As an AI engineer. Please learn: Foundation: - Vector Databases (Pinecone, Weaviate, Qdrant) - Embedding models & similarity search - Prompt Engineering & Chain-of-Thought - LLM Evaluation metrics - Token optimization & cost management Architecture: - RAG patterns (naive,

30 Essential Git Commands Every Developer Should Know 📒 🔹 Setup & Config (3–5 commands) 1. `git config --global https://t.co/6f5S1XN1gr "Your Name"` 2. `git config --global https://t.co/n4h7XXOXyS "you@example.com"` 3. `git config --list` 4. `git config --global

Top 30 Docker Commands Every Developer Must Know 🐳 🔹 Daily Docker Basics 1. docker --version – Check your Docker version 2. docker info – System-wide info (containers, images, driver…) 3. docker pull <image> – Download image from registry 4. docker images – List local

“Benchmarks are lying to you” Benchmarks test: > short context > clean inputs > single-turn reasoning Real agents deal with: > messy state > partial failures > long horizons > tool latency > retries > cost limits If your agent only works on benchmarks, it won’t survive

Real-time Multiplayer Frontend in 2026 – 26 Core Skills Every Serious Dev Needs (Extension to "Frontend Essentials 2026" => after Three.js, motion, AI-native, this is the next power move) Most frontend devs stop at "chat with https://t.co/VOjohXwYdL". Real pros build apps

Software engineers in 2026, especially now that AI is everywhere in the stack, actually focus on these if you want real value + jobs that pay: > Master fundamentals so deep (system design, algorithms, architecture, debugging at scale) > Pick one strong programming

- Ansible is free - Jenkins is free - Helm is free - Trivy is free - SonarQube community edition is free - Loki for logs is free - Vault is free - Podman is free (docker alternative) - Minikube or kind for local kubernetes is free still boils down to

Frontend engineers in 2026, especially with AI everywhere, please actually learn these: > JavaScript & TypeScript (deep, like you own the language) > Pick one serious framework and go extremely deep > React + Next.js is still eating most of the job market > Svelte or Solid

LLM business model: - Subscribe to them ($20/month) - Get tokens - Build my web app/website - Encounter errors - Fix them - Spend tokens - Face the same error repeatedly - Spend more tokens - Hit token limit - Subscribe again ($20/month)

3D Web / Three.js Essentials 2026 (front-end niche) 1. Core scene setup: Scene, Camera (Perspective/Orthographic), WebGLRenderer 2. Basic geometries & meshes (Box, Sphere, Plane, custom BufferGeometry) 3. Materials mastery (MeshStandard, MeshPhysical, custom NodeMaterials)

yeah remote work in hft n hedge funds isnt common coz latency matters a ton but few firms open up spots especially for engineers heres what i dug up from job sites n career pages in 2026 firms with remote engineer hires - citadel got virtual internships for india folks pays well

Cloud & Infrastructure Essentials 2026 1. Cloud Mental Models & Shared Responsibility 2. Identity & Access Management (IAM deep dive, RBAC, ABAC, MFA, SSO) 3. Networking Deep Dive (VPC, Subnets, Route Tables, Peering, Transit Gateway) 4. Hybrid & Multi-Cloud Connectivity

DevOps Essentials 2026 1. Linux Fundamentals & CLI Commands 2. Networking Basics (IP, DNS, Firewalls) 3. Scripting Languages (Bash, Python) 4. Version Control Systems (Git) 5. Branching Strategies & Git Workflows 6. CI/CD Fundamentals 7. Pipeline Automation (Jenkins,

"That's not a bug, it's a feature." - Abraham Lincoln

“It works on my machine." - Albert Einstein

Backend Essentials 2026 1. HTTP Protocols & Status Codes 2. RESTful API Design 3. GraphQL Fundamentals 4. Authentication (OAuth2, OIDC) 5. JWT & Session Management 6. Password Hashing & Salting 7. Encryption & Security Best Practices 8. Rate Limiting & Throttling

AI/ML Engineer Essentials 2026: 1. Python Mastery ( & C++ familiarity) 2. Linear Algebra & Calculus 3. Probability & Statistics 4. Data Manipulation (Pandas, Polars) 5. Deep Learning Frameworks (PyTorch) 6. Transformers Architecture 7. LLM Fundamentals & Tokenization 8.

Distributed Systems Backend Essentials 2026 1. Networking fundamentals (TCP, UDP, HTTP/2, HTTP/3) 2. API design (REST, gRPC, GraphQL trade-offs) 3. Data modeling for scale 4. CAP theorem & consistency models 5. Distributed transactions (Sagas, idempotency) 6. Event-driven

Frontend Essentials 2026 1. Semantic HTML5 2. Modern CSS Layouts (Flexbox, Grid) 3. CSS Custom Properties 4. CSS Variables Scope 5. CSS Preprocessors (Sass/SCSS) 6. CSS-in-JS (Emotion, Styled) 7. Responsive Design Principles 8. Mobile-First Approach 9. Tailwind

This is the reality check the AI hype needs. Everyone's racing to build "fully autonomous agents." Almost nobody should. Let me explain why constrained autonomy beats full autonomy in production. --- THE FANTASY: "Our agent runs 24/7, makes decisions, executes actions,

first stop the job or revert the code so no more duplicates come in then take a backup just in case find out how to spot the duplicates maybe by some key or timestamp keep the original ones and delete the extras in small batches so table doesnt lock up after clean add unique