Nibbles
An endeavor to share curated stuff that, we think, would be fun to read and useful. Occasionally, we might throw up something original. So, watch out!
Nibbles 50
For our 50th edition we wanted to go back and shortlist five articles that stand the test of time, in other words classical. In other
We need to start thinking of AI as “normal”
The article “Is AI Normal?” from MIT Technology Review examines the polarized discourse surrounding artificial intelligence. While some experts, like OpenAI’s Sam Altman, envision AI’s
How AI-assisted coding will change software engineering: hard truths
This Pragmatic Engineer Newsletter article, featuring insights from Addy Osmani, explores the evolving impact of AI-assisted coding on software engineering. It highlights the transformative potential
Emerging Patterns in Building GenAI Products
The article explores emerging patterns in building Generative AI products as they transition from prototypes to production. It identifies key challenges like hallucination, unbounded data
How AI-assisted coding will change software engineering: hard truths
The article highlights how AI coding tools (like GitHub Copilot) will automate much of the “grunt work” of programming, freeing engineers to focus on higher-level
Best of 2024
The Forty-Year Programmer You wish to be a ‘coder for life’. This article gives an insight. We love them. The Turing Trap: The Promise &
My Favourite Software Architecture Patterns
Matt Bentley explores several software architecture patterns, discussing their practical applications and the scenarios where they excel. He emphasizes that selecting the appropriate pattern depends
A simple programming productivity trick: leave work unfinished to reach flow
The article from Engineer’s Codex shares four productivity tips for software engineers: leaving tasks slightly unfinished to regain flow easily, mastering keyboard shortcuts, organizing frequently
27 Unique Dev Challenges: A Recent Study Explored the Top Challenges Faced by LLM Developer
This article explores 27 challenges faced by developers working with Large Language Models (LLMs), including issues around data handling, model optimization, and ethical concerns. It