One of my biggest weaknesses as a software engineer is procrastination when facing a new project. When the scope is unclear, I have a tendency to wait until I feel I’ve “felt out” the problem to start …
Project Xanadu: Even More Hindsight
Retrospective on Project Xanadu’s success and failure: a lack of design iteration, meaningful use-cases, or practicality stopped a valuable vision from maturing into something useful. (And contrasted with my approach.)
Software Bonkers
I’m software bonkers: I can’t stop thinking about software. And I can’t stop building software.
Don't Get Distracted
I’m going to tell you about how I took a job building software to kill people. But don’t get distracted by that; I didn’t know at the time.
It's getting harder to focus every day
I’m feeling it right now. I had to set a timer for 15 minute on my computer and block all distractions to write this. If I didn’t force myself to focus I would easily get distracted by something after few minutes. Even when I’m doing things that I’ve been waiting to do it, I still feel the urge to do something else. I don’t know how and when this happened. During the last few years I was always studying, working, and doing open source. And actually got stuff done. Doing all of those at the same time requires paying attention to what I wanted to do and ignore the noise.
‘AI fatigue’ is settling in as companies’ proofs of concept increasingly fail. Here’s how to prevent it | Fortune
Along with the excitement about the possibilities of generative AI is a great deal of pressure for leaders and employees participating in projects.
How I stopped worrying and learned to love the easy fix
On the balance between perfect solutions and pragmatic fixes in software engineering
The antidote to AI fatigue — Answer.ai Solveit
Magpai: A bird's-eye view of your business, powered by an AI brain.
Unify your tools and context, surface insights and risks, automate next steps, and keep everyone in sync. Zero technical expertise required...
DevEx in Action
Somewhere, right now, a software developer is pulling open a ticket from the project backlog, excited by the prospect of working on something new. As the developer begins reading through the description of the task, their laptop is suddenly flooded with alerts from the team’s production error-tracking system, disrupting the developer’s ability to focus. Eventually, returning to the task at hand, the developer studies the requirements described in the ticket. Unfortunately, the task lacks context and clarity, so the developer asks for help, which will take days to resolve.
My productivity app is a never-ending .txt file
The biggest transition for me when I started college was learning to get organized. There was a point when I couldn't just remember everything in my head. And having to constantly keep track of things was distracting me from whatever task I was doing at the moment.
Stop Begging Billionaires To Fix Software — Build Your Own
This is part one of a two-part series on using AI tools as one piece to fight back against tech company control of our lives. Part two shares the details of how I built my own task management tool …
Ship working code while you sleep with the Ralph Wiggum technique
Building a mental model for async programs
In my experience writing async programs in Dart and Rust, I have built up a model in my head to avoid bugs caused by async. These bugs are usually hard to find and reproduce, so there is a lot of value in taking preventive measures to avoid them. I hope to improve this model in my head by writing this post and to share them with anyone who might find it helpful.
AI fatigue is real and nobody talks about it | Siddhant Khare
You're using AI to be more productive. So why are you more exhausted than ever? The paradox every engineer needs to confront.
Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity
Despite widespread adoption, the impact of AI tools on software development in the wild remains understudied. We conduct a randomized controlled trial (RCT) to understand how AI tools at the February-June 2025 frontier affect the productivity of experienced open-source developers. 16 developers with moderate AI experience complete 246 tasks in mature projects on which they have an average of 5 years of prior experience. Each task is randomly assigned to allow or disallow usage of early 2025 AI tools. When AI tools are allowed, developers primarily use Cursor Pro, a popular code editor, and Claude 3.5/3.7 Sonnet. Before starting tasks, developers forecast that allowing AI will reduce completion time by 24%. After completing the study, developers estimate that allowing AI reduced completion time by 20%. Surprisingly, we find that allowing AI actually increases completion time by 19%--AI tooling slowed developers down. This slowdown also contradicts predictions from experts in economics (39% shorter) and ML (38% shorter). To understand this result, we collect and evaluate evidence for 20 properties of our setting that a priori could contribute to the observed slowdown effect--for example, the size and quality standards of projects, or prior developer experience with AI tooling. Although the influence of experimental artifacts cannot be entirely ruled out, the robustness of the slowdown effect across our analyses suggests it is unlikely to primarily be a function of our experimental design.
