200 Done on a 2nd Day of 2023 on @LeetCode #python #datastructures #coding #programming #Algorithms

Tomorrow folks P.S - This will be beneficial only: 1. If you've gone through the syllabus once 2. seen some practice questions 3. Last minute revision

Along with the HLD, LLD, backend series (this will have 5-6 more articles that's it), I'm gonna start working on DevOps-related articles (since I'm a DevOps engineer at work hehe)

If I had 6 months to become an Agentic AI Engineer. I'd do this. Stage 1: Python Async Foundations asyncio, FastAPI, event-driven architecture, error handling, API integration patterns. Stage 2: LLM Fundamentals for Agents Context management, model routing, token economics, latency tradeoffs, failure modes. Stage 3: Tool Calling Structured Outputs Pydantic validation, function calling schemas, error recovery, dynamic tool discovery. Stage 4: Memory State Management Short-term buffers, long-term vector recall, context compression, cross-session sync. Stage 5: Single Agent Workflows ReAct loops, plan-and-execute, self-reflection, iteration limits, graceful degradation. Stage 6: Multi-Agent Orchestration LangGraph/CrewAI, supervisor patterns, message passing, conflict resolution, handoffs. Stage 7: Human-in-the-Loop Systems Uncertainty detection, approval gates, audit trails, resume logic, intervention points. Stage 8: Evaluation Quality Assurance Automated eval harnesses, LLM-as-a-judge, regression testing, hallucination metrics. Stage 9: Observability Tracing Distributed tracing (LangSmith/Arize), cost dashboards, latency monitoring, alerting. Stage 10: Security Guardrails Prompt injection defense, output filtering, PII redaction, sandboxed execution, compliance. Stage 11: Production Deployment vLLM/SGLang, Kubernetes scaling, CI/CD for agents, canary releases, rollback strategies. Stage 12: Open Source Portfolio Ship autonomous agents publicly, write architecture docs, record demos, contribute to libs. Most people stay stuck watching tutorials. Builders get hired. (Bookmark it)

15 AI founders you should follow on Twitter: @sama = founded OpenAI @AravSrinivas = founded Perplexity AI @karpathy = ex-founding member at OpenAI @darioamodei = founded Anthropic @demishassabis = founded DeepMind @hwchase17 = founded LangChain @adcock_brett = founded Figure AI @AndrewYNg = founded DeepLearning. AI @jeremyphoward = founded fast. ai @DrJimFan = leads AI robotics at NVIDIA @natfriedman = ex-CEO of GitHub @swyx = founded Smol AI @levelsio = founded PhotoAI @fchollet = founded Keras @rasbt = underrated ML educator

A guy on Reddit with 10 years of engineering experience just shared the one thing he'd teach every vibe coder first. And it'll save you thousands in AI costs. 🤯 Most people using Claude Code use it the expensive way. They call the AI every time the tool runs. Every run burns tokens. Every token costs money. His advice: flip it. Use Claude Code to BUILD the tool once. Then run it forever without spending a single token. Simple example. You want to check a website daily for updates. The expensive way: have an LLM search the site every day. Burns tokens every single time. The free way: use Claude Code to write a script that scrapes the page and alerts you if anything changed. Build it once. Runs forever. Zero tokens. Then he took it further. He had Claude Code build him a full neural network something that used to take weeks and years of ML training while he cooked dinner. It runs for free. No tokens. No API calls. Forever. Spend tokens once to build it. Run it for free forever. That's it. That's the insight most vibe coders are missing.

RT @ghumare64: If you want to go from AI beginner → AI builder & engineer, don’t just watch tutorials. Build from great open-source repos.…

Getting hired as a SWE in 2026: round 1 - DSA on whiteboard round 2 - system design for 10M users round 3 - live debug a prod incident round 4 - solve P=NP in 45 mins round 5 - fight the CTO in hand-to-hand combat Round 6 is on you 👇

I've started a 25-day series on Scaling and Architecture. One topic per day. As a Principal Backend Engineer with 12 years of building systems at scale, I want to break down every concept I wish someone explained to me earlier in my career. Day 1 was Load Balancing. Today is Day 2: CDN. Follow along if you're serious about system design. This will be worth your time.

As a Backend Developer, Slap yourself if you cannot clearly explain at least 10 of the following: TCP congestion control algorithms TLS 1.3 handshake internals HTTP/2 multiplexing & HPACK HTTP/3 QUIC packet loss recovery Connection pooling pitfalls Zero-downtime deployment strategies Database transaction isolation levels (serializable vs snapshot) B-tree vs LSM-tree index internals Query planner & cost-based optimization Deadlock detection & prevention ACID vs BASE trade-offs Two-phase commit vs Saga pattern Distributed locking (Redlock pitfalls) CAP theorem in practice CRDTs & conflict-free replicated data types Eventual consistency anti-patterns Kafka partition rebalancing & exactly-once semantics RabbitMQ dead-letter queues & message ordering gRPC streaming flow control GraphQL resolver batching & N 1 problem OAuth2 token introspection vs JWT validation Rate limiting algorithms (token bucket vs leaky bucket) Circuit breaker bulkhead patterns Observability: OpenTelemetry tracing propagation Prometheus metric cardinality explosion Log aggregation with sampling Memory-mapped files vs traditional I/O Garbage collection tuning (G1 vs ZGC) Thread pools vs virtual threads (Project Loom) Actor model vs shared-memory concurrency Message-driven architecture (Akka / Orleans) CQRS Event Sourcing projections Outbox pattern for reliable events Sharding strategies & hot partition avoidance Read replicas lag monitoring Database connection pool exhaustion Prepared statement caching Index bloat & vacuum strategies Kubernetes pod disruption budgets Service mesh traffic shifting Serverless cold-start mitigation API gateway throttling & caching layers Background job queues (Celery / BullMQ) retry semantics Distributed cache invalidation (cache-aside vs write-through) Eventual consistency in cache Idempotency keys in API design Optimistic locking with version vectors Paxos / Raft consensus internals Byzantine fault tolerance basics Chaos engineering principles Database failover & split-brain prevention Microservices observability (distributed tracing) API contract testing (Pact / Spring Cloud Contract) Backward-compatible schema evolution Protobuf vs JSON performance trade-offs Binary protocol parsing Zero-copy networking (sendfile) epoll / kqueue internals Syscall overhead & context switching Memory barriers & CPU cache coherence Lock-free data structures Bloom filters & HyperLogLog in practice Consistent hashing for load balancing Virtual memory & page faults impact Container runtime security (seccomp, AppArmor) Sidecar pattern limitations Service discovery (Consul vs DNS) Blue-green vs canary deployments Feature flags with rollout strategies Data pipeline backpressure handling Exactly-once processing guarantees Idempotent consumers in event streams And if you only know 10 — kindly return the “Senior Backend Developer” title. 😄

This is genuinely brilliant fashion advice for Indian men.

Best YouTube Channels to Learn Languages & Communication: 1. English – English Addict with Mr Steve 2. Spanish – Dreaming Spanish 3. Japanese – Japanese Pod 101 4. Public Speaking – Charisma on Command 5. Writing – Brandon McNulty 6. Storytelling – Matthew Dicks 7. Debate Skills – Intelligence Squared 8. Body Language – Observe 9. Business Communication – Jeff Su 10. Vocabulary – Merriam-Webster 11. French – Français Authentique 12. German – Easy German 13. Mandarin Chinese – Yoyo Chinese 14. Arabic – Arabic Pod 101 15. Korean – Talk To Me In Korean 16. Italian – Italiano Automatico 17. Portuguese – Practice Portuguese 18. Hindi – Hindi Pod 101 19. Russian – Russian With Max 20. Sign Language – Bill Vicars 21. Accent Reduction – Rachel's English 22. Pronunciation – Sounds American 23. Grammar – English with Lucy 24. Listening Skills – TED Talks 25. Persuasion & Influence – Rhetoric & Persuasion 26. Presentation Skills – Toastmasters International 27. Confidence Building – Brian Tracy 28. Emotional Intelligence – Daniel Goleman Talks 29. Cross-Cultural Communication – Erin Meyer Insights 30. Speed Reading – Howard Berg 31. Memory & Vocabulary Retention – Loci Method 32. Slang & Informal English – English with Greg 33. IELTS Preparation – E2 IELTS 34. TOEFL Preparation – TST Prep 35. Debate & Critical Thinking – Jordan Peterson Lectures 36. Journalism & Reporting – Reuters Training 37. Screenwriting – StudioBinder 38. Poetry & Spoken Word – Button Poetry 39. Negotiation Skills – The Black Swan Group 40. Interviewing & Communication – Big Think

You need that serious start 🔥🔥

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AI Engineering from Scratch. 503 lessons. 20 phases. 320 hours. github.com/rohitg00/ai-engin… Phase 00: Setup & Tooling (12 lessons) Phase 01: Math Foundations (22 lessons) Phase 02: ML Fundamentals (18 lessons) Phase 03: Deep Learning Core (13 lessons) Phase 04: Computer Vision (28 lessons) Phase 05: NLP (29 lessons) Phase 06: Speech & Audio (17 lessons) Phase 07: Transformers Deep Dive (14 lessons) Phase 08: Generative AI (14 lessons) Phase 09: Reinforcement Learning (12 lessons) Phase 10: LLMs from Scratch (22 lessons) Phase 11: LLM Engineering (15 lessons) Phase 12: Multimodal AI (25 lessons) Phase 13: Tools & Protocols (23 lessons) Phase 14: Agent Engineering (42 lessons) Phase 15: Autonomous Systems (22 lessons) Phase 16: Multi-Agent & Swarms (25 lessons) Phase 17: Infrastructure & Production (28 lessons) Phase 18: Ethics, Safety & Alignment (30 lessons) Phase 19: Capstone Projects (85 lessons)

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If I had 6 months to become an ML Engineer. I'd do this. Stage 1: Python Data Engineering pandas, numpy, SQL, Parquet/Arrow, API ingestion, data validation, pipeline orchestration. Stage 2: ML Fundamentals Statistics Linear algebra, probability, bias/variance, supervised/unsupervised learning, evaluation metrics. Stage 3: Deep Learning Frameworks PyTorch, training loops, backprop, CNNs/Transformers, optimizers, learning rate schedulers. Stage 4: Feature Stores Pipelines Feature engineering, preprocessing, data versioning, DVC, Airflow/Prefect, dbt integration. Stage 5: Experiment Tracking Tuning MLflow/Weights & Biases, hyperparameter optimization, cross-validation, reproducibility, model registry. Stage 6: Model Deployment Serving FastAPI, Docker, model registries, batch vs real-time inference, REST/gRPC endpoints, scaling. Stage 7: LLM Integration GenAI RAG pipelines, fine-tuning (LoRA/QLoRA), prompt engineering, embedding models, vector databases. Stage 8: MLOps CI/CD GitHub Actions, automated testing, model validation gates, continuous training, deployment strategies. Stage 9: Monitoring Drift Detection Data/concept drift, performance metrics, structured logging, alerting, automated retraining triggers. Stage 10: Infrastructure Cloud Scale AWS/GCP ML stacks, distributed training, GPU orchestration, Kubernetes, autoscaling, cost tracking. Stage 11: Open Source Portfolio Ship end-to-end ML systems publicly, write architecture docs, record demos, publish benchmarks. Stage 12: Apply ML Engineer, MLOps Engineer, AI Infrastructure Engineer, Data Science Engineering roles. Most people stay stuck watching tutorials. Builders get hired.

When life gets hard, watch this scene 1000 times.

Be a world-class BACKEND ENGINEER once you complete watching all these courses: