Dashboard
Week — · Target: Google
MISSION
"In the intersection of logic
and creativity lies true innovation."
Technologist driven by the impulse to create.
Architecting systems at VIT Chennai.
Roadmap
0%
Phase 1 of 6
LeetCode
—
Connect LeetCode
GitHub
—
Connect GitHub
CF Rating
—
Connect Codeforces
COGNITIVE LOAD ANALYST
Productivity Recommendation
AI // ONLINE
OPTIMIZATION
High
Sprinting Phase
Analyzing your trajectory... Current LeetCode consistency suggests you should pivot
to **System Design (Phase 3)** to maximize your retention window. Focus on **Concurrency** patterns
today.
SYSTEM DIAGNOSTICS
UPTIME
0h 0m
COGNITIVE MEMORY
20.0%
Daily Mastery Checklist
2 LeetCode Mediums
Review 1 System Design Pattern
30m Tech Fundamental Reading
Update Journal / Reflection
Practice 1 Behavioral (STAR) Story
Analyze 1 Tech Blog (Engineering Feed)
Open Source Contrib / Bug Hunting
System Design Mock Sketch
Quick Jumps
Tech Intelligence
Live Global Industry Pulse
Today's Schedule
Deep Focus
⌛
Start Sprint
25:00 Pomodoro Protocol
Quick Intel
Profile
PIYUSH KUMAR · VIT CHENNAI
Academic Profile
COLLEGE
V.I.T CHENNAI
BATCH
2025 — 2029
AMBITION
SOFTWARE FIELD · GAME DESIGNER · WEB-TOOL
Languages Learnt
PYTHONCC++HTMLSQL
Contact Information
EMAIL[email protected]
INSTAGRAM@piyush_kumar
OS Personalization
CORE THEMES
SYSTEM COGNITION
Intelligence Index
842
Processing state: STABLE // Optimization:
ACTIVE
Projects
System implementations
AI Research Hub
Global search and AI integration for
personal knowledge management.
System Infrastructure
Cloudflare R2D1
SQLWorkers
Quantum Arcade
multi-game platform with shared
economy and persistent progress.
Canvas APIState
Machine
Cloud Vault
Secure storage for resumes, code
snippets, and private study materials.
Supabase StoragePostgreSQL
Cloud Vault
Secure storage & document management
Root
☁️
Secure Upload Zone
Drag & drop files here or click to browse
🔍
Placement Guide
Comprehensive FAANG Handbook
Domain Map
1. Understanding the Giants
PHILOSOPHY
Google: Algorithmic complexity & scale.
Amazon: Leadership Principles & Ownership.
Meta: Fast iteration & Product Sense.
Apple: Precision & Security.
Amazon: Leadership Principles & Ownership.
Meta: Fast iteration & Product Sense.
Apple: Precision & Security.
LEVELING
L3 (Junior/New Grad)
L4/L5 (Senior/LMM)
L6+ (Staff/Principal)
L4/L5 (Senior/LMM)
L6+ (Staff/Principal)
2. DSA Mastery — Pattern Guide
Structures Deep-Dive
- • Monotonic Stack: Next Greater Element problems.
- • Trie: Prefix matching & Dictionary search.
- • Segment Tree: Range queries (Sum/Min/Max).
- • Union-Find: Cycle detection & Disjoint sets.
Algorithmic Logic
- • Sliding Window: Subarrays/Substrings (O(n)).
- • Two Pointers: Sorted arrays (O(n)).
- • Backtracking: N-Queens, Sudoku, Subsets.
- • DP: Memoization vs Tabulation.
3. System Design — Architecture
CASE: URL SHORTENER
• Hashing: Base62 encoding for IDs.
• DB: NoSQL (Key-Value) for scale.
• Cache: Redis for high-read redirecting.
CASE: CHAT SYSTEM
• Protocol: WebSockets for bi-directional.
• Offline: Message queuing (Kafka).
• Status: Heartbeat mechanisms for presence.
Concepts: CAP Theorem (Consistency vs Availability), Load Balancers (Round Robin vs Least
Conn), Database Sharding.
4. Advanced Technical Stack
DISTRIBUTED SYSTEMS
Microservices, Service Discovery, Fault Tolerance (Circuit Breakers), Consensus
(Raft/Paxos).
CLOUD & DEV SEC
Docker/K8s, CI/CD pipelines, AuthN/AuthZ (OAuth2/JWT), OWASP Top 10.
5. Behavioral Question Bank
Conflict: Tell me about a time you disagreed with a peer.
Failure: Describe a project that didn't go as planned.
Impact: When did you take initiative above your role?
Adapt: How did you handle a drastic requirement change?
6. Impact Resume
- • Action Verbs: +Impact/Metrics.
- • Numbers: "Scaled to 1M requests".
- • Length: 1 Page strict.
7. Process
- • 1. Recruiter Screen
- • 2. Phone Screen
- • 3. Onsite (4-6 rounds)
8. Platforms
LeetCode, HackerRank, InterviewBit.
9. Mocking
Pramp, Interviewing.io, Peer Mocks.
Strategy
Elite Preparation Framework
Behavioral — STAR Method
Situation: Context of the event.
Task: What was required of you?
Action: What did YOU specifically do?
Result: Outcome with numbers (e.g., +20% efficiency).
Target: Prepare 10-12 core stories.
Selection Differentiators
- • Structured communication > Fast coding.
- • Explaining trade-offs (A vs B).
- • Handling ambiguity with clarifying questions.
- • Writing modular, production-grade code.
Core Fundamentals
OS
Threads, Deadlocks, Mutex, Paging.
DBMS
Indexing, ACID, Query Optimization.
Networking
TCP/UDP, HTTP, Load Balancing.
X. TECHNICAL
ARCHIVE (ADVANCED)
Distributed Computing Mastery
1. Consistent Hashing: Crucial for horizontal scaling. Solves the issue of re-mapping keys when nodes are added/removed. Ring structure with virtual nodes ensures uniform data distribution.
2. Gossip Protocols: Decentralized communication. Used for cluster membership and failure detection (e.g., Apache Cassandra, Redis Cluster).
3. Distributed Tracing: Observability in microservices. OpenTelemetry, Jaeger, and Zipkin. Context propagation across service boundaries.
4. Stream Processing: Kappa vs Lambda Architectures. Exactly-once processing guarantees in Apache Flink and Kafka Streams.
5. CAP Theorem & Trade-offs: Understanding when to prioritize Availability (AP) vs Consistency (CP) in systems like DynamoDB vs MongoDB.
6. Database Indexing Deep Dive: B-Trees vs B+ Trees, LSM Trees (Log-Structured Merge-Trees) for write-heavy workloads like Cassandra and InfluxDB.
High-Level Mental Models
1. First Principles Thinking: Breaking down complex problems into basic truths for innovation.
2. Pareto Principle (80/20): Focus on the 20% of patterns that cover 80% of LeetCode hard problems.
3. Second-Order Thinking: Considering the consequences of consequences (critical for System Design trade-offs).
4. Occam's Razor: Choosing the simplest architecture that solves the scale requirement.
Hard Algorithmic Patterns
• Heavy Hitters (Top K): Count-Min Sketch for frequency estimation. Misra-Gries algorithm for space-efficient top-k identification.
• Range Minimum Query (RMQ): Sparse Table (pre-processing O(N log N), query O(1)). Useful for LCA of trees.
• Network Flow: Ford-Fulkerson and Edmonds-Karp. Applications in bipartite matching and resource allocation.
• Advanced DP: DP on Trees (using DFS to propagate states), Digit DP (counting numbers with specific properties), Bitmask DP for TSP-style problems.
• Geometry Algorithms: Convex Hull (Monotone Chain), Segment Intersections, Sweep Line algorithms for range searches.
52-WEEK MASTER CURRICULUM
Week 1-4: Foundations & Logic. Big-O, Arrays, Strings, Two Pointers, Sliding Window.
Week 5-8: Core Structures. Linked Lists, Stacks, Queues, Recursion mastery.
Week 9-12: Hierarchical Data. Binary Trees, BSTs, Heaps, Treaps.
Week 13-16: Graph Theory. BFS, DFS, Connectivity, Topological Sort.
Week 17-20: Advanced Graphs. Dijkstra, Bellman-Ford, Prim's, Tarjan's SCC.
Week 21-26: Dynamic Programming. 1D, 2D, Knapsack, LIS, LCS, Matrix Chain.
Week 27-30: String Algorithms. KMP, Rabin-Karp, Z-Algorithm, Tries, Suffix Trees.
Week 31-35: System Design Fundamentals. Load Balancers, Proxies, Caching, Sharding.
Week 36-40: Advanced Systems. Microservices, Message Queues (Kafka), Consensus (Raft).
Week 41-45: Industrial Case Studies. Instagram, WhatsApp, Uber, Netflix, YouTube architecture.
Week 46-48: Behavioral Mastery. Leadership Principles, Conflict Resolution, STAR Method.
Week 49-52: High-Pressure Mocks. Peer interviews, Salary negotiation, Final Review.
"Consistency is the only superpower that matters."
Integrations
Connect your platforms — data is per-account
My Profiles
Live stats from your connected platforms
Activity Feed
Live Activity
Unified feed from all platforms
AI Hub
The Most Famous Apps in the AI Ecosystem
LLMs & Intelligent Chat
🌐
ChatGPT
By OpenAI · The standard
🎭
Claude
By Anthropic · Reasoning king
💎
Gemini
By Google · 2M Context link
Software Engineering
⌨️
GitHub Copilot
Autocomplete & Chat
🚀
Cursor
AI-Native Code Editor
🏗
Replit Agent
Full App Generator
Creative & Multimedia
Research & Productivity
🔍
Perplexity
Search Redefined
📓
Notion AI
Workspace Intelligence
📜
Consensus
Scientific Search Engine
Specialized Industrial AI
Data Science: Polymer, Akkio, MonkeyLearn, KNIME, DataRobot.
Design/UI: Galileo AI, Uizard, Attention Insight, Fontbolt.
Marketing: Jasper, Copy.ai, Anyword, AdCreative, Writesonic.
Sales/CRM: Apollo.io, Gong, Lavender, Regie.ai, Outreach.
Customer Support: Intercom Fin, Ada, Kustomer, Forethought.
Legal: Harvey, CoCounsel, Ironclad, Luminance, Spellbook.
Deep Dev & Architecture AI
Code Reviews: Codacy, CodeRabbit, Codiga, DeepSource.
Documentation: Mintlify, Swimm, Trelent, scribe.
Testing: Applitools, Testim, Mabl, Functionize.
Security: Snyk AI, Mend.io, Checkmarx, Veracode.
Infrastructure: Pulumi AI, Firefly, Brainboard.
Omni-Productivity & Automation
Meetings: Otter.ai, Fireflies, Grain, Rewatch.
Email: Superhuman, Shortwave, SaneBox.
Workflows: Zapier Central, Make.com, Bardeen.
Visualizations: Gamma, Beautiful.ai, Tome, Decktopus.
Production Workflow
State-of-the-Art Stack & Orchestration
Reflection Checklist
Is it liked by me?
Overall stack satisfaction
Production Ready?
Battle-tested for users
Roadmap
Learning Path
AI Skills & Importance Matrix
Tiered curriculum from Junior to Staff Engineer.
Tier 1: Junior/Entry
Foundations of utility.
• Prompt Engineering mastery
• Basic API integration (OpenAI/Groq)
• Understanding LLM limitations (Hallucinations)
• Basic API integration (OpenAI/Groq)
• Understanding LLM limitations (Hallucinations)
Tier 2: Mid/Senior
Building production systems.
• **RAG Pipelines**: Vector DBs & Retrieval
• Context Window management
• Agentic Workflows (Tool calling)
• Context Window management
• Agentic Workflows (Tool calling)
Tier 3: Staff/Architect
Strategic AI infrastructure.
• Multi-agent Orchestration (LangGraph)
• LLM Fine-tuning & Distillation
• AI Security & Compliance (Guardrails)
• LLM Fine-tuning & Distillation
• AI Security & Compliance (Guardrails)
Advanced Resources
AI Blueprints
Production-ready workflows for AI-native applications.
Workflow: RAG Pipeline
Advanced Document Retrieval
1. **Ingest**: PDF/Text processing with chunking.
2. **Embed**: Generate vectors via `text-embedding-3`.
3. **Store**: Index in Pinecone or pgvector.
4. **Retrieve**: Semantic search for top-k chunks.
5. **Augment**: Feed context to LLM for final answer.
2. **Embed**: Generate vectors via `text-embedding-3`.
3. **Store**: Index in Pinecone or pgvector.
4. **Retrieve**: Semantic search for top-k chunks.
5. **Augment**: Feed context to LLM for final answer.
Workflow: Agentic Loop
Autonomous Problem Solving
1. **Plan**: LLM decomposes goal into sub-tasks.
2. **Execute**: Call tools (Search, Code Exec, DB).
3. **Verify**: Self-correction based on tool results.
4. **Iterate**: Loop until termination criteria met.
2. **Execute**: Call tools (Search, Code Exec, DB).
3. **Verify**: Self-correction based on tool results.
4. **Iterate**: Loop until termination criteria met.
Operational Best
Practices
• **Evaluations**: Use `Ragas` for retrieval quality.
• **Cost**: Implement token-based rate limiting.
• **Latency**: Use streaming for responsive UX.
• **Cost**: Implement token-based rate limiting.
• **Latency**: Use streaming for responsive UX.
Calendar
Study sessions · Google Calendar sync
MON
TUE
WED
THU
FRI
SAT
SUN
This Week's Schedule
Upcoming Sessions
🤖 AI Study Planner
Tell the AI your availability and target
date
— it'll generate an optimal study plan.
Progress
Heatmap · Journal · Phase breakdown
Activity Heatmap — 6 Months
Less
More
Phase Progress
Journal
AI Coach
Groq API · Fastest inference · 6,000 TPM free
Model Selection
📋 Review my LeetCode approach for Two Sum
🏗 Explain URL shortener system design
🤖 What AI/ML skills matter most for FAANG?
📝 Help me write a STAR leadership story
🧠 Explain Transformers simply
💼 Give me a mock behavioral question
AI System Blueprints
Architectural templates for modern intelligence.
LLM RAG Pipeline
High-performance vector search architecture.
• **Ingestion**: Unstructured data -> Chunking -> Embeddings.
• **Retrieval**: Vector DB (Pinecone/Milvus) with semantic search.
• **Augmentation**: Context injection into LLM prompts.
• **Retrieval**: Vector DB (Pinecone/Milvus) with semantic search.
• **Augmentation**: Context injection into LLM prompts.
Agentic Workflows
Autonomous multi-agent systems.
• **Planning**: Chain-of-Thought (CoT) and ReAct patterns.
• **Execution**: Tool use, function calling, and API integration.
• **Memory**: Persistent state across agent steps.
• **Execution**: Tool use, function calling, and API integration.
• **Memory**: Persistent state across agent steps.
Intelligence Library & AI Skills
Curated resources and dynamic skill tracking.
My AI Skill Matrix
Recommended Learning Path
DSA Masters
The fundamental pillars of coding interviews.
System Design
Architecting at FAANG scale.
AI & ML Ops
Productionizing intelligence.
Career & Soft Skills
The "STAR" behind the engineer.
The Elite Intelligence Index (2026 Edition)
The most powerful technical and behavioral repositories on the web.
TOP 1% DSA BANKS
AI & SPECIALIZED BANKS
SYSTEM DESIGN ELITE
THE L6/L7 LEADERSHIP
VAULT
SYSTEM DESIGN (ARCHITECT)
Q: How do you design
a
URL shortener (TinyURL)?
A: Key components: Hash/Base64 encoding, NoSQL for high throughput (Key-Value),
Redis cache for hot URLs, and a Redirection service. Use consistent hashing for scaling.
Q: Explain the
difference between Push vs Pull in Notification systems.
A: Push: Server initiates (WebSockets/SSE); low latency but hard to scale with
millions.
Pull: Client polls periodically; easier to scale but introduces lag and overhead. Use Push for
real-time.
Q: What is the
"Thundering Herd" problem?
A: When many processes wake up to a single event (e.g., cache expiry) and crash the
backend.
Solve with: Jitter (random expiry), Mutex locks, or Probabilistic early expiration.
AI & ML ENGINEERING
Q: What is the
difference between RAG and Fine-tuning?
A: RAG (Retrieval-Augmented Generation) provides external knowledge at inference;
best
for dynamic info.
Fine-tuning updates model weights; best for learning style, domain-specific terminology, or new
capabilities.
Q: Explain
"Temperature" in LLM sampling.
A: Controls randomness. T=0 is deterministic (picks max probability).
Higher T (e.g., 0.7-1.0) flattens the distribution, allowing for more "creative" or diverse token
selection.
Q: What is a Vector
Database?
A: A database optimized for high-dimensional embedding storage and similarity search
(ANN search).
Essential for RAG. Examples: Pinecone, Weaviate, Milvus, Chroma.
CLOUD & INFRASTRUCTURE
Q: What is "Cold
Start"
in Serverless (FaaS)?
A: The latency when an idle function is invoked and the cloud provider must spin up a
new container instance.
Mitigate with: "Warm" pings, provisioned concurrency, or smaller code bundles.
Q: Explain
Infrastructure as Code (IaC) - Declarative vs Imperative.
A: Declarative (Terraform/CloudFormation): Define the "Desired State". Imperative
(Ansible/Scripts):
Define the "Steps" to reach a state. FAANG prefers Declarative for reproducibility.
Q: How does a
Content
Delivery Network (CDN) work?
A: Caches static content at "Edge Locations" geographically closer to users.
Uses Anycast routing to direct users to the nearest node, reducing latency and origin server load.
SECURITY & DEVSECOPS
Q: What is SQL
Injection (SQLi) and how to prevent it?
A: Malicious SQL code injected into inputs. Prevent using **Prepared Statements**
(Parameterized Queries)
and input sanitization. Never concatenate raw strings into SQL.
Q: Explain
Cross-Site
Scripting (XSS).
A: Injecting malicious scripts into web pages viewed by other users.
Mitigate with: CSP (Content Security Policy), HTML escaping, and "HttpOnly" cookies to prevent token
theft.
Q: What is "Zero
Trust" architecture?
A: A security model that assumes no actor (internal or external) is trusted by
default.
Requires continuous verification through MFA, identity-based access, and micro-segmentation.
ADVANCED SYSTEMS (L6+)
Q: Explain the CAP
Theorem.
A: In a distributed system, you can only pick 2 of: Consistency, Availability,
Partition
Tolerance.
Since network partitions are inevitable, real-world choices are CP or AP.
Q: What is "Eventual
Consistency"?
A: A consistency model where, given no new updates, all accesses will eventually
return
the last updated value.
Used in AP systems like DynamoDB or Cassandra for high availability.
Q: How does
Consistent
Hashing work?
A: Maps data and nodes to a circular hash ring. Minimizes data remapping when nodes
are
added or removed.
Essential for scalable caches and distributed databases.
BACKEND & SYSTEMS
Q: Optimistic vs
Pessimistic Locking.
A: Optimistic: Assume no collisions; use versioning (MVCC). Fast for read-heavy.
Pessimistic: Lock resources immediately. Best for high-contention write-heavy scenarios.
Q: How does a B-Tree
differ from an LSM Tree?
A: B-Tree: Optimized for Reads (RDBMS). LSM: Optimized for Writes (NoSQL).
LSM uses Log-Structured merge-and-compact; B-Tree uses sorted nodes.
Q: What is an Inode
in
Linux?
A: Metadata structure storing file attributes (permissions, block pointers) but not
the
name/data.
The Hall of Fame: Legendary Interview Questions
The most iconic questions you MUST know to survive a FAANG interview.
"What happens when you
type
google.com?"
The Expert Answer:
1. **DNS Lookup**: Local cache -> Resolver -> Root -> TLD -> Authoritative.
2. **TCP/TLS Handshake**: 3-way TCP handshake + TLS 1.3 certificate exchange.
3. **HTTP Request**: Browser sends GET request via the socket.
4. **Server Processing**: Load Balancer -> Web Server -> App Server -> Database.
5. **Rendering Phase**: CRP (Parsing HTML/CSS -> Layout -> Painting on GPU).
1. **DNS Lookup**: Local cache -> Resolver -> Root -> TLD -> Authoritative.
2. **TCP/TLS Handshake**: 3-way TCP handshake + TLS 1.3 certificate exchange.
3. **HTTP Request**: Browser sends GET request via the socket.
4. **Server Processing**: Load Balancer -> Web Server -> App Server -> Database.
5. **Rendering Phase**: CRP (Parsing HTML/CSS -> Layout -> Painting on GPU).
"Design a Rate
Limiter"
(System Design)
The Expert Answer:
1. **Algorithm**: Token Bucket (best for burst traffic) or Leaky Bucket (smooth traffic).
2. **Storage**: Redis (fast in-memory increment/decrement).
3. **Strategy**: Sliding Window Log (precise) vs Fixed Window (simpler, but burst issues).
4. **Scaling**: Use Redis clusters and a centralized Rate Limiting service to avoid local state issues.
1. **Algorithm**: Token Bucket (best for burst traffic) or Leaky Bucket (smooth traffic).
2. **Storage**: Redis (fast in-memory increment/decrement).
3. **Strategy**: Sliding Window Log (precise) vs Fixed Window (simpler, but burst issues).
4. **Scaling**: Use Redis clusters and a centralized Rate Limiting service to avoid local state issues.
"Reverse a Linked List"
(The
DSA Classic)
The Expert Answer:
1. **Iterative**: Use three pointers: `prev`, `curr`, `next`. Move them step-by-step to flip the `next` pointer of `curr`.
2. **Recursive**: Base case (head is null or head.next is null). Recursive call to reverse the rest, then set `head.next.next = head` and `head.next = null`.
3. **Complexity**: Time O(N), Space O(1) iterative, O(N) recursive (stack).
1. **Iterative**: Use three pointers: `prev`, `curr`, `next`. Move them step-by-step to flip the `next` pointer of `curr`.
2. **Recursive**: Base case (head is null or head.next is null). Recursive call to reverse the rest, then set `head.next.next = head` and `head.next = null`.
3. **Complexity**: Time O(N), Space O(1) iterative, O(N) recursive (stack).
"Two Sum" (The Legend)
The Expert Answer:
1. **Brute Force**: O(N^2) comparison.
2. **Optimized**: Use a Hash Map to store `complement = target - nums[i]`.
3. **Single Pass**: As you iterate, check if the current number's complement exists in the map. If not, add the current number and its index to the map.
4. **Complexity**: Time O(N), Space O(N).
1. **Brute Force**: O(N^2) comparison.
2. **Optimized**: Use a Hash Map to store `complement = target - nums[i]`.
3. **Single Pass**: As you iterate, check if the current number's complement exists in the map. If not, add the current number and its index to the map.
4. **Complexity**: Time O(N), Space O(N).
BEHAVIORAL (STAR)
Q: "Tell me about a
time
you failed."
A: Focus on a genuine professional mistake, explain the immediate correction (Action),
the lesson learned, and how you ensured it never happened again (Result). No "fake" failures.
Q: "What is your
biggest
weakness?"
A: Pick a technical skill or "soft" trait you previously lacked,
detail the structured steps you've taken to improve (courses, feedback loops), and show current
progress.
Q: "How do you handle
a
toxic teammate?"
A: Focus on Professionalism, Communication, and Escalation (if needed).
Show empathy but maintain team velocity and mental safety as the priority.
Engineering Blogs (Real-time Intel)
♾️
Meta Eng
🔍
Google Dev
🎬
Netflix Tech
☁️
AWS Arch
🏠
Airbnb Eng
🚗
Uber Eng
💳
Stripe Eng
🐙
GitHub Eng
🧡
Cloudflare
👾
Discord Eng
💬
Slack Eng
📌
Pinterest
🔗
LinkedIn Eng
📦
Dropbox Tech
🎈
DoorDash
🚕
Grab Eng
🎨
Lyft Eng
🤖
Reddit Eng
🎧
Spotify Eng
🖌️
Canva Eng
Video Mastery Academy (Free Courses)
DSA & ALGORITHMS
SYSTEM DESIGN
LLD & OOD
AI & MACHINE LEARNING
DEVOPS, SECURITY & WEB
AI Arsenal & Tooling (Force Multipliers)
Claude 3.5 Sonnet
Pro-tier code architecture
Cursor IDE
The AI native VS Code fork
v0.dev
UI/UX generation by Vercel
Perplexity AI
Live web search & research
Phind
Developer search engine
ChatGPT Plus
GPT-4o & Canvas mastery
Google Gemini
Multimodal 1.5 Pro model
ElevenLabs
Industry leading AI voice
Runway Gen-3
High-end AI video generation
Leonardo.ai
Dynamic image/asset generation
Pika Art
Cinematic video motion
HeyGen
AI Avatar & Video translation
Quillbot
Paraphrasing & Writing aid
Grammarly
Real-time comms optimization
Otter.ai
Meeting notes & transcription
Fireflies
Intelligence for meetings
The 100+ Knowledge Index
TOP TECH CREATORS
OPEN SOURCE DEEP DIVES
ESSENTIAL DEV TOOLS
CS CLASSICS & PAPERS
System Architecture Vault
Distributed systems, scalability, and high availability.
Load Balancing
Distributing traffic across clusters.
• Algorithms: Round Robin, Least Conn, IP Hash
• Layer 4 vs Layer 7 balancing
• Health checks & auto-scaling
• Layer 4 vs Layer 7 balancing
• Health checks & auto-scaling
Caching Layer
Reducing latency with Redis/Memcached.
• Eviction: LRU, LFU, FIFO
• Strategies: Cache-aside, Write-through, Write-back
• Cache stampede prevention
• Strategies: Cache-aside, Write-through, Write-back
• Cache stampede prevention
Database Sharding
Horizontal scaling of data layers.
• Key-based vs Range-based sharding
• Re-sharding challenges
• Consistent Hashing
• Re-sharding challenges
• Consistent Hashing
Message Queues
Asynchronous processing & decoupling.
• Pub/Sub vs Point-to-Point
• Kafka vs RabbitMQ vs SQS
• Idempotency in consumers
• Kafka vs RabbitMQ vs SQS
• Idempotency in consumers
Interview War Room
Behavioral frameworks and technical drills.
STAR Method Framework
Structuring behavioral responses for impact.
S // Situation
Brief context of the challenge.
T // Task
What was your specific responsibility?
A // Action
The detailed steps YOU took.
R // Result
Quantified impact & outcome.
Behavioral Story Bank
Your vault of STAR-formatted career milestones.
The "Conflict" Story
S: Disagreement on system architecture with senior dev.
T: Resolve conflict without delaying the sprint.
A: Data-driven POC to compare throughput and latency.
R: Selected the hybrid approach; improved perf by 40%.
T: Resolve conflict without delaying the sprint.
A: Data-driven POC to compare throughput and latency.
R: Selected the hybrid approach; improved perf by 40%.
The "Ownership" Story
S: Production outage on Saturday midnight.
T: Restore service and find root cause.
A: Led the war room; identified faulty DB migration.
R: Stabilized in 2h; implemented pre-deploy checks.
T: Restore service and find root cause.
A: Led the war room; identified faulty DB migration.
R: Stabilized in 2h; implemented pre-deploy checks.
The "Growth" Story
S: New team member struggling with the stack.
T: Onboard them effectively while hitting my goals.
A: Created 5-day bootcamp docs & pair programmed.
R: Independent contributor in 3 weeks; docs now standard.
T: Onboard them effectively while hitting my goals.
A: Created 5-day bootcamp docs & pair programmed.
R: Independent contributor in 3 weeks; docs now standard.
+
Add New Success Story
Technical Drills
Focused exercises for high-stakes execution.
Coding Patterns Drill
Master the templates that solve 80% of problems.
Sliding Window
Subarrays, substrings, constraints
Fast & Slow Pointers
Cycle detection, linked lists
Merge Intervals
Scheduling, overlapping timeframes
Top K Elements
Heaps, frequency analysis
Mock Simulator
Simulate a high-pressure 45-minute round.
45:00
Level: SDE-II (Generalist)
• Peer-mode: Share screen with a friend
• AI-mode: Record & Analyze with AI Coach
• Performance Metrics: Speed, Quality, Edge Cases
Gmail
Recent emails from your connected account
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Connect Gmail to see your emails
Google Drive
Your files & study documents
📁
Connect Google Drive to access your files