The Upskill Space retweeted
Kubernetes Ingress Vs Gateway API ๐
Most Kubernetes deployments today use Ingress to expose HTTP(S) traffic.
It works, but it has notable caveats:
Tied to HTTP
Cluster scoped
Limited Extensibility
No Fine grained Routing Control
and so on.,
If havenโt explored the Gateway API yet, you might be missing out on more flexible traffic control and multi protocol support.
๐ด 52K+ read my DevOps and Cloud newsletter: techopsexamples.com/subscribโฆ
What do we cover:
DevOps, Cloud, Kubernetes, IaC, GitOps, MLOps
๐ Consider a Repost if this is helpful
The Upskill Space retweeted
LIVE: Watch the @Space_Stationโs robotic arm guide @NorthropGrummanโs Cygnus XL spacecraft to a port on the station for installation. xitter.cf/i/broadcasts/1gqGvrzlMโฆ
The Upskill Space retweeted
Linear Regression clearly Explained!
Linear Regression models relationship between a dependant variable (y) & two or more independent variables (x1, x2 ...)
โ๏ธFor the sake of simplicity we discuss linear regression with a single independent variable.
Mathematical Representation:
y=mx+c
where:
- y is the dependent variable
- x is the independent variable
- m is the slope of the line
- c is the y intercept
Cost Function:
โ๏ธThe goal is to find the best values for m and c that minimize the error between the predicted values & the actual values for all data points.
This is how the cost function looks, a mean squared error:
MSE= 1/Nโ(yiโโ(m*xiโ+c))**2
Where:
- N is the number of data points.
- yiโ is the actual value for the ith data point.
- mxiโ+c is the predicted value for the ith data point.
How it works (Optimisation)โ
To minimize the MSE, we can use gradient descent.
The basic idea is to:
- Start with some initial value of m & c
- Compute the gradient of the MSE w.r.t. both m & c.
- Update m & c in the direction of the negative gradient.
Implementation from Scratch:
Now that we understand how things work it's fairly simple to implement linear regression in Python!
Check this out ๐
That's a wrap!
Share this with your network if you found this insightful !
Find me โ @akshay_pachaar โ๏ธ
For more insights and tutorials on AI and Machine Learning!
Cheers!! ๐
The Upskill Space retweeted
Advanced ML Courses- mltut.com/best-advanced-machโฆ
@KirkDBorne
@antgrasso
@ronald_vanloon
#MachineLearning #DeepLearning #BigData #Datascience #ML #HealthTech #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras #SQL #Statistics #AgenticAI #AIAgent
The Upskill Space retweeted
In 1976, we asked the late Robert Redford to follow the footsteps of the Wild Westโs greatest outlaws along the Outlaw Trailโthe legendary route stretching from Montana to Mexico. Discover his journey: on.natgeo.com/3VUdOhP
The Upskill Space retweeted
Free performance for full table scans in Postgres 17
You see, Postgres like most databases work with fixed size pages. Pretty much everything is in this format, indexes, table data, etc. Those pages are 8K in size, each page will have the rows, or index tuples and a fixed header. The pages are just bytes in files and they are read and cached in the buffer pool.
To read page 0, for example, you would call read on offset 0 for 8192 bytes, To read page 1 that is another read system call from offset 8193 for 8192, page 7 is offset 57,345 for 8192 and so on.
If table is 100 pages stored a file, to do a full table scan, we would be making 100 system calls, each system call had an overhead (I talk about all of that in my OS course).
The enhancement in Postgres 17 is to combine I/Os you can specify how much IO to combine, so technically while possible you can scan that entire table in one system call doesnโt mean its always a good idea of course and Ill talk about that.
This also seems to included a vectorized I/O, with preadv system call which takes an array of offsets and lengths for random reads.
The challenge will become how to not read too much, say Iโm doing a seq scan to find something, I read page 0 and found it and quit I donโt need to read any more pages. With this feature I might read 10 pages in one I/O and pull all its content, put in shared buffers only to find my result in the first page (essentially wasting disk bandwidth, memory etc)
It is going to be interesting to balance this out.
Note that true postgres issues a kernel system call to read 8k at a time the kernel can be configured to read โaheadโ little bit read more and cache it in the file system cache.
โโ-
Learn more about database and OS internals, check out my courses
Fundamentals of database engineering databases.win
Fundamentals of operating systems oscourse.win
The Upskill Space retweeted
5 Skills every Frontend Developer should have in 2025 ๐จโ๐ป
1.HTML, CSS & JavaScript Mastery โ the backbone of the web
2.Modern Frameworks โ React, Next.js, Vue (pick one & go deep)
3.TypeScript โ write safer & scalable code
4.UI/UX Sense โ building not just functional, but delightful interfaces
5.APIs & State Management โ know how to connect, fetch & manage data
๐ Learn these, and youโll be able to work on standard projects as a frontend dev.
Which one are you currently learning? ๐
The Upskill Space retweeted
Correlation is the skill that has singlehandedly benefitted me the most in my career.
In 3 minutes I'll demolish your confusion (and share strengths and weaknesses you might be missing).
Let's go:
The Upskill Space retweeted
๐๐ผ๐ ๐ฑ๐ผ๐ฒ๐ ๐ง๐ฒ๐น๐ฒ๐ด๐ฟ๐ฎ๐บ ๐ฑ๐ฒ๐น๐ถ๐๐ฒ๐ฟ ๐๐ผ๐๐ฟ ๐บ๐ฒ๐๐๐ฎ๐ด๐ฒ๐ ๐ถ๐ป๐๐๐ฎ๐ป๐๐น๐ ๐ฎ๐ป๐ฑ ๐๐ฒ๐ฐ๐๐ฟ๐ฒ๐น๐ ๐ฎ๐ฐ๐ฟ๐ผ๐๐ ๐๐ต๐ฒ ๐ด๐น๐ผ๐ฏ๐ฒ?
Telegram is built on a ๐ฑ๐ถ๐๐๐ฟ๐ถ๐ฏ๐๐๐ฒ๐ฑ ๐๐ฒ๐ฟ๐๐ฒ๐ฟ ๐ฎ๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ with data centers located in different parts of the world (DC1, DC2, etc.) for low latency and high availability.
When you send a message, it is first encrypted and sent to the nearest Telegram server cluster. For secret chats, it uses ๐ฒ๐ป๐ฑ-๐๐ผ-๐ฒ๐ป๐ฑ ๐ฒ๐ป๐ฐ๐ฟ๐๐ฝ๐๐ถ๐ผ๐ป (MTProto), meaning only you and the recipient can read it. Cloud chats are also encrypted but between the client and server.
The platform is designed for speed and reliability, using a custom ๐ฝ๐ฟ๐ผ๐๐ผ๐ฐ๐ผ๐น (MTProto) optimized for quick delivery over unreliable mobile connections, ensuring messages get through even on weak networks.
Telegram's core consists of ๐ต๐ถ๐ด๐ต๐น๐ ๐๐ฝ๐ฒ๐ฐ๐ถ๐ฎ๐น๐ถ๐๐ฒ๐ฑ ๐๐ฒ๐ฟ๐๐ถ๐ฐ๐ฒ๐. The Auth Key service manages user authentication and encryption keys. The Message Broker service routes traffic between users and data centers.
The Storage service handles massive amounts of media and files in a distributed manner, while the Sync service ensures your chats are updated across all your devices instantly. A dedicated Push service delivers notifications efficiently.
Behind the scenes, Telegram runs ๐บ๐ฎ๐๐๐ถ๐๐ฒ ๐ฟ๐ฒ๐ฎ๐น-๐๐ถ๐บ๐ฒ ๐ผ๐ฝ๐ฒ๐ฟ๐ฎ๐๐ถ๐ผ๐ป๐. It handles billions of messages daily with a relatively small team by focusing on efficiency and a clean, optimized codebase.
Data is distributed across multiple data centers for redundancy and speed. Media is heavily cached and replicated to be delivered from a location nearest to the user.
The entire system is designed for ๐ณ๐ฎ๐๐น๐ ๐๐ผ๐น๐ฒ๐ฟ๐ฎ๐ป๐ฐ๐ฒ and scalability, allowing it to handle massive traffic spikes and continue operating smoothly even if parts of the infrastructure encounter issues.
The most impressive part is the scale and efficiency. Telegram supports hundreds of millions of users, allows massive group chats and channels, and delivers media instantly, all while maintaining a strong focus on privacy and speed.
๐ง๐ฒ๐น๐ฒ๐ด๐ฟ๐ฎ๐บ ๐ง๐ฒ๐ฐ๐ต ๐ฆ๐๐ฎ๐ฐ๐ธ:
โ๏ธ ๐๐ฟ๐ผ๐ป๐๐ฒ๐ป๐ฑ: Telegram's own C++ MTProto library, Kotlin Native, Swift, React for web versions
๐ฅ๏ธ ๐๐ฎ๐ฐ๐ธ๐ฒ๐ป๐ฑ: C++, Go, Java, Python
๐ ๐ฃ๐ฟ๐ผ๐๐ผ๐ฐ๐ผ๐น: Custom MTProto
๐ฑ ๐ ๐ผ๐ฏ๐ถ๐น๐ฒ: Swift for iOS, Kotlin for Android (official apps)
๐ ๐๐ฎ๐๐ฎ ๐๐ฒ๐ป๐๐ฒ๐ฟ๐: Multiple geographically distributed clusters
๐๏ธ ๐๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ๐: PostgreSQL, custom distributed storage
๐ง ๐๐ฎ๐ฐ๐ต๐ถ๐ป๐ด: Redis, Memcached
โ๏ธ ๐ ๐ฒ๐๐๐ฎ๐ด๐ถ๐ป๐ด: Custom message queueing and broker systems
๐ ๐๐ป๐ฐ๐ฟ๐๐ฝ๐๐ถ๐ผ๐ป: MTProto (aes256, rsa2048, dh2048) for client-server/server-client and end-to-end for secret chats
๐น ๐ ๐ฒ๐ฑ๐ถ๐ฎ ๐ฆ๐๐ผ๐ฟ๐ฎ๐ด๐ฒ: Custom distributed file storage called BigFiles
โ๏ธ ๐ข๐ฝ๐ฒ๐ฟ๐ฎ๐๐ถ๐ผ๐ป๐: In-house tools for cluster management and monitoring
๐ Learn more in software design handbook: codewithdhanian.gumroad.com/โฆ
