Engineering efficiency, GPT-4's rivals, DynamoDB Architecture & more
Newsletter - Issue #01
Welcome to the first issue of groCTO 🚀.
We’ve handpicked:
Our week’s favorite - How (in)efficient are engineering organizations?
A few insightful pieces:
And lastly, a special podcast ‘The DORA Lab’ with Lena Reinhard, ex-CircleCI
How (in)efficient are engineering organizations? 🤔
The latest research by Abi Noda generously covers his insights on how large tech companies are responding to the current market conditions. Finding unique ways to cut costs and leverage their engineering departments using AI, many companies struggle to get beyond the fear-of-missing-out aspects of large language models brought on by sweet promises from highly successful big tech PR.
Executives want to go “better and faster”— meanwhile, many organizations still struggle with figuring out how to measure productivity and efficiency. Abi and his team lean on surveying large engineering organizations to combine objective data with qualitative human feedback.
On average, developers report 22% of their time being wasted. The average organization's inefficiency (interpreted as their average dev response) is nearly identical.
This is from a research report covering 180 large organizations with over 30000 surveyed employees. They explores the common notion that larger organizations are less efficient compared to smaller ones. What exactly sets apart the highly efficient large organizations from the lagging ones?
Keep an eye out for his upcoming article series on developer experience using this data. Stay sharp on trends and opportunities presented to you to bring proven 2024 practices to your own organization that will help you assess and improve the health and efficiency of your engineering department without developer churn or burnout.
👇 Read the full article with a link to the original research paper.
Thank you for your service, Abi!
Why so serious?
👀 Putting GPT-4's new rivals to the test
Nathan Warren researched the best LLM tools on the market for Exponential View by Azeem Azhar. He put his prompt engineering skills to the test by comparing and creating three test scenarios for Claude 3, GPT-4, and Gemini Ultra. As a spark of inspiration, he opted to reduce his own bias by asking the LLM’s to grade each other’s work instead.
🤖 His research article compares the AI’s overall quality, speed and reliability.
A Deep Dive into Amazon DynamoDB Architecture
Even executives and directors have to stay sharp on tools and technologies. We picked for you a deep dive into DynamoDB Architecture prepared by the
Some of the key lessons the DynamoDB team had were as follows:
🔋 The full article goes into detail on DynamoDB’s History, Operational Requirements, and finally its Architecture and how it manages Durability.
The DORA Lab | Episode 1 with Lena Reinhard
In our debut episode, host Kovid Batra welcomes Lena Reinhard, a tech mentor with a wealth of experience from renowned organizations such as Circle CI, Travis CI, and BuzzFeed.
Lena delves deeper into what drives teams to prioritize and implement DORA metrics and focuses on effectively communicating engineering metrics with non-engineering teams to ensure alignment with business goals.
And that’s it for today! See you next week, Ciao 👋
Credits 🙏
Curators- Diligently curated by our community members Denis & Kovid.
Sponsors- This newsletter is sponsored by Typo AI - Ship reliable software faster.
If you haven’t already, consider subscribing us 🔔
Great curation! specifically the podcast with Lena Reinhard. This is gold!
Loved this edition. Keep sharing such insights & experiences. Thanks!