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STYou can start electronics without actual components
#electronics #simulation #engineering
@standup.scientist
#System On Module Explained
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COComment “HLD” to get the complete flow or just take the screenshot.
One System Design Flow for every HLD interview.
Use the flow in your next interview, scale application from 1 to million users and modify your application services in between before servers.
Hope it helps! Follow for more!
#systemdesign #datastructure #hld #reelitfeelit
@codewithnishchal
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DRFull wave rectifier
#physics #jee #neet #ncert #physics12 #semiconductor #doctor #engineering #instagram #animation
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VOComment "SYSTEM" for the links.
You Will Never Struggle With System Design Again
📌 Learn System Design step by step with these beginner-friendly resources:
1️⃣ System Design was HARD until I Learned these 30 Concepts – Ashish Pratap Singh
2️⃣ System Design for Beginners Course – freeCodeCamp
3️⃣ System Design Primer – GitHub
Stop feeling overwhelmed by scalability, load balancers, databases, and caching. These resources break down 30+ core concepts, practical case studies, and real-world system architectures with simple explanations.
Whether you’re preparing for technical interviews, building scalable projects, or just starting to explore backend architecture, these are the best free resources to master system design.
Save this, share it, and turn confusion into clarity with practical System Design skills.
@volkan.js
827.2K
ELLet’s solve this control systems exercise together: find the steady-state value of the step response of the system illustrated in the block diagram.
Comprising the closed-loop system architecture are the following fundamental components that allow for self-correction:
* The Controller (G_c): The “brain” that processes the signal.
* The Process or Plant (G): The physical system we are trying to influence.
* The Output Transducer (H): Often a sensor that measures the output and feeds it back to the start.
🔄 The Power of Feedback
Without feedback, a system is “blind” to external disturbances. Feedback allows us to compare where we are (Output) with where we want to be (Reference Input). If there is any difference between the two, the system drives the plant, via the actuating signal, to make a correction.
In this specific problem, our output transducer, or sensor, has unity gain, which means that H(s)=1. This is a special case where the actuating signal is precisely the error signal as it is the actual difference between the input and output.
⏱️ Efficiency via the Final Value Theorem
One of the most elegant tools in a control engineer’s toolkit is the Final Value Theorem (FVT). Usually, finding the steady-state behavior of a system would require us to perform an Inverse Laplace Transform to get back into the time domain, y(t), and then calculate the limit as t approached infinity.
FVT lets us skip the heavy lifting. By analyzing the behavior as s tends to 0 in the frequency domain, we can predict the system’s long-term “resting point” without ever leaving the s-plane.
#electrical #electricalengineering #controlsystem #electronics
@electricalmath
97.8K
VIComment “sys” For A FULL GUIDE
If you’re preparing for cloud or backend job interviews, these concepts show up again and again; not as theory, but as how you think at scale.
From API design and rate limiting, to message queues that handle traffic spikes, and finally how microservices talk to each other without breaking everything, this is the backbone of modern systems.
If you understand why these exist and when to use them, you’re already ahead of most candidates.
What should I break down next in detail?
Let me know in the comments!
.
.
[system design interview, cloud interview prep, backend system design, microservices architecture, caching strategies, database scaling, rest api, what is api, cloud engineering concepts, bytebytego system design, system design case studies, backend interview, interview questions, system design for job interview, lld, hld, devops, cloud tutorial, women in tech, system design for faang]
@vishakha.sadhwani
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SSOperating systems in 60 seconds
@sshirg
17.0K
MSConnection diagram of mini Bluetooth module and battery charge module. Type miniblusb to get links
@ms3_workshop
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MIYour System design fails when an API returns conflicting data.
Unlock 200+ practical problem-solutions just like this one in the Ebook. Link in bio
1️⃣ Cache isn’t shared across servers
👉 Each server keeps its own cached copy, so one has new data and another still has the old one.
Example: Profile update shows on Server A but Server B still returns yesterday’s info.
⸻
2️⃣ Read replicas are lagging behind primary DB
👉 Writes go to the primary, but reads hit a replica that hasn’t synced yet.
Example: User updates email → replica hasn’t synced → API returns the old email.
⸻
3️⃣ Race conditions between parallel writes
👉 Two servers update the same record at the same time and overwrite each other.
Example: Cart quantity updated twice → you randomly see 1 or 2 items.
⸻
4️⃣ Event updates (Kafka/SQS) arrive at different times
👉 Some servers process the update message earlier, others later.
Example: Server A got the “order shipped” event, Server B is still waiting for it.
⸻
5️⃣ Different app versions running in production
👉 Some servers run new logic, others run old logic.
Example: Discount calculation is updated on v2, but v1 servers still return old prices.
⸻
6️⃣ Multiple sources of truth (SQL + NoSQL mismatch)
👉 One DB updates faster than the other, causing mismatched responses.
Example: Order marked delivered in SQL but still out for delivery in NoSQL.
Show your expertise. Add new points in the comments.
Follow for more ! 💡
#systemdesign #apidesign #distributedsystems #microservices #backenddeveloper #softwaredeveloper #caching #database #scaling #readreplica #racecondition #datainconsistency #loadbalancing #eventdriven #programming #coding #devops #techinterview #api #ai #mission_compile #backenddevelopment
( System Design Failure, API Conflicting Data, Distributed System Inconsistency, API Design Problems, Solving Data Inconsistency, Why API returns different results, Fixing data inconsistency in microservices, System design pitfalls data mismatch, Cache synchronization across servers, Handling race conditions in backend)
@mission_compile
36.5K
COHow system handled multiple requests??
Idempotency is not about duplicates.
It’s about retries.
In real systems:
• networks fail
• responses get lost
• clients retry
The server may have already processed the request —
the client just doesn’t know it.
Without idempotency → double charges, double orders, broken trust.
With idempotency → same request, same result, every time.
Retries are unavoidable.
Side effects are not.
That’s why payments, orders, and critical APIs are always idempotent.
Save this.
Most engineers learn this only after a production bug.
#idempotency #systemdesign #backendengineering #distributedSystems softwareengineering apiDesign developers techarchitecture
@codewithupasana
32.9K
PIComment “SYSTEM” to get links!
🚀 Want to learn system design in a way that actually sticks? This mini roadmap takes you from beginner to building and explaining real world backend systems like microservices, APIs, and scalable architectures.
🎓 Sys Design Beginner
Perfect starting point if you are new to system design. You will understand what system design really is, how interviews think, and the core building blocks like APIs, databases, caching, scaling, and reliability. Great for building a clean mental model before you dive into complex architectures.
📘 8 System Design Core
Now deepen your knowledge. This resource breaks down the most important system design concepts you will keep seeing everywhere like scalability, availability, consistency, sharding, caching, rate limiting, queues, and CAP tradeoffs. It helps you stop memorizing buzzwords and start explaining decisions clearly.
💻 Microservices + K8s
Time to go hands on. You will learn microservice architecture and system design by building real components with Python and deploying with Kubernetes. This connects architecture to real engineering: services, communication, containers, deployment, and production style thinking.
💡 With these system design resources you will:
Understand how scalable backend systems are structured
Learn microservices and how services communicate
Build practical skills for interviews and real projects
Get confident with concepts like caching, queues, databases, and reliability
Learn how Kubernetes fits into modern system architecture
If you are serious about backend engineering, cloud, DevOps or system design interviews, learning system design fundamentals is a must have skill.
📌 Save this post so you do not lose the roadmap.
💬 Comment “SYSTEM” and I will send you all the links.
👉 Follow for more content on system design, microservices, backend engineering, and scalable architecture.
@pirknn
48.8K
PESystem Design Fundamentals: Episode 1 - The Monolith
New to system design? This is your starting point. Learn the fundamental architecture that powers the internet: monoliths with load balancers, caching layers, and database replicas. No fluff, just the core concepts you need. Follow along as we build your system design knowledge from the ground up. #SystemDesign #TechEducation #CodingTutorial #SoftwareArchitecture
@peppermint_pai
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