Episode #16: Behind the Code – From Databases to Developer Superpowers
The Mysteries of Database Operations
At DjangoCon Europe, Karen Jex, a seasoned database expert and senior solutions architect at Crunchy Data, took the audience on a deep dive into the “Anatomy of a Database Operation.” Jex, who has spent over 20 years as a DBA and is now a recognized contributor to the PostgreSQL project, shared her wealth of knowledge and insights, shedding light on the intricate processes that occur behind the scenes when we interact with a database. Jex’s presentation covered the three key stages of a database operation: sending a request, executing the operation, and returning the results to the user. She delved into the client-server architecture of PostgreSQL, the parsing and planning of SQL statements, and the fascinating MVCC (Multiversion Concurrency Control) mechanism that ensures data consistency. Attendees were treated to a glimpse of the inner workings of a database, from the creation of new rows to the efficient handling of updates and deletions. Jex’s engaging style and practical examples made the complex topic accessible, leaving the audience with a deeper appreciation for the power and complexity of modern database systems.
The Real Reason Behind the Tech Job Meltdown—It’s All About Taxes
There’s been a lot of talk about why tech layoffs have hit so hard since 2023, with theories ranging from AI automation to post-pandemic overhiring. But a deep dive into the numbers reveals a less obvious, but critical culprit: changes to the U.S. tax code, specifically Section 174. For decades, companies could immediately deduct R&D expenses—think engineer salaries, software, and contractor costs—giving them a huge incentive to invest in innovation. But starting in 2022, those expenses have to be spread out over five years (or fifteen for overseas work), which means companies suddenly faced much higher tax bills and cash flow problems. The result? Many tech firms—especially startups and those with big research budgets—had little choice but to cut costs quickly, leading to widespread layoffs and even offshoring jobs to countries with more favorable tax rules. While other factors like rising interest rates and shrinking venture capital also played a role, this tax change was a major shock to the system. The big takeaway: policy changes that seem small on paper can have massive, real-world consequences for tech jobs, innovation, and the broader economy. If you’re wondering why the job market feels so different lately, it’s not just the usual tech cycle—it’s a fundamental shift in how the U.S. supports (or doesn’t support) R&D.
AI-Assisted Coding people are Nuts??
Is AI-Assisted Coding the Future of Software Development or they are complete lame? Despite skepticism from some of tech’s brightest minds, the author of this interesting blog makes a compelling case for the practical benefits of using large language models (LLMs) in software development. He argues that LLMs can automate much of the tedious, repetitive coding work, freeing up developers to focus on more impactful tasks. The author acknowledges concerns about code quality and hallucination, but suggests these can be managed through proper tooling and developer oversight. He believes LLMs will raise the “floor” of code quality while freeing developers to tackle more challenging problems.
Why Software Engineering Skills Are More Valuable Than Ever
Despite a tough job market, software engineering talent is incredibly powerful right now—not just for building products, but for automating tedious and formulaic tasks across all parts of a business. I read an article this week that highlights how engineers can now automate workflows that once required human input, thanks to advancements in AI tools like ChatGPT and Cursor. If you’re the type of person who actually automates repetitive tasks, you’re in a unique position to drive massive productivity. A real-world example comes from the author’s own startup journey: by automating the creation and marketing of 2D video game assets, he’s been able to handle what would traditionally require an entire marketing team. With just a well-written prompt and a few minutes, AI tools can now generate everything from sprite packs to showcase videos. So here’s the gist: If you have software engineering skills, you now have “superpowers” to tackle problems that seemed too painful or time-consuming before. This shift opens up new opportunities for engineers to add value far beyond traditional coding roles.
Platform Engineering Isn’t Just Infrastructure with a New Name
It’s become trendy to rebrand infrastructure teams as ‘platform engineering,’ but this week I came across an article that argues real platform engineering is much more than just managing Kubernetes clusters or writing Terraform modules. True platform teams don’t just provision and operate infrastructure—they build internal developer platforms (IDPs) that empower developers with real self-service. That means developers can spin up resources, deploy apps, and own their environments, while the platform team focuses on making the platform stable, maintainable, and user-friendly. The key insight is that platform engineering is about building a product for other engineers, not just automating away toil for your own team. This requires a product mindset: thinking about user experience, managing feature requests, testing, and release cycles—just like any customer-facing software. If your team is still the bottleneck for every config change or deployment, you’re not a platform team yet. The big takeaway: treat your platform as a product, and success will scale far beyond what managed infrastructure alone can deliver.