Mohan @itismohan

Classic Operating Systems: From Batch Processing to Distributed Systems The quote from the book's preface will lay the groundwork for what to expect in this book - a good reminder of how we have arrived here today, standing on the shoulders of the legends! Highly recommended to keep it as a reference! "In operating systems courses, you learn useful principles, but very little about the difficult art of software design. So I decided to go back to the masters who made the major discoveries in operating systems and put some of their best papers together in a book, and call it Classic Operating Systems: From Batch Processing to Distributed Systems. Well, here is that book. The papers illustrate the breakthroughs in operating system technology from the 1950s to the 1990s. All of them are written by the pioneers who designed these systems." - Per Brinch Hansen.

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The Ultimate Step-By-Step LLM Engineering Projects Roadmap (2026 Edition) - Build a tokenizer - Learn embeddings - Implement RoPE / ALiBi - Hand-wire attention - Build MHA - Build a Transformer block - Train a mini-former - Compare objectives - Build sampling - Speculative decoding - KV cache - MQA / GQA / MLA - Long context - FlashAttention - Hardware budgets - Toy MoE - Sparse model trade-offs - State-space / linear attention - Diffusion language models - Data pipelines - Synthetic data - Scaling laws - SFT / DPO / RLHF / GRPO - Quantization - Serving stacks - Eval harnesses - RAG - Tool use / agents - Vision-language adapters - Interpretability - Red-team suite - Full capstone model system One request: Choose an Opensource AI lab when you make it Opensource is where humanity gets to keep the tools DM me when you've made it ;)

Ahmad @TheAhmadOsman

a month ago

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Here are 20 companies that will dominate the AI age: 1. Micron While AI runs on GPUs, they are only as powerful as the memory that feeds them. Every new generation of AI models demands dramatically more memory, creating a supply crunch as demand far outpaces industry capacity. As AI accelerators become larger and more memory-intensive, $MU is emerging as one of the most important suppliers. Memory will determine who wins the AI race, and Micron is positioned at the center of that revolution.

$NVDA SOLD $ARM & $APLD IN 2026, AND BETTING SO BIG ON THESE COMPANIES: 1. $GLW | $3.2B 2. $IREN | Secured a 5y right to invest $2.1B + Selected for $NVDA DSX AI Infrastructure 3. $CRWV | $2B 4. $NBIS | $2B 5. $MRVL | $2B + Jensen’s next trillion dollar company. 6. $LITE | $2B 7. $COHR | $2B

The Tech Investor @TheTechInvest

5 days ago

The Most Attractive Giants of the Market 2027 EV/FCF Multiple 1. $MELI 7x | E-commerce + Fintech 2. $SAMSUNG 7.3x | Memory 3. $SK Hynix 8.3x | Memory 4. $CRM 10.4x | Software 5. $UBER 11.8x | Delivery +Mobility 6. $NOW 13.4x | Software 7. $MU 15.6x | Memory 8. $SNDK 16.4x

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UPDATED FIB LEVELS FOR POPULAR STOCKS Space • $RKLB $92 • $ASTS $90 • $PL $32 • $RDW $12 Quantum • $IONQ $55 • $RGTI $19 • $QBTS $20 • $INFQ $13 Drones • $ONDS $9 • $AVAV $169 • $KTOS $52 • $MRCY $104 Nuclear • $OKLO $50 • $UUUU $15 • $GEV $906 • $LEU $182 Photonics • $AAOI $160 • $LITE $825 • $COHR $369 • $AEHR $110 CPU Bottleneck • $AMD $512 • $INTC $125 • $ARM $420 • $AMKR $83 Physical AI • $TSLA $382 • $AMZN $230 • $GOOGL $357 • $ISRG $397 AI Utilities • $IREN $56 • $NBIS $206 • $CIFR $17 • $CRWV $111 AI Power • $EOSE $7 • $BE $265 • $NVTS $24 • $VRT $282 AI Hardware • $NVDA $192 • $TSM $437 • $ASML $1,732 • $MU $1,013 AI Applications • $PLTR $121 • $NOW $95 • $CRWD $624 • $SNOW $181 AI Connectivity • $AVGO $368 • $ALAB $372 • $CRDO $262 • $MRVL $292

You only need 7 AWS services to get hired: > EC2 - run apps > S3 - store files > IAM - manage access > RDS - databases > Lambda - run code without servers > CloudWatch - monitor > VPC - network setup Forget the 200+. Master these 7 and get ahead of 90%

Day 26/100 of Learning System Design. Design Dropbox/Google Drive – File Sync and Storage. Grab the System Design Ebook: codewithdhanian.gumroad.com/l/urcjee

Dhanian 🗯️ @e_opore

7 days ago

Day 25/100 of Learning System Design. Design Facebook – Social Graph, Friend Requests, and Posts. Grab the System Design Ebook: codewithdhanian.gumroad.com/l/urcjee

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Notes for system design interviews based on 'An Insider's Guide' github.com/liquidslr/syst…

AI Engineering from Scratch. 503 lessons. 20 phases. 320 hours. github.com/rohitg00/ai-en… 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)

I built this website to help people who want to prep for systems design interviews

Three filters to get our life right than wrong 👇🏽👇🏽 - Choose Optimism - Choose to work on hard things - Choose to do the things that excites #ThoughtForTheDay

Nobody explained AI this clearly. So I made this. 🔥 If you've been confused by LLM, RAG, AI Agents and MCP — stop overthinking it. Think of AI like a human body 👇 🩷 LLM = The Brain Reasones through problems. Generates language and logic. Without it? No AI. Period. 🟠 RAG = Brain + Books Gives the brain access to real knowledge. Eliminates hallucinations completely. Without it? AI just guesses. Every time. 🩵 AI Agent = Brain + Hands Doesn't just think — it ACTS. Executes tasks. Uses tools. Remembers context. Without it? AI talks. Never delivers. 🟡 MCP = The Nervous System Connects LLM + RAG + Agent into ONE system. Without it? All parts work in silos. Broken. 💡 The 1-line summary nobody gives you: LLM thinks RAG knows Agent acts MCP connects This is the AI stack. Learn it. Own it. Build with it. 📌 I break down AI concepts like this every week in plain English — plus how to BUILD and monetize them. Free. No jargon. Just clarity. 👉 Subscribe to The AI Revenue Playbook: avsl.beehiiv.com 🔖 Save this — it's the clearest AI explainer you'll find. 💬 Which layer surprised you most? Drop it below. 🔁 Share this with someone still confused about AI. Follow @AiswaryaVenkit1 for more such insights!!

Time to revisit the first principles

This is so wholesome & cute right??? ❤️

$CRDO $MRVL $AAOI $MU $GOOG

InvestmentGuru @InvestmentGuru_

2 weeks ago

10 Stocks Built to Create Generational Wealth The AI infrastructure supercycle is rewriting the rules. Here’s where I’d position across the full stack — from the chip to the cloud to the platform layer. The Connective Tissue → $CRDO — SerDes interconnect is the bloodstream of

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$NVDA CEO Jensen Huang indirectly told everyone to buy energy stocks. This is the largest AI bottleneck of the GPU armsrace. Here are 5 stocks that could 10x in the next few years:

5 NUCLEAR ENERGY stocks that could 10x by 2030 ☢️🚀

If machine learning only clicks when you build the pieces yourself, this is a useful repo to keep around. Build your own X - Machine Learning is a public build-from-scratch machine learning tutorial index for learners and builders who want implementation practice. It helps you move from reading algorithms to coding them by organizing ML topics into categories and linking several NumPy examples for core algorithms. Key features: • Build-from-scratch roadmap – starts at linear/logistic regression and KNN, then expands into deep learning and LLM topics • Core Python examples – includes NumPy code for regression, KNN, loss functions, and activation functions • Category navigation – groups ideas across recommendation systems, computer vision, NLP, forecasting, anomaly detection, and more • Implementation-first learning – matches the README’s goal of building ML pieces from scratch • Ongoing tutorial list – README says it will keep adding new tutorials It’s open-source (Apache-2.0 license). Link in the reply 👇