Api isn't working :(

awesome ❤

Hi, I am getting this below error after configure the application as you have mentioned in the video. "No adapter for endpoint " I have updated few of the dependency to support java 11 Please help me to resolve the issue

How to do graceful shutdown for spring boot with kafka streams?

pls create video using open ai

The intro video is so shitty and unnecessary!

Why did you not show Conflicts? It would have been so helpful.

Thanks for the explanation :) Note for self: In system design, particularly in the context of performance measurement and analysis, *P90*, *P99*, and other similar notations refer to *percentiles*. Percentiles are statistical measures that indicate the relative standing of a value within a dataset. They are often used to understand the distribution and variability of metrics like response times, latencies, or other performance indicators. # Understanding Percentiles A percentile is a value below which a given percentage of observations in a dataset fall. For example: - P90 (90th percentile): 90% of the data points are below this value. In system performance terms, it means that 90% of the requests or transactions complete within this time. - P99 (99th percentile): 99% of the data points are below this value, indicating that 99% of the requests complete within this time. - P50 (50th percentile, also known as the median): 50% of the data points are below this value. Percentiles are particularly useful for understanding the performance experienced by the majority of users and identifying outliers or rare occurrences that might indicate problems. # Common Percentiles in System Design - P50 (Median): Indicates the middle value of the dataset, where half the observations are faster and half are slower. It's a useful measure of central tendency. - P90: Often used to represent the performance experienced by the majority of users. If the 90th percentile latency is 200ms, it means 90% of users experience response times faster than 200ms. - P95: This percentile indicates the point below which 95% of the observations fall. It's useful for identifying the tail latency, where a small fraction of requests may experience slower performance. - P99: Focuses on the "worst" 1% of the performance spectrum. It's critical for understanding the behavior of the system under peak load or during rare, high-latency situations. - P99.9 (Three Nines): Provides even more granular insight into the worst-case scenarios, highlighting extreme outliers. Let's consider an example of a web service's response times to illustrate P90 and P99 percentiles. # Scenario Imagine we have recorded the response times (in milliseconds) for 1,000 requests made to a web service. The response times are as follows (in sorted order for simplicity): [10, 12, 14, 15, 16, ..., 195, 200, 210, ..., 400, 450, 500, 550, 600] # Calculating Percentiles To calculate the P90 and P99 percentiles, we find the values below which 90% and 99% of the response times fall, respectively. 1. P90 (90th Percentile): - Position: To find the 90th percentile, we calculate (0.90 times 1000 = 900). So, we look at the 900th value in the sorted list of response times. - Value: Let's say the 900th value in the sorted list is 200ms. This means that 90% of the requests were completed in 200ms or less. 2. P99 (99th Percentile): - Position: For the 99th percentile, we calculate (0.99 times 1000 = 990). So, we find the 990th value in the sorted list. - Value: Suppose the 990th value is 500ms. This indicates that 99% of the requests were completed in 500ms or less. # Interpretation - P90 = 200ms: 90% of the requests were processed within 200ms. This gives an idea of the typical response time experienced by most users. - P99 = 500ms: 99% of the requests were completed within 500ms. This indicates that 1% of the requests took longer than 500ms, highlighting potential issues or outliers in performance. These percentiles provide valuable insights into the service's performance, particularly in understanding both the common experience and the rare, worst-case scenarios. They help identify whether there are significant delays affecting a small portion of users, even if the average response time is satisfactory.

isnt rabbitmq more suitable for high throughput stuff since it can prioritize messages?

Is there an expiry on access key?

can you tell me how you make grafana dashboard in linkerd-viz dashboard

Great job explaining it !

Very nicely explained

Which chrome extension you are using that show Highlighter options on screen ?

Great, I appreciate a lot, but I could not see authorization in this code but I can applaud for authentication, kindly reply, Thank you

It's hard to find quality content about advanced topics like this. Well explained 👍

Why didnt these companies not moving to linux?

We are still recovering 😂

How we can share same session with other application and this could be the good use case of spring session data redis , what fo you say ?

clear explination, thanks

How to remove this "data:" from the final output?

Great video! I am implementing this in my CI/CD for everything it can cover.

❤❤💯.. this is what I was searching,, Thanks bro.❤

openAPI was nothing but...

Does this not work with localstack instead of actual kinesis? I could not find a way to configure this with localstack

Hi, I am creating automation rule in JSM but when rule trigger its showing the jira automation in heading of mail by default but i want to change to my project name. please let me know how to configure this implementation

The Best RAG architecture video ever seen…. Lot of people are complicate the concept a lot… Could you do another video with the how prompt is compared with embeddings and measured accurate results…

thank you Ajay. you made my day.. one query, is it possible to upload a resume file and get the required details like, name, contact, skills etc..

simple thing also you keep on repeating you could have reduce the video size and make it simple way

Thanks a lot.

open-webui appears to be a better alternative

Wisjxdb;🗺️[Arkgw 'bl]!

Great example. Your videos are always useful.

I thought you would be showing a demo on how to monitor.

Good demo to get started with Thanks Ajay

Calling a library in 5 lines of code

yes very helpful content with open source library for chatbot . But I think streamlit works similar way.

perfect

Login part that you are explaining is I guess OIDC because OAuth alone is just meant for Authorization, it is basically OIDC that enforces login and hence the Authentication

Thanks for the video. this is very helpful

We are enjoying this series <3

🙂👍

AI Primer❤

Hi, Kindly make a video of event source implementation. Thank you

Great job

Gr8 info buddy for beginners. I want to use microsoft azure openai with custom data and rag too. Also need a custom chatbotot (own). Can you help me with desing? How to build my app in python, call 3rd party api, how to use RAG, how to expose API to custom chatbot etc ?

good info

This is so cool👌

Amazing sir, understood

Good explanation brother! thanks a lot.