Kunal Darekar

Open Source contributor

[Kunal Darekar @kd_master99]

" Nothing is so necessary for a yound man as the company of intelligent women "

- Leo Tolstoy

[Kunal Darekar @kd_master99]

@0xalain_ @0xalain_ for pune location

[Kunal Darekar @kd_master99]

@aniketwarule1 Congrats 🎉🥳

[Rahul @sairahul1]

Complete Claude Code Training 6 HOURS.

The most comprehensive Claude training on the internet.

From A to Z: setup, workflow creation, website deployment, agent team creation, browser automation, client prospecting and pricing your services.

All of it without writing a single line of code.

In the end: you use Claude Code like a pro and you monetize your skills.

Beginner or advanced, everything is there in one place, this course covers it all.

It's worth more than all those $500 courses you almost bought.

Keep it bookmarked and watch later.

[Tech with Mak @techNmak]

The full AI engineering curriculum is now free.

It's called AI Engineering from Scratch. 20 phases, 428 lessons, roughly 320 hours end to end. Free. MIT license. Runs on your own laptop.

The design principle that makes it different from everything else => every algorithm gets built from raw math before a single framework loads. Backprop by hand. Tokenizer by hand. Attention by hand. Agent loop by hand. Then you implement the same thing in PyTorch or sklearn. By the time the production library appears, you already know what it's doing underneath.

Every lesson ends with something you keep:
→ Prompt templates for any AI assistant
→ Skill files for Claude, Cursor, Codex, OpenClaw, Hermes 
→ Agent definitions you wrote the loop for yourself 
→ MCP servers built from scratch in Phase 13

428 lessons means 428 artifacts by the end. Tools you built and actually understand.

The full 20 phases:
→ Phase 0 - Setup & Tooling (12 lessons) 
→ Phase 1 - Math Foundations (22 lessons) 
→ Phase 2 - ML Fundamentals (18 lessons) 
→ Phase 3 - Deep Learning Core (13 lessons) 
→ Phase 4 - Computer Vision (28 lessons) 
→ Phase 5 - NLP (29 lessons) 
→ Phase 6 - Speech & Audio (17 lessons) 
→ Phase 7 - Transformers Deep Dive (14 lessons) 
→ Phase 8 - Generative AI (14 lessons) 
→ Phase 9 - Reinforcement Learning (12 lessons) 
→ Phase 10 - LLMs from Scratch (22 lessons) 
→ Phase 11 - LLM Engineering (15 lessons) 
→ Phase 12 - Multimodal AI (25 lessons) 
→ Phase 13 - Tools & Protocols (23 lessons) 
→ Phase 14 - Agent Engineering (42 lessons) 
→ Phase 15 - Autonomous Systems (22 lessons) 
→ Phase 16 - Multi-Agent & Swarms (25 lessons) 
→ Phase 17 - Infrastructure & Production (28 lessons) 
→ Phase 18 - Ethics, Safety & Alignment (30 lessons) 
→ Phase 19 - Capstone Projects (17 projects, 20-40 hours each)

Python, TypeScript, Rust, Julia throughout.

GitHub Repo: https://t.co/E2Rg09gnrR

[Kones. @BCherich]

AI ML Scholarship *AWS AI ML Scholarship*

The AWS AI & ML Scholars Program (powered by AWS Skill Builder, Udacity, and Accenture) is a global learning initiative designed to help learners build strong foundations in AI and Agentic AI using real AWS cloud tools.

The program focuses on practical, industry-relevant skills in:

• Artificial Intelligence (AI) fundamentals
• Machine Learning (ML)
• Agentic AI systems
• Responsible AI practices
• AWS cloud-based AI development

Check out for details:

[Khushi Agarwal @Aicoder786]

ANDREJ KARPATHY COULD HAVE CHARGED $2,000 FOR THIS COURSE.

He put it on YouTube.

The full training stack. Tokenization. Neural network internals. Hallucinations. Tool use. Reinforcement learning. RLHF. DeepSeek. AlphaGo.

3 hours of the most comprehensive LLM education that exists anywhere at any price.

Not how to use the tools.

How the entire system was built from the ground up and why it behaves the way it does.

The engineers who understand this build things the ones who only use the tools cannot even conceive of.

The gap between those two groups is not 3 hours.

It is everything those 3 hours quietly unlock for the rest of your career.

[Rahul @sairahul1]

Anthropic pays $750,000+ a year for engineers who know how to build LLMs from scratch.

Stanford just released the exact lecture that teaches it - 1 hour 44 minutes, free, straight from CS229.

Bookmark and watch it this weekend.

It'll teach you more about how ChatGPT & Claude actually work than most people at top AI companies learn in their entire careers.

[Kunal Darekar @kd_master99]

@nitesh_singh5 Interested

[Kunal Darekar @kd_master99]

@nitesh_singh5 Interested

[Kunal Darekar @kd_master99]

@insanekrishnaa Interested

[Kunal Darekar @kd_master99]

@Ayushi_sharma02 interested

[Kunal Darekar @kd_master99]

@rom1trs UGC

[Kunal Darekar @kd_master99]

Choosing an OS is basically a personality test for developers

[Kunal Darekar @kd_master99]

@aniketwarule1 Happy Birthday 🎂

[Kunal Darekar @kd_master99]

@nitesh_singh5 interested

[Abhishek Singh @0xlelouch_]

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.

[Kunal Darekar @kd_master99]

@nitesh_singh5 Interested

[Leonard Rodman @RodmanAi]

System Design becomes EASY when you follow these:

1. Fundamentals
https://t.co/3El7u5v1at

2. API Design
https://t.co/y13ymiLR9c

3. Load Balancing
https://t.co/QP3ZH6iZxH

4. Message Queues
https://t.co/69i7a9FlDC

5. Rate Limiting
https://t.co/NcuCcfhY01

6. Caching
https://t.co/ZEmAMj5nb5

7. Sharding & Partitioning
https://t.co/KZcn6xrdPr

8. Database Replication
https://t.co/diWvyI4QZW

9. Consistent Hashing
https://t.co/JYZG24AXIf

10. CAP Theorem
https://t.co/8MQRmb3qy8

11. Microservices
https://t.co/pH29HRu3WJ

12. Fault Tolerance
https://t.co/hMAqL9i1Nv

13. Scalability
https://t.co/3sP0CkZTGF

14. Event-Driven Architecture
https://t.co/BAYffQm2td

15. Service Discovery
https://t.co/8BaRzHIaFk

[baba yaga @babayagatwt]

Master System Design

SYSTEM DESIGN MASTER TREE
│
├── 1. Foundations
│ ├── What is System Design
│ ├── Functional vs Non-Functional Requirements
│ ├── Scalability (Vertical / Horizontal)
│ ├── Latency vs Throughput
│ ├── Availability vs Consistency
│ └── CAP Theorem
│
├── 2. Core Architecture
│ ├── Client–Server Architecture
│ ├── 3-Tier Architecture
│ ├── Monolithic Systems
│ ├── Microservices
│ ├── Service Oriented Architecture
│ └── Event Driven Architecture
│
├── 3. Networking Layer
│ ├── HTTP / HTTPS
│ ├── REST APIs
│ ├── gRPC
│ ├── WebSockets
│ ├── API Gateway
│ └── Service Discovery
│
├── 4. Load Handling
│ ├── Load Balancers
│ │ ├── Round Robin
│ │ ├── Least Connections
│ │ └── Consistent Hashing
│ ├── Rate Limiting
│ ├── Throttling
│ └── Traffic Shaping
│
├── 5. Caching Systems
│ ├── Client Side Cache
│ ├── CDN
│ ├── Reverse Proxy Cache
│ ├── In-Memory Cache
│ │ ├── Redis
│ │ └── Memcached
│ └── Cache Strategies
│ ├── Cache Aside
│ ├── Write Through
│ └── Write Back
│
├── 6. Databases
│ ├── SQL Databases
│ │ ├── MySQL
│ │ └── PostgreSQL
│ ├── NoSQL
│ │ ├── MongoDB
│ │ ├── Cassandra
│ │ └── DynamoDB
│ ├── Data Modeling
│ ├── Indexing
│ └── Replication
│
├── 7. Database Scaling
│ ├── Vertical Scaling
│ ├── Horizontal Scaling
│ ├── Sharding
│ ├── Partitioning
│ ├── Read Replicas
│ └── Leader–Follower Replication
│
├── 8. Message Systems
│ ├── Message Queue
│ │ ├── RabbitMQ
│ │ └── SQS
│ ├── Event Streaming
│ │ └── Kafka
│ ├── Pub/Sub
│ └── Event Bus
│
├── 9. Storage Systems
│ ├── Object Storage
│ │ ├── S3
│ │ └── Blob Storage
│ ├── File Storage
│ └── Distributed Storage
│
├── 10. Reliability
│ ├── Fault Tolerance
│ ├── Failover
│ ├── Redundancy
│ ├── Health Checks
│ └── Circuit Breaker
│
├── 11. Observability
│ ├── Logging
│ ├── Monitoring
│ ├── Metrics
│ ├── Alerting
│ └── Distributed Tracing
│
├── 12. Security
│ ├── Authentication
│ │ ├── JWT
│ │ └── OAuth
│ ├── Authorization
│ ├── Encryption
│ ├── TLS
│ └── Secret Management
│
├── 13. Design Patterns (Industry)
│ ├── Microservices Patterns
│ │ ├── API Gateway Pattern
│ │ ├── Service Registry Pattern
│ │ ├── Sidecar Pattern
│ │ └── Saga Pattern
│ ├── Data Patterns
│ │ ├── CQRS
│ │ ├── Event Sourcing
│ │ └── Database per Service
│ ├── Communication Patterns
│ │ ├── Request-Response
│ │ ├── Publish-Subscribe
│ │ └── Event Streaming
│ └── Reliability Patterns
│ ├── Circuit Breaker
│ ├── Bulkhead
│ └── Retry Pattern
│
├── 14. Distributed Systems Concepts
│ ├── Consensus (Raft)
│ ├── Leader Election
│ ├── Distributed Locks
│ ├── Id Generation (Snowflake)
│ └── Time Synchronization
│
├── 15. Deployment Infrastructure
│ ├── Containers (Docker)
│ ├── Container Orchestration
│ │ └── Kubernetes
│ ├── CI/CD Pipelines
│ └── Infrastructure as Code
│
└── 16. Real-World Design Problems
├── URL Shortener
├── Social Media Feed
├── Chat System
├── Video Streaming Platform
└── Ride Sharing System