The params prop is part of the dynamic API and was made asynchronous in Next.js 15. Essentially, we just need to follow the instruction—await params before accessing its properties, like this:

const id = (await params).id

I didn't know the labelling/naming thing! Thanks for sharing! I can see its potential to significantly improve code readability and maintainability, because in my case positions of values are important!

The issue? val2 is typed as string | number, but ChildComp expects a string. Since I knew val1 would always be a number and val2 a string, I redefined the prop with a more specific structure:

props: [number, string];

#TypeSafty #DevTips

But when I spreaded it and then passed it to a child component,

const [va1, val2] = prop;
<ChildComp prop={val2}>

I got the error, Type 'string | number' is not assignable to type 'string'.

I wrote a post explaining the issue and possible solutions. Check it out here:
www.jessewei.net/articles/und...

A better approach is to keep secrets on the server and never send them to the client. This is where Next.js Server Components come in handy. #ServerComponents #Security

To make env vars accessible in the browser, we can prefix them with NEXT_PUBLIC_, but that completely defeats the purpose of keeping them secret.

At first, I didn’t fully get this, thinking I could manage sensitive data using environment variables in client components to keep them hidden. But that doesn’t work, because env vars are only available on the server by default.

For a deeper dive into views vs. copies with accessible examples, check out my blog post here ↓

www.jessewei.net/articles/und...

Chained assignment is discouraged because it creates temporary intermediate objects, which may be views in some cases and copies in others. This unpredictability can lead to unexpected bugs, and that’s exactly what the SettingWithCopyWarning is designed to warn us about.

To ensure our code behaves as expected, we need to be explicit:
• If we want to modify the original data, we should operate on it directly.
• If we need to preserve the original, we should explicitly create a copy and modify the copy.

The root cause lies in how #NumPy handles data: depending on the indexing operation, it may return either a view or a copy to optimize performance. The problem? We can’t always predict which one we’re getting.

Once the data is parsed into a datetime object, pandas automatically standardizes it to its default YYYY-MM-DD format. To display the date in a different format, use .dt.strftime().