fix: typos

ds-with-mac/content/posts/unpopular-opinion-hard-good-ds/index.md

@@ -322,7 +322,7 @@ If you have chosen the path of a Data Scientist, you're likely someone who enjoy
 
 The choices don't stop there. Should you buy or build? Fine-tune or prompt-engineer, especially as LLM capabilities continue to improve? What tasks are still considered core to Data Science? Sure, evaluation (evals) is essential, but how engaging is it to write evals all day? And with the rise of techniques like using LLMs as evaluators, is there even a need for traditional approaches anymore?
 
-My point is that **technology**—and **Data Science** with it—continues to change rapidly. And we as practioners need to stay ahead of the curve and adopt a continious learning mind set. If you caught the recent announcements from Anthropic, demonstrating <cite>Claude taking over your computer [^8]</cite>, it raises an even bigger question: *do we still need programmers*, or will AI soon take over these tasks? We'll likely need to be present and involved, but filtering out what's genuinely relevant from the constant stream of new developments is becoming increasingly challenging.
+My point is that **technology**—and **Data Science** with it—continues to change rapidly. And we as practitioners need to stay ahead of the curve and adopt a continuous learning mind set. If you caught the recent announcements from Anthropic, demonstrating <cite>Claude taking over your computer [^8]</cite>, it raises an even bigger question: *do we still need programmers*, or will AI soon take over these tasks? We'll likely need to be present and involved, but filtering out what's genuinely relevant from the constant stream of new developments is becoming increasingly challenging.
 
 ### Key Challenges and Considerations
 * **Overwhelming Choice of Tools and Technologies**: With the rapid release of new programming languages, frameworks, and libraries, Data Scientists face the daunting task of deciding which tools to invest their time. Should you learn Rust for performance benefits or stick to Python, which remains the industry standard for data science? Every new tool claims to be better, but not all are worth the investment.