ML teams need software engineering mindset and software engineering teams need ML mindset to be successful in today’s era. "ML teams need to have software engineering mindset and software engineering teams need to have ML mindset to be successful in today’s era!"
Decision: Focus on depth over breadth – complete a few high‑impact courses thoroughly, take on a small number of real‑world projects, and align with teams that have already secured good opportunities. (Quote: "Aspiring software engineers! Don't fall into this trap, rather focus on just completing few courses but in depth, take up real live projects even if less, just do it at depth rather than doing too many projects")
I observed a recurring challenge for early‑career engineers that hampers product impact.
A common challenge for software engineers in their early years (2-5 years of experience) is overlooking the importance of deeply understanding customer use cases.
Often, understanding the customer's pain point feels like a “non‑tech” task and is considered boring.
This leads to spending less time on problem discovery and more on coding.
The result? Rework, endless back‑and‑forth, increased cycle times, and lower customer satisfaction—even if the feature is shipped quickly.
True impact comes from building with empathy—taking the time to understand the problem as the customer feels it.
It’s not just about shipping fast; it’s about solving right.
Elite software engineer is the new fresher in this AI era!
On 2025-10-06 I observed that many software engineers feel “lazy” about updating JIRA tickets while they are deep in coding work.
The remark was phrased “it lazy it lazy” to highlight the inertia.