Varun Madupu 💙 @devmvk

@f_a_r_i_n_x @Naveen_X69 Why did he abscond when he gave complaint two hours before

13+ Attention mechanisms you should know ▪️ Self-attention ▪️ Cross-attention ▪️ Causal attention ▪️ Linear Attention ▪️ Softmax attention ▪️ Sliding Window (local attention) ▪️ Global attention ▪️ FlashAttention ▪️ Multi-Head Attention (MHA) ▪️ Multi-Query Attention (MQA) ▪️ Grouped-Query Attention (GQA) ▪️ Multi-Head Latent Attention (MLA) ▪️ Interleaved Head Attention (IHA) + Slim Attention, KArAt, XAttention, Mixture-of-Depths Attention (MoDA) Save the list and explore more about them here: turingpost.com/p/attention-ty…

These are actual high-demand next-level AI skills, not RAG: - Data prep for instruction fine-tuning - @UnslothAI ecosystem for fine-tuning, reasoning models, quantization, etc. - Fine-tuning embedding models - Backend design using FastAPI, Redis caching, queue workers, rate limiting - Making LLM inference layer using @vllm_project - @DeepSpeedAI for multi-GPU training - Learn NVIDIA Triton for running any vision and object detection models along with LLMs - Learn about Docker and CUDA setup - Use of @huggingface ecosystem - Distributed systems using @anyscalecompute - Kubernetes and Terraform setup for serving your model - Learn about @modal for low-config setup deployment - Deploying LLM on Ollama for easier calls - Context engineering - Agent memory harness - Multi-agent orchestration - LLM as a judge These skills are more focused towards inference engineering to ensure that your model runs smoothly in production. Memory, inference, and orchestration matter a lot more than frontier models. I am learning inference engineering and agent harness nowdays. Totally worth exploring!

90% of Applied AI Engineering interviews are just these 7 concepts repeated:

Research papers you must read for AI Engineer interviews: 1. Attention is all you need (Transformers) 2. LoRA (Low rank adaption) 3. PEFT ( Parameter Efficient Fine Tuning) 4. VIT (Vision Transformers) 5. VAE (Variational Auto Encoder) 6. GANs ( Generative Adversarial Networks) 7. BERT ( Bidirectional Encoder Representation from Transformers) 8. Diffusion Models (Stable Diffusion) 9. RAG (Retrieval Augment Generation) 10. GPT (Generative Pre-trained Transformers)

In the era of vibe coding, study the fundamentals and read books to keep your fundamentals clear. My career has benefited a lot from reading these books: 1. Designing Data Intensive Applications 2. Operating Systems: Three Easy Pieces 3. A Philosophy of Software Design 4. System Design Interview: An Insider‘s Guide

I came across this blog from Chip Huyen in 2022 where she discussed the ai courses to do. i feel they are relevant to this day! Here they are - STATS 202 :- data mining basics CS109 :- probability foundations CS231N :- computer vision DL CS224N :- NLP deep learning CS229 :- theoretical machine learning CS221 :- intro artificial intelligence CS228 :- probabilistic graphical models CS234 :- reinforcement learning CS238 :- decision theory RL CS224W :- graph machine learning CS246 :- large-scale data mining CS230 :- applied deep learning CS236 :- generative models EE263 :- linear dynamical systems CS336 :- robot perception CS103 :- discrete math foundations CS124 :- NLP basics CS223A :- robotics fundamentals i have done a few from here, would reccomend

“Build an AI agent” isn’t the right starting point. Real systems need architecture, determinism, and integration—not just better prompts. Here’s what actually matters: hackernoon.com/why-build-an-a…

AI Engineering Buildcamp is project-driven. You learn AI engineering by building. 15 projects you can build during the course 👇🏼 (You get lifetime access to the course if you sign up)

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Places to get your first 100 users: 1. Reddit (niche subreddits) 2. Indie Hackers 3. Hacker News 4. Product Hunt 5. BetaList 6. AlternativeTo 7. SaaSHub 8. Uneed 9. Launching Next 10. StartupBase 11. PitchWall 12. MicroLaunch 13. Dev Hunt 14. Side Project Ideas 15. Niche newsletters 16. Founder communities 17. Waitlist directories

Best Skills That Actually Get You Hired (2026) Backend – APIs (REST + GraphQL), Auth (JWT/OAuth, RBAC/ABAC), DB Design (SQL + NoSQL), Caching (Redis/CDN), Rate Limiting, Queues (Kafka/RabbitMQ), Event-driven architecture, Idempotency, API versioning, Observability (logs/metrics/traces), Background jobs, File storage (S3/GCS), WebSockets, gRPC, Feature flags Frontend – State Management (Redux/Zustand/Signals), Performance (code splitting, lazy loading), Accessibility (a11y), Responsive design, Testing (unit + e2e), SSR/SSG (Next.js), Hydration, Edge rendering, API integration patterns, Error boundaries, Form handling, Design systems, Micro-frontends, Web security (XSS, CSP) DevOps / Infra – Docker, CI/CD pipelines, Kubernetes (basics → deployments), Infrastructure as Code (Terraform), Cloud (AWS/GCP/Azure), Monitoring (Prometheus/Grafana), Logging (ELK), Tracing (OpenTelemetry), CDN + edge (Cloudflare), Secrets management, Autoscaling, Blue-green / Canary deploys, Cost optimization System Design – Scalability patterns, Tradeoffs (latency vs consistency), Distributed systems, CAP theorem, Consistency models, Data partitioning (sharding), Replication, Load balancing, Caching strategies, API gateways, Service discovery, Fault tolerance, Circuit breakers, Backpressure, Multi-region design Data / AI (increasingly expected) – Basic ML intuition, LLM APIs, Prompt engineering, Embeddings, Vector DBs (Pinecone/Weaviate), RAG pipelines, Data pipelines (ETL), Streaming (Kafka), Feature stores, Model evaluation basics Security (non-optional now) – Auth flows, OWASP Top 10, Input validation, Rate limiting abuse, Secrets handling, HTTPS/TLS basics, Secure storage, API security, Dependency vulnerabilities Testing / Reliability – Unit testing, Integration testing, E2E testing, Test coverage, Load testing, Chaos testing basics, Observability-driven debugging, SLOs/SLAs, Incident handling Developer Productivity – Git (advanced workflows), Debugging complex systems, Reading large codebases, Writing clean PRs, Code reviews, Documentation, CLI tools, Automation scripting AI-Assisted Development – Using tools like GitHub Copilot, Cursor IDE, ChatGPT effectively, Prompting for code, Code validation (not blind trust), Generating tests/docs, Refactoring with AI Soft Skills (still underrated, still decisive) – Communication (clear + structured), Ownership, Breaking down problems, Asking good questions, Explaining tradeoffs, Writing design docs, Handling ambiguity, Prioritization

Skills That Pay in the AI Era (2026) AI/ML: Python, PyTorch, Transformers, Fine-tuning, Evaluation AI Stack: RAG, Vector Databases, Embeddings, Prompt Engineering, Agents Backend: Node.js, Go, Rust, API Design, System Design Frontend: React, Next.js (for AI apps & dashboards) APIs: REST, GraphQL, gRPC Databases: PostgreSQL, Redis, Vector DBs DevOps: Docker, Kubernetes, CI/CD, Terraform Cloud: AWS/GCP/Azure, Serverless, Edge (Cloudflare, Vercel) MLOps: Model serving, MLflow, Monitoring, Data pipelines Security: Auth, API Security, Zero Trust AI Productivity: Copilot, Cursor, Code review with AI Core CS: Data Structures, Networking, OS, Scalability

Don’t overthink it. • Build a Calculator to master logic & loops • Build a Weather App using live APIs • Build a CRUD Web App with Flask + DB • Build a Chatbot UI with Streamlit + GPT • Build a File Organizer with os & shutil • Build a Resume Parser using NLP • Build a Stock Predictor using ML • Build a Job Tracker that updates Notion

If you're hunting for remote work, Reddit is one of the most underrated job boards on the internet. You just need to know where to look. Bookmark this thread ↓

If I had to start System Design from scratch again, I’d ignore 90% of the internet… …and just study these 40 articles. No random YouTube hopping. No endless tabs. No confusion. Just a clean, structured path that actually works. This is the roadmap I *wish* I had during my interview prep 👇 You’ll learn: • How to think in systems (not just memorize answers) • Real trade-offs (scalability vs consistency, latency vs cost) • How to design like a senior engineer And the best part? You can even: → Ask questions via voice in real-time → Get instant feedback → Practice HLD even as a beginner Here’s the full breakdown: 1. HLD Basics → lnkd.in/gmbzpdjV 2. Core Concepts & Trade-offs → lnkd.in/gUFeCcpT 3. Networking & DNS → lnkd.in/gENhQVgS 4. Load Balancing & Scaling → lnkd.in/giQkSPc7 5. Application Architecture → lnkd.in/gx8NQwKG 6. Databases → lnkd.in/gX9VK-MM 7. Caching → lnkd.in/gtcfXJAH 8. Async Processing → lnkd.in/gYeJ2hca 9. Communication Protocols → lnkd.in/giSSdMJR 10. Performance & Monitoring → lnkd.in/giiKTJE4 11. Cloud Design Patterns → lnkd.in/gNTmsgHA 12. Reliability Patterns → lnkd.in/g6fvAknr Save this. This is easily 50+ hours of scattered learning—compressed into one roadmap. Follow this, and System Design will finally start making sense.

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Full Course On How To Speak Brilliantly

If you’re hunting for a remote job, you just need to figure out how Reddit works, and you’ll never be unemployed for a long time. Here’s a list of subreddits you should bookmark right now:

In 2013, Yale professor Ben Polak gave a legendary 1-hour lecture on Game Theory. It will change how you make decisions in negotiations, business, and life. His frameworks: • Dominance arguments • Backward induction • The proactive bias 12 lessons to make better decisions:

You don't need random papers, just learn these concepts sequentially to kickstart your LLM engineer journey. Here’s what actually matters if you’re an engineer: - Tokenization and embeddings - Attention and transformer blocks - Training and fine tuning - LoRA and QLoRA - DPO and alignment - Quantization - KV cache and inference systems - FlashAttention and PagedAttention Not theory. Systems. I wrote a complete guide covering everything step by step. If you want to build with LLMs, not just study them, this is for you. Also breaking down inference and deployment at scale in upcoming posts on hands-on level.

Karan🧋 @kmeanskaran

4 months ago

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