SAGAR SINGH

Java | Javascript

[SCR @SCR01111]

RUBRIK SOFTWARE ENGINEER ROADMAP📌

How to get into Rubrik

Rubrik's engineering roles involve high-scale problems across areas such as the following:

distributed systems, cloud, data protection, and security.

1. DSA

Focus on:

• Arrays
��� Strings
• Hashing
• Binary Search
• Linked List
• Trees
• Graphs
• BFS / DFS
• Heaps
• Tries
• Dynamic Programming
• Greedy

Target:

150–200 quality problems

Focus on Medium → Hard.

Practice explaining:

Brute Force → Optimization → Complexity → Edge Cases

Resources:

LeetCode:
https://t.co/g0z6SxImHp

NeetCode:
https://t.co/SfgneaijB4

Striver A2Z DSA:
https://t.co/PvgHeH5QGQ

2. OPERATING SYSTEMS

Important topics:

• Processes vs Threads
• Memory Management
• Virtual Memory
• CPU Scheduling
• Context Switching
• Synchronization
• Deadlocks
• Race Conditions
• File Systems

Resources:

MIT Operating Systems:
https://t.co/f0THkRO9Hy

MIT xv6:
https://t.co/0g0YyPC3q0

xv6 Book:
https://t.co/bQazkGuyCo

3. CONCURRENCY

This is an important area for systems-oriented preparation.

Learn:

• Threads
• Mutex
• Locks
• Semaphores
• Condition Variables
• Thread Safety
• Producer Consumer
• Reader Writer
• Thread Pools
• Job Scheduling
• Race Conditions
• Deadlocks

Resources:

MIT Operating Systems:
https://t.co/f0THkRO9Hy

C++ Concurrency:
https://t.co/CH7glp1eFw

Java Concurrency:
https://t.co/sowBcaALjn

4. SYSTEM CODING

Don't just learn system design theory.

Practice implementing:

• Thread Safe Queue
• Thread Pool
• Job Scheduler
• LRU Cache
• Rate Limiter
• Connection Pool
• Concurrent Data Structures
• Memory Allocator

Resources:

Concurrency in Practice:
https://t.co/gWOjK3CckU

C++ Thread Library:
https://t.co/CH7glp1eFw

Java Executor Framework:
https://t.co/7V00P2Wjw2

5. LOW LEVEL DESIGN

Focus on:

Requirements
↓
Classes / Interfaces
↓
Data Structures
↓
Concurrency
↓
Error Handling
↓
Thread Safety
↓
Implementation

Practice:

• LRU Cache
• Parking Lot
• Logger
• Rate Limiter
• Task Scheduler
• File System
• Connection Pool

Resources:

Refactoring Guru:
https://t.co/3iZGOwVQxm

Design Patterns:
https://t.co/99axzmkoMm

System Design Primer:
https://t.co/kj8QBHCZvh

6. DISTRIBUTED SYSTEMS

Learn:

• CAP Theorem
• Replication
• Sharding
• Partitioning
• Consistency
• Distributed Locks
• Leader Election
• Message Queues
• Fault Tolerance
• Failure Recovery

Resources:

MIT 6.5840 Distributed Systems:
https://t.co/QSR25wcSNe

MIT Distributed Systems Labs:
https://t.co/yZnEuTj3g7

Designing Data-Intensive Applications:
https://t.co/o8qZS3Yjlz

7. STORAGE SYSTEMS

This deserves special attention for Rubrik.

Understand:

• Object Storage
• Block Storage
• Distributed File Systems
• Snapshots
• Backup & Recovery
• Deduplication
• Compression
• Encryption
• Replication
• Metadata Management

Resources:

MIT 6.5840:
https://t.co/QSR25wcSNe

Google File System Paper:
https://t.co/NtgArvuTwS

Amazon S3 Documentation:
https://t.co/3MqLxXBqyZ

Ceph Documentation:
https://t.co/UzmP2InbHj

8. RUBRIK-SPECIFIC SYSTEM DESIGN

Practice designing:

• Distributed Backup System
• Distributed File System
• Large Scale Storage System
• Deduplication Service
• Snapshot System
• Distributed Job Scheduler

For every design, think about:

Scale
Reliability
Consistency
Failure Recovery
Performance
Security

Resources:

Rubrik Engineering:
https://t.co/HDRT0ySJXZ

MIT Distributed Systems:
https://t.co/QSR25wcSNe

System Design Primer:
https://t.co/kj8QBHCZvh

DDIA:
https://t.co/o8qZS3Yjlz

9. PROJECTS

Build at least one systems-focused project.

PROJECT 1

Distributed Backup System

File Chunking
↓
Hashing
↓
Deduplication
↓
Compression
↓
Encryption
↓
Distributed Storage
↓
Restore

PROJECT 2

Concurrent Job Scheduler

Job Queue
↓
Worker Pool
↓
Task Execution
↓
Retries
↓
Timeouts
↓
Failure Handling

Resources:

MIT xv6:
https://t.co/f0THkRO9Hy

MIT Distributed Systems Labs:
https://t.co/yZnEuTj3g7

GitHub:
https://t.co/0z5pja4Zzr

10. INTERVIEW PREPARATION

Prepare for:

DSA
→ System Coding
→ OS / Concurrency
→ HLD
→ Technical Deep Dive
→ Hiring Manager

Do not assume every Rubrik interview follows exactly the same sequence.
The process can vary by role and hiring cycle.

Resources:

Rubrik Careers:
https://t.co/glcA9HvwxT

Rubrik Engineering Careers:
https://t.co/HDRT0ySJXZ

Rubrik Interview Experiences:
https://t.co/jypmP1ZOaV

LeetCode:
https://t.co/AVtz0lyLVN

11. RUBRIK INTERVIEW QUESTIONS

DSA

• LRU Cache
• Sliding Window Maximum
• 4Sum
• Edit Distance
• Graph Traversal
• Random Pick With Weight

System Coding

• Implement a Thread Safe Queue
• Implement a Thread Pool
• Implement Reader Writer Lock
• Design a Job Scheduler
• Implement a Memory Allocator

System Design

• Design a Distributed Backup System
• Design a Distributed File System
• Design a Storage System
• Design a Deduplication Service
• Design a Snapshot System

Practice Resources:

LeetCode:
https://t.co/g0z6SxImHp

Interview Experiences:
https://t.co/jypmP1ZOaV

System Design Primer:
https://t.co/kj8QBHCZvh

12. 8 WEEK PLAN

Week 1–2

DSA + Problem Solving

Resources:
https://t.co/SfgneaijB4

Week 3–4

Advanced DSA + OS

Resources:
https://t.co/f0THkRO9Hy

Week 5–6

Concurrency + System Coding + LLD

Resources:
https://t.co/CH7glp1eFw

https://t.co/sowBcaALjn

https://t.co/3iZGOwVQxm

Week 7

Distributed Systems + HLD

Resources:
https://t.co/QSR25wcSNe

https://t.co/kj8QBHCZvh

Week 8

Rubrik-specific Practice + Mock Interviews

Resources:

Rubrik Careers:
https://t.co/glcA9HvwxT

Rubrik Engineering:
https://t.co/HDRT0ySJXZ

Interview Experiences:
https://t.co/jypmP1ZOaV

FINAL PREPARATION STACK

DSA
→ Medium / Hard

OS
→ Threads + Memory + Processes

Concurrency
→ Locks + Synchronization

System Coding
→ Implement

LLD
→ Concurrent Components

Distributed Systems
→ Scale + Reliability

Storage
→ Backup + Snapshots + Deduplication

HLD
→ Distributed Storage

Projects
→ Systems-focused

Interview Prep
→ DSA + System Coding + HLD

Company Research
→ Rubrik Products + Engineering + Job Description

START HERE

1. Finish DSA fundamentals
2. Learn OS + concurrency
3. Build one systems project
4. Learn distributed systems
5. Practice Rubrik-style system coding
6. Study storage systems
7. Practise HLD
8. Apply through Rubrik's official careers page
9. Practice mock interviews
10. Prepare your resume around systems and engineering depth

[Swapna Kumar Panda @swapnakpanda]

If you want to go GOD LEVEL in SYSTEM DESIGN, learn these concepts NOW:

[Puneet Patwari @system_monarch]

I'm a Principal with 12 years of experience. If I was coaching you to crack system design rounds for Sr to Staff+ AI/ML roles at companies like Meta, Google, Salesforce, Amazon, etc.

I would 100% ask you to work on these fundamentals before we would start talking about interviews.

Because AI system design is still system design.

The only difference is that now your bottlenecks are not just databases, caches, and queues.

They are tokens, context windows, retrieval quality, inference cost, hallucinations, model latency, evals, and user trust.

Here are the fundamentals I would start with:

➤ LLM Basics
↬ Tokens
↬ Context Window
↬ Prompt Design
↬ System Prompts
↬ Temperature
↬ Top-p Sampling
↬ Structured Outputs
↬ JSON Mode
↬ Function Calling
↬ Tool Calling
↬ Agents
↬ Memory
↬ Guardrails
↬ Hallucinations
↬ Model Latency
↬ Model Routing
↬ Small vs Large Models
↬ Fine-tuning vs Prompting
↬ Open-source vs Closed Models

➤ RAG & Retrieval
↬ Embeddings
↬ Vector Search
↬ Vector Databases
↬ Chunking
↬ Chunk Overlap
↬ Metadata Filtering
↬ Hybrid Search
↬ Keyword Search
↬ Semantic Search
↬ Reranking
↬ Retrieval Recall
↬ Retrieval Precision
↬ Query Rewriting
↬ Document Freshness
↬ Permission-aware Retrieval
↬ Citation Grounding
↬ Evidence Selection
↬ Context Packing
↬ Missing Information Detection

➤ AI System Architecture
↬ API Gateway
↬ Request Routing
↬ Model Gateway
↬ Prompt Service
↬ Inference Service
↬ Retrieval Service
↬ Ranking Service
↬ Feature Store
↬ Offline Pipelines
↬ Online Serving
↬ Async Processing
↬ Queueing
↬ Streaming Responses
↬ Rate Limiting
↬ Fan-out/Fan-in
↬ Batch Inference
↬ Real-time Inference
↬ Human-in-the-loop Systems
↬ Fallback Workflows

➤ Cost & Performance
↬ Token Budgeting
↬ Prompt Compression
↬ Prompt Caching
↬ Semantic Caching
↬ Response Caching
↬ Batch Requests
↬ Model Quantization
↬ Distillation
↬ Latency Budgets
↬ Cold Starts
↬ GPU Utilization
↬ Throughput
↬ Cost per Query
↬ Cost per User
↬ Model Selection
↬ Inference Scaling
↬ Backpressure
↬ Load Shedding

➤ Evaluation & Quality
↬ Offline Evals
↬ Online Evals
↬ Golden Dataset
↬ Human Review
↬ LLM-as-Judge
↬ A/B Testing
↬ Regression Testing
↬ Answer Relevance
↬ Factual Accuracy
↬ Faithfulness
↬ Groundedness
↬ Toxicity Checks
↬ Safety Checks
↬ Drift Detection
↬ Feedback Loops
↬ Confidence Scoring
↬ Escalation Criteria
↬ Quality Monitoring

➤ Reliability & Security
↬ Timeouts
↬ Retries
↬ Circuit Breakers
↬ Failover
↬ Model Fallbacks
↬ Graceful Degradation
↬ Observability
↬ Tracing
↬ Prompt Logs
↬ Token Metrics
↬ Error Budgets
↬ PII Redaction
↬ Data Privacy
↬ Access Control
↬ Prompt Injection
↬ Jailbreak Defense
↬ Audit Logs
↬ Compliance

[Shubham Mishra 🐐 @shubh6200]

This playlist is super underrated when it comes to understanding the engineering behind trillions parameter LLM inference from first principles by @thecommitlog

Save it for your weekend watch :
https://t.co/2VNA1WteiU

[Norvex @norvex1029]

Anthropic just published the official guide to building a knowledge graph with Claude.

You build one when the answer lives in no single document. Who works with the people who worked on project X. Which vendors are connected to this incident. Retrieval gives you the closest chunks, but the full chain exists in none of them.

This used to mean training an entity recognizer on your domain, training a relation classifier, and writing merge rules by hand. Three systems to maintain, all three going stale as your data shifts. In Anthropic’s guide, each one becomes a prompt.

The cheap model handles extraction. You define the output shape once, and every document comes back as typed entities and triples that fit it. It also writes one sentence about each entity, which looks like a throwaway until the next step.

Then the smart model merges duplicates, and this is where the old stack often broke. Edit distance catches NASA against National Aeronautics and Space Administration. It does nothing for Edwin Aldrin and Buzz Aldrin, which share no characters but are the same person. The model reads those one-line descriptions and merges them based on meaning.

Queries run the other way. Take the neighborhood around one node, hand Claude the raw triples, and every claim in the answer points back to the edge it came from.

It ships with a gold set and a scorer, so you change one line of the extraction prompt, rerun it, and watch precision move.

Full breakdown in the article below.

Bookmark this.

[Noisy @noisyb0y1]

She built 100+ agents for Anthropic and made $1.3M - and in 60 minutes leaked everything she knows at Stanford:

02:07 - her first agent for Anthropic brought her $1.3M
08:34 - agents replace a team of 50 engineers worth $200k a month
19:47 - one agent did overnight what the company planned for 5 years

after watching I launched my first agent - $7k in the first week and zero employees needed.

Save & watch - the article below is step by step how to build your first agent like hers.

[Khairallah AL-Awady @eng_khairallah1]

this is f*cking gold

Andrej Karpathy joined Anthropic five weeks ago.

Two Anthropic seniors just made Karpathy's loop 1000x better with "Graph Engineering"

the agentic systems got 1000x better the moment you wired agents into a graph

I dropped it into my setup. The very first response was different.

Not slightly different. Completely different.

Claude stopped giving generic answers and started working exactly the way I think.

Bookmark it before it gets lost in your feed.

Read it now, then check the article below.

[Neo Kim @systemdesignone]

If you want to become GOAT-level with system design, then learn these 17 case studies (save now):

[Suraj Sharma @suraj_sharma14]

As a Backend Engineer, you must build these projects.

Systems that prove you can scale, secure & ship.

1.) High-Throughput API Service
Build: FastAPI or Go service handling 10k+ RPS with async processing.
Why: Proves you understand concurrency, latency & load balancing.

2.) Real-Time Data Pipeline
Build: Kafka or Flink pipeline processing events with exactly-once semantics.
Why: Shows you can handle streaming data not just batch jobs.

3.) Vector Search Engine
Build: Similarity search over millions of embeddings with metadata filtering.
Why: Modern backends need semantic search not just keyword matching.

4.) Distributed Cache Layer
Build: Redis-based caching with invalidation strategies and cache warming.
Why: Database performance depends on your caching strategy.

5.) Multi-Tenant Auth System
Build: OAuth2/OIDC provider with RBAC, SSO & session management.
Why: Security is non-negotiable. You must understand identity deeply.

6.) Background Job Queue
Build: Celery or Bull alternative with retry logic, dead-letter queues, priorities.
Why: Async task processing is the backbone of scalable systems.

7.) Observability Platform
Build: Distributed tracing, structured logging, metrics dashboards, alerting.
Why: You cannot fix what you cannot see. Debugging at scale requires visibility.

8.) Event-Driven Microservices
Build: Pub/Sub architecture with saga pattern for distributed transactions.
Why: Monoliths do not scale. You need to manage service communication.

9.) Infrastructure as Code
Build: Terraform or Pulumi scripts provisioning entire environments reproducibly.
Why: Manual deployment is dead. Automation is mandatory.

10.) Database Sharding Strategy
Build: Horizontal partitioning with consistent hashing and rebalancing logic.
Why: Vertical scaling has limits. You must know how to scale data.

11.) Security Middleware
Build: Rate limiting, WAF rules, input validation, DDoS protection layers.
Why: One vulnerability can destroy trust. Security is a feature.

12.) CI/CD Pipeline
Build: GitHub Actions with automated testing, canary deployments, rollback.
Why: Shipping fast means shipping safely. Automation reduces risk.

13.) Real-Time WebSocket Server
Build: Bidirectional communication for chat, notifications or live updates.
Why: HTTP is not enough for modern interactive experiences.

14.) Cost Optimization Dashboard
Build: Track cloud spend per service, identify waste, automate resource scaling.
Why: Engineering decisions impact profitability. FinOps is part of the job.

15.) Disaster Recovery Plan
Build: Backup strategies, failover testing, data restoration procedures.
Why: Systems fail. Your ability to recover defines reliability.

Most people watch tutorials. Builders ship systems.

Bookmark & Repost.

[Dan Kornas @DanKornas]

AI system design interviews are messy. This repo gives you a map.

AI System Design Guide is a practical GitHub reference for engineers preparing for AI system design interviews and building production AI systems.

It helps you avoid random tutorial-hopping by organizing the path around interview prep, RAG, agents, model selection, evaluation, security, reliability, and case studies.

Key features:

• Goal-based navigation – jump straight to interview prep, RAG, agents, model picking, evals, role transition, or the glossary
• Production AI coverage – chapters span retrieval systems, agentic systems, MLOps, security, reliability, safety, and observability
• Interview prep included – README points to a 110-question bank, answer frameworks, and whiteboard exercises
• Case-study library – architecture prompts cover search, coding agents, multi-tenant SaaS, support automation, document intelligence, and more
• Bonus eval guides – companion guides cover Phoenix, Langfuse, LangWatch, judges, RAG evals, tracing, and drift detection

It’s open-source (MIT license).

Link in the reply 👇

[Harsh @harshsinghsv]

AI Engineer Roadmap v4 is live 🔥

What's new:
→ Harness Engineering

https://t.co/mgOoIvaA2g

[Vaishnavi @_vmlops]

Cracking system design interviews shouldn’t feel like wandering through 47 browser tabs and a vague YouTube playlist.

https://t.co/Z0WI4qkkkV is basically “LeetCode for system design + behavioral interviews” with AI mock interviews, guided walkthroughs, and real interview experiences from companies like Meta, Amazon & Google

If you’re preparing for SWE, ML, or data interviews in 2026, this is worth checking out

[Harsh @harshsinghsv]

I built the most complete AI Engineer roadmap on the internet.

10 stages. 247 topics. 89 resources. All free.

Math → LLMs → RAG → Agents → MCP → MLOps → System Design

Nothing paywalled. Nothing held back.

https://t.co/mgOoIvaA2g

[Avinash Singh @AvinashSingh_20]

10 GitHub Repos To Learn in-depth Ai engineering !

1 :- https://t.co/H0GU9laXzT
2 :- https://t.co/cMUDbPKojT
3 :- https://t.co/6ZROJxbfYp
4 :- https://t.co/cOKeyPyERS
5 :- https://t.co/vlreU5F1QN
6 :- https://t.co/fw3opQU7wV
7 :- https://t.co/jjigzQKG1p
8 :- https://t.co/H3wnY4EH9r
9 :- https://t.co/v7LJeyMPz6
10 :- https://t.co/r3eOXdZ1sF

Bookmark it!

[Ajay @ajay_2512x]

System Design Interview Problems 🚀

➤ Easy
Design URL Shortener like TinyURL
Design Autocomplete for Search Engines
Design Load Balancer
Design Content Delivery Network (CDN)
Design Parking Garage
Design Vending Machine
Design Distributed Key-Value Store
Design Distributed Cache
Design Authentication System
Design Unified Payments Interface (UPI)

➤ Medium
Design WhatsApp
Design Spotify
Design Instagram
Design Notification Service
Design Distributed Job Scheduler
Design Tinder
Design Facebook
Design Twitter
Design Reddit
Design Netflix
Design Youtube
Design Google Search
Design E-commerce Store like Amazon
Design TikTok
Design Shopify
Design Airbnb
Design Rate Limiter
Design Distributed Message Queue like Kafka
Design Flight Booking System
Design Online Code Editor
Design an Analytics Platform (Metrics & Logging)
Design Payment System
Design a Digital Wallet

➤ Hard
Design Location Based Service like Yelp
Design Uber
Design Food Delivery App like Doordash
Design Google Docs
Design Google Maps
Design Zoom
Design File Sharing System like Dropbox
Design Ticket Booking System like BookMyShow
Design Distributed Web Crawler
Design Code Deployment System
Design Distributed Cloud Storage like S3
Design Distributed Locking Service

[千寻｜AI 分享 🌸 @Crypto_QianXun]

如果我要在90天内找到一份20万美元的AI工程师工作，我不会去读学位。

我会精通这10个GitHub仓库。

1. awesome-llm-apps
生产级AI指南。RAG、智能体、多模态应用，附完整代码。10.6万+ stars。
仓库 → https://t.co/0ZZKDLflhd

2. LangChain
基础框架。被Klarna、Replit、Elastic及2026年大多数AI创业公司用于生产环境。
仓库 → https://t.co/XqfpGJReBi

3. LangGraph
驱动生产智能体的编排层。资深AI工程师岗位描述中的必备技能。
仓库 → https://t.co/aHDtWGRsF0

4. CrewAI
多智能体协调。财富500强团队首选框架。
仓库 → https://t.co/JFbdjgk4Mt

5. Ollama
在自己机器上运行任何开源LLM。学习模型工作原理的最快方式。
仓库 → https://t.co/yySYBNIw7X

6. awesome-mcp-servers
MCP是2026年所有主流AI实验室���用的标准。掌握它让你领先99%的工程师。
仓库 → https://t.co/O6zfXs66cK

7. Qdrant
大规模生产RAG使用的向量数据库。嵌入和语义搜索是AI岗位的必需技能。
仓库 → https://t.co/mRXnbqF2XP

8. AI-Agents-for-Beginners
微软免费12课程，教建立智能体。真实代码、真实练习、真实面试准备。
仓库 → https://t.co/nbhJglVFHl

9. system-design-primer
生产级AI就是系统设计。FAANG工程师用来面试准备的仓库。
仓库 → https://t.co/ojgvZFVG25

10. awesome-claude-code
这个工具的使用指南，现被FAANG、OpenAI、Anthropic及大多数YC创业公司内部使用。
仓库 → https://t.co/D8EjUYiz3w

[Nav Toor @heynavtoor]

If I had to land a $200K AI engineer job in 90 days, I would not get a degree.

I would master these 10 GitHub repos.

1. awesome-llm-apps
The production AI playbook. RAG, agents, multimodal apps, all in working code. 106K+ stars.
Repo → https://t.co/oXrD5A8K6a

2. LangChain
The foundational framework. Used in production by Klarna, Replit, Elastic, and most AI startups in 2026.
Repo → https://t.co/alIh6rDDIu

3. LangGraph
The orchestration layer powering production agents. The skill on every senior AI engineer job description.
Repo → https://t.co/bzVBn9uecV

4. CrewAI
Multi-agent coordination. The framework most Fortune 500 teams reach for first.
Repo → https://t.co/0xohE065sD

5. Ollama
Run any open-source LLM on your own machine. The fastest way to learn how models actually work.
Repo → https://t.co/gyZhUdzsnZ

6. awesome-mcp-servers
MCP is the standard every major AI lab adopted in 2026. Knowing it puts you ahead of 99% of engineers.
Repo → https://t.co/ejVOgkRJDX

7. Qdrant
The vector database used for production RAG at scale. Embeddings and semantic search are non-negotiable for AI roles.
Repo → https://t.co/ziSSXW2dzZ

8. AI-Agents-for-Beginners
Microsoft's free 12-lesson course on building agents. Real code, real exercises, real prep.
Repo → https://t.co/7dNsDw6bTj

9. system-design-primer
Production AI is system design. The repo FAANG engineers use to prep for interviews.
Repo → https://t.co/AypwqcL1Xz

10. awesome-claude-code
The playbook for the tool now used inside FAANG, OpenAI, Anthropic, and most YC startups.
Repo → https://t.co/VhNjDoz7YM

Here's the wildest part:

A $200K AI engineer in 2026 isn't paid for a degree.

They are paid for what these 10 repos teach.

The market doesn't care where you learned it. It only cares if you can ship.

90 days. 10 repos. One portfolio that proves you can do the work.

That's it. That's the whole game.

Save this before you forget.

100% free. 100% open source.

[Ajay @ajay_2512x]

Auth0 Engineering Blogs 🚀

Build an AI Assistant with LangGraph, Vercel, and Next.js: Use Gmail as a Tool Securely
https://t.co/NhgUEMvMAB

Building a Secure RAG with Python, LangChain, and OpenFGA
https://t.co/qoIy0reGBh

Identity Challenges for AI-Powered Applications
https://t.co/8nwvzX6xxI

[Ajay @ajay_2512x]

Top 3 Projects of Week 15 on Peerlist — worth your attention

1. OCR Markdown
Turn PDFs, scans, and images into clean, editable Markdown in seconds.
No signups for basic OCR, runs right in your browser, and the premium model handles complex layouts like tables, math, and images with impressive accuracy. Huge time-saver if you deal with messy docs.
🔗 https://t.co/oGDRt4rmJ9

2. GhostlyX
Privacy-first analytics that actually respects your users.
No cookies, no creepy tracking — just fast, real-time insights with a clean dashboard. Perfect if you want actionable data without compromising GDPR compliance or site performance.
🔗 https://t.co/DlziJnvOUO

3. AI Opportunities Map
If you’ve ever felt stuck at the “what should I build?” stage — this is gold.
A massive, open-source map of the AI ecosystem with 2,500+ opportunities across 200 domains. Built using multiple research agents, so it’s not just random idea noise — it’s structured, validated insight.
🔗 https://t.co/MrocxVa5Ga

Which one would you try first?

[Ajay @ajay_2512x]

😤 Tired of manually reading LeetCode compensation posts?

Just came across an absolute gem 💎

Someone built LeetCode Comp Tracker and it basically turns all those scattered compensation posts into a clean, searchable dashboard.

https://t.co/uiU6oKEnWe

Why it’s awesome:
• Filter by role, company, YOE, currency, etc.
• Visual charts — YOE vs Total Comp, salary distribution, top-paying companies
• Export data as CSV

Honestly, this saves so much time if you’re prepping for offers or negotiations.

Big props to whoever built this 🙌
Definitely worth checking out.

https://t.co/8hYVwinEz1

#compensation #salary #tools #opensource