Shiven Dave

[Neo Kim @systemdesignone]

If I had to design an API, I'd consider these 12 techniques:

1 Clear Resource Names
2 Standard Methods
3 Idempotency
4 API Versioning
5 Correct Status Codes
6 Pagination
7 Filtering & Sorting
8 Security
9 Rate Limiting
10 Caching
11 API Docs
12 Pragmatic

What else should make this list?

[Nandkishor @devops_nk]

Linux Command Cheat Sheet 🐧

[sysxplore @sysxplore]

How traceroute works?

[Dhanian 🗯️ @e_opore]

Docker Complete Roadmap
|
| | |-- Fundamentals
| |-- Introduction to Docker
| | |-- What Docker is
| | |-- Containers vs Virtual Machines
| | |-- Why Docker matters
| |-- Docker Architecture
| | |-- Docker Engine
| | |-- Docker Daemon
| | |-- Docker CLI
| |-- Installation & Setup
| | |-- Docker Desktop
| | |-- Linux installation
| | |-- Verifying Docker setup

| | |-- Core Docker Concepts
| |-- Docker Images
| | |-- Pulling images
| | |-- Building custom images
| | |-- Image layers
| |-- Docker Containers
| | |-- Running containers
| | |-- Stopping and removing containers
| | |-- Container lifecycle
| |-- Dockerfile
| | |-- FROM, RUN, CMD
| | |-- COPY and WORKDIR
| | |-- Multi-stage builds

| | |-- Docker Commands
| |-- docker build
| |-- docker run
| |-- docker ps
| |-- docker exec
| |-- docker logs
| |-- docker images
| |-- docker stop & rm

| | |-- Storage & Volumes
| |-- Named Volumes
| |-- Bind Mounts
| |-- Data persistence
| |-- Volume management

| | |-- Docker Networking
| |-- Bridge network
| |-- Host network
| |-- Overlay network
| |-- Custom networks
| |-- Container communication

| | |-- Docker Compose
| |-- Multi-container applications
| |-- docker-compose.yml
| | |-- Services
| | |-- Volumes
| | |-- Networks
| |-- Environment variables
| |-- Scaling services

| | |-- Docker Registries
| |-- Docker Hub
| |-- Private registries
| |-- Pushing and pulling images
| |-- Image versioning

| | |-- Security
| |-- Container security basics
| |-- Image scanning
| |-- Secrets management
| |-- Least privilege principle
| |-- Secure Dockerfiles

| | |-- Performance Optimization
| |-- Reducing image size
| |-- Layer caching
| |-- Optimizing builds
| |-- Resource limits

| | |-- Container Orchestration
| |-- Introduction to Kubernetes
| | |-- Pods
| | |-- Services
| | |-- Deployments
| |-- Docker Swarm basics
| |-- Scaling containers

| | |-- CI/CD with Docker
| |-- GitHub Actions
| |-- Jenkins pipelines
| |-- GitLab CI
| |-- Automated deployments

| | |-- Monitoring & Logging
| |-- Docker logs
| |-- Prometheus
| |-- Grafana
| |-- ELK Stack
| |-- Health checks

| | |-- Cloud & Deployment
| |-- Deploying containers to cloud
| | |-- AWS
| | |-- Azure
| | |-- GCP
| |-- Container hosting platforms
| |-- Serverless containers

| | |-- Real World Projects
| |-- Dockerize a Node.js app
| |-- Deploy MERN stack with Docker
| |-- Containerize a Python API
| |-- Build microservices with Docker
| |-- Setup CI/CD pipeline using Docker

| | |-- Advanced Topics
| |-- Multi-stage production builds
| |-- Service mesh basics
| |-- Sidecar containers
| |-- Infrastructure as Code
| |-- GitOps concepts

| | |-- Interview Preparation
| |-- Docker interview questions
| |-- Debugging containers
| |-- Networking scenarios
| |-- Kubernetes integration questions

| | |-- Community and Growth
| |-- Build Docker projects
| |-- Share on GitHub
| |-- Write technical blogs
| |-- Contribute to open source
| |-- Stay updated with Docker ecosystem

Grab this ebook to Master Docker
https://t.co/sC9bTrAJWt

[Level Up Coding @LevelUpCoding_]

35 resources to learn system design:

1. Microservices
↳ https://t.co/7W3w38s5nF

2. Redis
↳ https://t.co/02Ppx7ieKp

3. Event-driven architecture
↳ https://t.co/5vgAD2wJ61

4. Database indexing
↳ https://t.co/19PMGzxh9d

5. Circuit breakers
↳ https://t.co/MknsklCl5D

6. ACID vs BASE
↳ https://t.co/l2mdVSNLqT

7. Rate limiting
↳ https://t.co/BHbpQdzRbK

PS - if you want a structured path, get our free 142-page System Design Handbook when you join our free weekly newsletter → https://t.co/PwztWFwF6v

8. Observability
↳ https://t.co/H1ewSTKg0g

9. System design quality attributes
↳ https://t.co/ozKy29UbBn

10. CAP theorem
↳ https://t.co/uO0fcYF7fF

11. Idempotency
↳ https://t.co/sGY9AEYIYF

12. REST APIs
↳ https://t.co/uAHDRtJUt1

13. Sync vs Async
↳ https://t.co/BRIF05jn8j

14. Network protocols
↳ https://t.co/PppArCVi80

15. Change Data Capture (CDC)
↳ https://t.co/fpZnlYPzmD

16. CI/CD pipelines
↳ https://t.co/jqxmbzfXwf

17. SSO (single sign-on)
↳ https://t.co/WmjJ6Gv4LD

18. JWT
↳ https://t.co/7IsbabOMCB

19. gRPC
↳ https://t.co/2shjriQa9k

20. Health checks vs heartbeats
↳ https://t.co/Cb05qsG5al

21. API gateway vs load balancer vs reverse proxy
↳ https://t.co/FiODhLn3HJ

22. HTTPS
↳ https://t.co/Y9H19Bo5me

23. Load balancing algorithms
↳ https://t.co/kPbIWqmkDI

24. Database caching
↳ https://t.co/GPQelMXSj6

25. API protocols
↳ https://t.co/XUNY6sbyKx

26. CDN
↳ https://t.co/lPKsBWuLfU

27. Database types
↳ https://t.co/yqHPxZJgDA

28. Message Queues
↳ https://t.co/e5FyPqYMaT

29. Hashing vs encryption vs tokenization
↳ https://t.co/8M0mptGEV3

30. Service Discovery
↳ https://t.co/Hs4ZchDhRV

31. Pub/Sub
↳ https://t.co/bT6uWiev1J

32. Connection pooling
↳ https://t.co/JV2OWUrBYP

33. Forward proxy vs reverse proxy
↳ https://t.co/d0nywsmG27

34. Consistent hashing
↳ https://t.co/6QjQq2LHtn

35. SQL vs NoSQL
↳ https://t.co/J78gAicVVb

——

👋 PS: Get my free 142-page System Design Handbook when you join my free weekly newsletter.

Join 33,000+ engineers → https://t.co/WTV0H4kkp2

——

♻️ Repost to help others learn system design.

[Alex Xu @alexxubyte]

How does SSH work?

[Shiven Dave @ShivenDave]

📦 Stop fighting Dockerfiles! 🤖 AWS open-sources ml-container-creator to instantly generate BYOC inference containers for SageMaker. Focus on your ML model, not boilerplate code. 🔗 https://t.co/YzPKlhQCOk #AWS #SageMaker #BYOC #MLOps #OpenSource #DataScience

[Shiven Dave @ShivenDave]

🛑 Stop unpredictable AI agents! 🤯 AWS open-sources Strands Agent SOPs: Write reliable logic in simple Markdown. ✅ Solves the control vs. flexibility trade-off. 🔗 https://t.co/NfUvQS1f5w #AWS #AIagents #OpenSource #GenerativeAI #LLMs #Strands

[Shiven Dave @ShivenDave]

🗣️ No more GenAI lag! 🔄 SageMaker AI launches Bidirectional Streaming. Get real-time, two-way dialogue over a single connection (HTTP/2 + WebSocket). ⚡ Perfect for voice and video agents. 🔗 https://t.co/rriNF8XC3F #AWS #SageMaker #GenAI #Streaming #LowLatency #AIInference

[Shiven Dave @ShivenDave]

🤖 The future is Physical AI. 🌎 Systems that Understand, Reason, and Act in the real world (The Autonomy Flywheel). Example: Moxi robots saved hospitals 600K hours! Learn the tech: 🔗 https://t.co/TBxJN9ngpv #AI #Robotics #PhysicalAI #ML #AWS #Future #DigitalTwins

[Shiven Dave @ShivenDave]

🚨 AI Fixes SecOps Speed. ⚡ AWS Security now uses an AI Agent to instantly automate evidence gathering (CloudTrail, IAM, EC2). Go from HOURS to MINUTES ⏱️. Stop digging through logs: 🔗 https://t.co/ybig62MesQ #AWS #Security #GenAI #SecOps #CloudSecurity #IncidentResponse #Auto

[Shiven Dave @ShivenDave]

🔥 GenAI Cost Problem SOLVED! 🔥
SageMaker HyperPod now supports NVIDIA MIG (Multi-Instance GPU).
💰 6x Utilization: Partition your A100/H200 GPUs.
⚡ Faster: Get isolated, dedicated performance instantly.
🧠 Smarter: End resource waste today.
🔗 Details: https://t.co/NFl5emnf1C

[The Linux Foundation @linuxfoundation]

🚀 Keynote speakers announced for #OSSummit Japan, #AIDev & #AutoLinux, December 8–10 in Tokyo: Linus Torvalds (Creator of Linux & Git), Stefania Druga (Sakana AI), Kazuo Tsubouchi (Honda) + more! View keynote speakers: https://t.co/VDQ9iAFDtU Register: https://t.co/A0hxln4820

[Mayank Tayal @Ai_here202]

Become a full-stack Developer in 4 weeks 🧑💻

This PDF Includes:

✅ HTML
✅ CSS
✅ JavaScript
✅ React
✅ Node and Express
✅ MongoDB
✅ Font-end to Back-end Integration
✅ Deployment
✅ Testing
✅ Personal Project

To get it:

- Like, Retweet and Follow
- Reply "Dev"

[LetsDefend @LetsDefendIO]

Layers of OSI Model

[Alex Xu @alexxubyte]

Algorithms you should know before taking System Design Interviews

[Dr Milan Milanović @milan_milanovic]

𝗧𝗼𝗽 𝟮𝟬 𝗦𝗤𝗟 𝗾𝘂𝗲𝗿𝘆 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘁𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀

Here is the list of the top 20 SQL query optimization techniques I found important:

1. Index for query access patterns (composite, selective, covering); not by row count; keep stats fresh.
2. Use EXISTS for presence checks; use COUNT(*) only when you need the count.
3. Select explicit columns; avoid SELECT * to cut I/O and enable covering indexes.
4. Prefer sargable predicates; rewrite slow correlated subqueries with JOIN/EXISTS.
5. Avoid DISTINCT as a band-aid; fix joins/keys; use it only when dedup is required.
6. Filter in WHERE; reserve HAVING for post-aggregate filters.
7. Use explicit JOIN ... ON; avoid implicit joins in WHERE.
8. Use keyset pagination; avoid OFFSET/LIMIT on big sets; for sampling use TABLESAMPLE (if available).
9. Use UNION ALL instead of UNION when duplicates are acceptable.
10. Replace wide OR predicates with UNION ALL only when each branch can seek different indexes.
11. Schedule heavy queries off-peak; apply resource caps/queues if available.
12. Avoid OR in join predicates; use computed columns or UNION ALL when it enables index seeks.
13. Pick GROUP BY when you need grouped rows; use window functions when you need row detail with aggregates.
14. Use derived/temp tables when they reduce work or add stats; beware of blocking pushdowns.
15. For bulk loads: disable/drop nonclustered indexes, batch insert, then rebuild; keep PK/clustered when useful.
16. Use materialized views for slow-changing, expensive aggregates; plan refresh/invalidation.
17. Avoid non-sargable comparisons (e.g., <>) on low-selectivity columns; rewrite to ranges when possible.
18. Minimize correlated subqueries over large sets; prefer set-based joins/EXISTS.
19. Choose INNER vs LEFT/RIGHT by semantics; INNER often performs best when applicable.
20. Cache repeated result sets: temp tables (session), result cache, or materialized views, with freshness rules.

Do you know what is 𝗤𝘂𝗲𝗿𝘆 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗿? Its primary function is to determine 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝘄𝗮𝘆 to execute a given SQL query by finding the best execution plan.

The query optimizer works by taking the SQL query as input and analyzing it to determine how best to execute it.

The first step is to parse the SQL query and create a syntax tree.

The optimizer then analyzes the syntax tree to determine the various ways the query can be executed.

Next, the optimizer generates 𝗮𝗹𝘁𝗲𝗿𝗻𝗮𝘁𝗶𝘃𝗲 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝗽𝗹𝗮𝗻𝘀, which are different ways of executing the same query.

Each execution plan specifies the order in which the tables should be accessed, the join methods to use, and any filtering or sorting operations to be performed.

The optimizer then assigns a 𝗰𝗼𝘀𝘁 to each execution plan based on factors such as the number of disk reads and the amount of CPU time required to execute the query.

Finally, the optimizer chooses the execution plan with the lowest cost as the optimal solution for the query.

This plan is then used to execute the query.

[Sahn Lam @sahnlam]

Data Structures in Everyday Tech

[Tom Dörr @tom_doerr]

kanban board project management you can self-host

[sysxplore @sysxplore]

Linux grep command basics