#software-development

#software-development

AI tools don't always boost productivity. A recent study from Model Evaluation and Threat Research found that when 16 software developers were asked to perform tasks using AI tools, the they took longer than when they weren't using the technology, despite their expectations AI would boost productivity. The research challenges the dominant narrative of AI driving a workplace efficiency boost.

AI is not destroying professional careers - it's just helping every professional and field evolve for the next generation. AI, especially recent generative AI innovations, has started helping everyone boost their day-to-day working productivity with human-like intelligent automation. Programmers had good old days - they used to have a geeky working style with interesting planning, experimenting, designing, development, testing, and collaboration activities.

But LLMs took it a notch even further, coders have started morphing into LLM prompters today, that is primarily how software is getting produced. They still must baby sit these LLMs presently, reviewing and testing the code thoroughly before pushing it to the repo for CI/CD. A few more years and even that may not be needed as the more enhanced LLM capabilities like "reasoning", "context determination", "illumination", etc. (maybe even "engineering"!) would have become part of gpt-9

I've spent 15+ years building across multiple tech ventures and cultures - starting in Vietnam, sharpening my craft in Japan and Singapore, then expanding to the U.S., Australia and Europe. Each stop taught me how different ecosystems turn constraints into capability: how to ship products under pressure, build companies from zero, grow talent pipelines and lead teams through the hardest execution challenges.

At Google, we tried to study productivity to determine which factors strongly predicted developers' self-rated productivity, condensing research into 48 productivity factors.

"DevOps was supposed to be the silver bullet that would solve everything. But nobody really implemented it fully."

Lovable AI lets you build full-stack web apps just by describing your idea in plain English, allowing creators to move fast without needing programming knowledge.

FogBugz's outdated interface and clunky workflows are holding agile teams back, as modern software development demands real-time collaboration and intuitive design.

User Acceptance Testing (UAT) is the final checkpoint where real users validate the product in real-world scenarios to ensure it meets expectations and business goals.