Shaun Abram
Technology and Leadership Blog
Subscribe to RSS FeedArticle summary: Is AI code sabotaging its own ROI case?
Is AI code sabotaging its own ROI case? is an article on leaddev.com where tech journalist Bill Doerrfeld interviews Charity Majors, CTO of Honeycomb.
Some summary notes below, but the TLDR is that the true value of AI in software development isn’t just writing code faster, but accelerating the entire Software Development Life Cycle (SDLC). Ownership is more important than authorship. Keep focussing on the basics of SLOs, stress-testing, canary deployments, feature flags etc. Leaning on Platform Engineering and AIOps to augment operational work. Real ROI won’t come from generating more code faster – it will come from streamlining the lifecycle around it.
Tags: aiops, charitymajors, leaddev, platformengineering, summary
Talk summary: Leading a Team of Developers Using AI by Venkat Subramaniam
I attended NFJS’s Tech Leader Summit conference in Florida last week. One of the talks was “Leading a Team of Developers Using AI” from Venkat Subramaniam.
AI has changed software development irrevocably. We should lean in and play to AI’s strengths, while utilizing the skills that we as engineers still bring to the table. Machines are fast at finding patterns in large data sets, but humans have the critical thinking and intelligence to see beyond existing data sets.
While typing raw code may soon be over, many skills remain essential:
- Problem solving
- Understanding requirements (which may be incomplete and contradictory)
- Handling trade offs
- Communicating
Maintainability, reliability, performance, security and robustness all remain important aspects of software projects that AI may not address sufficiently.
AI has been trained on vast amounts of code written by humans. But much of it is bad (we’ve all written bad code). And now humans complain that AI generates bad code. That is karma!
AI can’t differentiate good code from bad. But you can, especially in the context of your current environments and needs.
Use AI for
- ideas, not solutions
- prototypes, not production code (AI can allow you to bring new ideas to test the market much faster. Similar to how 3D printing did for physical objects)
- finding bugs in and documenting existing code, not writing new code (AI excels at finding issues in complex code that humans struggle to understand)
Tags: conferencetalks, nfjs, nofluffjuststuff, summary, venkatsubramaniam
Book summary: Deep Work
Deep Work: Rules for Focused Success in a Distracted World (amazon.com) is a book from professor and author Cal Newport (wikipedia.org).
Highly recommended.
Deep Work refers to professional activities performed in a state of distraction-free concentration that push your cognitive capabilities to their limit. It is how we quickly learn hard things and produce at an elite level. Deep work, cultivating craftsmanship, and the associated “flow” state, allow to you understand your world as rich in meaning and importance, and ignore shallow negative things.
The book advocates for creating regular habits that build deep work into your routine. Great creative minds think like artists but work like accountants.
Shallow work, such as email & instant messaging, distracts us while simultaneously degrading our capacity to remain focused.
Ruthlessly reduce shallow work, where possible. Be careful what you say yes to; avoid social media and internet entertainment in general; embrace boredom. The ability to concentrate intensely is a skill that must be trained. Constant attention switching online has a lasting negative effect on your brain. If every moment of potential boredom in your life (e.g., waiting in line) is relieved with a quick glance at your smartphone, then your brain becomes rewired to not be ready for deep work. Proactively schedule what you will do with your time each day.
The ability to concentrate is a skill that gets valuable things done. Deep work is much more powerful than most people understand. To leave the distracted masses and join the focused few is a transformative experience.
What follows is a more detailed summary of the book. The majority of it is direct quotes. Again, it is worth reading the original.
Tags: bookreviews, books, deepwork, selfimprovement, summary, timemanagement
Report Summary: 2025 DORA report
I’ve been a huge fan of the annual DORA (DevOps Research and Assessment) report since reading the Accelerate book that explains the DORA metrics and how to improve them.
The 2025 DORA report (pdf attached here) just came out and has a particularly interesting section on AI Capabilities, The DORA AI Capabilities Model. I summarize some of the points here that I found most interesting here, but the original is well worth a read.
Tags: AI, artificialintelligence, dora, dorareport, genai, summary
Blog post summary: The role of developer skills in agentic coding, by Birgitta Böckeler
Birgitta Böckeler, a Distinguished Engineer at Thoughtworks, recently posted a list of links that “describe what my colleagues and I are learning and thinking” around generative AI and LLMs. One in particular that I liked was “The role of developer skills in agentic coding” (written by Böckeler herself).
Böckeler states that agentic coding assistants are becoming ever more capable but, as the engineer, you still need to intervene, correct and steer. As well as detailing types of problems she sees with these tools, recommendations are provided on how to work around and mitigate.
(more…)Tags: agenticcoding, AI, artificialintelligence, summary
AI-First at Shopify; a memo from Tobi Lutke
CEO of Shopify, Tobi Lutke, posted an “internal” memo entitled “Reflexive AI usage is now a baseline expectation at Shopify“. It very publicly leaked and he has since shared it himself on twitter. It is generating a lot of discussion (e.g. CNBC, Forbes, Techcrunch).
Tags: AI, artificialintelligence, shopify, summary
Blog post summary: Designing data products by Kiran Prakash
This article from Kiran Prakash on martinfowler.com provides “a step-by-step, methodical approach to identifying data products”.
It starts with what a data product is: the smallest valuable unit of analytical data, packaged to deliver meaningful business value. More specifically, it must have these eight characteristics (from Zhamak Dehghani’s book Data Mesh)…
Tags: data, datalake, datamesh, dataproducts, martinfowler, summary
Summary: The Observability Crisis
The Observability Crisis is an article from Jaya Gupta & Ashu Garg from Foundation Capital, a Silicon Valley based venture capital (VC) firm investing in tech startups.
TLDR:
Companies in the first wave of the observability space (such as Splunk, AppDynamics, Datadog and New Relic) focused on solving data storage and analysis problems. However, with ever-increasing data volumes, costs increase and extracting actionable insights becomes more difficult.
Companies in the second wave (such as Honeycomb, Grafana and Cribl) helped organizations cope with complexity and cost by better supporting an increase in the volume, velocity, and the cardinality of data. However, the engineering overhead (such as SREs) to manage these tools, control costs, and triage alerts remains high.
The third wave could involve a shift from volume-based to value-based pricing, where you pay for observability tools based on their ability to accurately identify root causes and even prevent them from recurring. This is likely to involve using LLMs along with ML techniques that:
- Offer predictive maintenance by recognizing patterns across heterogeneous data sources
- Automatically generate incident playbooks
- Synthesize data from post-mortem reports
So, while I do like this article, my main gripe is that the article leaves it unclear whether any such “3rd wave” tools exist yet.
Read on for a summary of that article. The original is ~1600 words. This is ~600.
Tags: apm, devops, incidents, observability, RCA, reliability, rootcauseanalysis, sitereliabilityengineering, sre, summary
Blog post summary: Why we do incident drills
Why we do incident drills and why you should too is an article by Fína Mádrová, an SRE at Mews. This is a summary (600 words) of the original (1400 words).
Tags: incidents, sitereliabilityengineering, sre, summary
Blog post summary: Periodic Face-to-Face by Martin Fowler
I remain a big fan of Martin Fowler’s writings, and his most recent “Periodic Face-to-Face” article struck a chord. Some highlights below but the tldr:
Remote teams benefit from face-to-face gatherings every few months, to build trust and rapport. During those gatherings, focus on that tasks that require input from many people with rapid feedback, such as debates about product strategy and explorations of systems architecture. View the travel expenses as an investment and a cost effective way to set direction, reduce conflict and increase motivation.
Read on for other highlights…
Tags: culture, remote, summary
Summary: The SPACE of Developer Productivity
The SPACE of Developer Productivity is a 2021 paper by researchers at GitHub, University of Victoria, and Microsoft (including Dr Nicole Forsgren, co-author of Accelerate) that looks into ways to measure and predict productivity for both individuals and teams.
The following is a summary of the paper. The original is ~5400 words. This is ~2000.
Tags: developerproductivity, metrics, productivity, summary
Blog post summary: What test coverage means by John Gluck
In this What QA Wolf means by “test coverage” post, the author John Gluck starts with a simple definition of test coverage, discusses what tests to write and how, and ultimately cautions against using coverage targets as an end goal.
Tags: integrationtesting, summary, unittesting
Blog post summary: Accountability in Software Development by Kent Beck
A summary of Accountability in Software Development, by Kent Beck
Tags: accountability, kentbeck, summary
Blog post summary: Quality Assurance is Not About Testing
The following is a summary of Quality Assurance is Not About Testing by Matt Lievertz. I have also incorporated some elements of his earlier The Death of the Non-Coding QA Role post too.
Tags: qa, quality, summary, Testing
2022 VOID Report Summary
The following is a summary of the 2022 VOID report (pdf can be found directly here)
The original is ~10,000 words. This is ~1500.
Tags: incidents, summary, voidreport