Karan Tikoo

Kashmiri Pandit🇮🇳,
Software Engineer - Backend Tech Lead at Accenture👨‍💻,
Avid Traveller🧳✈️,
UFC and cricket Lover🤩🤼

[Shubh Jain @shubh19]

How to clear FAANG in 120 days:

- Set up LeetCode, VSCode, & GitHub
- Track progress in Notion daily
- Days 1-20: Learn 1 core topic + 4 problems
- Days 21-60: Deep dives + 5 problems/day
- Days 61-100: 2 advanced problems daily
- Take 2 mock interviews every week

Can you actually grind 6 hours a day for this?

[SCR @SCR01111]

If your strategy is memorizing 500 solutions...

the 501st question will humble you.

Learn patterns instead!

[Squat University @SquatUniversity]

POV: you start doing reverse planks every day, and now your pelvic tilt is finally going away, your stomach looks flatter and you finally feel what targeting your deep core feels like too...

[shalini @ShalsX]

I'll be honest with you guys because I don't know how to pretend everything is going well.

I've been learning DSA for about a month now. I know the basics of Java, then I jumped into LeetCode. I started following Striver's sheet, learning patterns like two pointers, sliding window, HashMap, etc.

I'll admit it—I looked at a lot of solutions. After seeing enough problems, I can often recognize the pattern. I'll look at a question and think, "This is probably a two-pointer problem," or "This should use a HashMap."

But here's where I'm stuck:

I can recognize the pattern, but I can't implement it.

I know how a HashMap works. I know HashSet. I've learned Java Collections. But when I sit down to solve a problem—especially ones involving characters, strings, frequencies, or multiple concepts together—my mind just goes blank.

It's like I know the individual tools, but I don't know how to make them work together to build a solution.

Sometimes I wonder if I skipped something important. Should I have spent more time on arrays? Java Collections? Problem-solving basics? Or is this just how learning DSA feels?

Right now, I'm overwhelmed. My confidence has dropped so much that I've even thought about quitting. I know I'm only a month in, but I expected that by now I'd at least be able to solve a few easy problems on my own. Instead, I still find myself looking at solutions more often than I'd like.

If you've been through this stage and now use DSA in your job or got better at it, I genuinely want to ask:

• Is this normal? • Did you ever reach a point where you understood the concepts but couldn't code the solution? • What helped everything finally click? • Am I missing something, or do I just need more practice?

I'm not looking for motivation or sugar-coated answers. I just want honest advice from people who've already been where I am.

If you don't want to reply publicly, my DMs are open. I'd genuinely appreciate your guidance.

[Suraj Sharma @suraj_sharma14]

If I had 6 months to become a Forward Deployed Engineer.

I'd do this.

Stage 1: Python and API Foundations
FastAPI, async, error handling, webhooks, REST/GraphQL, third-party SDKs.

Stage 2: Customer Integration Patterns
OAuth2, SAML, SSO, Salesforce/Slack/HubSpot APIs, webhook verification, retry logic.

Stage 3: Multi-Tenancy and Data Isolation
Row-level security, tenant identifiers, separate databases vs schemas, data partitioning.

Stage 4: Security and Compliance Basics
SOC 2, GDPR, HIPAA, PII redaction, encryption at rest and in transit, audit logging.

Stage 5: RAG for Customer Data
Secure ingestion pipelines, access control per document, citation tracking, permission filtering.

Stage 6: Deployment Automation
Docker, Kubernetes, Helm charts, CI/CD pipelines, one-click customer environments.

Stage 7: Observability and Dashboards
Distributed tracing, customer-facing health pages, usage metrics, SLA monitoring, alerting.

Stage 8: Incident Management and Debugging
Production debugging, log analysis, rollback strategies, post-mortems, customer communication during outages.

Stage 9: Documentation and Runbooks
Deployment guides, API docs, troubleshooting playbooks, onboarding checklists, knowledge base articles.

Stage 10: Requirements Gathering and Translation
Talking to non-technical stakeholders, translating business needs to technical specs, managing expectations.

Stage 11: Customer Success and Adoption
Usage analysis, feature adoption tracking, identifying expansion signals, reducing churn, proving ROI.

Stage 12: Public Portfolio and Case Studies
Document deployed projects, write implementation guides, share integration patterns, publish success metrics.

Most people stay stuck watching tutorials.

Builders get hired.

(Bookmark it)

[Karan Tikoo @TikooKaran]

@THEREALPUNK Usman vs gaethje is a very intriguing matchup!
But i think they will give him someone like hooker, chandler or charles so they can build him and ask him to KO for title shot and then they can give him
It!

[Suraj Sharma @suraj_sharma14]

As a Machine Learning Engineer, you must build these projects.

Systems that prove you can ship models that scale.

1. End-to-End ML Pipeline
Build: Automated pipeline from data ingestion to model deployment using Airflow or Prefect.
Why: Shows you can operationalize ML not just train models in isolation.

2. RAG System with Evaluation Harness
Build: Retrieval-Augmented Generation with grounded citations and automated quality metrics.
Why: Proves you can build reliable LLM applications not just chat demos.

3. Real-Time Inference Service
Build: Low-latency API serving models with <100ms response time using vLLM or Triton.
Why: Demonstrates understanding of production performance constraints.

4. Feature Store Implementation
Build: Centralized store for feature engineering with point-in-time correctness and online serving.
Why: Shows you can handle data consistency between training and inference.

5. Model Monitoring and Drift Detection
Build: System to track data drift, concept drift and performance degradation in production.
Why: Proves you understand models decay and need continuous observation.

6. Distributed Training Setup
Build: Multi-GPU training pipeline using PyTorch DDP or DeepSpeed for large models.
Why: Shows you can scale training beyond single-machine constraints.

7. Model Registry and Versioning
Build: Centralized registry tracking model versions, metrics, artifacts and lineage using MLflow.
Why: Demonstrates reproducibility and governance capabilities.

8. A/B Testing Framework for Models
Build: System to route traffic between model versions and measure business impact statistically.
Why: Shows you can validate model improvements beyond accuracy metrics.

9. Vector Search Engine
Build: Similarity search over millions of embeddings with metadata filtering and hybrid search.
Why: Modern ML systems require semantic retrieval not just keyword matching.

10. Automated Retraining Pipeline
Build: Trigger-based retraining when data drift exceeds threshold or performance drops.
Why: Proves you can build self-healing systems that maintain quality over time.

11. Cost Optimization Dashboard
Build: Track inference spend per model, per endpoint, per user with anomaly detection.
Why: Engineering decisions impact profitability. FinOps is part of the job.

12. Security and Guardrails Middleware
Build: Input validation, prompt injection defense, PII redaction and output filtering layers.
Why: Security is non-negotiable. One breach destroys trust forever.

13. Multi-Model Router
Build: Dynamic routing system selecting models based on task complexity, cost and latency.
Why: Shows you can optimize tradeoffs not just default to the largest model.

14. Data Versioning System
Build: Implement DVC or LakeFS for tracking dataset versions alongside code versions.
Why: Reproducibility requires versioning data not just code.

15. Production Agent Orchestrator
Build: Multi-agent system with state management, tool use and human-in-the-loop approval.
Why: Agents are the future of ML applications. Orchestration is the moat.

Bookmark & Repost...

[SCR @SCR01111]

One small tip:

create a folder called DSA Notes and save every note i post

by day 30, you'll have your own revision handbook.

[Shubh Jain @shubh19]

DSA Monster Sheet Learning Plan - From Basics to FAANG Level

Phase 0: Preparation (Day 0)
Setup:
- Create profiles on: LeetCode, Codeforces, AtCoder, GeeksforGeeks
- Choose IDE: VSCode, Sublime Text, or your preferred setup
- Initialize a GitHub repository for notes and code solutions
- Prepare a Notion page or Google Sheet tracker for daily progress

Phase 1: Core Foundation (Days 1–20)
Daily Routine:
- Learn 1 core topic from DSA pattern list
- Solve 3–5 problems based on the topic (LeetCode / GFG / Codeforces)
- Revise using notes or flashcards

Phase 2: Intermediate and Interview Level (Days 21–60)
Daily Routine:
- Study one deep-dive topic per day
- Solve 3 medium + 2 hard problems
- Participate in one coding contest per week (LeetCode or Codeforces)

Phase 3: Advanced, MIT-Level, and Competitive (Days 61–100)
Focus:
- Solve two advanced problems daily
- Participate in three competitive contests per week (Codeforces / AtCoder)

Phase 4: Final Stretch (Days 101–120)
Focus Areas:
- Re-solve problems from weak topics
- Attempt FAANG company-specific past questions (Blind 75, Google/Amazon tags on LeetCode)
- Take two mock interviews per week (use Pramp, Interviewing .io)
- Conduct weekly review of mistakes and missed patterns

Tools and Platforms
LeetCode: Use Tag Filters (Top 100 Liked, Google, Amazon)
Codeforces: Practice in Gym or by problem rating (1300–2000+)
GeeksforGeeks: Topic-wise problem sets for revision
Notion / Google Sheets: Track topics, problems, and performance over time

[Karan Tikoo @TikooKaran]

@SCR01111 @Google Bhai mera google mai krwado mai apko 3 lakh rs dene ko tyar hu

[SCR @SCR01111]

Google SDE Roadmap (3 to 4 Months) 📌

Everything I'd focus on if my goal was Google.

1. DSA (40%)
NeetCode 150: https://t.co/8BQxApC4rC
LeetCode Google Tag: https://t.co/cPkGYeufFj
Tech Interview Handbook: https://t.co/vHRyYhCWTq

Target 250 to 300 problems, weighted toward graphs, trees, DP, and design a data structure questions since these show up disproportionately in Google loops.

2. System Design (20%)
ByteByteGo: https://t.co/TMv2Tbm1x5
System Design Primer: https://t.co/kj8QBHCZvh
Designing Data Intensive Applications: https://t.co/ADGlSKPZEw

3. Core CS (10%)
Master OS, DBMS, Computer Networks, and OOP.
OSTEP: https://t.co/4NrVmK3xo8
GeeksforGeeks: https://t.co/dmEWLbs4ij

4. Low Level Design (10%)
Refactoring Guru: https://t.co/JxYHXG17L1
Head First Design Patterns

5. Behavioral and Googleyness (10%)

Google scores this as its own interview, not small talk. Prep 6 to 8 STAR stories covering conflict with a teammate, a failure, an ambiguous problem, leading without authority, and disagreeing with a decision.

6. Projects

Build 2 to 3 production-grade projects. Know the scaling decisions and trade-offs cold. Google interviewers probe project depth harder than most companies.

7. Mock Interviews (5%)
Pramp: https://t.co/LMYYuURD1c
https://t.co/dyQfNTZcrD: https://t.co/0M4UjYnjuP
Exponent: https://t.co/eZImnYtA9R

Do 15 to 20 mocks, and start early so feedback shapes your DSA and LLD prep instead of just validating it at the end.

8. Resume and Referral
Jake's Resume: https://t.co/4Nyn6vZlGH

A referral meaningfully improves your recruiter screen odds versus a cold apply. Worth chasing early.

9. AI Knowledge

This is the cherry on the cake, not the cake itself. Know how to use AI coding tools well, understand prompting, and be able to explain when and why you would or wouldn't use AI assistance in a real engineering workflow. It won't carry you through a DSA round, but it shows up in how you talk about your projects and workflow.

10. YouTube

Striver, William Fiset, Abdul Bari, Gaurav Sen, ByteByteGo

11. Must Read
Google Developer Blog: https://t.co/6rMrtzSsOJ
Google Testing Blog: https://t.co/Zns52NBw8T
Google Research Blog: https://t.co/hGr4RTj799

If you can consistently solve 250 to 300 quality LeetCode problems, build 2 to 3 production-grade projects, understand System Design and Core CS, nail your behavioral stories, practice 15 to 20 mock interviews, and stay sharp with AI tools on the side, you'll be in a strong position for Google SDE interviews.

Bookmark this.

[Karan Tikoo @TikooKaran]

@specsycoder Are bhai yar tum bhi naa

[Karan Tikoo @TikooKaran]

@specsycoder Out sick daldo

[Anu Sharma @O_Anu_O]

Been deep in agentic system design lately, so this was one of the most interesting reads in a while.

PAST-Bench finally asks the question most agent benchmarks skip: do agents actually get better from experience or just accumulate more state?

Turns out the answer is messier than expected. Two agents can show the same improvement and get there completely differently. Sound familiar? Its just like humans, agents persist past mistakes in different ways.

If you're into research papers, highly recommend this one

[Aryan Mittal @aryan_mittal007]

Sorry for linkedin slop but this is all it takes for you to reach GOOD levels of System design -

[Georges St-Pierre @GeorgesStPierre]

This nerve flossing exercise has been a game changer for my lower back stiffness. It’s helped me a lot. If you struggle with back issues, it might be worth looking into. 🙏
Check out for more:
https://t.co/8Jum1ilOYo

[Karan Tikoo @TikooKaran]

@FightluckBet Islam vs usman - islam would maul usman islam was a big lw!
Islam vs khabib is something i would have loved to see prime version islam 2024 vs khabib 2020 although i know khabib would have won somehow he can never loose in that octagon he has that level of confidence

[Venkatesh @venkateshdotdev]

🚨11 Backend engineering concepts every developer should understand:

𝟏. 𝐇𝐓𝐓𝐏: Request-Response Lifecycle
𝟐. 𝐑𝐄𝐒𝐓: Resource-Oriented APIs
𝟑. 𝐀𝐮𝐭𝐡𝐞𝐧𝐭𝐢𝐜𝐚𝐭𝐢𝐨𝐧: JWT, OAuth
𝟒. 𝐀𝐮𝐭𝐡𝐨𝐫𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Roles & Permissions
𝟓. 𝐂𝐚𝐜𝐡𝐢𝐧𝐠: Redis
𝟔. 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐨𝐧 𝐏𝐨𝐨𝐥𝐢𝐧𝐠: Efficient Database Access
𝟕. 𝐌𝐞𝐬𝐬𝐚𝐠𝐞 𝐁𝐫𝐨𝐤𝐞𝐫𝐬: Kafka, RabbitMQ
𝟖. 𝐑𝐞𝐭𝐫𝐢𝐞𝐬: Handling Transient Failures
𝟗. 𝐂𝐢𝐫𝐜𝐮𝐢𝐭 𝐁𝐫𝐞𝐚𝐤𝐞𝐫: Prevent Cascading Failures
𝟏𝟎. 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲: Logs, Metrics, Traces
𝟏𝟏. 𝐇𝐨𝐫𝐢𝐳𝐨𝐧𝐭𝐚𝐥 𝐒𝐜𝐚𝐥𝐢𝐧𝐠: Distributed Systems

[SumitM @SumitM_X]

As a Frontend Engineer in 2026, please learn:

- JavaScript & TypeScript (deeply, not just syntax)

- Responsive Design (CSS, Flexbox, Grid, Container Queries)

- Performance Optimization
(lazy loading, code splitting, tree shaking, memoization, Core Web Vitals)

- Web Security
(CORS, XSS, CSRF, Content Security Policy, secure cookies)

- Testing
(Jest, Cypress, Playwright)

- Build Tools
(Vite, Webpack basics, Babel, bundling strategies)

- Version Control
(Git, PR workflows, rebasing)

- Browser DevTools & Debugging

- Accessibility
(WCAG, ARIA, keyboard navigation, screen readers)

- Progressive Web Apps (PWA)

- Component Architecture
(composition, Atomic Design, HOCs, hooks patterns)

- Micro Frontends (when and when not to use them)

- API Integration
(REST, GraphQL, WebSockets, caching, error handling)

- CI/CD for Frontend
(automated builds, previews, deployments)

- Browser Rendering & DOM Performance

- State Management
(Redux, Zustand, signals, server state vs client state)

- Framework Internals
(e.g. React Fiber, Angular Ivy )

- UX/UI Collaboration
(design systems, Figma handoff, accessibility audits)

- WebAssembly (Wasm)

Pick ONE framework/library.

Stop jumping from one framework to another.