Charan S

I'm an Indian🇮🇳. 😊🆒☀️🌏🏐🏃‍♂️🏞🌄🎶📺👨‍💻

[Priyank Ahuja @ahuja_priyank]

Apple sold you an iPhone with its top camera settings switched off. On purpose.

$1,200 for a camera you've never actually used. Six settings are off by default, and they're the ones that can make your photos look dramatically better.

Fix these 6:

[Viora Tech @Viora_Tech_Ai]

Apple CarPlay is installed in over 800 million vehicles worldwide.

Most drivers use it for 3 things: Maps. Music. Phone calls.

That's a fully integrated car operating system doing the job of a $20 phone mount and a Bluetooth speaker.

A sales rep who drives 35,000 miles a year told me: "I spend 2–3 hours a day in my car. For 4 years I used CarPlay for directions and Spotify. That's it. Then a friend who works at Apple showed me what CarPlay actually does. My car became a mobile office. My commute became productive time. I haven't missed a meeting, forgotten an errand, or answered a spam call in my car since."

"CarPlay in 2026 has widgets that predict your next destination before you type it. It has Shortcuts that run 5 automations the moment you start your car. It has a Driving Focus mode that silences everything except the people who matter. It has offline maps that work in zero-signal dead zones. It has smart home controls that open your garage door before you pull into the driveway. And it remembers where you parked every single time."

"Most drivers are using maybe 15% of what CarPlay can do. The other 85% is sitting right there on the dashboard, waiting."

Here are 11 CarPlay features that turn your commute from dead time into productive time:

🧵

[Swapna Kumar Panda @swapnakpanda]

If you want to go GOD LEVEL in SYSTEM DESIGN, learn these concepts NOW:

[Akshay Dawra @IronSage_]

I uploaded my resume to Claude Al.
3 days later, I got an interview call.

Here are the 5 prompts l used:

[Neo Kim @systemdesignone]

If you want to become good at system design (in 2 weeks), then learn these 15 case studies:

1 How Apache Kafka Works
↳ https://t.co/8rOy9KgCMo

2 How Google Search Works
↳ https://t.co/jwOaC4bhnv

3 How YouTube Works
↳ https://t.co/kHk3g6jz6t

4 How Google Docs Works
↳ https://t.co/W57IkAjXpT

5 How WhatsApp Works
↳ https://t.co/VScq8QwHMr

6 How Airbnb Works
↳ https://t.co/Bi5SAjfv5S

7 How Spotify Works
↳ https://t.co/BxrH3oHIFS

8 How Slack Works
↳ https://t.co/eIo29uOQOJ

9 How Reddit Works
↳ https://t.co/o6Pw2hhj3T

10 How Bluesky Works
↳ https://t.co/2rLYlRlky0

11 How Twitter Works
↳ https://t.co/pF2RYmPaIG

12 How Uber Computes ETA
↳ https://t.co/hw1hYJqQmj

13 How Bitly Shortener Works
↳ https://t.co/tGndgdhH0V

14 How Stock Exchange Works
↳ https://t.co/iFNSX9TM9O

15 How Payment System Works
↳ https://t.co/ARiLxGR43G

What else should make this list?

===

👋 PS - Want my System Design Playbook (for Free)?

Join my newsletter with 200K+ software engineers now:
→ https://t.co/ByOFTtOihX

===

💾 Save now & RT to help others ace system design.
👤 Follow @systemdesignone + turn on notifications.

[SumitM @SumitM_X]

10 Terms Every Developer Should Know in 2026 :

1. LLM (Large Language Models)

2. RAG (Retrieval Augmented Generation)

3. Edge Computing

4. Zero Trust

5. Idempotency

6. Event-Driven Architecture

7. Serverless

8. Observability

9. Feature Flags

10. Data Mesh

[SumitM @SumitM_X]

Trending Interview Questions these days :

How do you decide when to shard and when to partition DB ?

[Makakmayum @makakmayum_sid]

Saga Pattern for Java/backend interviews:

When a user places an order, you need to withdraw money, reserve the product, and notify the warehouse. All in different services. What to do if one of them crashes?

A regular transaction won't help here. Welcome to Saga.

The idea is simple: break a large distributed transaction into a chain of small local ones. Each step is its own transaction in its own service. If something goes wrong, we launch compensating transactions in reverse order.

[Two approaches]:-

1. Choreography

Services communicate via events. Each one knows what to do next.
OrderService → [OrderCreated] → PaymentService
PaymentService → [PaymentDone] → InventoryService
InventoryService → [Reserved] → NotificationService

- Simple, no single point of failure
- Hard to track the entire flow, spaghetti of events

2. Orchestration

There's a conductor — the Saga Orchestrator. It knows the entire script and commands the services.
(code eg. in the image)

- The flow is visible in one place, easy to debug
- The orchestrator can become a bottleneck

[Important to understand]: a compensating transaction ≠ a DB rollback. This is a business operation. If the money has already been withdrawn, we don't roll back the DB row, we issue a refund.

In the Java ecosystem, Saga is used with:

→ Apache Kafka / RabbitMQ - for events between services
→ Axon Framework - built-in Saga support out of the box
→ Spring State Machine - for managing the orchestrator's state
→ Temporal / Conductor - infrastructure-level workflow orchestration

[When to use] :-

✔️ Microservice architecture
✔️ Multiple DBs, no possibility to use 2PC
✔️ Long-lived business transactions
❌ A monolith with one DB, just use @Transactional

Saga isn't a silver bullet, it's a compromise. You sacrifice isolation for scalability. Data can be temporarily inconsistent, and that's okay if the business is fine with it.

[Kshitij Mishra | AI & Tech @DAIEvolutionHub]

20 Backend Development Complete Courses for 2026:

1. Git
https://t.co/JikeQJ8amX

2. Java + Spring Boot
https://t.co/2KUpbnBGKw

3. Node.js
https://t.co/ciDBNLtkoR

4. REST API
https://t.co/0MNIaMSwqh

5. GraphQL API
https://t.co/0OEunrDtdS

6. gRPC
https://t.co/dbiezjRaCF

7. Webhooks
https://t.co/iez5Ah1mmG

8. Authentication
https://t.co/KOV7ImlC3Q

9. Payment Gateway Integration
https://t.co/Psef3yZFKm

10. Stripe Payment Integration
https://t.co/md6sGt99qr

11. PayPal Integration
https://t.co/OObrPTFBlU

12. PhonPe Integration
https://t.co/tvZMZ1Mbwg

13. Razorpay Integration
https://t.co/eP1YRWnO4w

14. Express.js
https://t.co/D1m1x01LXT

15. Kafka
https://t.co/aqXBT4ZcC1

16. Redis
https://t.co/8oCC21bhbj

17. Docker
https://t.co/q4kKg589S7

18. Kubernetes
https://t.co/fsJ4xkBDCt

19. Design Patterns
https://t.co/nqufTIsrCQ

20. Backend Complete Course
https://t.co/vhFJ1m4nSn

[Swapna Kumar Panda @swapnakpanda]

System Design becomes EASY when you follow these:

1. Fundamentals
https://t.co/IDGW3De1EA

2. API Design
https://t.co/AUx3IzQkir

3. Load Balancing
https://t.co/KlhVjDQyy6

4. Message Queues
https://t.co/UiFwA4ZB56

5. Rate Limiting
https://t.co/s6vyKcgnZE

6. Caching
https://t.co/E73QXGlNDV

7. Sharding & Partitioning
https://t.co/ZsLV5rVcrz

8. Database Replication
https://t.co/dtEvSPXrB8

9. Consistent Hashing
https://t.co/Oy1BUf23xe

10. CAP Theorem
https://t.co/jFfObbFc39

11. Microservices
https://t.co/nYizg4BLrA

12. Fault Tolerance
https://t.co/HpLji5RTod

13. Scalability
https://t.co/uovZHvzSBf

14. Event-Driven Architecture
https://t.co/cAkbLpPfqd

15. Service Discovery
https://t.co/tdjRfah2re

[SumitM @SumitM_X]

As a developer in 2026, how many of these AI related concepts do you understand?

- LLM vs SLM
- Tokens vs Context Window
- Prompt Engineering vs Prompt Chaining
- RAG (Retrieval Augmented Generation)
- Embeddings
- Vector Databases
- Agents vs Workflows

[SumitM @SumitM_X]

As a Java developer,

Please learn:

1. Core Java Mastery
- OOP principles (SOLID, DRY, KISS)
- Generics, Lambda expressions, Functional interfaces
- Java Streams API (map/reduce, collectors)
- Java Collections framework
- Java Reflection API
- Exception handling

2. Multithreading & Concurrency
- Thread synchronization, Executors, Locks
- Fork/Join framework
- Understanding of race conditions, deadlocks, and thread pools
- Concurrency utilities (java.util.concurrent)

3. Design Patterns & Architecture
- Common design patterns (Singleton, Factory, Builder)
- Architectural patterns (MVC, - Microservices, Event-Driven Architecture)
- Dependency Injection (DI), Inversion of Control (IoC)

4. Java Memory Management
- Garbage Collection (G1, CMS, ZGC)
- JVM heap and stack management
- Profiling tools (JProfiler, VisualVM)
- Analyzing memory leaks, thread dumps, and heap dumps

5. Classloaders and Reflection
- Custom class loaders
- Dynamic class loading
- Reflection for runtime behavior manipulation

6. Spring Framework & Spring Boot
- Spring Core (Dependency Injection, AOP)
- Spring Boot (Auto-configuration, Microservices support)
- Spring Security (OAuth2, JWT)
- Spring Data (JPA, Hibernate integration)
- Spring Cloud (Netflix OSS, Circuit Breakers)

7. Microservices Architecture
- Service discovery (Eureka, Consul)
- Load balancing, distributed tracing, and circuit breaking
- API Gateway (Zuul, NGINX)
- Asynchronous communication with Kafka, RabbitMQ

8. RESTful Web Services
- REST principles, building APIs
- JSON/XML handling
- API versioning, OpenAPI/Swagger documentation

9. Java I/O and NIO
- Blocking vs non-blocking I/O (NIO)
- Asynchronous I/O, channels, selectors
- File handling, serialization and deserialization

10. Reactive Programming
- Project Reactor, RxJava
- Event-driven architecture, backpressure
- Reactive streams, non-blocking IO

11. JPA/Hibernate
- ORM principles, entity relationships
- Lazy vs eager loading
- Caching strategies, query optimization

12. Database Optimization
- SQL optimization, indexing, and transactions
- NoSQL databases (MongoDB, Cassandra)
- ACID principles, CAP theorem

13. Distributed Systems
- Consistency, availability, partitioning (CAP)
- Event sourcing, CQRS (Command Query Responsibility Segregation)
- Distributed caching (Redis, Hazelcast)
- Tools: Apache ZooKeeper, Consul, etcd

14. Testing & TDD/BDD
- Unit testing (JUnit, Mockito)
- Integration and functional testing
- Behavior-driven development (Cucumber)

15. CI/CD & DevOps
- Continuous integration (Jenkins, CircleCI)
- Containerization with Docker
- Orchestration with Kubernetes
- Source control (Git), versioning, branching strategies

16. Performance Tuning & Optimization
- JVM tuning, garbage collection optimization
- Caching strategies, load balancing
-Tools for profiling and monitoring (Prometheus, Grafana)

17. Security
- Authentication/authorization (OAuth2, JWT)
- Secure coding (OWASP guidelines)
- Encryption (TLS/SSL), CSRF/XSS prevention

18. Cloud Platforms & API Gateways
- AWS, Azure, GCP deployment
-API Gateway (Zuul, NGINX) for rate-limiting and routing
- Cloud-native microservices tools

19. Logging & Monitoring
- Centralized logging (ELK Stack, Graylog)
- Distributed tracing (Jaeger, Zipkin)
- Application Performance Monitoring (APM)

20. Caching Mechanisms
- In-memory caching (Ehcache, Caffeine)
- Distributed caching (Redis, Memcached)
- Cache invalidation strategies (LRU, LFU)

21. Build Tools
- Maven and Gradle for dependency management
- Multi-module project handling
- Continuous delivery pipelines

22. Message Queuing Systems
- Kafka, RabbitMQ for event-driven microservices
- Pub/Sub models, distributed message brokers

[Neo Kim @systemdesignone]

If you want to become good at system design, learn these 19 case studies (save this):

1 How Stock Exchange Works:
↳ https://t.co/iFNSX9TM9O

2 How Payment System Works:
↳ https://t.co/ARiLxGR43G

3 How YouTube Works:
↳ https://t.co/kHk3g6jz6t

4 How Google Docs Works:
↳ https://t.co/W57IkAjXpT

5 How Kafka Works:
↳ https://t.co/8rOy9KgCMo

6 How URL Shortener Works:
↳ https://t.co/tGndgdhH0V

7 How WhatsApp Works:
↳ https://t.co/VScq8QwHMr

8 How Airbnb Works:
↳ https://t.co/Bi5SAjfv5S

9 How Spotify Works:
↳ https://t.co/BxrH3oHIFS

10 How Slack Works:
↳ https://t.co/eIo29uOQOJ

11 How Reddit Works:
↳ https://t.co/o6Pw2hhj3T

12 How Bluesky Works:
↳ https://t.co/2rLYlRlky0

13 How Tinder Works:
↳ https://t.co/4E1zfgfvlw

14 How Twitter Timeline Works:
↳ https://t.co/pF2RYmPaIG

15 How Uber Finds Nearby Drivers:
↳ https://t.co/kJ2t8dtmch

16 How Amazon Lambda Works:
↳ https://t.co/lx0BjeSRZt

17 How Amazon S3 Works:
↳ https://t.co/iReWAEHwmj

18 How Do Apple AirTags Work:
↳ https://t.co/upWcgsXwKh

19 How LLMs Like ChatGPT Actually Work:
↳ https://t.co/5lCKxq2g4N

What else should make this list?

——

👋 PS - Want my System Design Playbook for FREE?

Join my newsletter with 200K+ software engineers right now:

→ https://t.co/ByOFTtOihX

———

💾 Save this for later & RT to help other software engineers ace system design.

👤 Follow @systemdesignone + turn on notifications.

[SumitM @SumitM_X]

DON'T GO FOR JAVA INTERVIEW WITHOUT UNDERSTANDING GENERICS .

Common Java generics interview questions you can expect -

- Difference between List<?> and List<Object> in Java?

- Can we use Generics with Array?

- Can you pass List<String> to a method which accepts List<Object>?

[Shraddha Bharuka @BharukaShraddha]

Stop wasting hours trying to learn AI. 📘📚

I have already done it for you.

With one list. Zero confusion. And no fluff

📹 Videos:

1. LLM Introduction: https://t.co/kyDon6qLrb

2. LLMs from Scratch: https://t.co/2hyMhuKoiI

3. Agentic AI Overview (Stanford): https://t.co/FXu6cAqITC

4. Building and Evaluating Agents: https://t.co/ZigR1tdOFL

5. Building Effective Agents: https://t.co/uYwfwO55mO

6. Building Agents with MCP: https://t.co/4arFTW1b3i

7. Building an Agent from Scratch: https://t.co/eOmveyM9Hz

8. Philo Agents: https://t.co/zLu7x1tx9m

🗂️ Repos

1. GenAI Agents: https://t.co/eXCl2YaRPv

2. Microsoft's AI Agents for Beginners: https://t.co/3CSW4zPAwf

3. Prompt Engineering Guide: https://t.co/GVzvxPYDVO

4. Hands-On Large Language Models: https://t.co/0rgDvhx3pI

5. AI Agents for Beginners: https://t.co/3CSW4zPAwf

6. GenAI Agentshttps://lnkd.in/dEt72MEy

7. Made with ML: https://t.co/9z5KHF9DMe

8. Hands-On AI Engineering:https://t.co/dldAj5Xkr6

9. Awesome Generative AI Guide: https://t.co/U2WZhT4ERV

10. Designing Machine Learning Systems: https://t.co/sYAZX34YdQ

11. Machine Learning for Beginners from Microsoft: https://t.co/NjFxHbC9jZ

12. LLM Course: https://t.co/N34YTPu1OK

🗺️ Guides

1. Google's Agent Whitepaper: https://t.co/bW3Ov3vMW0

2. Google's Agent Companion: https://t.co/wredwWAbBA

3. Building Effective Agents by Anthropic: https://t.co/fxtE4alVrJ.

4. Claude Code Best Agentic Coding practices: https://t.co/lLSwJ9pG7C

5. OpenAI's Practical Guide to Building Agents: https://t.co/xgkEIogGfh

📚Books:

1. Understanding Deep Learning: https://t.co/CjcKpTemmV

2. Building an LLM from Scratch: https://t.co/DaWBxOx8o3

3. The LLM Engineering Handbook: https://t.co/ZA1n0N41Mf

4. AI Agents: The Definitive Guide - Nicole Koenigstein: https://t.co/boLkl1VlKb

5. Building Applications with AI Agents - Michael Albada: https://t.co/H1Xf5EkJLL

6. AI Agents with MCP - Kyle Stratis: https://t.co/JI3ELQZE6a

7. AI Engineering: https://t.co/Xk0JzMIf7o

📜 Papers

1. ReAct: https://t.co/QNqE4UU55w

2. Generative Agents: https://t.co/CwEpoJgY1U.

3. Toolformer: https://t.co/5m9xZd5teZ

4. Chain-of-Thought Prompting: https://t.co/KjVlgdWi77.

🧑🏫 Courses:

1. HuggingFace's Agent Course: https://t.co/7FSUYKxIdG

2. MCP with Anthropic: https://t.co/IkZGiWm2yS

3. Building Vector Databases with Pinecone: https://t.co/2YRoMfLdXd

4. Vector Databases from Embeddings to Apps: https://t.co/23A50ixbHJ

5. Agent Memory: https://t.co/uc3L9BrNF7

Repost for your network ♻️

[Dipti Sharma @Diptish09]

I GAVE GEMINI MY BIRTH DATE AND TIME

It broke down my entire life with unsettling precision.
No horoscopes. No tarot. Just pure artificial intelligence.

Here are 7 prompts you should try:

[Tech with Mak @techNmak]

What is RAG? What is Agentic RAG?

> Retrieval-Augmented Generation (RAG) <
----------------------------------------------

Retrieval-Augmented Generation (RAG) is an architecture that enhances a language model’s outputs by grounding them in external knowledge sources at inference time.

Instead of relying solely on parameters learned during training, RAG systems dynamically retrieve relevant information and inject it into the model’s context before generation.

=> Canonical RAG workflow
> A user submits a query.
> The query is embedded and matched against a pre-indexed corpus (commonly stored in a vector database).
> The top-K most relevant document chunks are retrieved.
> Retrieved context is appended to the original query.

A language model generates a response conditioned on this augmented input.

=> Primary objective
To reduce hallucinations and improve factual accuracy by grounding generation in verifiable, external context.

=> Key limitation
> Traditional RAG is a single-shot pipeline:
> No explicit reasoning or planning
> No validation of retrieved evidence

No iterative refinement if retrieval or generation is suboptimal

The system assumes the first retrieval and generation pass is sufficient, which often breaks down for complex, ambiguous, or multi-hop queries.

> Agentic RAG <
------------------

Agentic RAG extends standard RAG by introducing autonomous decision-making agents that can reason, plan, evaluate, and adapt across multiple steps.

Rather than a static retrieval → generation flow, Agentic RAG operates as a closed-loop, goal-driven system.

=> Core idea
Retrieval and generation are no longer treated as isolated steps, they become actions taken by agents in pursuit of a higher-level objective: producing a correct, complete, and useful answer.

=> Typical Agentic RAG Architecture
1./ Planning Agent
> Interprets the user’s intent
> Decomposes complex queries into sub-tasks
> Determines what information is required and from which sources

2./ Retrieval Agent
> Dynamically reformulates search queries
> Retrieves information from:
- Vector databases
- Structured databases
- APIs
- Tools or live data sources
> Can perform multi-hop retrieval when needed

3./ Generation Agent
> Synthesizes retrieved evidence into a coherent response
> Reasons across multiple sources
> Maintains traceability between claims and evidence

4./ Evaluation (Judge) Agent
> Critically reviews the generated output
> Checks for completeness, correctness, and alignment with the original query
> Decides whether to:
- Accept the answer
- Refine retrieval
- Re-plan and regenerate

This feedback loop can repeat until predefined quality criteria are met.

[SumitM @SumitM_X]

As a Java developer,

Please learn:

1. Core Java Mastery
- OOP principles (SOLID, DRY, KISS)
- Generics, Lambda expressions, Functional interfaces
- Java Streams API (map/reduce, collectors)
- Java Collections framework
- Java Reflection API
- Exception handling

2. Multithreading & Concurrency
- Thread synchronization, Executors, Locks
- Fork/Join framework
- Understanding of race conditions, deadlocks, and thread pools
- Concurrency utilities (java.util.concurrent)

3. Design Patterns & Architecture
- Common design patterns (Singleton, Factory, Builder)
- Architectural patterns (MVC, - Microservices, Event-Driven Architecture)
- Dependency Injection (DI), Inversion of Control (IoC)

4. Java Memory Management
- Garbage Collection (G1, CMS, ZGC)
- JVM heap and stack management
- Profiling tools (JProfiler, VisualVM)
- Analyzing memory leaks, thread dumps, and heap dumps

5. Classloaders and Reflection
- Custom class loaders
- Dynamic class loading
- Reflection for runtime behavior manipulation

6. Spring Framework & Spring Boot
- Spring Core (Dependency Injection, AOP)
- Spring Boot (Auto-configuration, Microservices support)
- Spring Security (OAuth2, JWT)
- Spring Data (JPA, Hibernate integration)
- Spring Cloud (Netflix OSS, Circuit Breakers)

7. Microservices Architecture
- Service discovery (Eureka, Consul)
- Load balancing, distributed tracing, and circuit breaking
- API Gateway (Zuul, NGINX)
- Asynchronous communication with Kafka, RabbitMQ

8. RESTful Web Services
- REST principles, building APIs
- JSON/XML handling
- API versioning, OpenAPI/Swagger documentation

9. Java I/O and NIO
- Blocking vs non-blocking I/O (NIO)
- Asynchronous I/O, channels, selectors
- File handling, serialization and deserialization

10. Reactive Programming
- Project Reactor, RxJava
- Event-driven architecture, backpressure
- Reactive streams, non-blocking IO

11. JPA/Hibernate
- ORM principles, entity relationships
- Lazy vs eager loading
- Caching strategies, query optimization

12. Database Optimization
- SQL optimization, indexing, and transactions
- NoSQL databases (MongoDB, Cassandra)
- ACID principles, CAP theorem

13. Distributed Systems
- Consistency, availability, partitioning (CAP)
- Event sourcing, CQRS (Command Query Responsibility Segregation)
- Distributed caching (Redis, Hazelcast)
- Tools: Apache ZooKeeper, Consul, etcd

14. Testing & TDD/BDD
- Unit testing (JUnit, Mockito)
- Integration and functional testing
- Behavior-driven development (Cucumber)

15. CI/CD & DevOps
- Continuous integration (Jenkins, CircleCI)
- Containerization with Docker
- Orchestration with Kubernetes
- Source control (Git), versioning, branching strategies

16. Performance Tuning & Optimization
- JVM tuning, garbage collection optimization
- Caching strategies, load balancing
-Tools for profiling and monitoring (Prometheus, Grafana)

17. Security
- Authentication/authorization (OAuth2, JWT)
- Secure coding (OWASP guidelines)
- Encryption (TLS/SSL), CSRF/XSS prevention

18. Cloud Platforms & API Gateways
- AWS, Azure, GCP deployment
-API Gateway (Zuul, NGINX) for rate-limiting and routing
- Cloud-native microservices tools

19. Logging & Monitoring
- Centralized logging (ELK Stack, Graylog)
- Distributed tracing (Jaeger, Zipkin)
- Application Performance Monitoring (APM)

20. Caching Mechanisms
- In-memory caching (Ehcache, Caffeine)
- Distributed caching (Redis, Memcached)
- Cache invalidation strategies (LRU, LFU)

21. Build Tools
- Maven and Gradle for dependency management
- Multi-module project handling
- Continuous delivery pipelines

22. Message Queuing Systems
- Kafka, RabbitMQ for event-driven microservices
- Pub/Sub models, distributed message brokers

[SumitM @SumitM_X]

This is a real Interview report which are sent to clients ....
I was part of this interviewing firm and had access to these reports...
AI summarizes the whole interview , very helpful for clients to see what all was covered in the Interview..

Have attached multiple reports like this in the book.

[Abhishek Singh @0xlelouch_]

I appeared for Google interview last year. Dynamic programming is their favourite interview topic.

Here are 10 Must-Know Dynamic Programming patterns for coding interviews
(with LeetCode style examples so people can map easily)

1. 1D DP (Linear DP)
You make decisions based on previous index.
Classic starter problems.
Examples: Climbing Stairs, House Robber, Fibonacci.

2. 2D DP (Grid / Matrix DP)
State depends on row and column.
Very common in interviews.
Examples: Unique Paths, Minimum Path Sum, Dungeon Game.

3. Knapsack Pattern (Pick or Not Pick)
At every step you decide take or skip.
Most DP problems reduce to this mentally.
Examples: 0/1 Knapsack, Subset Sum, Partition Equal Subset Sum.

4. Longest Subsequence / Subarray
You compare past states to build the longest answer.
Tricky transitions, very popular.
Examples: Longest Increasing Subsequence, Longest Common Subsequence.

5. Interval DP
You solve smaller ranges and expand.
Usually O(n³), scary but powerful.
Examples: Burst Balloons, Matrix Chain Multiplication.

6. DP on Strings
State usually based on two indices.
Edit operations, matching, skipping.
Examples: Edit Distance, Regular Expression Matching.

7. DP on Trees
DFS + DP values returned from children.
Very common in system style interviews.
Examples: House Robber III, Diameter of Binary Tree (DP variant).

8. DP on Graphs (DAG DP)
Topological order + DP relaxations.
Only works when no cycles.
Examples: Longest Path in DAG, Course Schedule variants.

9. Bitmask DP
State compressed into bits.
Looks hard, but brute force optimized.
Examples: Traveling Salesman Problem, Assign Tasks to Workers.

10. State Machine DP
You track states like buy/sell, hold/not hold.
Very common in trading style questions.
Examples: Best Time to Buy and Sell Stock I, II, III, with Cooldown.

Most DP questions are not new problems.
They are the same old patterns.

If we can identify the pattern, the solution writes itself slowly but surely.

Consider repost if this saves you hours of confusion before interviews!!