SAAD ALI

[SAAD ALI @ALIAli9381]

Cisco CCNA Training and Exam Complete Package #giveaway https://t.co/yolpWm9dMa

[Dhairya @dkare1009]

DSA Preparation Roadmap (From Zero to OA Ready)

1. Sliding Window: 3, 76, 209, 424, 567, 904
2. Two Pointers: 11, 15, 16, 18, 42, 167
3. Fast/Slow Pointers (Linked List): 141, 142, 19, 876, 160, 234
4. Binary Search on Sorted 33, 34, 35, 153, 162, 704
5. Binary Search on Answer: 875, 1011, 410, 774, 1283, 1482
6. Hashing / Frequency Maps: 1, 49, 128, 217, 242, 347
7. Prefix Sum / Running Sum: 303, 560, 724, 930, 974, 523
8. Difference Array / Range Updates: 370, 1094, 1109, 1893, 1943, 2381
9. Monotonic Stack: 739, 496, 503, 84, 85, 901
10. Monotonic Queue / Deque: 239, 862, 1425, 1438, 1499, 1696
11. Heap / Top K: 215, 347, 692, 703, 973, 1046
12. Intervals: 56, 57, 252, 253, 435, 452
13. Greedy Scheduling / Sorting: 45, 55, 406, 621, 763, 134
14. Linked List Manipulation: 21, 23, 24, 25, 92, 138
15. Tree DFS: 104, 112, 113, 543, 124, 226
16. Tree BFS / Level Order: 102, 103, 199, 515, 637, 116
17. BST Problems: 98, 99, 230, 235, 450, 700
18. Backtracking Basics: 46, 47, 77, 78, 90, 39
19. Backtracking with Constraints: 40, 17, 79, 131, 51, 52
20. Graph BFS / DFS: 200, 695, 733, 994, 1091, 1254
21. Topological Sort / DAG: 207, 210, 802, 1462, 1203, 2115
22. Union Find / DSU: 547, 684, 1319, 1579, 990, 1202
23. Shortest Path: 743, 787, 1514, 1631, 1334, 1976
24. MST / Graph Greedy: 1584, 1135, 1168, 1489, 778, 1102
25. Trie: 208, 211, 212, 648, 677, 1268
26. Bit Manipulation: 136, 137, 191, 338, 268, 190
27. 1D DP Basics: 70, 198, 213, 322, 279, 300
28. Knapsack / Subset DP: 416, 494, 518, 474, 1049, 879
29. Grid DP: 62, 63, 64, 221, 931, 120
30. String DP / Sequence DP: 1143, 72, 115, 583, 97, 1312

to fix exactly this:

1. 200+ Partenered companies for jobs
2. Pattern-based DSA, System design from LLD up, Core CSE, GenAI fundamentals, and Application strategy in one place.
3. Gets you job ready with mock interviews and resume reviews

How to use this list?
- These numbers are Leetcode problem numbers
- Do 3 patterns at a time, not all 30 together.
- For each pattern, solve the first 2 to understand the idea, the next 2 to get repetition, and the last 2 to stretch yourself.
- After every pattern, write one reusable template from memory.
- Do not just “solve and move on.” Ask: what signal in the question pointed to this pattern?
- If you get stuck, revisit the same pattern after 3 to 4 days. Pattern recognition is built by spacing, not cramming.

[Venkatesh @venkateshdotdev]

📚 Backend Engineer

Stage 1: Programming Language

Stage 2: Git

Stage 3: SQL

Stage 4: REST APIs

Stage 5: Authentication

Stage 6: Caching

Stage 7: Message Queues

Stage 8: System Design

Stage 9: Distributed Systems

Stage 10: Performance Optimization

Stage 11: Observability

Stage 12: Build systems that scale

🏆 Backend Engineer unlocked.

[SumitM @SumitM_X]

Some Java interview questions for junior Java devs (0 - 2 yoe), which you can face in the interview room -

- Can a static block throw exception?

- Why is String Immutable in Java?

- LinkedList in Java is a doubly or singly linked list?

[Python Programming @PythonPr]

The Ultimate Git Commands Cheat Sheet for Developers 🚀

[Khairallah AL-Awady @eng_khairallah1]

this is f*cking gold

How to build your first AI agent (Full guide)

if I had this a year ago, I would've shipped my first agent in a day instead of 2 weeks

in the right hands, this changes everything:

[Uday👨‍💻 @uday_devops]

🤖 AI Engineer

Stage 1: Python
Stage 2: Git + GitHub
Stage 3: SQL
Stage 4: Statistics & Linear Algebra
Stage 5: Machine Learning
Stage 6: Deep Learning (PyTorch / TensorFlow)
Stage 7: LLM Fundamentals
Stage 8: Prompt Engineering
Stage 9: RAG (Retrieval-Augmented Generation)
Stage 10: AI Agents & MCP
Stage 11: AI Deployment (Docker + Cloud)
Stage 12: Build AI applications

🏆 AI Engineer unlocked 🙌

[Shubh Jain @shubh19]

Which Coding Platform is best for practice coding?

1.Leetcode

2.Codeforce

3.Hackerrank

4.Codechef

5.Geekforgeeks

6.HackerEarth

[Austin @IamAroke]

Let's talk about Object-Oriented Design Principles.

1. SOLID

S — Single Responsibility Principle (SRP)

A class should have only one responsibility, one reason to change.

For example, it's a bad for a class to handle user authentication, send emails, generate reports e.t.c

[AsyncTrix @asynctrix]

☸️ Kubernetes in Plain English:

• Pod → the smallest running app unit
• Deployment → keeps your app running always
• Service → gives stable access to Pods
• Ingress → routes external traffic to apps
• Namespace → separates teams/environments
• ConfigMap → app configuration storage
• Secret → stores sensitive data securely
• Volume → persistent storage for containers
• Node → the machine running workloads
• Cluster → group of nodes managed together
• kube-apiserver → brain entry point of Kubernetes
• Scheduler → decides where Pods should run
• kubelet → worker agent on every node
• ReplicaSet → maintains desired Pod count
• HPA → auto-scales apps based on load
• DaemonSet → runs one Pod on every node

Master these and Kubernetes suddenly feels far less scary ☸️

[Dhairya @dkare1009]

Watching 1–2 videos daily from this banger playlist to stay sharp on system design. 🚀

Small, consistent learning > binge-watching.

#SystemDesign #SoftwareEngineering #Learning

[Rajat Jain - Data Analyst @rajatjain1107]

Want to stand out as a Data Analyst?
Save this for Future

Build these 5 Excel dashboards instead of just watching tutorials:

• Sales Performance Dashboard → Revenue, profit, top products, regional performance, monthly trends

• HR Analytics Dashboard → Employee attrition, headcount, hiring, department-wise analysis, diversity metrics

• Finance Dashboard → Budget vs Actual, expenses, cash flow, profit & loss, variance analysis

• Customer Analytics Dashboard → Customer segmentation, retention, repeat purchases, CLV, churn analysis

• Inventory Dashboard → Stock levels, reorder alerts, inventory turnover, supplier performance, dead stock

Hiring managers don't care how many courses you've completed.

They care about what you've built.

[Shubh Jain @shubh19]

How to find a Job as a Software Engineer in 2026

1. DSA Patterns
- Pattern based prep
- 250-300 focused questions
- Daily timed practice
- Explain approach out loud

2. CS Fundamentals
- OS, CN, DBMS

3. System Design
- HLD + LLD for real apps
- Focus on tradeoffs and constraints
- Draw clean diagrams
- Practice mock design rounds

4. AI Skills
- Use LLMs to read code and docs
- Generate tests and edge cases
- Debug faster with AI
- Always verify and improve the output

5. Projects
- Build 3-4 apps on problems you see in life
- Host everything and share links
- Add tests, logs, basic monitoring
- Write strong READMEs and case studies

6. Networking & LinkedIn
- Clear Headline & Niche
- Post for your target companies
- Warm DMs and referral ask
- Alumni, events and online communities

[Rajat Jain - Data Analyst @rajatjain1107]

30 Days to Learn SQL for Data Analysts

Week 1: SQL Fundamentals → Understand what databases and tables are
→ Learn SELECT, FROM, WHERE
→ Use ORDER BY, LIMIT, DISTINCT
→ Practice filtering data every day

Week 2: Data Analysis with SQL → Learn aggregate functions (COUNT, SUM, AVG, MIN, MAX)
→ Master GROUP BY and HAVING
→ Learn string and date functions
→ Solve 10-15 practice questions daily

Week 3: Intermediate SQL → Master INNER, LEFT, RIGHT, and FULL JOIN
→ Learn CASE WHEN
→ Practice subqueries and Common Table Expressions (CTEs)
→ Build small business queries using real datasets

Week 4: Interview-Level SQL → Learn window functions (ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD)
→ Solve scenario-based interview questions
→ Work on one end-to-end SQL project
→ Revise all concepts and practice mock interview questions

By Day 30, you should be able to: → Analyze business data confidently
→ Solve most Data Analyst SQL interview questions
→ Build portfolio-ready SQL projects
→ Feel ready for Data Analyst interviews

Bookmark this roadmap and follow it consistently for 30 days.

[Tech with Mak @techNmak]

This is literally one of the best LLM playlists out there!

43 lectures from Vizuara where you build an entire LLM from scratch, then finetune it.

You're writing the tokenizer yourself, coding attention from scratch, building the transformer block piece by piece.

Here's everything it covers:

Foundations
→ LLM basics, pretraining vs finetuning, what transformers are
→ How GPT-3 really works + the 3 stages of building an LLM

Data Pipeline
→ Coding a tokenizer from scratch
→ Byte Pair Encoding (the actual GPT tokenizer)
→ Input-target pairs, token embeddings, positional embeddings
→ The complete data preprocessing pipeline

Attention Mechanism (the deep part)
→ Simplified attention → self-attention with key/query/value
→ Causal self-attention, coded from scratch
→ Multi-head attention, both the intuition and the full math

Building the Architecture
→ Birds-eye view of the LLM architecture
→ Layer normalization, GELU activation, shortcut connections
→ Coding the entire transformer block
→ Building the actual 124M parameter GPT-2 model

Training
→ Defining the loss function + evaluating performance
→ Coding the full pretraining loop
→ Decoding strategies: temperature scaling, top-k sampling
→ Saving/loading weights, loading pretrained GPT-2 weights

Finetuning
→ Classification finetuning (spam classifier, built from scratch)
→ Instruction finetuning with Alpaca-format prompts
→ Data batching, dataloaders, the full finetuning training loop
→ Evaluating the finetuned model with Ollama

most complete free resource on LLMs I've found.

Heads up: quite a few of the lectures were recently moved to Members only, so you may need a YouTube membership to unlock the full series. Worth every penny.

🔗 [Playlist link in comments]

Don't sleep on this. Start learning.

[Shubh Jain @shubh19]

DSA Pattern Roadmap (From Zero to Placement Ready)

┣  1. Foundations You Cannot Skip
┃ ┣ Time & Space Complexity
┃ ┣  Big-O Notation
┃ ┣  Recursion Basics
┃ ┣  Basic Math (GCD, Prime, Factorization, Modulo)
┃ ┗  Problem Solving Basics (Brute Force → Better Approach)

┣  2. Core Data Structures
┃ ┣  Arrays
┃ ┣  Strings
┃ ┣  Linked Lists
┃ ┣  Stack
┃ ┣  Queue / Deque
┃ ┣  HashMap / HashSet
┃ ┗  Heap / Priority Queue Basics

┣  3. First Pattern Layer
┃ ┣  Two Pointers
┃ ┣  Sliding Window
┃ ┣  Prefix Sum
┃ ┣  Binary Search on Array
┃ ┣  Sorting + Observation
┃ ┗  Frequency Counting / Hashing Patterns

┣  4. Recursion & Search Patterns
┃ ┣  Recursion on Arrays / Strings
┃ ┣  Backtracking
┃ ┣  Subsets / Subsequences
┃ ┣  Permutations / Combinations
┃ ┣  Decision Tree Thinking
┃ ┗  Pruning Techniques

┣  5. Tree Patterns
┃ ┣  Binary Tree Traversals
┃ ┣  BST Basics
┃ ┣  DFS on Trees
┃ ┣  BFS / Level Order
┃ ┣  Height / Diameter / Balanced Tree
┃ ┣  Path Sum / Tree DP Basics
┃ ┗  Lowest Common Ancestor

┣  6. Graph Patterns
┃ ┣  Graph Representation
┃ ┣  BFS
┃ ┣  DFS
┃ ┣  Connected Components
┃ ┣  Cycle Detection
┃ ┣  Topological Sort
┃ ┣  Shortest Path Basics
┃ ┗  Union Find / Disjoint Set

┣  7. Advanced Pattern Layer
┃ ┣  Monotonic Stack
┃ ┣  Monotonic Queue
┃ ┣  Heap / Top K Problems
┃ ┣  Trie
┃ ┣  Bit Manipulation
┃ ┣  Greedy Patterns
┃ ┣  Interval Problems
┃ ┗  Merge / K-way Processing

┣  8. Dynamic Programming Roadmap
┃ ┣  1D DP
┃ ┣  2D DP
┃ ┣  Take / Not Take Pattern
┃ ┣  Knapsack Variants
┃ ┣  LIS Pattern
┃ ┣  String DP
┃ ┣  Grid DP
┃ ┣  DP on Subsequences
┃ ┣  DP on Trees
┃ ┗  State Design Practice

┣  9. Advanced Algorithms
┃ ┣  Dijkstra’s Algorithm
┃ ┣  Floyd Warshall Basics
┃ ┣  Minimum Spanning Tree
┃ ┣  Kahn’s Algorithm
┃ ┣  Shortest Path Variants
┃ ┣  Binary Search on Answer
┃ ┣  Segment Tree / Fenwick Tree Basics
┃ ┗  Advanced Graph + DP Mix Problems

┣  10. Pattern Recognition Training
┃ ┣  How to Identify the Input Type
┃ ┣  How to Spot Trigger Words
┃ ┣  Map Problem → Pattern
┃ ┣  Brute Force → Optimized Thinking
┃ ┣  Similar Problem Grouping
┃ ┣  Pattern Notes / Cheat Sheets
┃ ┗  Revision by Buckets, Not Random Questions
┃
┣  11. Practice System
┃ ┣  Easy → Medium → Hard Progression
┃ ┣  LeetCode / NeetCode / Codeforces
┃ ┣  Daily Timed Practice
┃ ┣  Weekly Mixed Contest Practice
┃ ┣  Topic-wise Sheets
┃ ┣  Re-solving Old Problems
┃ ┗  Mistake Log / Weak Area Tracker

[YourCloudDude | AWS & Python @yourclouddude]

Python Roadmap: Zero → Job-Ready Projects

• Syntax + variables
• Conditions + loops
• Functions + modules
• Data structures
• OOP concepts
• File handling
• APIs + `requests`
• Databases + SQL
• Flask or FastAPI
• GitHub portfolio projects

Learn in this order.
Build after every step.

[Tanuj @tanujDE3180]

Want to become a backend engineer that companies like Google, Uber, Netflix and Stripe want to hire in 2026?

Please learn

- Networking:
DNS, TCP/IP, HTTP/2, HTTP/3, TLS

- Database Internals:
B+ Trees, MVCC, WAL, Query Optimizer, Indexes

- API Design:
REST, gRPC, GraphQL, Idempotency, Pagination

- Distributed Systems:
CAP Theorem, Consensus, Replication, CQRS, Saga

- Event-Driven Architecture:
Kafka, Outbox Pattern, CDC, Event Streaming

- Performance Engineering:
Connection Pooling, Caching, Async I/O, Profiling

- Cloud Native:
Docker, Kubernetes, Service Mesh, Autoscaling

- Observability:
OpenTelemetry, Prometheus, Grafana, ELK

- Security:
OAuth2, JWT, mTLS, Zero Trust, OWASP Top 10

- Production Engineering:
Blue-Green Deployments, Canary Releases, Chaos Engineering, Disaster Recovery

[YourCloudDude | AWS & Python @yourclouddude]

Want to become a Data Analyst in 2026?

Learn Python in this order. 📊🐍

Not everything.

Just the skills that actually get used on the job 👇

[Karan🧋 @kmeanskaran]

You need AI/ML interview strategies before roadmaps.

In this article, I share my experiences from both sides of the interview table as a candidate and a recruiter, along with insights from hundreds of DMs I've received.

- What the AI/ML interview pipeline looks like in 2026
- 1 end-to-end project > 5 GenAI chatbots
- Do ATS-friendly resumes really work?
- How much DSA do you actually need for AI/ML jobs?
- When do recruiters ask you to implement ML algorithms from scratch?
- How to maximize your take-home assignments
- AI System Design vs. ML System Design
- The minimum MLOps preparation you should have
- What recruiters actually look for in candidates
- The best resources for AI/ML System Design and MLOps

This is an open-ended article, so feel free to share your own insights and experiences in the comments.