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As a Backend dev , how many concepts can you explain from below : 1. Event-Driven Architecture 2. Saga Pattern 3. CQRS (Command Query Responsibility Segregation) 4. Event Sourcing 5. Circuit Breaker Pattern 6. Distributed Tracing 7. CAP Theorem 8. Idempotency 9. Data…
Want to understand B-trees better? Try btree.app and bplustree.app. These are standalone sandboxes of the visuals I built for my "B-trees and database indexes" article. Helpful for learning B-tree insertion, search, and node splits.
Concepts You’ll Only Learn After a Year in Production
This Claude Code prompt literally turns Claude Code into ultrathink visionary🤯
This guy literally revealed the one prompt that rewires ChatGPT’s brain.
DevOps project for beginners: Terraform + Kubernetes Build real infrastructure, not tutorials. What you'll build: Deploy a complete web application on Kubernetes using Terraform. Part 1 - Infrastructure with Terraform: 1. Setup (Local machine): - Install Terraform CLI -…
"Cache‑Oblivious Algorithms and Data Structures" by Erik D. Demaine erikdemaine.org/papers/BRICS20… This is a foundational survey on designing cache‑oblivious algorithms and data structures that perform as well as cache‑aware approaches that require hardcoding cache size (M) and block…
The question: "Why do CPUs have multiple cache levels?" often gets many CS students and professionals thinking and researching. This article from Fabian Giesen narrates a "cache story" in a relatable way, only to delve into the details - a must-read! fgiesen.wordpress.com/2016/08/07/why…
The question: "Why do CPUs have multiple cache levels?" often gets many CS students and professionals thinking and researching. This article from Fabian Giesen narrates a "cache story" in a relatable way, only to delve into the details - a must-read! fgiesen.wordpress.com/2016/08/07/why…
Learn These Patterns to Solve Many Coding Problems
If you're a backend engineer who want to get better at system design, read these 14 articles: ↓
How LLMs work under the hood? This is the best place to visually understand the internal workings of a transformer-based LLM. Explore tokenization, self-attention, and more in an interactive way:
I finally understand how large language models actually work After reading the 2025 textbook “Foundations of LLMs” It blew my mind and cleared up years of confusion Here’s everything i learned (in plain english):
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Fuck it. I'm sharing the 10 Gemini prompts that built my entire SaaS from scratch. These prompts literally replaced my CTO, lead dev, and product manager. Comment 'send' and I'll DM you the complete Gemini guide to master it:
THIS IS INSANE…🔥 Someone just leaked 1,200+ plug-and-play N8N workflows and it’s breaking the internet 👀 They scraped everything from the official docs, GitHub, AND hidden forums 😮💨 ⚡ AI-powered lead gen ⚡ Auto content repurposing ⚡ Sales & CRM automation This vault…
6 GitHub repositories that will give you superpowers as an AI Engineer:
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