Pruthaviza

[Swapna Kumar Panda @swapnakpanda]

This one is the BEST for System Design Interviews.

You will find everything at one place. BOOKMARK IT NOW.

https://t.co/jwy2GMXmBD

[Jaydeep @_jaydeepkarale]

This repo is a goldmine if you are preparing forn Systemd Design Interviews

https://t.co/GDNl9D7DiR

[Ayaan 🐧 @twtayaan]

This repo is a gold mine for DevOps engineers 🤯

If you're preparing for a DevOps interview, this single repo can replace half the courses you'd normally pay for.

It covers:

→ Linux, Networking, and Git fundamentals
→ Kubernetes, Docker, and Terraform
→ AWS, Azure, and GCP → Ansible, Prometheus, and OpenStack
→ SQL, NoSQL, and system design
→ Real interview-style questions with actual written answers, not just topics to Google

Bookmark it and actually go through it before your next interview.

Repo: https://t.co/ulbnupaMKk

[Ayaan 🐧 @twtayaan]

You don't need a Kubernetes cluster to get good at Kubernetes 😎

Found a repo with 50 hands-on Kubernetes labs you can run directly in your browser:

→ kubectl instead of just reading docs
→ Pods, Deployments, StatefulSets, DaemonSets
→ HPA, scheduling, taints & tolerations
→ RBAC, Secrets, namespaces & quotas
→ Services, Ingress & real troubleshooting

You get a real Kubernetes environment in the browser, with labs built around actually doing the work.

Just open a lab and start breaking things.

Repo: https://t.co/0C4dEqYONg

[Pruthaviza @pruthaviza]

@BrandonFalco121 No way it's real

[Pruthaviza @pruthaviza]

@akillian11 @KantInEastt You mean AAP??

[Pruthaviza @pruthaviza]

@iamvicky_sharma @ajeetbharti Your suggestion is to just ignore large chunk of hindu population so that congress can win ??

[Puneet Patwari @system_monarch]

I'm a Principal engineer & I passed system design rounds of Amazon, Atlassian, Walmart, Saleforce, and Deliveroo.

Trust me, learning system is not hard. Start from these fundamental concepts:

1) Load Balancing: https://t.co/3jKCLiI6vl

2) CDN: https://t.co/dxzCmm9gAf

3) Caching: https://t.co/pRgn0FTPp2

4) Cache Invalidation: https://t.co/QrfRjJ57gd

5) Rate Limiting: https://t.co/LE5ECM2tGt

6) API Gateway: https://t.co/DgU8cBDUVr

7) CAP Theorem: https://t.co/a8WydnAIxd

8) Sharding: https://t.co/XQLU6eDriD

9) Replication: https://t.co/KuDkFH0fjx

10) Partitioning: https://t.co/3WXKeZLbLa

11) Queues: https://t.co/JchEoCcFmF

12) Microservices: https://t.co/aAQfM6AWMq

13) Microservices Vs Monoliths: https://t.co/bTaIIWkPU3

14) Fault Tolerance: https://t.co/qXNBoyOqYT

15) Database Scaling: https://t.co/D2lvPm1wkB

16) Service Discovery: https://t.co/z2DpwbJBVI

17) Consistency models: https://t.co/K2r3nMcCQu

18) Eventual Consistency: https://t.co/SWiz4ckIKR

19) Distributed Transactions: https://t.co/xqL7BTJxXn

20) Leader Election: https://t.co/ApNaYSnSFj

21) Horizontal vs Vertical Scaling: https://t.co/IFuEmzMfob

22) Back of the Envelope Estimation: https://t.co/7ntEmtVggQ

23) Idempotency, Data Latency & Finale: https://t.co/fNArLx4MrW

Let me know what you'd like me to cover, would love to help :)

[Pruthaviza @pruthaviza]

@AllBoutCrypto26 @BeingPolitical1 You think marrying minor is equivalent to polygamy??

[Pruthaviza @pruthaviza]

@rickysharma_08 @TajinderBagga Yeh jhatu bate india me hoti hai

Bangladesh/Pakistan me jinda jala dete hai

Tab tum jaise gardan jameen me ghused lete ho

You are praying for your own genocide

Congratulations 🎉🎉🎉

[Pruthaviza @pruthaviza]

@rickysharma_08 @TajinderBagga AL taqqiye ki aulaad
Bhag yaha se tera sharia nahi aayega yaha

[Pruthaviza @pruthaviza]

@piyush8905 Kasab ki g chat rahe the ac room me bitha ke

Yeh hai congress

[Pruthaviza @pruthaviza]

@rickysharma_08 @TajinderBagga Abdul , sharma surname , dojakh me jayega , kufr hai kufr , khuda se dar abdul

[Jack @Tech_Rose1]

𝟯𝟬 𝗕𝗹𝗼𝗴𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝟯𝟬 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀:

1. APIs: https://t.co/y6ztym5wqB
2. API Gateways: https://t.co/xXvTIUNofb
3. JWTs: https://t.co/crZbmceFIn
4. Webhooks: https://t.co/OkbepiiRvn
5. REST vs GraphQL: https://t.co/F9l4ddljNZ
6. Load Balancing: https://t.co/EQSafao8WR
7. Proxy vs Reverse Proxy: https://t.co/lfL82laXTD
8. Scalability: https://t.co/cXjB33LW0I
9. Availability: https://t.co/H0ubqxIkqo
10. SPOF: https://t.co/S0Fuu7mB7K
11. CAP Theorem: https://t.co/C6dHfjqyOY
12. SQL vs NoSQL: https://t.co/VghLHzR2U2
13. ACID Transactions: https://t.co/H2sSvyaH7q
14. Database Indexes: https://t.co/EzfqiXpcyD
15. Database Sharding: https://t.co/GGxDxDhYak
16. Consistent Hashing: https://t.co/y66XHode6O
17. CDC: https://t.co/P3vfViQAKG
18. Caching: https://t.co/qzxZBGpem1
19. Caching Strategies: https://t.co/qmcf3ujaoX
20. Cache Eviction Policies: https://t.co/tJWYyAse7i
21. CDN: https://t.co/LBHfBMoSqP
22. Rate Limiting Algorithms: https://t.co/Z0sKBbjhYX
23. Message Queues: https://t.co/CauS58jAht
24. Bloom Filters: https://t.co/HnagKALF5C
25. Idempotency: https://t.co/mTLi3DiKKW
26. Concurrency vs Parallelism: https://t.co/EPkKGUqZnv
27. Long Polling vs WebSockets: https://t.co/KQscrEA9tX
28. Stateful vs. Stateless Architecture: https://t.co/5eGwEfI46L
29. Batch vs Stream Processing: https://t.co/JDTwO09yNt
30. Geohashing: https://t.co/HgrE8hdRbD

🔁 Repost to help a friend.
📌 Save this post for later

Follow me @Tech_Rose1 for more AI IDEA.

[Akshay 🚀 @akshay_pachaar]

LLM quantization techniques I'd learn if I had to fit a 70B model on a single GPU:

(bookmark this)

A 70B model in FP16 needs 140GB for weights alone. At 4-bit, that drops to 35GB, which fits on one card.

But naive rounding fails on large models. Roughly 0.1% of hidden dimensions carry values up to 20x larger than anything else in the tensor, and they wreck the quantization grid for everything else.

Each of these 5 methods handles those outliers at a different point:

1. RTN: ignores them. Rounds every weight to the nearest grid level with no calibration data. Cheapest option, weakest at low bit widths.

2. GPTQ: repairs after rounding. Quantizes a layer column by column and adjusts the remaining weights to absorb the error before moving on.

3. AWQ: protects before rounding. Finds the ~1% of weight channels that matter most and scales them up so they survive quantization. Everything still ends up in plain INT4.

4. LLM. int8(): isolates at inference. Outlier dimensions run in FP16, the other 99.9% run in INT8, and the results are merged.

5. QAT: solves it during training. The model is fine-tuned with rounding baked into every forward pass, so it adapts to the damage before quantization is actually applied.

All five produce the same artifact, a model at a fraction of its trained precision. They differ only in where the outlier problem gets addressed.

The visual below nicely summarise these techniques.

There's a really good paper that provides a comprehensive study of LLM quantization techniques: https://t.co/AqYE8FXspZ

---
That said, quantization shrinks a model, but fine-tuning is how it gets adapted to a specific use case in the first place.

I wrote a full breakdown on fine-tuning LLMs with RL in 2026, including how to skip manual reward engineering with automatic LLM-graded rewards.

The article is quoted below!

[mohit @mohitwt_]

Day 6/30 of Inference Engineering

Part 3: CUDA Graphs - Memory Management, Capture Safety & Graph Updates (FINAL)

- why stable memory addresses matter during CUDA Graph capture and replay, and how data can still change between replays
- what can and cannot be safely captured, including synchronization, CPU-side logic, and capture-incompatible operations
- how existing CUDA Graphs can adapt when parts of the workload need to change
- how individual node parameters can be updated and how cudaGraphExecUpdate() handles compatible graph-level changes
- what happens with incompatible topology changes and when a graph needs to be re-instantiated

to watch on youtube, the link is shared below

[Nikki Siapno @NikkiSiapno]

How SSO works.

[prasanna @jaga_prasanna]

https://t.co/IBYdzJZM2k

[Codez @0xCodez]

https://t.co/WVztfCUH4u

[GitHub Projects Community @GithubProjects]

Dioxus is a Rust framework for building cross-platform apps with instant hot-reloading and signals-based state management.

- Build for web, desktop, mobile, and server from a single codebase
- Signals-based state management combining ideas from React, Solid, and Svelte