amruth mv

[Royal Challengers Bengaluru @RCBTweets]

That hug represents RCB nation’s admiration for Bhuvi. 🥹

Good night, 12th Man Army. ❤️

[Tathvam-asi @tathvamasi6]

Work from holiday 🤣🤣🤣🤣

[Tech with Mak @techNmak]

The most mass-complete list of CS video courses on the internet.

cs-video-courses. 78K+ stars.

MIT. Stanford. Berkeley. Harvard. CMU. IIT. Princeton. Caltech.

All free. All video lectures. All in one repo.

Topics covered:

→ Data Structures and Algorithms
→ Operating Systems
→ Distributed Systems
→ Database Systems
→ Computer Networks
→ Machine Learning
→ Deep Learning
→ Natural Language Processing
→ Computer Vision
→ Computer Graphics
→ Security
→ Quantum Computing
→ Robotics
→ Blockchain

From beginner (CS 50) to advanced (6.824 Distributed Systems).

The curriculum is free. The commitment is yours.

GitHub link in comments.

[Tech with Mak @techNmak]

The dirty secret of fine-tuning LLMs:

Every time you teach it something new, it forgets something old.

It's called catastrophic forgetting. And it's why your fine-tuned model suddenly can't do basic tasks anymore.

MIT dropped a paper that fixes this.

Self-Distillation Fine-Tuning (SDFT).

The idea is almost too simple:

Use the same model as both teacher and student.

Teacher: Model + demonstration in context
Student: Model without the demonstration

Train the student to match the teacher. On its own outputs.

That's it.

The results:
→ Higher accuracy on new tasks
→ 99% retention of prior capabilities
→ Works for skills AND knowledge injection

SFT teaches the model to copy. SDFT teaches it to understand.

[Dan Kornas @DanKornas]

Building an effective machine learning model is only about 10% of the effort required, the remaining 90% involves developing and maintaining the supporting ML systems.

The Machine Learning Solutions Architect Handbook by David Ping is one of the best books that I have read so far and I highly recommend it to everyone.

This book will help you design, build, and secure scalable ML systems to solve real-world business problems with Python and AWS.

What you will learn:
- Apply ML methodologies to solve business problems across industries
- Design a practical enterprise ML platform architecture
- Gain an understanding of AI risk management frameworks and techniques
- Build an end-to-end data management architecture using AWS
- Train large-scale ML models and optimize model inference latency
- Create a business application using artificial intelligence services and custom models
- Dive into generative AI with use cases, architecture patterns, and RAG

[Math Cafe @Riazi_Cafe_en]

Harvard's "Linear Algebra with Probability" by Oliver Knill

PDF: https://t.co/Lsu1IdbAFn

[Mustafa. @oprydai]

books are still the best way to learn something.

they force linear thinking in a non-linear world.
no shortcuts. no feeds. no dopamine hacks. just ideas stacked on ideas.

a good book compresses decades of thinking into a few hundred pages.
you inherit the author’s mental models, failures, edge cases, and tradeoffs; not just conclusions.

videos show what. books explain why.
tools teach usage. books teach judgment.

if you want opinions, scroll.
if you want understanding, read.

[spidey @lochan_twt]

one of the best playlist for learning backend

[Santiago @svpino]

This book just arrived.

95% of machine learning solutions in the real world are for tabular data. Not agents, not fancy stuff.

I read the original Kaggle Book. It's one of the best resources I've always recommended people to read.

If you want to learn how to tackle the problems most companies face, this is a great book.

The new edition now covers Gen AI competitions as well!

By the way, the authors are 3 Kaggle Grandmasters. These are some of the smartest people you'll ever meet!

Here is a link to Amazon: https://t.co/FxOVpghRgG

[Swapna Kumar Panda @swapnakpanda]

15 Completely FREE System Design Courses for 2026:

1. Fundamentals
https://t.co/IDGW3De1EA

2. Load Balancing
https://t.co/KlhVjDQyy6

3. Caching
https://t.co/E73QXGlNDV

4. Sharding & Partitioning
https://t.co/ZsLV5rVcrz

5. Database Replication
https://t.co/dtEvSPXrB8

6. Consistent Hashing
https://t.co/Oy1BUf23xe

7. CAP Theorem
https://t.co/jFfObbFc39

8. Microservices
https://t.co/nYizg4BLrA

9. Message Queues
https://t.co/UiFwA4ZB56

10. Rate Limiting
https://t.co/s6vyKcgnZE

11. API Design
https://t.co/AUx3IzQkir

12. Fault Tolerance
https://t.co/HpLji5RTod

13. Scalability
https://t.co/uovZHvzSBf

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

15. Service Discovery
https://t.co/tdjRfah2re

[Aadhaar @UIDAI]

Participate in the National Data Hackathon to generate data-driven insights on Aadhaar.

The top 5 innovative submissions will receive cash awards and certificates:
1st prize: Rs. 2,00,000/-
2nd prize: Rs. 1,50,000/-
3rd prize: Rs. 75,000/-
4th prize: Rs. 50,000/-
5th prize: Rs. 25,000/-

Registration opens on 5th Jan 2026.
For more details, visit: https://t.co/LNZ8CkOIsg

[Shraddha Bharuka @BharukaShraddha]

Stop watching tutorials.
Start building projects that actually work.

This GitHub repo contains 28 production-ready AI projects — not toy demos, but real-world applications you can run, modify, and learn from.

What’s inside 👇

Machine Learning
• Airbnb price prediction
• Flight fare calculator
• Student performance tracker

AI in Healthcare
• Chest disease detection
• Heart disease prediction
• Diabetes risk analyzer

Generative AI Apps
• Live Gemini chatbot
• Working medical assistant
• Document analysis tool

Computer Vision
• Hand tracking system
• Medicine recognition app
• OpenCV implementations

Data Analytics Dashboards
• E-commerce insights
• Restaurant analytics
• Cricket performance tracker

Coming next (advanced builds 🚀)

• Deepfake detection
• Brain tumor classification
• Driver drowsiness alerts

These aren’t just code files —
they’re fully working applications you can showcase in portfolios.

🔗 Check it out: https://t.co/c9o3Uy7pK8

💾 Save for later
♻️ Repost for builders
👋 More AI resources on my profile

[Tech with Mak @techNmak]

The ultimate guide to understanding and engineering ML systems - no fluff, all depth.

Happy to share the PDF if you’d like to read it.

[Mohit Mishra @chessMan786]

Building A CPU From Scratch

[Hasan Toor @hasantoxr]

Best YouTube Channels to learn AI in 2026:

1. Cole Medin
https://t.co/Fudp9wYUCP

2. Matthew Berman
https://t.co/KUqkIWa3B9

3. Tech With Tim
https://t.co/toAoMfdKB1

4. Two Minute Papers
https://t.co/qPkZE6LuEt

5. Skill Leap AI
https://t.co/RhJVKrgiXC

6. AI Explained
https://t.co/bWjMnPO80p

7. DeepLearningAI
https://t.co/bdwkpuud9O

8. Futurepedia
https://t.co/mEFNilA7t0

9. Tina Huang
https://t.co/ycT8PL0rLl

10. Andrej Karpathy
https://t.co/sDa5sFMpOM

[vixhaℓ @TheVixhal]

If you’re in AI/ML, software engineering, management, quant finance, or marketing/growth, learn mathematical physics. You’ll never regret it, you’ll thank me in the future.

Mathematical Physics:
- Vector Algebra
- Vector Calculus
- Matrices & Linear Algebra
- Differential Equations
- Fourier Analysis
- Probability & Statistics
- Integral Transforms
- Tensor Analysis