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When It's 3AM and Your App is on Fire: How Distributed Tracing Saves the Day

Distributed tracing is crucial for tracking performance issues in complex microservices architectures.
Without distributed tracing, troubleshooting involves manual correlation of timestamps and guesswork.
With distributed tracing, one can easily identify latency issues and pinpoint problematic components.
The three pillars of observability are logs, metrics, and traces, each serving a different purpose.
Distributed tracing provides end-to-end visibility of request flows, aiding in detecting failures and performance bottlenecks.
Real-world examples include Shopify using tracing to resolve Black Friday issues and Uber identifying timeouts through tracing.
Metrics signal when something is wrong, logs offer detailed context, and traces reveal the 'why' and 'where' of issues.
Starting with an observability framework like OpenTelemetry or Jaeger and instrumenting code is vital for distributed tracing.
Effective data collection, meaningful tags, and scaling the tracing implementation are key steps in maximizing its benefits.
Common pitfalls to avoid include excessive data collection, poor sampling, and siloed analysis.
The future of distributed tracing includes AI-powered anomaly detection, enhanced privacy controls, and business-centric observability.

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Go Is for Developers, Zig Is for Masochists — And I Love Both

Go, released in 2009 by Google, prioritizes simplicity, productivity, readability, and scalability over complex programming paradigms.
Zig, on the other hand, is a new and challenging language that delves deep into systems programming with minimal hand-holding.
The contrast between Go and Zig reflects different preferences in coding approaches, with Go being for developers and Zig being for those who enjoy the challenge.
Despite their differences, the author appreciates both Go and Zig for what they reveal about coding and human nature.

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Dev

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JavaScript string manipulation technique Every Developer Should Know [Part 1]

JavaScript string manipulation techniques are crucial for developers to enhance productivity and code faster.
Common methods like toUpperCase(), toLowerCase(), substring(), substr(), slice(), split(), replace(), and trim() are highlighted.
Advanced techniques include trimStart(), trimEnd(), charAt(), charCodeAt(), includes(), indexOf(), lastIndexOf(), startsWith(), and endsWith().
Additional manipulations like repeat(), padStart(), padEnd(), using template literals, and formatting currency with toLocaleString() are showcased.

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Medium

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Transforming Azure DevOps Engineering with AI Tools

AI tools are reshaping the future of software development, particularly in Azure DevOps engineering, by automating tasks, optimizing workflows, and driving innovation.
The integration of AI in Azure DevOps boosts efficiency and productivity by automating manual tasks like code reviews, testing, and deployment, enabling developers to focus on strategy and creativity.
AI tools empower DevOps engineers to make better decisions, optimize performance, and proactively minimize downtime by analyzing data, identifying patterns, and predicting failures.
AI fosters innovation and collaboration within Azure DevOps teams, enabling experimentation, exploration of new ideas, and continuous improvement in software development, with endless possibilities for reshaping the industry in the future.

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Dev

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How to Set Album Art for MP3 Files using Python?

Many people want to enhance their MP3 collections by adding visual elements like album art, as it improves the listening experience by providing context to the music.
Mutagen is a Python module that offers a simple way to handle audio metadata, supporting various formats including MP3, FLAC, and others.
To set album art for an MP3 file using Mutagen, you need to install it using pip and then follow a step-by-step process, including importing necessary libraries, loading the MP3 file, setting the album art by reading and encoding an image file, and checking if the art was added successfully.
If Mutagen doesn't meet your needs, alternatives like eyed3 and tinytag can be considered for working with ID3 tags in MP3 files or reading metadata respectively.

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Replit’s AI Revolution: Why This Coding Tool is Your Ticket to Building Anything, Anywhere

Replit is an AI-powered, cloud-based coding platform that allows users to write, run, and deploy code in various languages without the need for downloads or server configurations.
Replit's key features include AI functionalities like code suggestions, error detection, and auto-debugging, making it a versatile tool for developers, students, freelancers, startups, and hobbyists.
While the free plan is suitable for small projects, heavy users might encounter limits on storage and compute power, with paid plans offering more capabilities at a reasonable price.
In 2025, Replit is considered a must-have tool for coding, providing accessibility, collaboration, and an enjoyable coding experience, especially in a world dominated by AI technology.

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Disaster Recovery Planning for Databases: Because Murphy’s Law Loves Your Data

Data disasters can occur due to various reasons such as code bugs, accidental deletions, ransomware, hardware failures, or human error.
Having a solid disaster recovery plan is crucial to recovering data and resuming operations after an unexpected catastrophe.
Disaster recovery involves more than just backups; it includes knowing how to utilize backups effectively, the speed of recovery, and the acceptable data loss.
Think of disaster recovery as a fire escape plan for your data, essential for survival in case of a data emergency.

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How To Build End-To-End AI Voice Agentic App Using AI/ML API and OpenAI Realtime API

A participant in a LocalDown hackathon built Wei, an AI agent for habit-building, and won 3rd place.
Wei is an AI agent that makes habit-building effortless through natural dialogue and offers rewarding experiences.
The tutorial covers building Wei from scratch using the official OpenAI Agents Python SDK and AI Agents SDK.
Wei is powered by tools like shadcn/ui for Next.js, motion-primitives, and prompt-kit.
Approximately 6 to 7 AI Agents handle different aspects of the app, transferring control to each other as needed.
Agents like Greeter, General, Rewards, Points, Habits, and Info Agents manage various functionalities in Wei.
Functions such as getting user data, managing habits, calculating points, and providing rewards are handled by specific agents.
The API logic for Wei involves handling requests, managing database functions, and using cached user data for efficient processing.
UI components like ChatInterface, ChatInput, and associated contexts are crucial for enabling interactions with the AI agent.
The tutorial delves into building the app's UI/UX, running the app locally, and deploying it using Vercel.

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Medium

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Why Vibe Coding is Taking Over

Vibe coding is a paradigm where developers describe what they want in plain language, and AI generates the code, shifting the programmer’s role to guiding and refining.
Vibe coding is popular due to accessibility, speed, voice-to-code tools, rapid prototyping, and blurring traditional roles between designers and developers.
In vibe coding, developers describe their idea, AI generates code based on the description, testing and refining are done iteratively, and the final product is deployed once satisfactory.
Challenges of vibe coding include ensuring code quality, debugging AI-generated code, and the continued need for human oversight in decision-making and problem-solving.

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The Pragmatic Engineer

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Building, launching, and scaling ChatGPT Images

ChatGPT Images experienced rapid growth, with 100M new users signing up in the first week after its launch in March.
Engineering leaders Sulman Choudhry and Srinivas Narayanan share insights on the ChatGPT Images project.
Challenges included unexpected load, rewriting image generation to asynchronous, and ensuring reliability.
The launch went viral in India, with popular figures using the GPT for image generation.
The team experienced high traffic, reaching 1M new sign-ups per hour and 700M images generated in the first week.
The technology stack for ChatGPT Images includes Python, FastAPI, C, and Temporal for asynchronous workflows.
Adapting to peak loads, the team rewrote the image generation engine to handle asynchronous requests and maintain service availability.
Isolating systems and improving performance were important for managing reliability challenges under high load.
The team prioritized accessibility over latency during high traffic periods to ensure platform usability.
Efforts to optimize code, improve system efficiency, and increase capacity led to a successful and reliable ChatGPT Images launch.

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