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@Bradgohtrades WORKBOOK

@Bradgohtrades Workbook

pitching hotpath-rs youtube.com/watch?v=ir1WC_…

You're not a developer until you understand these files: • 🔐 .env → Secret keys & env variables. NEVER push to GitHub • 🙈 .gitignore → Files Git should ignore (node_modules, .env...) • 🔒 package-lock.json → Locks dependency versions across all machines • 🟢 .nvmrc → Pins the Node.js version for your whole team • 📐 .editorconfig → Consistent formatting across all editors • 🔴 .eslintrc → Linting rules to keep JS/TS clean • ✨ .prettierrc → Auto code formatting (goodbye tabs vs spaces wars) • 🎨 tailwind.config.js → Custom colors, spacing & themes • 🔵 tsconfig.json → Controls TypeScript compilation • 🔄 .babelrc → Babel config for backward JS compatibility • 🐶 .huskyrc → Git hooks to catch bugs before pushing • 🐳 .dockerignore → Keeps Docker builds clean & lean • ⚡ vite.config.js → Vite dev server & build config • ▲ next.config.js → SSR, redirects & Next.js behavior • 🚫 .prettierignore → Files Prettier should skip 📑 Save this before it disappears...

Singapore’s AI obsession just hit Everest peak. The Foreign Minister is self-hosting Claude on a Raspberry Pi and building a diplomatic knowledge graph using Karpathy’s LLM Wiki pattern. Wahlao! SG devs, the minister is coming for your job. And he’s not even using Cursor — he’s on NanoClaw running locally. Can someone git pull his code and give it a test. Only bad thing? He dropped this on Facebook instead of X. Minister, we need to talk. gist.github.com/VivianBalakris…

Zac @Zac_labs

3 months ago

Singapore’s obsession with AI is hitting a new peak. 🇸🇬 🤖 Today, 4 of the top 5 most downloaded apps in SG are AI chatbots. Both the tech migrants and the aunties in hawkers are doing it. And what’s with this vpn at number 4.

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If you are a software engineer who want to get better at algorithms and system design, read these 16 curated articles: ↓

As a backend engineer. Please learn: - DB - System Design - Algorithms - API design - API Security - CI/CD - DS - Docker/ Kubernetes - Caching - Messaging Stop jumping from one language to the other

Preparing for DSA interview, do these leetcode problems! 1. Sliding Window: 3, 76, 424, 904 2. Two Pointers: 11, 15, 167, 42 3. Binary Search: 33, 153, 875, 1011 4. Hashing + Prefix: 1, 49, 560, 128 5. Monotonic Stack: 739, 503, 84, 901 6. Heap (Top K): 215, 347, 973, 703 7. Intervals: 56, 435, 57, 252 8. BFS/DFS: 200, 994, 102, 133 9. Trees: 104, 236, 543, 124 10. DP basics: 70, 198, 322, 300 Just 2-3 per pattern, write the template from memory, then speedrun variants.

After giving 10+ Backend interviews, I found only 20 topics you should really master - Backend Fundamentals: 1. How the Server Works (Request–Response Cycle) 2. REST API Design Principles 3. HTTP Methods & Status Codes 4. Middleware & Request Flow 5. Authentication vs Authorization Node.js & Backend Runtime: 6. Event Loop & Non-Blocking I/O 7. Async/Await & Promises 8. Error Handling & Global Error Middleware 9. Environment Variables & Config Management 10. Logging & Debugging Databases: 11. SQL vs NoSQL (When to Use What) 12. Database Schema Design 13. Indexing & Query Optimization 14. Transactions & ACID Properties 15. Relationships (1–1, 1–Many, Many–Many) Security & Scalability: 16. JWT, Sessions & Cookies 17. Password Hashing & Security Best Practices 18. Rate Limiting & Caching (Redis) 19. Scalability Concepts (Load Balancing, Horizontal Scaling) 20. API Versioning & Deployment Basics If you master these, most backend interviews become predictable 🚀 Follow up for more @Heymaxi01

Top 7 skills required for Java devs to get a job these days. Which one are you struggling with ? 1. Core Java (8–22) : Streams, records, virtual threads, collections, OOP 2. Spring Boot: REST APIs, validation, profiles, AOP, actuator, config management, devtools, custom starters, error handling. 3. Microservices: Feign/WebClient, API versioning, service discovery, config server, circuit breakers, retries, distributed tracing, API Gateway,SAGA, CQRS. 4. Spring Security : JWT, OAuth2, role-based auth 5. Hibernate/JPA : Mappings, lazy/eager, JPQL, performance tuning 6. DSA : Arrays, trees, graphs, DP, hashing 7. Concurrency : Thread pools, locks, CompletableFuture, Loom

If it’s real-time → WebSockets If it’s scale → Kafka If it’s simplicity → REST If it’s chaos → GraphQL If it’s AI → Python If it’s infra → Go If it’s logs → ElasticSearch If it’s low-latency → Redis If it’s high-availability → Postgres If it’s streaming → Flink If it’s low-level → C If it’s high-performance → C++ If it’s enterprise → Java

Typed a Gmail username once and the UI instantly said: “Username already taken.” I asked an ex-Staff Google engineer the same problem (he was director of engineering in a startup i worked at), “You’re not doing an Elasticsearch query on every keypress, right?” He laughed. “No. That’d be a crime.” My classy approach: 1. Keep an in-memory trie of reserved usernames. 2. Update it async (delta pushes), not per keystroke. 3. UI checks locally in O(k) where k = username length. Numbers (why this is feasible): 1. Assume 2B usernames, avg length 10 chars. 2. Raw chars = 2B × 10 = 20B chars. 3. Even if you store 1 byte/char (not true in a trie, but baseline) that’s ~20GB just for characters. 4. A trie is about prefix sharing, so common prefixes collapse hard. Real memory is “nodes + edges”, not “strings”. 5. If we model ~1 node per char worst-case: ~20B nodes. - If a node is 8 bytes (tight packed arrays, bitsets, offset indices; no pointers), worst-case is 160GB. - With prefix sharing, you can easily cut multiples of that depending on distribution (gmail-like usernames are not random). 6. Shard by first 2 chars (36 possible: a-z, 0-9). 36² = 1296 shards. - Worst-case per shard: 160GB / 1296 ≈ 123MB. - Suddenly “instant check” fits in memory per front-end pod or edge POP. Yes, you can also do it with WebSockets: 1. Client streams “candidate username” events. 2. Server replies with availability. 3. Works fine, but now you’ve built a hot, stateful, low-latency service for… a UI hint. Most people will ship: 1. Elasticsearch prefix search. 2. Debounce 150ms. 3. Cache a bit. 4. Pray at peak signup traffic. And it works. But the trie approach is the kind of solution where the UI feels like magic tbh and it's something novel that i thought of. Things are just different at google scale.

SumitM @SumitM_X

3 months ago

As a developer, Have you ever wondered : You type a Gmail username and UI instantly shows "Username already taken"... There are millions of users globally How is this check so fast?

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𝗧𝗼𝗽 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 𝘁𝗼 𝗜𝗺𝗽𝗿𝗼𝘃𝗲 𝗔𝗣𝗜 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝟭. 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 A cache hit never touches the database. On a miss, you query the DB and write to cache so the next caller doesn't pay the same cost. The part that engineers usually get wrong is invalidation. TTL is easy to implement and will absolutely serve stale data at the worst moment. Event-driven invalidation is accurate, but now you have a new thing that can break. 𝟮. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 𝗣𝗼𝗼𝗹𝗶𝗻𝗴 When you open a new connection, for each request, a few things happen: TCP handshake, TLS, Auth, etc. This takes 50–200ms, or even more. Pool your connections. 𝟯. 𝗔𝘃𝗼𝗶𝗱 𝗡+𝟭 𝗤𝘂𝗲𝗿𝗶𝗲𝘀 Every slow codebase I've worked in had this problem. You fetch a list of records, then loop through them and query related data for each. And it works fine locally with 10 rows, but in production with 2,000, it's 2,001 database round-trips per request. We can fix this with one JOIN. Also, we need to index the columns in your WHERE clause. Before you change anything, run a profiler and verify this is actually the problem. I've assumed N+1 before and been wrong. 𝟰. 𝗣𝗮𝘆𝗹𝗼𝗮𝗱 𝗖𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 This is something usually forgotten. A 120 KB JSON response becomes roughly 18 KB. If you're not doing this, you're sending the client unnecessary work. You can choose Brotli or gzip. 𝟱. 𝗔𝘀𝘆𝗻𝗰 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 Operations that take seconds don't belong inside an HTTP response. Return 202, put the job on a queue, process it in the background, fire a webhook when it's done. Your p99 will thank you. 𝟲. 𝗛𝗧𝗧𝗣/𝟮 HTTP/1.1 runs one request at a time per connection. That made sense in 1997. HTTP/2 multiplexes everything over a single TCP connection, allowing all requests to be in flight at once, with header compression on top. If your infrastructure supports it and you haven't switched, worth looking at why not. 𝟳. 𝗕𝗮𝘁𝗰𝗵𝗶𝗻𝗴 Ten API calls are 10 round-trips, but also 10 times the latency cost. Let clients bundle operations into one request and process them in parallel on the server. In REST: POST /batch or GET /users?ids=1,2,3. GraphQL handles this without you having to think about it. 𝟴. 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝗙𝗶𝗿𝘀𝘁 Set up OpenTelemetry and look at actual traces before touching anything. I've watched teams spend weeks optimizing the wrong layer. Here is a real example: API handler: 12ms, network: 113ms, DB query: 680ms. Everyone was looking at the API layer, but the problem was that it was sitting in the database the whole time. An afternoon of instrumentation would have shown them that almost immediately. 𝟵. 𝗣𝗮𝗴𝗶𝗻𝗮𝘁𝗶𝗼𝗻 Never return 10,000 rows. Return 20 with a cursor for the next page. Offset pagination scans and discards rows on every call, at page 500 you're scanning 10,000 rows to show 20. Cursor-based picks up exactly where it left off.

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Anthropic is offering 13 AI courses & certificates. It's free by following these 13 links: 1 - Claude 101. Learn Claude for everyday work. Core features and best practices. ↳ anthropic.skilljar.com/claude-101 2 - AI Fluency: Framework & Foundations. The foundational thinking course. Must need. ↳ anthropic.skilljar.com/ai-fluency-fra… 3 - Introduction to Agent Skills Build, configure, and share Skills in Claude Code — reusable instructions Claude applies automatically. ↳ anthropic.skilljar.com/introduction-t… 4 - Building with the Claude API Full spectrum: function calling, tool use, streaming, SDKs, and production patterns. ↳ anthropic.skilljar.com/claude-with-th… 5 - Claude Code in Action Integrate Claude Code into your dev workflow. Hands-on, practical, ship-focused. ↳ anthropic.skilljar.com/claude-code-in… 6 - Intro to Model Context Protocol Build MCP servers and clients from scratch in Python. Tools, resources, and prompts. ↳ anthropic.skilljar.com/introduction-t… 7 - MCP: Advanced Topics Sampling, notifications, file system access, and transport for production MCP servers. ↳ anthropic.skilljar.com/model-context-… 8 - AI Fluency for Students AI skills for learning, career planning, and academic success through responsible collaboration. ↳ anthropic.skilljar.com/ai-fluency-for… 9 - AI Fluency for Educators For faculty and instructional designers applying AI Fluency into teaching and institutional strategy. ↳ anthropic.skilljar.com/ai-fluency-for… 10 - Teaching AI Fluency Teach and assess AI Fluency in instructor-led settings. Curriculum-ready. ↳ anthropic.skilljar.com/teaching-ai-fl… 11 - AI Fluency for Nonprofits Increase organizational impact and efficiency while staying mission-true. ↳ anthropic.skilljar.com/ai-fluency-for… 12 - Claude with Amazon Bedrock The full AWS accreditation course, now open to everyone. ↳ anthropic.skilljar.com/claude-in-amaz… 13 - Claude with Google Cloud's Vertex AI Work with Claude through Google Cloud's Vertex AI, from setup to production. ↳ anthropic.skilljar.com/claude-with-go… 14 - How to master AI with words (not code) Shameless plug: it's my own (free) newsletter. Join 369,000+ weekly readers at how-to-ai.guide. I made how-to-claude.ai to start mastering Claude. And then claude-co.work to master Claude Cowork. ♻️ Repost this to help others access AI courses.

Ruben Hassid @rubenhassid

3 months ago

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Anthropic just announced the "Claude Certified Architect" program. And you can start today. In 16 years of my professional career, I haven't done a single certification. Not one. Not AWS. Not Azure. Not Google Cloud. Not PMP. Not Scrum. Not any of the alphabet soup. I learned by building. By breaking things. By shipping. But I'm about to break that streak. I'm going for my first-ever certification: Claude Certified Architect — Foundations Here's why this matters — especially if you're a developer, engineer, or any professional who feels like the AI wave is moving too fast. Claude Code launched a few weeks ago. And it feels like a paradigm shift. Not an incremental upgrade. Not another chatbot wrapper. A fundamentally different way of building software. Agentic architecture. Tool orchestration. MCP integration. Context management at a systems level. If those words sound intimidating — that's exactly why this certification exists. It covers everything from agentic orchestration to prompt engineering to Claude Code workflows. Not surface-level content. And here's what got me: It costs nothing. Free. Zero. $0. So if you've been feeling left behind... If you've been watching others ship AI agents while you're still figuring out where to start... If you've been telling yourself "I'll learn this next quarter"... This is your sign. Stop scrolling. Start building. First certification in 16 years. Let's see how this goes. Links in the comments 👇 Cc : Brij Pandey

12 must-know data structures for coding interviews: 1. 𝐀𝐫𝐫𝐚𝐲𝐬 2. 𝐌𝐚𝐭𝐫𝐢𝐱 (2𝐃 𝐀𝐫𝐫𝐚𝐲) 3. 𝐋𝐢𝐧𝐤𝐞𝐝 𝐋𝐢𝐬𝐭 4. 𝐒𝐭𝐚𝐜𝐤 5. 𝐐𝐮𝐞𝐮𝐞 6. 𝐇𝐚𝐬𝐡𝐌𝐚𝐩 7. 𝐓𝐫𝐞𝐞 8. 𝐁𝐢𝐧𝐚𝐫𝐲 𝐒𝐞𝐚𝐫𝐜𝐡 𝐓𝐫𝐞𝐞 (𝐁𝐒𝐓) 9. 𝐇𝐞𝐚𝐩 (𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐲 𝐐𝐮𝐞𝐮𝐞) 10. 𝐓𝐫𝐢𝐞 11. 𝐆𝐫𝐚𝐩𝐡 12. 𝐔𝐧𝐢𝐨𝐧-𝐅𝐢𝐧𝐝 (𝐃𝐢𝐬𝐣𝐨𝐢𝐧𝐭 𝐒𝐞𝐭) ♻️ Repost to help others preparing for interviews

DSA prep can be demotivating if you are not doing the right kind of problems. I tell people to learn 10 patterns, then do 15–25 target questions per pattern until you can explain the invariant. 1. Two pointers: 125, 167, 15 2. Sliding window: 3, 76, 424 3. Prefix sum + hashmap: 560, 974, 525 4. Binary search: 33, 153, 875 5. Monotonic stack: 739, 84, 901 6. Heap / Top K: 215, 347, 295 7. Intervals: 56, 435, 57 8. BFS/DFS grid: 200, 994, 417 9. Trees: 102, 236, 543 10. DP starter pack: 70, 198, 322

Basic OOP Concepts Explained with Clear Examples: 1. 𝐄𝐧𝐜𝐚𝐩𝐬𝐮𝐥𝐚𝐭𝐢𝐨𝐧 Hide internal data behind public methods. - Example: A BankAccount class keeps balance and pin private. The only way to interact with it is through deposit() and getBalance(). 2. 𝐀𝐛𝐬𝐭𝐫𝐚𝐜𝐭𝐢𝐨𝐧 Expose a simple interface, hide the complexity behind it. - Example: An EmailService class gives you sendEmail(to, body). Internally, it handles SMTP connections, authentication, and retry logic. The caller doesn't need to know any of that. They just call one method and it works. 3. 𝐈𝐧𝐡𝐞𝐫𝐢𝐭𝐚𝐧𝐜𝐞 Let child classes reuse and override behavior from a parent class. - Example: An Animal class defines speak(). Dog extends it and returns "Woof!", Cat extends it and returns "Meow!". Shared logic lives in one place, and each subclass customizes what it needs. 4. 𝐏𝐨𝐥𝐲𝐦𝐨𝐫𝐩𝐡𝐢𝐬𝐦 Write code that works with multiple types through a common interface. - Example: Define a Shape interface with a draw() method. Now Circle, Rectangle, and Triangle each implement draw() their own way. A single drawShape(Shape s) method works with all of them. ♻️ Repost to help others in your network

PATCH vs PUT : PUT is used when you want to replace an entire resource. You send the complete object, and the server overwrites the old one. If a field is missing, it might get removed or reset. Example: PUT /users/101 Body: { "name": "John", "age": 30 } This replaces the whole user object with the new data. PATCH is used when you want to update only part of a resource. You send only the fields that need changes, and the server modifies just those. It’s more efficient for small updates. Example: PATCH /users/101 Body: { "age": 31 } This only changes the user’s age, keeping everything else intact.