B @codingisezy

SYSTEM DESIGN INTERVIEW Weak candidate: - Jumps to Kafka, Redis, microservices - Draws 12 boxes in 4 minutes - Never asks QPS, data size, or latency - Says “we’ll shard it” for 10K users - Ignores failure cases High-value candidate:

If you are preparing for System Design Interviews, I have prepared this 13-step guide. SAVE THIS:

This is the best site on the internet to learn how LLMs actually work. Free. Completely. 0xkato.xyz/how-llms-actua… Bookmark this site. Then read this ↓

Rahul @sairahul1

2 weeks ago

Most developers are learning AI wrong You don't need another prompt engineering course You need to learn how production AI agents actually work - orchestration, RAG, evals, context engineering, inference, and more I put together the entire course here: x.com/i/article/2093…

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SOFTWARE ENGINEERS ONLY... Go GOD LEVEL in DSA in JUST 4 months. Crack any interviews easily:

System Design was hard until I learned these concepts:

working in tech can change your entire life feat: @kunalstwt

AI ENGINEERING is the FUTURE. If you want to go GOD LEVEL, learn these concepts NOW:

Andrew Ng co-founded Google Brain and taught half this industry its first AI course. "Prompting will be dead in 6 months. Loops and graphs are replacing it". In 100 minutes at Stanford he shows how to build agents that finish the work and sharpen themselves. LLMs -> Agents -> Loops -> Graphs The first 20 minutes go past where most $700 AI courses stop. Most people are still learning prompts while the leverage moved two layers up. Same model, same tokens, and the only thing that changes is the shape you run it in. Watch the lecture today, then save the full graph engineering guide below ↓

slash1s @slash1sol

4 weeks ago

x.com/i/article/2087…

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Fast inference makes a new class of real-time LLM applications possible. In our new short course, Fast LLM Inference with Cerebras, built in partnership with @Cerebras and taught by @zhennydez, @duerr_seb, and @MilksandMatcha, you'll build them on the Wafer-Scale Engine, where a model's weights sit on-chip and tokens come out several times faster than a typical GPU setup. You'll build a webpage that personalizes itself as users interact with it, assemble a multi-tool workflow that analyzes market signals in one response, and adopt habits for cleaner agentic coding with Codex. Enroll for free: hubs.la/Q04pypry0

New: A map of the most important skills in AI Engineering. x.com/i/article/2088…

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

From 1.84LPA to 75LPA in 9 years. No BTech. Passed out from a Tier-3 college. This is how he achieved it:

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Don't waste 2 years learning to become an AI agentic engineer in 2026. Andrew Ng, the godfather of AI, gave the complete playbook to become one from scratch. 1 hour course. Free: • 00:00 - AI agent basics • 12:12 - AI Agentic workflows & design patterns • 53:27 - Practical tips for building AI agents • 1:20:30 - self-improving AI agent loops • 1:30:19 - multi-agent AI systems I watched it last night. Halfway through, I realized I could get into Anthropic in weeks, not years. Bookmark now. Watch it. Then build your own AI agent.

This repo is a goldmine if you are preparing forn Systemd Design Interviews github.com/liquidslr/syst…

As a SOFTWARE ENGINEER, are you afraid of DSA? BOOKMARK THIS THREAD. Practice Data Structures and Algorithms in the EASIEST and FASTEST way.

Google SDE Roadmap (3 to 4 Months) 📌 Everything I'd focus on if my goal was Google. 1. DSA (40%) NeetCode 150: neetcode.io LeetCode Google Tag: leetcode.com/company/google/ Tech Interview Handbook: techinterviewhandbook.org 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: bytebytego.com System Design Primer: github.com/donnemartin/sy… Designing Data Intensive Applications: dataintensive.net 3. Core CS (10%) Master OS, DBMS, Computer Networks, and OOP. OSTEP: pages.cs.wisc.edu/~remzi/OSTEP/ GeeksforGeeks: geeksforgeeks.org 4. Low Level Design (10%) Refactoring Guru: refactoring.guru 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: pramp.com Interviewing.io: interviewing.io Exponent: tryexponent.com 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: overleaf.com/latex/template… 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: developers.googleblog.com Google Testing Blog: testing.googleblog.com Google Research Blog: research.google/blog/ 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.

𝗗𝗦𝗔 𝗶𝘀 𝗼𝘃𝗲𝗿𝘄𝗵𝗲𝗹𝗺𝗲𝗱 𝘄𝗶𝘁𝗵 "𝗖𝗵𝗲𝗮𝘁 𝗦𝗵𝗲𝗲𝘁𝘀" 𝘁𝗵𝗮𝘁 𝗽𝗿𝗼𝗺𝗶𝘀𝗲 𝗺𝗮𝘀𝘁𝗲𝗿𝘆 𝗶𝗻 15 𝗱𝗮𝘆𝘀.  If you have a full-time job, that is a lie. I’ve revamped the ultimate "DSA for Working Professionals" roadmap. Realistic timeline: 3.5 months. Effort: 2-3 hours/day. Goal: FAANG Ready. Here is the complete Week 1-15 breakdown.

Nobody warned me the final boss was Kubernetes.

Want to become a Software Engineer at NVIDIA? Here's a free roadmap. ━━━━━━━━━━━━━━━━━━━━ 1. Learn C++ NVIDIA uses C++ extensively for high-performance systems. 📚 Learn C++ learncpp.com ━━━━━━━━━━━━━━━━━━━━ 2. Master Data Structures & Algorithms 📚 Striver A2Z takeuforward.org/strivers-a2z-d… 📚 NeetCode Roadmap neetcode.io/roadmap Practice: leetcode.com ━━━━━━━━━━━━━━━━━━━━ 3. Learn Computer Science Fundamentals Operating Systems pages.cs.wisc.edu/~remzi/OSTEP/ Computer Networks gaia.cs.umass.edu/kurose_ross/ Database Systems postgresql.org/docs/ ━━━━━━━━━━━━━━━━━━━━ 4. Learn System Design System Design Primer github.com/donnemartin/sy… ByteByteGo bytebytego.com ━━━━━━━━━━━━━━━━━━━━ 5. Learn Linux & Git GitHub Skills skills.github.com Learn Git Branching learngitbranching.js.org Linux Journey linuxjourney.com ━━━━━━━━━━━━━━━━━━━━ 6. Learn CUDA (Huge Advantage) CUDA Documentation docs.nvidia.com/cuda/ CUDA Samples github.com/NVIDIA/cuda-sa… CUDA Programming Guide docs.nvidia.com/cuda/cuda-c-pr… ━━━━━━━━━━━━━━━━━━━━ 7. Parallel Programming NVIDIA DLI Free Courses nvidia.com/en-us/training/ OpenMP openmp.org/resources/tuto… ━━━━━━━━━━━━━━━━━━━━ 8. Build Projects Ideas: • Image Processing with CUDA • Matrix Multiplication Optimizer • GPU Path Tracer • Mini Deep Learning Framework • Parallel File Compressor ━━━━━━━━━━━━━━━━━━━━ 9. Open Source CUDA Samples github.com/NVIDIA/cuda-sa… NVIDIA GitHub github.com/NVIDIA TensorRT github.com/NVIDIA/TensorRT RAPIDS github.com/rapidsai ━━━━━━━━━━━━━━━━━━━━ 10. AI / Deep Learning PyTorch pytorch.org/tutorials/ NVIDIA NGC catalog.ngc.nvidia.com TensorRT developer.nvidia.com/tensorrt ━━━━━━━━━━━━━━━━━━━━ 11. Resume Jake's Resume overleaf.com/latex/template… Resume Worded resumeworded.com ━━━━━━━━━━━━━━━━━━━━ 12. Apply NVIDIA Careers nvidia.com/en-us/about-nv… ━━━━━━━━━━━━━━━━━━━━ If your dream company is NVIDIA, don't just learn AI. Master C++, CUDA, parallel programming, and strong CS fundamentals. Bookmark this.

If I had 6 months to become an AI Engineer, I'd do this. Stage 1 — Programming Fundamentals (Weeks 1–2) Learn Python, Git, Linux, SQL, APIs, OOP, NumPy, and Pandas. Stage 2 — Math & ML Foundations (Weeks 3–4) Master linear algebra, probability, statistics, calculus, regression, classification, clustering, and evaluation metrics. Stage 3 — Deep Learning (Weeks 5–6) Learn neural networks, backpropagation, CNNs, RNNs, PyTorch, TensorFlow, and model training. Stage 4 — LLM Fundamentals (Weeks 7–8) Understand transformers, attention, tokens, embeddings, context windows, prompting, and function calling. Stage 5 — RAG & Vector Databases (Weeks 9–10) Learn embeddings, chunking, retrieval, reranking, LangChain, LlamaIndex, FAISS, Chroma, Pinecone, and Qdrant. Stage 6 — AI Agents (Weeks 11–12) Build agents using LangGraph, CrewAI, MCP, tool calling, memory, planning, and multi-agent workflows. Stage 7 — Fine-Tuning & Open Models (Weeks 13–14) Learn LoRA, QLoRA, PEFT, Unsloth, Hugging Face, model quantization, and inference optimization. Stage 8 — Multimodal AI (Weeks 15–16) Work with vision models, OCR, speech-to-text, text-to-speech, image generation, and document understanding. Stage 9 — Production AI Engineering (Weeks 17–20) Master FastAPI, Docker, Redis, PostgreSQL, authentication, caching, queues, monitoring, logging, and cloud deployment. Stage 10 — AI Evaluation & MLOps (Weeks 21–22) Learn LangSmith, W&B, hallucination testing, latency, cost optimization, CI/CD, Docker, Kubernetes, and model monitoring. Stage 11 — Build a Portfolio (Weeks 23–24) Ship: Production RAG Assistant AI Coding Agent Customer Support SaaS Multimodal Document Parser AI Research Agent AI Workflow Automation Deploy every project publicly with a live demo, README, and architecture diagram. Stage 12 — Job Preparation Practice Python, SQL, LeetCode basics, system design, ML concepts, LLM interviews, and AI engineering case studies. Apply for: +AI Engineer +GenAI Engineer +Machine Learning Engineer +Applied AI Engineer +AI Platform Engineer +AI Software Engineer Don't spend 6 months watching AI tutorials. Spend 6 months building AI systems people would actually use.