惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
V
V2EX
V
Visual Studio Blog
博客园_首页
Last Week in AI
Last Week in AI
Apple Machine Learning Research
Apple Machine Learning Research
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
S
SegmentFault 最新的问题
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
Martin Fowler
Martin Fowler
Recent Announcements
Recent Announcements
J
Java Code Geeks
月光博客
月光博客
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
F
Fortinet All Blogs
P
Privacy & Cybersecurity Law Blog
C
CERT Recently Published Vulnerability Notes
C
CXSECURITY Database RSS Feed - CXSecurity.com
B
Blog RSS Feed
S
Schneier on Security
酷 壳 – CoolShell
酷 壳 – CoolShell
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
W
WeLiveSecurity
A
Arctic Wolf
U
Unit 42
博客园 - 司徒正美
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
有赞技术团队
有赞技术团队
Recorded Future
Recorded Future
Engineering at Meta
Engineering at Meta
Google DeepMind News
Google DeepMind News
大猫的无限游戏
大猫的无限游戏
Microsoft Security Blog
Microsoft Security Blog
Hacker News: Ask HN
Hacker News: Ask HN
量子位
B
Blog
T
The Exploit Database - CXSecurity.com
C
Cisco Blogs
博客园 - 三生石上(FineUI控件)
H
Help Net Security
博客园 - 叶小钗
C
Cyber Attacks, Cyber Crime and Cyber Security
L
LINUX DO - 热门话题
Hugging Face - Blog
Hugging Face - Blog
Google DeepMind News
Google DeepMind News
小众软件
小众软件
雷峰网
雷峰网
TaoSecurity Blog
TaoSecurity Blog
Schneier on Security
Schneier on Security

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant Common SOC 2 Failures (Real World) Stop Vibe-Checking Your AI App: A Practical Guide to Evals How to Use SonarQube and SonarScanner Locally to Level Up Your Code Quality Your Next To-Do App Is Dead — I Replaced Mine with an OpenClaw AI Sign a Nostr event in 60 lines of Python using coincurve — no nostr-sdk, no nbxplorer, no rust toolchain ITGC Audit Explained Like You’re in Big 4 Patch Tuesday abril 2026: Microsoft parcha 163 vulnerabilidades y un zero-day en SharePoint Stop scraping everything: a better way to track competitor price changes Listing on MCPize + the Official MCP Registry while routing payments OUTSIDE the marketplace — how I kept 100% of my x402 revenue Building an AI-Powered Risk Intelligence System Using Serverless Architecture Why We Ripped Function Overloading Out of Our AI Toolchain Testing AI-Generated Code: How to Actually Know If It Works SaaS Churn Is Killing Your Business. Here Is What to Do About It (Without a Support Team) The Speed of AI Is No Longer Linear - And Self-Improving Models Are Why How to Implement RBAC for MCP Tools: A Practical Guide for Engineering Teams From Standard Quote to Persuasive Proposal: AI Automation for Arborists I built a CLI that scaffolds complete multi-tenant SaaS apps Axios CVE-2025–62718: The Silent SSRF Bug That Could Be Hiding in Your Node.js App Right Now The dashboard that ended our friendship Data Pipelines Explained Simply (and How to Build Them with Python) The Hidden Cost of AI Systems Nobody Talks About. undefined vs undeclared, and how typeof behaves Switching from file-based jobs to NATS/Kafka in Rust without changing code io_uring Adventures: Rust Servers That Love Syscalls Why Agentic AI is Killing the Traditional Database The POUR principles of web accessibility for developers and designers Quantum Neural Network 3D — A Deep Dive into Interactive WebGL Visualization How To Install Caveman In Codex On macOS And Windows Automation Pipeline Reliability: Why Your Workflow Breaks When Nobody Is Watching I Built an 'Open World' AI Coding Agent — It Works From ANY Folder From Freelancing to Product: A Tech Service Company's SaaS Transformation China's AI Giants: Adding Tencent Hunyuan & ByteDance Doubao to AI University (74 Providers) On the Vibe Coders and Their Lies clerk: Auto-Summarize Your Claude Code Sessions AI Weekly — 2026/04/10–04/17 | The Model Lockdown Is Here, but the Toolchain Is the Real Battleground AI 週報 — 2026/04/10–2026/04/17 模型封鎖潮來了,但工具鏈才是真戰場 Maybe this is how Open-Source apps are born... 🚀 Fine-Tune LLMs with LoRA and QLoRA: 2026 Guide tRPC v11 + Next.js App Router: End-to-End Type Safety Without the Boilerplate ShadCN UI in 2026: Why I Stopped Installing Component Libraries and Started Owning My Components SaaS Billing in React Server Components: Stripe + Supabase Without a Single `useEffect` Join our DEV Weekend Challenge — $1,000 in Prizes Across TEN winners! Submissions Due April 20 at 6:59 AM UTC. Implementing FSRS Spaced Repetition in Flutter + Supabase — Adding Memory Science to an AI Learning App "I Texted My Localhost From the Train — Claude Code Fixed the Bug Before I Got Home" I Built a Sales Prep AI and It Went Deeper Than Expected Design to Code #2: One JSON, Eleven Outputs Solving the 100M-Row Problem: A Summary Table Pattern for High-Volume Push Notification Logs Flutter Web With Wasm: What Actually Changes For Developers I Built 50 Royalty-Free Soundtracks for My Side Project in a Weekend Using AI Music Generation The Vibe Coding Security Checklist: 7 Things to Check Before You Ship Stop Letting Googlebot Guess Fix Your React App's SEO Right Desconstruindo o Streaming do LinkedIn: Como Criar um Engine de Extração de Vídeo de Alta Performance com HLS e FFmpeg (EDA Part-1) EDA (Exploratory Data Analysis) Explained With Real Life — Why Looking at Your Data Is the Most Important Step in Machine Learning Brand Relationship Management at Scale: Our 4-Touch Outreach System for 200+ Brands Why String.fromEnvironment() Might Return an Empty String in Dart JGuardrails 1.0.0 — Hardening Java LLM Apps Against Jailbreaks, Toxicity, and Prompt Injection Plan and Schedule a Full Week of Threads Content From One Claude Conversation Coding Cat Oran Ep3, Five Tables Changed Everything Updated: BFF Pattern I'm done watching freelancers get buried by 200 proposals. So I'm building the alternative. This is my first post BFS Algorithm in Java Step by Step Tutorial with Examples Tracking LLM Pricing Monthly: An Open Dataset for 22 AI Models How We Measure Content ROI on a Comparison Site: Revenue Attribution Without Perfect Data Introducing Nova AI Ops: The AI-Native Operating System for SRE Teams I built a free desktop video downloader for Windows — Grabbit How Talkie OCR Helps Vision-Impaired & Dyslexic Users Read the World Around Them VRCFaceTracking安装和iPhone面捕配置教程,有bug Even CrowdStrike Can't See Your Agents The Automation Gold Rush: What n8n Workflows and Claude Are Opening Up for Developers Right Now
What the Meta software engineer interview is actually like (and how I'd prep)
Mahesh · 2026-06-12 · via DEV Community

A friend got a Meta recruiter ping last month and immediately texted me "what do I even do now." I'd been through the loop, so I wrote her a long reply. This is basically that reply, cleaned up.

The first thing worth saying is that Meta's process is predictable, and that's good news. Once you know the shape of it, most of the panic goes away and you can spend your energy on the parts that actually move the needle.

It starts with a recruiter call, usually around half an hour. Low pressure. They want your background, what you're looking for, and roughly where you'd fit. Be honest about your timeline here, because it sets the pace for everything after.

Then comes the technical phone screen, about 45 minutes in a shared editor like CoderPad, with one or two coding problems. The mistake people make is treating it like a silent LeetCode session. The interviewer is listening to how you think, so narrate. Talk through your approach before you write, mention the edge cases you're weighing, say it out loud when you spot a better time complexity. A correct answer delivered in silence scores worse than a slightly slower one they can actually follow.

If that goes well, you get the onsite, or the "full loop." It's usually four to five rounds, and Meta has these internal nicknames you'll hear people throw around.

The coding rounds are called "Ninja." You'll do two of them, roughly 45 minutes each, a couple of medium problems per round. Nothing exotic. Arrays, strings, hash maps, trees, graphs, a little dynamic programming. They lean on patterns far more than obscure tricks.

If you're going for a senior level (E5 and up) there's a system design round, nicknamed "Pirate." They hand you something open-ended like "design the news feed" or "design a rate limiter" and watch how you reason about scale. There's no single right answer they're fishing for. They want to see you clarify requirements, estimate load, sketch something sensible, and then talk honestly about the trade-offs.

And then there's behavioral, which they call "Jedi." I'll be blunt: this is the round people underestimate the most, and it's the one that quietly sinks otherwise strong candidates. Meta takes it seriously and maps your answers to how they actually operate, things like moving fast, owning outcomes, and focusing on impact. Have real stories ready, use a loose STAR structure so you don't ramble, and put numbers on your impact wherever you honestly can.

Quick note on levels, because it changes what they expect from you. Meta runs from E3 (new grad) through E4, E5 (senior), E6 (staff) and up. The higher you go, the more the system design and the "how do you handle ambiguity" signals matter. For real compensation by level and city, just look it up on Levels.fyi. Those numbers move around and I'd rather you see current data than trust something I half-remember.

So how do you prep without losing your mind? Less grinding than you'd think. For coding, a focused set of around 150 problems you genuinely understand beats blasting through 500 you'll forget by Friday. Drill the common patterns: two pointers, sliding window, BFS and DFS, heaps, intervals, the usual DP shapes. For system design, learn the building blocks (load balancers, sharding, caching, queues) and rehearse one repeatable way of walking through any prompt. For behavioral, write out six to eight stories and get comfortable actually saying them.

The single biggest thing that helped me wasn't more problems, though. It was practicing out loud under something close to real conditions. Solving quietly at your desk does nothing for the moment a stranger is watching and your mind goes blank. Once I started running mock interviews where I had to speak my answers, that gap finally closed. I've been using LastRound AI for it lately. It runs voice mock interviews that score how you actually respond, and it keeps interview breakdowns for Meta and a few hundred other companies that pull the round structure and question patterns from public sources, so you walk in already knowing the format.

If I had to compress all of this into one line: stop trying to be impressive and start being clear. Meta interviewers are genuinely rooting for you. Give them a clean view of how you think, treat the behavioral round like it's technical, and the rest tends to fall into place.

If you've been through a Meta loop recently, I'd love to hear what caught you off guard. The comments are the best part of these posts.