Using GitHub Copilot To Speed Up Your Development Workflow
- Grady Salzman tl;dr: “Let’s dive into three features of Copilot I use everyday to increase my productivity and write better code: Copilot Edits, workplace contexts, and customizing Copilot for your workspace.”featured in #583
Five Voice-Based Workflows To Try in 2025
tl;dr: From brainstorming on the go to neatly structured retrospectives, fully prepared meetings, or ready-to-share strategy documents—you can now do real work without a keyboard. LLMs are unlocking new ways of working, these are our favorite voice-based workflows for managers tired of being tethered to their screens.featured in #581
The Productivity Apps I Use In 2024
- Cassidy Williams tl;dr: “This post will not cover my code editor(s), terminals, or other developer tools. This is just a list of the tools I use daily to get my tasks done! Also, all of them work across operating systems. I use both a PC and a Mac, so that’s important to me.”featured in #579
The Productivity Apps I Use In 2024
- Cassidy Williams tl;dr: “This post will not cover my code editor(s), terminals, or other developer tools. This is just a list of the tools I use daily to get my tasks done! Also, all of them work across operating systems. I use both a PC and a Mac, so that’s important to me.”featured in #578
Are You Shipping More With GitHub Copilot? Is More Roadmap Work Being Done?
tl;dr: Engineering leaders are trying to figure out if their team is using GitHub Copilot, how much they're using it, and its impact on their work. Download this slide deck from Jellyfish, analyzing data from 4,200+ developers at 200+ companies, and start understanding whether you're getting adequate return on your AI investments.featured in #571
Expanding The Solution Size With Multi-File Editing
- Birgitta Böckeler tl;dr: “A very powerful new coding assistance feature made its way into GitHub Copilot at the end of October. This new “multi-file editing” capability expands the scope of AI assistance from small, localized suggestions to larger implementations across multiple files. Previously, developers could rely on Copilot for minor assistance, such as generating a few lines of code within a single method. Now, the tool can tackle larger tasks, simultaneously editing multiple files and implementing several steps of a larger plan. This represents a step change for coding assistance workflows.”featured in #568
The Impact of Generative AI on Software Developer Performance
tl;dr: BlueOptima's study of 200,000+ enterprise developers uncovered surprising insights on the reality of Generative AI: (1) Only 12% of developers commit GenAI code without modification, suggesting limited real-world integration. (2) Modest productivity gains of only 4%, indicating marketing claims are premature. (3) A decline in quality with high AI usage.featured in #559
featured in #557
Modeling Impact Of LLMs On Developer Experience
- Will Larson tl;dr: “Models are imperfect representations of reality, but this one gives us a clear sense of what matters the most: if we want to increase our velocity, we have to reduce the rate that we discover errors in production. That might be reducing the error rate as implied in this model, or it might be ideas that exist outside of this model. For example, the model doesn’t represent this well, but perhaps we’d be better off iterating more on fewer things to avoid this scenario. If we make multiple changes to one area, it still just represents one implemented feature, not many implement features, and the overall error rate wouldn’t increase.”featured in #556
OpenAI Is Shockingly Good At Unminifying Code
- Frank Fiegel tl;dr: “Usually I would just power through reading the minimized code to understand the implementation. However, I realized that I never tried asking ChatGPT to do it for me... So I copied all of the above code and asked ChatGPT to "explain the code”.”featured in #545