Forem: Priya Yadav

How do you actually get back into a project after being away for a few days?

Priya Yadav — Tue, 10 Mar 2026 17:32:40 +0000

Every time I switch back to a project I haven't touched in a few days, I lose 1-2 hours just trying to remember where I left off.

I work across 3 projects at once. The switching itself is fine — it's the coming back that kills me. What was I doing? Why did I stop? What did the team decide last week?

I'll dig through Slack, reread notes, check the task board — and still feel like my brain hasn't fully loaded in yet.

Is this just the cost of juggling multiple projects, or have you found something that actually helps?

Asking because I'm trying to figure out if this is a me problem or a everyone problem. If you've got a system that works — even a messy one — I'd genuinely love to hear it.
How do you actually get back into a project after being away for a few days?

contextSwitching #deepWork #knowledgeWorkers #brainFog #asyncTeams #remoteWork #startupLife #productivityTips #cognitiveLoad #focusedWork #decisionAmnesia #techFounders

Context switching help needed

Priya Yadav — Mon, 09 Mar 2026 10:38:53 +0000

Quick question — when you switch back to a project you haven't touched in 2-3 days, how long does it take you to get back up to speed? What do you do to re-orient?

Which Database to choose?

Priya Yadav — Tue, 28 Jan 2025 08:58:17 +0000

Yesterday, during an interview, I was asked about the differences between MySQL, PostgreSQL, MongoDB, and DynamoDB. Honestly, I felt stuck. It's one thing to use databases in projects, but explaining their differences and use cases under pressure is another challenge altogether. So, I decided to dive deeper, and here’s what I learned:

1. MySQL
Type: Relational Database (SQL)
Best for: Structured data with defined schemas. Ideal for web apps with transactional operations, like e-commerce websites.

Pros:
Simple and easy to set up.
Great for read-heavy workloads.
Widely supported with robust community support.
Cons:
Not as flexible for complex queries as PostgreSQL.
Limited scalability for massive datasets.
Example Use Case: A blog platform where relationships between users, posts, and comments are predefined.

2. PostgreSQL
Type: Relational Database (SQL) with advanced features.
Best for: Complex data relationships, custom functions, and high scalability.

Pros:
Supports advanced queries, JSON storage, and geospatial data.
Highly extensible with custom data types and indexes.
ACID compliance ensures data integrity.
Cons:
Slightly steeper learning curve compared to MySQL.
Example Use Case: A financial app requiring complex transactions and analytics.

3. MongoDB
Type: NoSQL (Document-based)
Best for: Unstructured or semi-structured data, rapid development, and flexible schemas.

Pros:
Stores data as JSON-like documents, making it flexible.
Great for scaling horizontally.
Ideal for real-time data apps.
Cons:
Doesn’t enforce relationships like relational databases.
Can get expensive for massive datasets.
Example Use Case: A social media app where user data is dynamic and doesn’t fit a strict schema.

4. DynamoDB
Type: NoSQL (Key-Value and Document-based) by AWS
Best for: Applications needing ultra-low latency and high scalability with minimal management.

Pros:
Fully managed by AWS, no server maintenance required.
Built-in scalability for high read/write traffic.
Great for event-driven apps.
Cons:
Limited querying capabilities compared to other databases.
Vendor lock-in with AWS.
Example Use Case: A serverless real-time leaderboard for a gaming app.

How I’d Answer Now
Choosing the right database depends on the application’s requirements:

Relational Databases (MySQL, PostgreSQL): Go for structured, consistent data. PostgreSQL is the winner for complex queries.
NoSQL Databases (MongoDB, DynamoDB): Perfect for flexible, schema-less data. DynamoDB shines for serverless, high-traffic applications, while MongoDB is excellent for rapid prototyping.
I’ve learned that being clear on use cases, strengths, and limitations is key in interviews. Next time, I’ll be ready—and I hope this helps you too!

Your Turn:
What’s your go-to database for your projects? Share your thoughts in the comments!

Digital Declutter: My GitHub Copilot 1-Day Build submission

Priya Yadav — Mon, 20 Jan 2025 04:42:52 +0000

This is a submission for the GitHub Copilot Challenge: New Beginnings.

What I Built
Digital Declutter is a Next.js web application designed to help users clean up their inbox efficiently. It integrates with the Google API to fetch emails, identify potentially suspicious emails, and offers the ability to delete them with ease.

The goal is to provide a fresh, simplified beginning for your email experience—free of clutter and suspicious emails. This app emphasizes user security and convenience, giving people more control over their inboxes in a user-friendly way.

Key Features:
Fetch Emails: Retrieves emails from your Gmail inbox via the Google API.
Suspicious Email Detection: Highlights emails that might be spam or suspicious based on certain parameters.
Email Deletion: Easily delete selected emails to maintain a clean inbox.
Modern UI: Built with Tailwind CSS for a sleek and responsive interface.

Demo
You can try out Digital Declutter here: Digital Declutter Live Demo

*Here are some visuals of the app:
*

Dashboard

Suspicious Email Highlighting

Bulk Email Deletion

Repo
The source code for Digital Declutter is available here:
Email cleaner repo

Copilot Experience
Building Digital Declutter was an exciting experience, made even more seamless by GitHub Copilot. Here's how Copilot helped throughout the development process:

Code Autocomplete: Copilot provided valuable autocomplete suggestions for repetitive patterns, such as setting up API endpoints, Implementing suspicious email highlighting algorithm and defining TypeScript interfaces.
Prompts and Chat: With Copilot Chat, I asked questions about the Google API integration and received instant guidance, saving hours of manual research.
Model Switcher: Leveraging different GitHub Models helped me brainstorm and refine my code structure for efficient email fetching and deletion logic.
Error Handling: Copilot suggested meaningful error-handling approaches for API requests, ensuring smooth functionality even when issues occurred.
GitHub Models
I explored GitHub Models to prototype the logic for:

Efficiently fetching emails using the Gmail API.
Highlighting suspicious emails using specific filtering logic.
Bulk deletion of emails by mapping through email IDs.
The iterative suggestions helped me refine complex workflows like asynchronous API calls and UI interactivity.

Conclusion
Participating in the GitHub Copilot Challenge has been an incredible journey of learning and growth. Building Digital Declutter not only gave me the opportunity to create something impactful but also allowed me to explore the power of AI-assisted development.

By leveraging GitHub Copilot, I was able to focus on solving real-world problems instead of being bogged down by tedious boilerplate code. Digital Declutter is a small but meaningful step toward creating cleaner, more manageable inboxes for users.

I’m excited to continue exploring Copilot and GitHub Models to bring more innovative ideas to life!

If you enjoyed this post or have feedback on Digital Declutter, feel free to connect with me here on DEV or through my GitHub profile https://github.com/PriyaYadav12.

Happy coding! 🚀