Episode #12: AI Pair Programming Pitfalls, Four-Day Workweek Wins, Stepping Back for Perspective, Debunking the 10x Developer Myth, Human Ingenuity vs. LLMs and the 'Who Cares' Era
The Four-Day Workweek
Employees love the four-day workweek, but do companies benefit too? Apparently, yes - the average company rates the trials an 8.2 out of 10 for success, and only 10% discontinued the program after a year. The key seems to be boosting productivity, often by cutting down on meetings and allowing more creative time. But if these productivity hacks are so effective, why haven’t companies implemented them already? The article suggests the four-day week makes these changes easier, as workers are motivated to find new efficiencies. As AI threatens more jobs, could the four-day week be a win-win solution for both employees and employers?
It’s worth taking a day off once in a while to contemplate where you’re going in life.
When deep in the throes of a technical challenge, it’s easy to lose sight of the bigger picture. I found this thoughtful blog post that explores the delicate balance between tenacious problem-solving and the need to periodically step back and reassess one’s approach. What happens when an engineer becomes so fixated on a particular task that they lose touch with their original goals? The author recounts a personal experience of getting overly invested in porting C code to Rust, only to realize they had strayed from their initial intent of simply observing the language’s capabilities. This raises an intriguing question: how do we know when we’ve gone too far down a rabbit hole, and when it’s time to take a step back and reevaluate? The article suggests that periodic “rituals” of reflection, such as taking breaks at regular intervals, can help provide the necessary perspective. By stepping away and asking ourselves fundamental questions about our motivations and priorities, we can avoid the pitfalls of monomania and ensure that our efforts remain aligned with our broader objectives. This is a valuable lesson for developers and tech professionals alike, who must navigate the constant pull of urgent tasks while maintaining a strategic view of their work.
Myth of the 10x Developer: True Productivity Comes from Teamwork
The tech industry is obsessed with the idea of the “10x developer” – the coding superhero who can single-handedly outperform their peers. The reality is far more nuanced. True productivity often comes not from individual heroics, but from effective teamwork and collaboration. What makes someone truly productive? It’s not just about coding speed or technical prowess, but the ability to communicate clearly, learn continuously, and delegate tasks effectively. The best developers aren’t necessarily the ones who can write the most complex algorithms, but those who can elevate the entire team through their teamwork and problem-solving skills. Rather than chasing the mythical 10x developer, companies would do better to focus on building strong, collaborative teams where everyone contributes to the collective success. After all, “The real 10x productivity comes from the collective strength of the team, not just the speed of one person’s typing fingers.”
First Week as an Engineering Manager: A Roadmap for Success
Transitioning into an engineering management role can be daunting, but this insightful article from Alex Ponomarev offers a clear blueprint for making a strong start. The key? Preparation, communication, and a willingness to let go of hands-on coding. Ponomarev emphasizes the importance of doing your homework before diving into meetings with your team, manager, and other stakeholders. What are the company’s goals, systems, and pain points? Understanding the context upfront allows you to ask the right questions and identify critical information gaps. But the real magic happens when you actively engage your team - not just to introduce yourself, but to truly understand their challenges, ideas, and concerns. How can you best support them during this shift? As an engineering manager, your role evolves from individual contributor to strategic leader. While it may be bittersweet to step back from coding, Ponomarev offers practical advice for managing that transition, from delegating tasks to adjusting deadlines. The message is clear: by prioritizing learning, relationship-building, and continuous improvement, new engineering managers can lay a solid foundation for long-term success.
Human coders are still better than LLMs
While large language models (LLMs) have become increasingly useful tools for developers, a recent blog post by antirez highlights how human coders can still outshine AI in certain areas. Antirez, a well-known figure in the tech community, shares a story of tackling a complex bug in his Redis project, where he was able to devise a creative solution that outperformed the suggestions from an LLM assistant. What makes human coders so special? Antirez argues that the human capacity for “thinking out of the box” and envisioning “strange and imprecise solutions” is something that current AI still struggles to match. By combining his deep domain expertise with lateral thinking, Antirez was able to come up with a clever approach involving hashing and XOR operations to efficiently detect and resolve the issue. While the LLM provided useful input, it ultimately fell short of the human’s ability to synthesize a truly novel solution. This article serves as a reminder that despite the rapid advancements in AI, there are still many areas where human ingenuity and problem-solving skills remain unparalleled. As the tech industry continues to grapple with the implications of AI, developers and leaders would do well to recognize the unique value that human coders can bring to the table.
As a developer, my most important tools are a pen and a notebook
In a surprising blog post, one developer argues that the most important tools in his arsenal are not the latest coding frameworks or cutting-edge IDEs, but rather a simple pen and notebook. Juha-Matti Santala, a seasoned software engineer, explains that when it comes to problem-solving and designing software, stepping away from the computer and embracing analog tools can unlock new levels of creativity and clarity. Santala emphasizes that writing and sketching on paper helps him turn abstract ideas into tangible artifacts, exposing gaps in his understanding that might otherwise be overlooked. By documenting his thought process, he can later revisit and refine his solutions, making it a powerful “refactoring tool” in its own right. The ability to easily reference past notes also proves invaluable, as Santala can quickly recall his previous thought patterns and design decisions. This unconventional approach to software development raises an intriguing question: in an increasingly digital world, could the humble pen and notebook be the secret weapons that set the most effective developers apart?
Why agents are bad pair programmers: Because they are too fast to collaborate with humans?
In a thought-provoking blog post, Justin Searls explores the challenges of using AI-powered “agent” tools as pair programmers. While these agents can code faster than humans, Searls argues that their lightning-fast speed can actually undermine the collaborative nature of pair programming. What’s the problem with AI pair programmers? Searls remembers working with programmers who would suddenly take the keyboard and type code really fast, without saying anything. He couldn’t even keep up with what they were doing. This same dynamic, he says, can occur with AI agents, leading to disengagement and a lack of shared understanding. Worse, the agent may be building the “wrong thing all along,” leaving the human partner to clean up the mess. So what’s the solution? Searls suggests abandoning editor-based “agentic pairing” in favor of asynchronous workflows where the agent’s work can be reviewed via pull requests. He also recommends “throttling down” the agent’s speed and introducing features that encourage more collaborative, human-like behavior, such as pausing to discuss direction and expressing self-doubt. By designing AI tools that emulate the back-and-forth of human pair programming, Searls believes the tech industry can unlock the benefits of this practice without the pitfalls.
Who Cares Era: When AI Chatbots Infiltrate the Media
In a startling revelation, it was discovered that the Chicago Sun-Times and the Philadelphia Inquirer had both published “special supplements” containing entirely fabricated facts, experts, and book titles generated by an AI chatbot. What’s most disheartening is that at every step, from the writers to the editors to the readers, no one seemed to care. This incident encapsulates the “Who Cares Era” we find ourselves in, where mediocre, disposable content is churned out and largely ignored. As AI becomes a “mediocrity machine” producing “good enough” simulations of normalcy, the question arises: When does the illusion fall apart, and how much do people truly care? That’s why I’m making my tech weekly myself. Even if an AI voice might sound better, authenticity is required. In a world of shortcuts, real human effort and genuine care make all the difference.