Hambira @DDevilhunter

Goldmine for AI Engineers! 📌 If you're learning AI, ML, LLMs, or AI agents, don't waste hours jumping between random tutorials. These are 10 repositories I'd actually keep bookmarked - from Python fundamentals to ML, LLMs, agents, and production AI. 1. Python - 100 Days jackfrued/Python-100-Days A 100-day Python learning path covering fundamentals, data analysis, web development, and more. GitHub: github.com/jackfrued/Pyth… 2. Generative AI for Beginners microsoft/generative-ai-for-beginners A practical introduction to building Generative AI applications. Covers: • LLM fundamentals • Prompt engineering • RAG • AI agents • Fine-tuning • AI application development GitHub: github.com/microsoft/gene… 3. LLMs From Scratch rasbt/LLMs-from-scratch Want to understand what's actually happening inside an LLM? Build one step by step. Covers: • Tokenization • Embeddings • Attention • Transformers • Training • Fine-tuning GitHub: github.com/rasbt/LLMs-fro… 4. Machine Learning for Beginners microsoft/ML-For-Beginners A structured 12-week, 26-lesson curriculum covering classical machine learning. A good starting point if you want ML fundamentals before jumping into LLMs. GitHub: github.com/microsoft/ML-F… 5. OpenAI Cookbook openai/openai-cookbook A collection of practical examples and guides for building applications with OpenAI models. Useful when you want to move from: Learning → Building GitHub: github.com/openai/openai-… 6. Stable Diffusion CompVis/stable-diffusion Interested in generative image models? This repository contains the original Stable Diffusion implementation and research code. GitHub: github.com/CompVis/stable… 7. AI Agents for Beginners microsoft/ai-agents-for-beginners A practical course for understanding and building AI agents. Covers: • Agentic AI • RAG • Agent frameworks • Tool use • Multi-agent systems GitHub: github.com/microsoft/ai-a… 8. AI for Beginners microsoft/AI-For-Beginners A structured 12-week, 24-lesson introduction to AI. Covers: • Neural networks • Computer vision • NLP • Deep learning • Classical AI GitHub: github.com/microsoft/AI-F… 9. LLM App pathwaycom/llm-app Focused on building practical LLM applications. Explore: • RAG • AI pipelines • Enterprise search • Real-time data • Vector search GitHub: github.com/pathwaycom/llm… 10. Segment Anything facebookresearch/segment-anything A foundation model for promptable image segmentation. Worth exploring if you're interested in computer vision and multimodal AI. GitHub: github.com/facebookresear… Don't bookmark all 10 and forget about them. Pick based on where you are: Python → Python-100-Days ML → ML-For-Beginners AI Fundamentals → AI-For-Beginners LLMs → LLMs-from-scratch Generative AI → Generative-AI-for-Beginners Agents → AI-Agents-for-Beginners Building → OpenAI Cookbook / LLM App Computer Vision → Segment Anything Pick one. Build something. Then move to the next.

Transformer Architecture Explained in 3 ways here: - Encoder only - Deconder only - Encoder - Decoder both

I learned basic system design. ↓ Drew a client, server, and database. ↓ "Looks scalable enough." ↓ Added 1 million users to the question. ↓ Server died. ↓ Added another server. ↓ "Wait, how does traffic know where to go?" ↓ Learned load balancers. ↓ Database became the bottleneck. ↓ Added read replicas. ↓ Writes were still slow. ↓ Learned indexing. ↓ Added a cache because everyone kept saying "use Redis." ↓ "Wait, what happens when the cache is stale?" ↓ Learned cache invalidation. ↓ One service started taking 8 seconds. ↓ Added a message queue. ↓ "Wait, what if the message gets processed twice?" ↓ Learned idempotency. ↓ Users started uploading huge files. ↓ Stopped storing them in the database. ↓ Learned object storage and CDNs. ↓ One region went down. ↓ Learned replication and failover. ↓ Realized every new box creates another problem. ↓ Finally understood system design. Jokes aside. Beginner system design feels overwhelming because people throw around terms like sharding, Kafka, Redis, CDNs, queues, replicas, and microservices. You do not need to memorize all of them. Start with one simple system: Client → Server → Database Then keep asking: "What breaks if traffic becomes 10x?"

🚨11 Software Engineering principles I wish I understood earlier: 𝟏. 𝐒𝐎𝐋𝐈𝐃: Better Object-Oriented Design 𝟐. 𝐃𝐑𝐘: Avoid Repetition 𝟑. 𝐊𝐈𝐒𝐒: Keep Things Simple 𝟒. 𝐘𝐀𝐆𝐍𝐈: Don't Build Unnecessary Features 𝟓. 𝐒𝐑𝐏: One Responsibility 𝟔. 𝐎𝐩𝐞𝐧/𝐂𝐥𝐨𝐬𝐞𝐝: Extend Without Breaking 𝟕. 𝐃𝐞𝐩𝐞𝐧𝐝𝐞𝐧𝐜𝐲 𝐈𝐧𝐯𝐞𝐫𝐬𝐢𝐨𝐧: Loose Coupling 𝟖. 𝐂𝐨𝐦𝐩𝐨𝐬𝐢𝐭𝐢𝐨𝐧: Build Flexible Components 𝟗. 𝐒𝐞𝐩𝐚𝐫𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐂𝐨𝐧𝐜𝐞𝐫𝐧𝐬: Cleaner Design 𝟏𝟎. 𝐅𝐚𝐢𝐥 𝐅𝐚𝐬𝐭: Detect Problems Early 𝟏𝟏. 𝐌𝐞𝐚𝐬𝐮𝐫𝐞 𝐅𝐢𝐫𝐬𝐭: Optimize What Actually Matters

40 graph you must practice for sde interviews 1. Find if Path Exists in Graph 2. Number of Islands 3. Flood Fill 4. Connected Components in Undirected Graph 5. Clone Graph 6. BFS of Graph 7. DFS of Graph 8. Rotten Oranges 9. Surrounded Regions 10. Number of Provinces 11. Detect Cycle in Undirected Graph 12. Detect Cycle in Directed Graph 13. Bipartite Graph Check 14. Topological Sort (DFS) 15. Topological Sort (Kahn's BFS) 16. Course Schedule I 17. Course Schedule II 18. Number of Islands II (Union Find) 19. Dijkstra's Shortest Path 20. Bellman-Ford Algorithm 21. Floyd-Warshall Algorithm 22. Cheapest Flights Within K Stops 23. Network Delay Time 24. Path With Minimum Effort 25. Swim in Rising Water 26. Minimum Spanning Tree (Prim's) 27. Minimum Spanning Tree (Kruskal's) 28. Redundant Connection 29. Accounts Merge 30. Strongly Connected Components (Kosaraju's) 31. Strongly Connected Components (Tarjan's) 32. Bridge Finding in Graph 33. Articulation Points 34. Word Ladder I 35. Word Ladder II 36. Alien Dictionary 37. Critical Connections in a Network 38. Minimum Cost to Connect All Points 39. Bus Routes 40. Shortest Path in a Grid with Obstacles Elimination

Everyone's suddenly talking about 𝗵𝗮𝗿𝗻𝗲𝘀𝘀 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴. Weren't we just getting the hang of context engineering? 😵‍💫 Here's the 3-step evolution of how we work with AI in 2026, and why each stage matters: 𝟭. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 It's about what you 𝘴𝘢𝘺 to the model. The practice of figuring out what instructions/framing/examples a model needs to nail a task or answer a question in your domain. Do this right and the model does in one shot what used to take five. But it can only work with what you put in that one ask. 𝟮. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 It's about what you 𝘣𝘳𝘪𝘯𝘨 to the model. Your context window is finite (200K tokens, maybe 1M on the smartest models). Every token you load upfront (memory, AGENTS.md, skills, docs) is budget spent. Context engineering is the practice of deciding what earns a spot in that window, and what stays out. Do it well and the model feels 10x smarter. Overpack, and it drowns. 𝟯. 𝗛𝗮𝗿𝗻𝗲𝘀𝘀 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 It's about what you build 𝘢𝘳𝘰𝘶𝘯𝘥 the model. A model on its own does one thing: take an input → return an output → stop. Harness engineering is the practice of building the system that runs it in a loop, and gives it tools to act, memory to remember, and guardrails to stay on track. → Prompt and context are what you hand the model for 𝘰𝘯𝘦 turn. → The harness is what strings 𝘩𝘶𝘯𝘥𝘳𝘦𝘥𝘴 of them together into an agent that actually goes and does the work. That's why everyone's so hyped about harness engineering. In only a few years, we've gone from 𝘵𝘢𝘭𝘬𝘪𝘯𝘨 to a model → 𝘧𝘦𝘦𝘥𝘪𝘯𝘨 a model → 𝘣𝘶𝘪𝘭𝘥𝘪𝘯𝘨 𝘢 𝘴𝘺𝘴𝘵𝘦𝘮 around it. Which stage is your team at? 👇

As a System Design Engineer, It will be good if you have an understanding of the below 40 System Design Case Studies👇 1. URL Shortener (TinyURL / Bitly) 2. Pastebin 3. Rate Limiter 4. Distributed Cache 5. Load Balancer 6. API Gateway 7. Notification System 8. Web Crawler 9. Search Autocomplete 10. Distributed Key-Value Store 11. Twitter/X Feed 12. Instagram 13. Facebook News Feed 14. YouTube 15. Netflix 16. Video Streaming Platform 17. WhatsApp / Real-Time Messaging 18. Uber / Ride-Sharing System 19. Google Maps 20. Food Delivery System 21. Amazon / E-Commerce Platform 22. Payment Processing System 23. Ticket Booking System 24. Hotel Booking System 25. Flight Booking System 26. Online Auction System 27. Distributed File Storage (Dropbox / Google Drive) 28. Cloud Storage System 29. Google Search Engine 30. Recommendation System 31. Ad Serving System 32. Metrics and Monitoring System 33. Logging and Log Aggregation System 34. Distributed Job Scheduler 35. Message Queue / Event Streaming Platform 36. Real-Time Collaboration System 37. Code Deployment Platform 38. Video Conferencing System 39. Ride-Sharing Location Tracking System 40. Distributed Transaction and Order Management System 📘 Grab System Design Case Studies Handbook: codewithdhanian.gumroad.com/l/yetjrf These case studies provide a strong foundation for learning how to design scalable, reliable, fault-tolerant, and high-performance distributed systems used by modern technology companies.

LLM fine-tuning techniques I'd learn if I were to customize them: Bookmark this. 1. LoRA 2. QLoRA 3. Prefix Tuning 4. Adapter Tuning 5. Instruction Tuning 6. P-Tuning 7. BitFit 8. Soft Prompts 9. RLHF 10. RLAIF 11. DPO (Direct Preference Optimization) 12. GRPO (Group Relative Policy Optimization) 13. RLAIF (RL with AI Feedback) 14. Multi-Task Fine-Tuning 15. Federated Fine-Tuning Since we're talking about fine-tuning, I wrote a full breakdown on fine-tuning LLMs with RL in 2026. Including how to skip manual reward engineering with automatic LLM-graded rewards. And this is done using a 100% open-source solution: github.com/openpipe/art The article is quoted below.

Akshay 🚀 @akshay_pachaar

8 months ago

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“Do Hard Things” book summary in 4k

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Computer Networking 101: Sockets 🛠️ The best way to understand what sockets are and how they work is to write a few simple client-server programs. The good news is that it's only a few lines of Python or Go. Or if you feel courageous, try C - the code will be more verbose, but it's as close as it gets to the actual system API. A few problems to practice (in a friendly, controlled environment with helpful hints): - Write a simple TCP Echo server labs.iximiuz.com/challenges/wri… - (simpler) Write a client for a telemetry server labs.iximiuz.com/challenges/wri… - (harder) Write a client for a chat server labs.iximiuz.com/challenges/wri… - Make one server work with both TCP and Unix sockets labs.iximiuz.com/challenges/wri… Happy hacking!

Best accounts to follow from each frontier lab to stay constantly up to date Anthropic @karpathy - must-follow account for AI; recently joined Anthropic @bcherny - Claude Code creator, always shares great tips @trq212 - also a Claude Code developer; writes amazing articles on CC OpenAI @polynoamial - works on reasoning research, shares a lot of technical details @gabriel1 - Sora developer, great career path @jxnlco - works on dev experience, shares a lot about Codex Google AI @OfficialLoganK - all the major Google Gemini and AI Studio updates @ammaar - product and design; shares great things about vibe-coding in Google AI Studio @fofrAI - cool use cases for generative models Cursor @leerob - the loudest voice behind Cursor updates @ericzakariasson - shares great insights on using Cursor @mntruell - Cursor’s CEO; major releases and usage updates xAI @milichab - recently joined xAI, shares updates on Grok @skcd42 - also covers major Grok releases @ai_explorer25 - covers all ai content and free resources

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手描きでDMCアニメを作ってみた

15 Must-Know Software Design Patterns: 1. Singleton 2. Factory Method 3. Builder 4. Adapter 5. Decorator 6. Facade 7. Proxy 8. Composite 9. Observer 10. Strategy 11. Command 12. Iterator 13. State 14. Template Method 15. Chain of Responsibility ♻️ Repost to help others in your network

20 must-know DSA patterns for coding interviews: 1. Prefix Sum 2. Two Pointers 3. Sliding Window 4. Fast & Slow Pointers 5. LinkedList In-place Reversal 6. Frequency Counting 7. Monotonic Stack 8. Bit Manipulation 9. Top ‘K’ Elements 10. Overlapping Intervals 11. Binary Search Variants 12. Binary Tree Traversal 13. Depth-First Search (DFS) 14. Breadth-First Search (BFS) 15. Shortest Path 16. Matrix Traversal 17. Backtracking 18. Prefix Search (Trie) 19. Greedy 20. Dynamic Programming Patterns ♻️ Repost to help others master coding interviews

MCP vs Skills

How to Tell a Story book summary in 4k

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Someone just dropped a 9-layer production AI architecture and it's the most honest breakdown I've seen. services/ - RAG pipeline, semantic cache, memory, query rewriter, router. Not one file. Five. agents/ - document grader, decomposer, adaptive router. Self-correcting by design. prompts/ - versioned, typed, registered. Never hardcoded. security/ - input, content, output. Three guards not one. evaluation/ - golden dataset, offline eval, online monitor. Most people skip this entire layer and ship blind. observability/ - per-stage tracing, feedback linked to traces, cost per query. .claude/ - agent context so your AI coding assistant knows the codebase before it touches a file. The demo is one file. Production is this.

Mastering System Design: 10 Key Pillars Every Engineer Should Know Designing scalable, reliable, and efficient systems starts with understanding these 10 core pillars that form the foundation of modern system architecture. 1. APIs & Security Covers REST, gRPC, GraphQL, rate limiting, authentication methods (JWT, OAuth), TLS/SSL, and protection against DDoS and MITM attacks. 2. Caching Improves performance and reduces load with strategies like LRU, session persistence, and multi-level caching. 3. Proxies Includes reverse and forward proxies, load balancing methods, and advanced routing (e.g., path-based, least outstanding requests). 4. Messaging Ensures smooth inter-service communication with queues, pub/sub models, polling, streaming, and idempotent processing. 5. Features Clarifies system goals and constraints, aligning stakeholder needs while balancing consistency, availability, and tradeoffs. 6. Users Considers user types, access patterns, usage spikes, accessibility, and scalability based on demographics and behavior. 7. Data Model Explores relational vs NoSQL databases, indexing, horizontal scaling, sharding, ETL pipelines, and distributed query models like MapReduce. 8. Geography & Latency Tackles region-aware system performance using CDNs, DNS, network latency optimization, and response-time tuning. 9. Server Capacity Focuses on compute and storage provisioning (CPU, RAM, SSD), scaling strategies, parallel processing, and partitioning. 10. Availability & Microservices Addresses fault tolerance, microservices orchestration, leader election, redundancy, observability, and service mesh principles. Use this blueprint to build high-performance architectures that are scalable, secure, and production-ready. Whether you're preparing for system design interviews or building the next big product - these pillars are your foundation.

Someone on GitHub uploaded System Design Interview Notes 😭 Now I don't need to Spend $1000 on Expensive courses 😭🙂

Everyone is still prompting claude a few teams are structuring it and the difference is insane once you add: CLAUDE.md → project memory skills/ → auto-activated workflows agents/ → parallel workers hooks → lifecycle automation MCP → external tools settings.json → permissions & guardrails Claude stops being a chatbot and starts acting like a senior engineer that knows your repo it remembers conventions runs tests before commits refactors in your style calls tools automatically splits work across agents this is not prompting anymore this is giving Claude a brain + muscle + memory the teams using structured Claude will ship 10x faster because they’re not asking AI for answers they’re assigning it responsibilities bookmark this before everyone discovers CLAUDE.md RT if you’re building with Claude Code already