@SumitM_X That extra OPTIONS request is the browser sending a CORS preflight request.
Adding the Authorization header makes the request non simple under CORS rules, so the browser first asks the server for permission before sending the actual request.
The bottleneck is often not the email API itself, but the queueing, provider rate limits, retries, throughput limits, or asynchronous processing pipeline behind it.
A 200 OK usually only means the provider accepted the request, not that the email was actually delivered immediately.
At that scale you need proper queue architecture, retry handling, backpressure control, provider monitoring, batching strategies, and sometimes multiple email providers for reliability.
@itsaaroshi Freedom.
Android gives users far more control over customization, file management, app installation, default apps, and hardware choices across different budgets.
@Umesh__digital Usually rebase for a cleaner linear history before merging.
But if the branch is heavily shared with multiple developers already working on it, merge can be safer to avoid rewriting commit history and creating conflicts for everyone else.
@knowRowan Currently building LifeStack.
It is focused on productivity, activity tracking, and helping people understand where their time actually goes instead of losing everything inside random notes and unfinished to do lists.
A growing DLQ usually means messages are repeatedly failing processing and never recovering successfully.
Common causes are bad payloads, schema mismatches, downstream service failures, poison messages, retry logic problems, or consumers that cannot handle certain edge cases correctly.
The circuit breaker pattern is one of the main protections here.
It stops repeatedly calling unhealthy downstream services, preventing thread exhaustion, cascading failures, and system wide overload.
Timeouts, retries with backoff, bulkheads, and load shedding are also commonly used together.
Bcoz training LLMs is not limited by Python execution speed alone.
Python became dominant because of its AI ecosystem, simplicity, research friendliness, and massive library support like PyTorch, TensorFlow, NumPy, and JAX.
Also, the heavy computation is usually happening underneath in highly optimized C, C++, or CUDA code running on GPUs. Python often acts more like the orchestration layer than the raw compute layer itself.
@jahirsheikh8 These jobs mostly go to people from top colleges like IIT or NIT. Or they need years of strong work experience. Normal college grads rarely get in.
You are doing great bro.
Have some break and come back stronger again.
A lot of creators underestimate how mentally exhausting daily posting becomes after the initial excitement phase.
At some point, the pressure to constantly perform, stay visible, and maintain engagement starts feeling more like a job than creative expression.