitest, Storybook, and Playwright, based on the testing needs.
The mix of tests in your portfolio should consider factors like application complexity, team size, stability requirements, and development phase.

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26 Likes

Medium

380

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5 Lesser Known Best Practices For Dockerfiles

Setting shell options in Dockerfiles is a lesser-known best practice.
It improves the overall robustness of the Dockerfile.
Docker by default builds Dockerfiles with undefined variables and non-zero exit codes without any complaints.
Adding the directive for setting shell options can help to detect and handle these issues.

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Towards Data Science

427

How I Would Learn To Code (If I Could Start Over)

Learning to code is a valuable skill that can lead to higher income and enjoyable work.
The author shares their journey as a data scientist and machine learning engineer for 4 years.
Starting with Fortran in physics undergrad, the author struggled initially but later found passion in coding.
Transitioning to AI and learning Python and SQL opened new career paths for the author.
Choosing a language based on career goals, like Python for AI, is recommended.
Emphasizing hands-on projects over excessive tutorials is advised for faster learning.
Focus on mastering fundamentals like data types, design patterns, and problem-solving skills over chasing trends.
Documenting learning publicly on platforms like GitHub and LinkedIn can enhance career prospects.
AI is seen as a tool rather than a threat to coding professions in the foreseeable future.
Avoiding distraction from shiny new technologies and focusing on consistent learning and skill-building can pave the way for a successful coding career.

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Medium

389

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Python Doesn’t Support True Constants – So I Built “setconstant” to Fix That

Python lacks built-in support for true constants, which can lead to variables being overwritten and unpredictable behavior.
To address this problem, the 'setconstant' Python package was created.
With 'setconstant', developers can declare constants that cannot be changed or overridden.
The package provides a simple and powerful way to define and use constants in Python, improving code stability.

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Towards Data Science

259

Creating an AI Agent to Write Blog Posts with CrewAI

The author shares their journey of starting to write about Data Science and Artificial Intelligence to create a professional image and share knowledge with others.
With a background in Data Science, the author developed an interest in Natural Language Processing and Large Language Models, leading to experimenting with CrewAI to build AI agents.
They explain the concept of a crew of AI agents working together towards a common goal, such as producing a blog post on a given topic.
The crew includes agents like Writer Stylist, Planner, Writer, and Editor, each tasked with specific roles in the blog post creation process.
By using CrewAI, the process of setting up agents and tasks is simplified through YAML configuration files and Python code.
Agents are defined with roles, goals, and backstories, while tasks are defined with descriptions and expected outputs.
The Crew Base class is used to specify where agents and tasks configuration files are located for the project.
The workflow involves configuring agents, tasks, and the crew process and then running the crew to generate the blog post content.
The author provides code examples for setting up agents, tasks, crews, and running the project, emphasizing the sequential process flow.
The result is a blog post created by an AI agent, showcasing the potential of using AI for automated content generation.

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13 Likes

Medium

227

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The Limits of Accuracy: A Broader Approach to Model Evaluation

Model evaluation can be done by counting the number of correct predictions.
The accuracy of the model can be calculated by dividing the number of correct predictions by the total number of predictions.
A confusion matrix can provide more insights into the model's performance.
The model can make different predictions based on the data set.

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13 Likes

Medium

113

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How to Build a Local LLM Chatbot with CAG: Streamlit, vLLM, and Smart Context Caching

RAG and CAG are two different paradigms for building chatbots with LLMs.
RAG is useful for large amounts of data that may change over time.
CAG is suitable for limited data that doesn't change much, and it utilizes cached knowledge.
CAG involves preprocessing the data once and storing it on disk for efficient question answering.

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6 Likes

Medium

398

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How to Use Continuations in Racket

Continuations in Racket allow a program to pause and resume execution by capturing the current point of control flow.
In Racket, a continuation captures the current state of the program and provides a way to return to that state later.
call/cc in Racket captures a continuation, allowing the program to resume where it left off when the continuation is invoked.
Continuations work like bookmarks in code, enabling you to return to a specific point in the program's execution.
A captured continuation is a function that, when called, resumes the program from the saved point where call/cc was invoked.
Continuations can be reused multiple times and provide flexibility in managing program flow.
Continuations can be used for early exits, pausing and resuming computations, and escaping from nested structures.
They are useful for implementing generators, backtracking, and custom control structures in Racket.
Using call/cc, you can create retry mechanisms, exit loops, and handle backtracking scenarios efficiently.
Continuations in Racket offer a powerful way to manipulate program flow and solve complex problems with unique control structures.

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23 Likes

Medium

258

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What is a Rug Pull? Unmasking Crypto’s Sneakiest Scam

A rug pull is a scam in the cryptocurrency space where project creators vanish with the funds, leaving behind worthless digital assets.
Rug pulls often occur in decentralized finance (DeFi) due to the lack of rules and vetting, making it easier for scammers to exploit investor trust.
These scams come in different flavors and have hit hard in recent years, resulting in significant financial losses for investors.
To protect yourself from rug pulls, it is important to exercise caution, verify the credibility of projects, and stay informed about potential scams.

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15 Likes

Medium

380

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Creativity in LLMs: Optimizing for Diversity and Uniqueness

Creative writing emphasizes diversity and multiple valid answers.
Despite the ability to generate text, large language models (LLMs) lack creativity.
Midjourney proposes a solution to LLMs' lack of creativity.
Techniques like instruction tuning and alignment reduce LLMs' variety of responses.

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22 Likes

Medium

175

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What’s driving today’s crypto market rise?

The recent rise in the crypto market is synchronized with the rebound of the US stock market.
President Donald Trump is planning to announce reciprocal tariffs to balance global trade.
Inflows into crypto investment products are increasing, indicating growing confidence among investors.
Technical analysis suggests a potential breakout in the crypto market, with a target of $2.76 trillion.

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10 Likes

Medium

144

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Teach Your GBM to Extrapolate with Model Stacking

LightGBM, a popular tool for predicting UK property prices, struggled to extrapolate across time
Time-based features and the linear_tree extension of LightGBM improved performance, but were not a complete solution
Model stacking is proposed as a potential solution to help the struggling GBM
The dataset for the models consists of UK property sales data spanning over thirty years, with randomly sampled 1 million examples

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