Avinash

[Jian Wang @jianw851]

Goldmine for AI Engineers! 📌

If you're learning AI, ML, LLMs, or AI agents, don't waste hours jumping between random tutorials.

These are 10 repositories I'd actually keep bookmarked - from Python fundamentals to ML, LLMs, agents, and production AI.

1. Python - 100 Days

jackfrued/Python-100-Days

A 100-day Python learning path covering fundamentals, data analysis, web development, and more.

GitHub:
https://t.co/ED5Fm7Ei3Q

2. Generative AI for Beginners

microsoft/generative-ai-for-beginners

A practical introduction to building Generative AI applications.

Covers:

• LLM fundamentals
• Prompt engineering
• RAG
• AI agents
• Fine-tuning
• AI application development

GitHub:
https://t.co/kdQ2b6jBYB

3. LLMs From Scratch

rasbt/LLMs-from-scratch

Want to understand what's actually happening inside an LLM?

Build one step by step.

Covers:

• Tokenization
• Embeddings
• Attention
• Transformers
• Training
• Fine-tuning

GitHub:
https://t.co/lzCKdk7kl1

4. Machine Learning for Beginners

microsoft/ML-For-Beginners

A structured 12-week, 26-lesson curriculum covering classical machine learning.

A good starting point if you want ML fundamentals before jumping into LLMs.

GitHub:
https://t.co/AlXqh5KO9b

5. OpenAI Cookbook

openai/openai-cookbook

A collection of practical examples and guides for building applications with OpenAI models.

Useful when you want to move from:

Learning → Building

GitHub:
https://t.co/FrBiKTbIS6

6. Stable Diffusion

CompVis/stable-diffusion

Interested in generative image models?

This repository contains the original Stable Diffusion implementation and research code.

GitHub:
https://t.co/25e7FVkHIf

7. AI Agents for Beginners

microsoft/ai-agents-for-beginners

A practical course for understanding and building AI agents.

Covers:

• Agentic AI
• RAG
• Agent frameworks
• Tool use
• Multi-agent systems

GitHub:
https://t.co/oAn2ptn2T5

8. AI for Beginners

microsoft/AI-For-Beginners

A structured 12-week, 24-lesson introduction to AI.

Covers:

• Neural networks
• Computer vision
• NLP
• Deep learning
• Classical AI

GitHub:
https://t.co/p4PdLnL89o

9. LLM App

pathwaycom/llm-app

Focused on building practical LLM applications.

Explore:

• RAG
• AI pipelines
• Enterprise search
• Real-time data
• Vector search

GitHub:
https://t.co/LYMOE4PgWj

10. Segment Anything

facebookresearch/segment-anything

A foundation model for promptable image segmentation.

Worth exploring if you're interested in computer vision and multimodal AI.

GitHub:
https://t.co/j0mPn2or2V

Don't bookmark all 10 and forget about them.

Pick based on where you are:

Python → Python-100-Days

ML → ML-For-Beginners

AI Fundamentals → AI-For-Beginners

LLMs → LLMs-from-scratch

Generative AI → Generative-AI-for-Beginners

Agents → AI-Agents-for-Beginners

Building → OpenAI Cookbook / LLM App

Computer Vision → Segment Anything

Pick one.

Build something.

Then move to the next.

[Avinash Singh @AvinashSingh_20]

10 GitHub repositories to learn the Agentic AI concepts you need before 2027 👇

1- Harness Engineering
https://t.co/GaBptbI9Li

2- Loop Engineering
https://t.co/vntHvqr5Z8

3- Context Engineering
https://t.co/WMSnpLWLCb

4- Tool Design & MCP
https://t.co/2BBQPGEIB9

5- Agent Memory
https://t.co/u1khzk1ShW

6- Agent Orchestration
https://t.co/UHrE2bTLmy

7- AI Guardrails
https://t.co/bPDcwGrxlK

8- Agent Evals
https://t.co/lZdNMrxYXQ

9- Human-in-the-Loop
https://t.co/A8oCRldLlx

10- Agent Observability
https://t.co/BmnkE0mxy9

These cover the production side of agentic AI:

→ Harness
→ Loops
→ Context
→ Tools
→ Memory
→ Orchestration
→ Guardrails
→ Evals
→ Human oversight
→ Observability

✅ Save this for your Agentic AI learning journey.

Join the tech community for more resources:
https://t.co/GXwVvESAPV

Follow for more developer resources, AI tools, interview prep, jobs & opportunities.

[Avinash @upadhya28]

Shame on you @SattvaGroup
https://t.co/l5u6PdfV4D
Never expexted this big goalmaal

[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

[Avinash @upadhya28]

#IndianFlag @narendramodi @CMofKarnataka
Flag seems to be not in good condition near MG Road bangalore
@CNNnews18 @publictvnews

[Ivan Velichko @iximiuz]

SSH Tunnels Cheat Sheet 🧐

- Local port forwarding: Access private endpoints, potentially through a bastion host, as if they were locally opened ports

- Remote port forwarding: Expose local ports or private network addresses publicly via an exit node

Both with good old SSH 👇

[Avinash @upadhya28]

@TheDataHubX @narendramodi @grok is it true

[Avinash @upadhya28]

@MichaelVerstrae @narendramodi Indian leadership has independent decision makers. Not like puppet leadership like in Ukraine.. it's because of poor leadership Ukraine is in this mess

[Swapna Kumar Panda @swapnakpanda]

Stanford's FREE Courses on AI & ML:

❯ CS221 - Artificial Intelligence
❯ CS229 - Machine Learning
❯ CS230 - Deep Learning
❯ CS234 - Reinforcement Learning
❯ CS229M - ML Theory
❯ CS224N - NLP with Deep Learning
❯ CS224U - NL Understanding

Course links inside:

[Ashish Pratap Singh @ashishps_1]

30 Blogs to Learn 30 System Design Fundamentals:

1. APIs: https://t.co/SFR7c1yi1Z

2. Webhooks: https://t.co/789CPy1rXE

3. REST vs GraphQL: https://t.co/p0aR5Xxnk3

4. API Gateway: https://t.co/kjlIJy9ElI

5. Load Balancers: https://t.co/O4WkWoVCuC

6. Load Balancing Algorithms: https://t.co/zrVmh7rURa

7. Proxy vs Reverse Proxy: https://t.co/AR8L9F2met

8. Scalability: https://t.co/Gbpjax24gd

9. Availability: https://t.co/GfakejdGQz

10. SPOF: https://t.co/5FtrH1Shrc

11. CAP Theorem: https://t.co/Aaq5s96WFU

12. Database Types: https://t.co/zhPaltn2bo

13. SQL vs NoSQL: https://t.co/vDTE5PqNel

14. ACID Transactions: https://t.co/eQBBIIM6w9

15. Database Indexes: https://t.co/CKk39deBkk

16. Sharding vs. Partitioning: https://t.co/RnMbJLzrpJ

17. Consistent Hashing: https://t.co/cg1mZ4iuQy

18. CDC: https://t.co/YCesZ6Y8AN

19. Caching: https://t.co/Jed9uIFFim

20. Caching Strategies: https://t.co/xhxmMgVQvW

21. Cache Eviction Policies: https://t.co/eGUV3zzG3A

22. CDN: https://t.co/DVnDPGUsFC

23. Rate Limiting Algorithms: https://t.co/mjnf3S9PUA

24. Message Queues: https://t.co/BTX85efXZu

25. Bloom Filters: https://t.co/vNV9yFrixf

26. Idempotency: https://t.co/lsMSkZrS38

27. Concurrency vs Parallelism: https://t.co/DWmMDNjkxR

28. Stateful vs. Stateless Architecture: https://t.co/w7OkmUQLR8

29. Long Polling vs WebSockets: https://t.co/MB628lZb2w

30. Batch vs Stream Processing: https://t.co/q3MCCs26wd

♻️ Repost to help others in your network.

[Avinash @upadhya28]

@bigbasket_com Number of items available has drastically reduced.. most of the items are not available now a days. Before @TataCompanies merger BB was very good. Fresh vegetables and organic items list was high. Bb needs backward upgrade to survive now

[Avinash @upadhya28]

@TheGopalan Please share the download link

[Hasan @Ubermenscchh]

Cancel your Netflix subscription if you haven't watched these 12 must-see movies. 🎬

A thread 🧵

[Tech Fusionist | Kushal Gangil @techyoutbe]

✅ Docker Sessions ✅ Kubernetes Sessions ✅ Terraform (Step by Step)

🔥 Docker Sessions 🔥

Day 6 : Containerization
(https://t.co/1pDZ12sfG6)

Day 7 : Docker
(https://t.co/12pBBB475j)

Day 8 : Docker Architecture
(https://t.co/rSZhMhLw2E)

Day 9 : Docker Hands-on
(https://t.co/2p7riddvsI)

🔥 Kubernetes Sessions 🔥

Day 10 : Container Orchestration
(https://t.co/TQvL5EFrzt)

Day 11: Container Orchestration Tools
(https://t.co/z2x4T3Rxoo)

Day 12: Kubernetes - Quick Start
https://t.co/z2x4T3Rxoo

Day 13: Kubernetes Concepts, Architecture and Comparison with Cloud
https://t.co/A9KLFDk3v7

🔥 Terraform Sessions 🔥

Day 14: Terraform (IaC) Overview - Concepts, components and providers
https://t.co/oi6bqbmKGy

Day 15: Terraform (IaC) Overview - Configuration Files (Simplified)
https://t.co/tK8rXLL8cG

[Avinash @upadhya28]

@sagar_lele Shame on people who give importance to the quantity of hours rather than quality.. I strongly believe person who spend more than expected hours in office regularly is incompetent in his role.

[Riti Kumari @riti2409]

Operating System resources that helped me in clearing interviews at top MNCs.

It includes videos, blogs, resources & notes for revision.

A thread 🧵

(1/n)

[Ryan Peterman @ryanlpeterman]

If you want to learn system design and Kubernetes, read these famous engineering blog posts from top companies (OpenAI, Airbnb, Netflix, Stripe, etc)

I spent hours digging for them so you don't have to:

[Igor De Souza @Igfasouza]

A curated list of Apache Kafka learning resources.

#apachekafka

https://t.co/KOgTFwEQi9

[Ashish Pratap Singh @ashishps_1]

10 of the Best Company Engineering Blogs I read this year:

1) How Discord stores trillions of messages: https://t.co/5YRnNKTMXh

2) Real time messaging at Slack: https://t.co/5i8oC5GlHV

3) How Canva scaled Media uploads from Zero to 50 Million per Day: https://t.co/44O5lxb76u

4) How Facebook uses AI to power it’s Marketplace: https://t.co/Fr9wJCIWj7

5) How Pinterest built a real-time user action counting system for ads: https://t.co/Ko4hzrvngI

6) How Airbnb avoids double payments: https://t.co/jMUACVjafB

7) How Uber optimizes the Timing of Push Notifications using ML: https://t.co/1IbWgMs2qP

8) Capturing a billion emojis at Hotstar: https://t.co/ODM25yYVwo

9) Stripe’s payments APIs - The first 10 years: https://t.co/ek5bcVDhbH

10) Building Faster Indexing at DoorDash: https://t.co/5wOk1j67Al

[Alex Xu @alexxubyte]

IBM MQ -> RabbitMQ -> Kafka ->Pulsar, How do message queue architectures evolve?

🔹 IBM MQ
IBM MQ was launched in 1993. It was originally called MQSeries and was renamed WebSphere MQ in 2002. It was renamed to IBM MQ in 2014. IBM MQ is a very successful product widely used in the financial sector. Its revenue still reached 1 billion dollars in 2020.

🔹 RabbitMQ
RabbitMQ architecture differs from IBM MQ and is more similar to Kafka concepts. The producer publishes a message to an exchange with a specified exchange type. It can be direct, topic, or fanout. The exchange then routes the message into the queues based on different message attributes and the exchange type. The consumers pick up the message accordingly.

🔹 Kafka
In early 2011, LinkedIn open sourced Kafka, which is a distributed event streaming platform. It was named after Franz Kafka. As the name suggested, Kafka is optimized for writing. It offers a high-throughput, low-latency platform for handling real-time data feeds. It provides a unified event log to enable event streaming and is widely used in internet companies.

Kafka defines producer, broker, topic, partition, and consumer. Its simplicity and fault tolerance allow it to replace previous products like AMQP-based message queues.

🔹 Pulsar
Pulsar, developed originally by Yahoo, is an all-in-one messaging and streaming platform. Compared with Kafka, Pulsar incorporates many useful features from other products and supports a wide range of capabilities. Also, Pulsar architecture is more cloud-native, providing better support for cluster scaling and partition migration, etc.

There are two layers in Pulsar architecture: the serving layer and the persistent layer. Pulsar natively supports tiered storage, where we can leverage cheaper object storage like AWS S3 to persist messages for a longer term.

Over to you: which message queues have you used?

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