AlexP @alexdocoding

Instead of watching an hour of Netflix, watch this 2 hour hour Stanford lecture will teach you more about how LLMs like ChatGPT and Claude are built than most people working at top AI companies learn in their entire careers.

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Thyroid cancer used to hit people in their 60s. Now it's the #1 most common cancer in young adults aged 16-33. According to the National Cancer Institute, diagnoses have tripled since the 1990s. Here are 8 things destroying your thyroid without you realizing: 1. Tap water

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📊 Module 3 of Data Engineering Zoomcamp done! - BigQuery & GCS - External vs materialized tables - Partitioning & clustering - Query optimization My solution: shorturl.at/dY4ay Free course by @DataTalksClub: github.com/DataTalksClub/…

@antmillionsbot You can never give me enough money unless you give me skills how to earn more money. At least I don't have to worry about when it comes to money at all.

🐳 Module 1 of Data Engineering Zoomcamp done! ✅Docker containers ✅Postgres & SQL ✅Terraform & GCP ✅NYC taxi data pipeline My solution: github.com/Alexpeain/Data… Free course by @DataTalksClub: github.com/DataTalksClub/…

Time to open a new ch 📖 ​After years in Backend Development, I’m officially diving into AI Engineering. The fundamentals of the web are my foundation, but it’s time to learn how to build "intelligent" systems. ​Starting withFoundation Models. Day 1 starts today. #AIEngineering

2026 first activity of the year. Gonna participate in Data engineering courses host by Datatalk . The cohort is going to start soon on 12,Jan. github.com/DataTalksClub/…

Become AI Engineer for Free -> Complete GitHub AI/ML resources! Programming & Fundamentals 1- github.com/practical-tuto… – Curated list of project-based tutorials (includes Python basics + hands-on programming projects) 2- github.com/ageron/handson… – Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow (code from the bestselling book, free notebooks) Machine Learning & Deep Learning 3- github.com/josephmisiti/a… – Curated list of awesome Machine Learning frameworks, libraries, and resources (by language) 4- github.com/ChristosChrist… – Curated list of awesome Deep Learning tutorials, projects, books, and communities 5- github.com/microsoft/ML-F… – Microsoft's free 12-week ML curriculum with lessons and quizzes Generative AI & LLMs 6- github.com/krishnaik06/Ro… – Comprehensive roadmap for Generative AI with free resources and projects 7- github.com/huggingface/tr… – State-of-the-art library for NLP, LLMs, and transformers (examples, models, and tutorials) Hands-on Projects & Labs 8- github.com/ashishpatel26/… – 500+ AI/ML/DL/CV/NLP projects with free code 9- github.com/DataTalksClub/… – Free ML engineering bootcamp with practical projects and deployment MLOps & Production 10- github.com/DataTalksClub/… – Free MLOps course covering experimentation, deployment, monitoring, and CI/CD 11- github.com/visenger/aweso… – Curated list of awesome MLOps tools, platforms, and references Roadmaps & Interviews 12- github.com/Pandeycoder/AI… – AI Engineer Roadmap 2025 – Learn AI, ML, Deep Learning, MLOps, and Big Data with projects & resources 13- github.com/krishnaik06/Co… – Comprehensive AI roadmap (Data Science, Generative AI, Agentic AI) with week-by-week plans and projects ✅Like & Repost for others!

Machine Learning from Stanford- by Andrew Ng 227-pages lecture notes for the course CS229 is now AVAILABLE. Reply if you want to get a copy.

10 GitHub repos that will level up your AI Agent skills (SAVE THIS)🔖 1. Hands-On Large Language Models Complete code notebooks from basics to advanced fine-tuning. 🔗 lnkd.in/dxaVF86w 2. AI Agents for Beginners A free 11-part intro course to build your first agents. 🔗 lnkd.in/dHvTmJnv 3. GenAI Agents Tutorials and code for building generative AI agents. 🔗 lnkd.in/dEt72MEy 4. Made with ML Learn to design, build, and deploy real ML apps. 🔗 lnkd.in/d2dMACMj 5. Prompt Engineering Guide Learn to write powerful and effective prompts. 🔗 lnkd.in/dBUCsVJ8 6. Hands-On AI Engineering Practical LLM-powered apps and agent examples. 🔗 lnkd.in/dgQtRyk7 7. Awesome Generative AI Guide Curated hub for genAI research and tools. 🔗 lnkd.in/dJ8gxp3a 8. Designing Machine Learning Systems Summaries and resources from the popular ML systems book. 🔗 lnkd.in/dEx8sQJK 9. ML for Beginners (Microsoft) Free beginner-friendly ML curriculum. 🔗 lnkd.in/dBj3BAEY 10. LLM Course Roadmaps and hands-on notebooks to build LLM apps. 🔗 lnkd.in/diZgGACG I'm curating 50+ AI Agent resources on my profile worth checking out 👋

I love this figure from Anthropic's new talk on "Skills > Agents". Here are my notes: The more skills you build, the more useful Claude Code gets. And it makes perfect sense. Procedural knowledge and continuous learning for the win! Skills essentially are the way you make Claude Code more knowledgeable over time. This is why I had argued that Skills is a good name for this functionality. Claude Code acquires new capabilities from domain experts (they are the ones building skills). Claude Code can evolve the skills as needed and forget the ones it doesn't need anymore. It's a collaborative effort, which can easily be expanded to entire teams, communities, and orgs (via plugins). Skills are particularly useful for workflows where information and requirements constantly change. Finance, code, science, and human-in-the-loop workflows are all great use cases for Skills. You can build new Skills using the built-in skill creation tool, so you are always building new skills with all the best practices. Or you can do what I did, which is build my own skill creator to build custom skills catered to the work I do. Just more levels of customization that Skills also enables. Skills flexibility enables future capabilities to be easily integrated everywhere. Competitors don't have anything remotely close to this type of ecosystem. The deep understanding of Anthropic engineers on the importance of better context management tools and agent harnesses is something to admire. Very bullish on Claude Code.

As a backend developer living in 2025 , Plase learn 👇 1. Cloud-Native Development (AWS/GCP/Azure + Kubernetes) Backend is shifting heavily to scalable, distributed cloud systems. Knowing Kubernetes, Docker, CI/CD, and cloud SDKs is important. 2. Event-Driven Architecture & Messaging Systems learn tools like Apache Kafka, RabbitMQ, and Redis Streams for decoupled, real-time systems. 3. API Design & Documentation (REST + GraphQL + OpenAPI) Clean, versioned, well-documented APIs are non-negotiable. Postman, Swagger, GraphQL skills . 4. Performance Optimization & Observability Know how to profile, trace, and debug performance bottlenecks using Actuator, Prometheus, Grafana, or Jaeger. 5. Security Best Practices Be strong with OWASP, OAuth2, JWT, rate limiting, and zero trust principles. 6. Multithreading, Concurrency, and Reactive Programming Learn Reactor, CompletableFuture, Virtual Threads, and how to design non-blocking systems. 7. AI/ML Integration & Data Engineering Basics Understand how to work with model APIs, data pipelines, I need to invest more time on it Stop jumping from one language to another

Backend isn’t just APIs and CRUD. This is the roadmap companies like Google, Meta, Amazon expect you to know. DSA > Companies like Google don’t care about your projects if you fail here. > This is the real filter. > Conquer this, and every door in tech opens. > Struggle here, and those doors stay shut. 0. Ground Setup Before going crazy with topics, you need: > One main backend language (JavaScript/TypeScript, Go, Java, Rust — pick one and master it) > Git + GitHub: branching, PRs, rebases, conflict resolution > Linux basics: ls, cd, cat, grep, find, permissions, SSH > Editor mastery: VS Code or any solid setup First principles here: > What is a program? How does source code become a running process? > What is an OS process vs thread? > What does it mean to run a server on a port? You should be able to: > SSH into a Linux box, pull a repo, install deps, run a server on a port, view it via browser --- 1. Core Backend Fundamentals 1.1 Networking and HTTP You cannot be a strong backend engineer if HTTP is just “that thing Express uses”. Understand: > DNS → IP → TCP → HTTP > Difference between IP / TCP / UDP / HTTP / HTTPS > HTTP methods, status codes, headers, idempotency, safe methods > REST vs RPC vs GraphQL Practice: > Build a backend without any framework using native http > Parse JSON body manually > Implement basic rate limiting > Add API versioning (/v1, /v2) --- 1.2 API Design and Backend Architecture Basics First principles: > Resource modeling > Stateless vs stateful > Consistent naming, validation, clear errors > Pagination, filtering, sorting Layered architecture: > Controller → Service → Repository (DAO) You should be able to: > Design APIs for Task Manager or Expense Tracker > Keep error and response structure consistent everywhere --- 2. Databases and Data Modeling Big companies will grill you here. 2.1 Relational Databases SQL is non-negotiable. Understand: > Table, row, column, primary key, foreign key > Normalization: 1NF, 2NF, 3NF basics > Joins: inner, left, right, full > Transactions and ACID > Indexes and their impact You should be able to: > Instagram schema: users, posts, likes, comments, followers > Query top posts, mutual followers > Use migrations, not manual DB edits --- 2.2 NoSQL and When to Use It Understand: > Document DB vs relational DB > Denormalization > Event logs and analytics needs > CAP theorem basics > Collections, documents, indexes, aggregation pipeline Be able to decide: > When a feature needs SQL vs NoSQL --- 3. Authentication, Authorization and Security 3.1 Auth Basics Understand: > Authentication vs authorization > State vs stateless auth > Password hashing (bcrypt/argon2) > Cookies: HttpOnly, Secure, SameSite > Access vs refresh tokens > Token revocation and rotation > Basics of OAuth2 / OpenID Connect You should be able to: > Implement email/password auth with verification and forgot password > Explain cookies vs localStorage > Explain token invalidation strategies --- 3.2 Web Security Core Understand: > SQL injection, XSS, CSRF, IDOR (OWASP Top 10) > Input validation and output encoding > CORS and preflight You should be able to: > Identify common vulnerabilities in APIs > Configure CORS correctly --- 4. Concurrency, Performance and Scalability 4.1 Concurrency and Asynchrony Understand: > Event loop, callback queue, microtasks > Blocking vs non-blocking I/O > CPU-heavy work blocks Node You should be able to: > Explain when to use worker threads and offloading --- 4.2 Caching Concepts: > In-memory vs Redis caches > TTL, LRU, invalidation > HTTP caching: ETag, Last-Modified, Cache-Control You should be able to: > Use Redis to cache DB queries and rate limit > Plan cache keys well --- 4.3 Scaling and Architecture Patterns Understand: > Vertical vs horizontal scaling > Stateless services for scale-out > Load balancers > Monolith vs microservices > Sync vs async flows (queues) Patterns: > Circuit breaker > Retry with backoff > Idempotency keys for POST You should be able to: > Split a monolith into services and make them communicate --- 5. Messaging, Queues and Async Systems Understand: > RabbitMQ, Kafka, Redis streams > Producers, consumers, delivery semantics Use cases: > Emails, uploads, payment pipelines, logs You should be able to: > Offload heavy jobs to background workers > Build async workflows 6. Testing and Quality Understand: > Unit, integration, E2E tests > Test pyramid > Deterministic vs flaky tests You should be able to: > Test auth flows and business logic > Run tests in CI (GitHub Actions or similar) 7. DevOps Basics for Backend Engineers 7.1 Containers and Deployment Understand: > Dockerfile basics (FROM, COPY, RUN…) > Docker Compose for app + DB > Environment variables and config You should be able to: > Containerize backend and DB > Deploy to any cloud provider 7.2 CI/CD and Observability Understand: > CI pipelines: tests, lint, build > Auto deploy workflows > Logging and metrics You should be able to: > Add logging middleware with request IDs > Monitor latency, error rate, throughput 8. System Design This becomes crucial for big tech interviews. Understand: > Load balancer, app server, DB, cache, queue, file storage > Consistency models > API Gateway and BFF patterns > Unique ID generation (UUID, Snowflake) Practice on paper: > URL shortener > Instagram feed > E-commerce checkout > Notification systems Consider: > DB schema, caching, queue usage, failure handling 9. Domain-Specific Knowledge Depending on role and product: > Fintech: strong consistency, audit logs, idempotency > E-commerce: carts, pricing, orders, inventory > Real-time: WebSockets, SSE, backpressure Choose what aligns with your career direction and keep grinding.

🤖 Built a Django app with AI in @Al_Grigor's AI Dev Tools Zoomcamp! ✨ TODO app from scratch 📝 Models & migrations 🎨 Views and templates ✅ Tests My repo: github.com/Alexpeain/data… Zero Django knowledge → working app in one session! Join me: github.com/DataTalksClub/…

It's already over a year after my life suddenly turned into IDP(former),mental breakdown to self-healing: relocated to new place started everything from scratch! Here COMEBACK! I'm going building #backend projects by learning with learning logs or documents. Prepfor JobInterview

@Shri_73_ Let's connect

Create a RESTful API for a personal blogging platform. API can perform Create a new blog post Update an existing blog post Delete an existing blog post Get a single blog post Get all blog posts Filter blog posts by a search term. #BackendJourney roadmap.sh/projects/blogg…

Learning without building is insufficient for skill mastery. While looking for jobs ,I try to build projects as much as I can .This is simple Todo list Cli build by Python and import argparse module. #BackendJourney #opentowork roadmap.sh/projects/task-…

The first freelance project I got offered from a local Education center to build their school website this just happened before I was even ready but when chances come don't just say 'No' I am going to put so much effort in this.Thank you for trusting me. 🔥🔥 #BackendJourney