at this age? focus on your career & goals. save money & travel. we can no longer afford to waste time again. focus on things that matter in the long run. you will never be this young again.

somebody said "rockbottom is actually a trampoline" and i have never felt so alive

You’re not an AI Engineer until you understand these terms: • 🧠 Embeddings → Numerical meaning of text/data • 🔍 Vector DB → Similarity search storage • 📚 RAG → Retrieval-Augmented Generation • 🎯 Fine-Tuning → Task-specific model training • 🪶 LoRA → Lightweight fine-tuning method • ⚡ Quantization → Smaller/faster models • 🧵 Context Window → Model memory limit • 🔄 Function Calling → Structured tool usage • 🛡 Guardrails → Output constraints/safety • 📏 Eval Frameworks → Measure model quality • 🧮 Tokenization → How text becomes tokens • 🚀 KV Cache → Faster inference reuse • 🔥 Hallucination → Confident wrong output • 🪝 Prompt Chaining → Multi-step workflows Building demos is easy. Production AI is not.

AI engineers are printing money right now. But only if they know this: Most people are learning the wrong things. Courses won’t get you hired. Skills will. Here’s what actually pays in 2026: → Building end-to-end LLM systems (not just calling APIs) → Working with real data pipelines (cleaning, chunking, retrieval) → RAG that actually works in production (not tutorial-level demos) → Inference optimization (vLLM, batching, caching) → Evaluations (DeepEval, human feedback loops) → Agents (only where needed) (LangGraph > hype wrappers) What companies actually want: → Someone who can ship (not just experiment) → Someone who understands tradeoffs (latency vs cost vs quality) → Someone who can debug broken outputs (not blame the model) → Someone who thinks in systems (not prompts) The gap is simple: Most people are learning tools. Few people are building systems. That’s where the money is. If you’re learning AI right now: Stop collecting certificates. Start shipping projects.

Kobe Bryant once said: “I have nothing in common with lazy people who blame others for their lack of success. Great things come from hard work and perseverance. No excuses.”

Imagine looking back one day and realizing you were brave enough to chase the life you truly wanted and it worked.

12 GitHub repos to improve at AI engineering (categorized): 𝗟𝗟𝗠𝘀, 𝗥𝗔𝗚 & 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 1) Guides to build RAG, agents, vector search ↳ lucode.co/ai-developer-hub-o… 2) RAG patterns ↳ github.com/NirDiamant/RAG_Te… 3) E2E guide for building agents ↳ github.com/NirDiamant/agents… 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 4) Prompt engineering ↳ github.com/dair-ai/Prompt-En… 5) Hands-on ML deep learning ↳ github.com/ageron/handson-ml… 6) Neural networks ↳ github.com/karpathy/nn-zero-… 𝗖𝘂𝗿𝗮𝘁𝗲𝗱 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗟𝗶𝘀𝘁𝘀 7) Examples building with openAI ↳ github.com/openai/openai-coo… 8) Ready to run templates ↳ github.com/Shubhamsaboo/awes… 9) ML frameworks, libs, & software ↳ github.com/josephmisiti/awes… 10) Data Science resources ↳ github.com/academic/awesome-… 𝗛𝗮𝗻𝗱𝘀-𝗢𝗻 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 11) 100 projects ↳ github.com/patchy631/ai-engi… 12) E2E project with Claude Code ↳ github.com/shareAI-lab/learn… If you're serious about AI engineering, this repo is worth bookmarking (star it): lucode.co/ai-developer-hub-o… It’s not just theory; you’ll find: • Full AI apps and reference implementations • Jupyter notebooks for RAG, agents, and vector search • Practical guides on agent architecture and memory systems If you found this list useful, star the repo so you can come back to it later (and support more content like this) What other AI GitHub repos should be on the list? —— ♻️ Repost to help others learn AI engineering. 🙏 Thanks to @Oracle for sponsoring this post. ➕ Follow me ( Nikki Siapno ) to improve at AI engineering.

I'm in love with this sentence: “Forgive yourself for not knowing earlier what only time could teach.”

SOFTWARE ENGINEERS, This 24 minutes video will teach you how to actually prompt Claude. Not only Claude, basically any LLM. Anthropic's own team leading this workshop. Worth more than thousand bucks, Available FREE. Watch NOW. Bookmark for LATER.

Why is no one talking about this? @nvidia is offering around 80 AI models via hosted APIs absolutely for free. You get access to MiniMax M2.7, GLM 5.1, Kimi 2.5, DeepSeek 3.2, GPT-OSS-120B, Sarvam-M etc. This plugs straight into OpenClaude, OpenCode, Zed IDE, Hermes agent and even with Cursor IDE. Setup: – Grab API key: build.nvidia.com/models – base_url = "integrate.api.nvidia.com/v1" – api_key = "$NVIDIA_API_KEY" – select model (e.g. minimaxai/minimax-m2.7) If you’re building or experimenting, this is basically free inference. Lock in and start building today anon. Thank me later.

Best YouTube Channels To Crack Tech Interviews (2026) 1. DSA – NeetCode 2. LeetCode Patterns – Abdul Bari 3. System Design – Gaurav Sen 4. Mock Interviews – Pramp 5. FAANG Prep – Tech Dummies 6. Coding Rounds – Nick White 7. Behavioral – Jeff H Sipe 8. Problem Solving – Back To Back SWE 9. Deep DSA – Errichto 10. Interview Strategy – Exponent 11. Resume Career – Self Made Millennial 12. Real Interview Qs – Clément Mihailescu 13. Advanced DSA – William Lin 14. CS Basics – MIT OpenCourseWare

I’m in love with this sentence: “Discipline looks boring until you see what it builds.”

You can't dream beyond your level of exposure, that's what travelling and reading more gives you.

A small thread of JavaScript questions asked in 12 LPA interviews.

DAY 02 of #100DaysOfCode Here is my activity log : 🎯Problems Solved: 1- Merge Sorted Array 2- Count Pairs Whose Sum is Less than Target 3- Two Sum 4- Two Sum II - Input Array Is Sorted 5- 3Sum #DSA #LearningInPublic #TwoPointers #DSAinJava

DAY 01 of #100DaysOfCode Here is my activity log : Pattern: Two Pointers 🎯Problems Solved: 1. Valid Palindrome 2. Reverse a String 3. Squares of a Sorted Array 4. Valid Palindrome II 5. Valid Word Abbreviation #DSA #LearningInPublic #TwoPointers #DSAinJava

Today marks the initial day of my #100DaysOfCode where i will share my DSA preparation journey! I'm shifting my focus from traditional topic-wise learning to a Pattern-Based Approach. I'll be posting my daily progress here to stay accountable. #DSA #LearningInPublic #Java

Instead of watching a 2-hour movie, watch this masterclass on AI coding and thank me later.

I never relied on just one playlist/resource for DSA. In my 4 years of college I explored almost all the YT channels and this is what I felt (personally). For a well structured resource - Striver (best) Specifically DSA in java - kunal kushwaha Topic wise -> Arrays - Striver Linked List - striver Binary Search - striver Tree - Striver (best) Graph - Striver (best) Recursion - Aditya Verma Stack - Aditya Verma (best) Sliding Window - Aditya Verma Dynamic Programming - Aditya Verma (best) To track your progress with curated problems -> As a beginner -> striver's A2Z sheet (tuf) -> Love Babbar's 450 For interview purpose -> Striver's Blind 75/sde sheet (tuf) These all resources are my personal choices which I personally felt best. No biasness.