vinith Alampally

Tech Buff | Cricket | Computer Science

[Matt Dancho (Business Science) @mdancho84]

Understanding probability is essential in data science.

In 4 minutes, I'll demolish your confusion.

Let's go!

[Deedy @deedydas]

If you feel like giving up, you must read this never-before-shared story of the creator of PyTorch and ex-VP at Meta, Soumith Chintala.

> from hyderabad public school, but bad at math
> goes to a "tier 2" college in India, VIT in Vellore
> rejected from all 12 universities for US masters despite 1420 on the GRE
> fuckit.jpg
> goes to the US anyway on a J-1 visa to CMU with no plan
> applies for masters (again) to 15 universities
> rejected from all except USC and with late admissions, NYU in 2010
> finds this guy called Yann LeCun (before he was famous)
> starts getting into open source
> rejected from all jobs including DeepMind
> only job is Amazon as test engineer
> his PhD mentor helps him get a job at a small startup (MuseAmi)
> rejected from DeepMind
> couldn't get H-1B because of J-1 home return issue; gets waiver through months of approval with USCIS and US State Dept
> very low on confidence
> In 2011/12 builds one of the fastest AI inference engines on phones
> rejected from DeepMind
> emailed Yann again and joins FAIR because of Torch7 open-source work
> scrapes through bootcamp at Facebook, struggling on an HBase task
> L8/L9 engineers at Facebook struggle to get ImageNet working
> figures out numerics / hyperparam issue as an L4
> first big win!
> FAIR goes well, runs 3 person torch7 team and co-creates PyTorch
> because of politics, management wants to shut down PyTorch
> cries-at-bar.jpg, literally
> eventually some people save PyTorch and it launches in 2017
> gets a EB-1 green card!
> the rest is history...

Think about that. He went to a tier 2 college. Was rejected from all Masters programs 2x. Rejected from every single job except Amazon test engineering. Rejected from DeepMind 3x. Nearly had his baby project shut down. Struggled with visa issues. After 12 years of failures (2005-17), he eventually rose to became a VP at Meta one of the most influential people in AI!

Soumith's story is one of resilience and he's living proof that no matter how down in the dumps you are, there's always hope.

[🥱 Sleepy (ML/DL) @KrishnaNaraKun]

MIT 6.006 Introduction to Algorithms, Spring 2020

Still the best playlist on YouTube, teaching the algorithms. Taught by Prof. Erik Demaine, Dr. Jason Ku, Prof. Justin Solomon.

Link in comments 😎👇

[ℏεsam @Hesamation]

you cannot find a better course than this by DeepMind and UCL to get cracked at reinforcement learning.

just 20 hours.

[Skilled Coder @theskilledcoder]

How a File is Read from Disk

[Skai. @superSaiyanSkai]

On the days I feel lost, I return to this, and it always reignites me.

[Mat Velloso @matvelloso]

We actually had to slow down the demo today because otherwise people wouldn't see it working 😂

[🥱 Sleepy (ML/DL) @KrishnaNaraKun]

"Intro to Deep Learning and Generative Models "

- 44 hours playlist, taught by legendary @rasbt .
- Almost all basic to advanced level topics explained by Sebastian Raschka.

[🥱 Sleepy (ML/DL) @KrishnaNaraKun]

Deep Learning

CS 182 , UC Berkeley 2021 , total 66 vedios teaching all details from very basics.

(Link in comments)

[🥱 Sleepy (ML/DL) @KrishnaNaraKun]

Introduction to Machine Learning -by Sudeshna Sarkar

One of the oldest and best machine Learning courses, offered by NPTEL. The entire playlist mainly concentrates on fundamentals and mathematical intuition of machine Learning

[ℏεsam @Hesamation]

this is actually so gold! AI Engineering roadmap from 0 to 100.

save this and look at it every morning:

𝗖𝗼𝗱𝗶𝗻𝗴 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀:
- Python.
- Typescript (Optional).
- Bash.

𝗟𝗟𝗠 𝗔𝗣𝗜𝘀:
- Types of LLMs.
- Structured Outputs.
- Prompt Caching.
- Multi-modal models.

𝗟𝗟𝗠 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻:
- Prompt Engineering.
- Tool Use.
- Finetuning.

𝗦𝘁𝗼𝗿𝗮𝗴𝗲 𝗳𝗼𝗿 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹:
- Vector Databases.
- Graph Databases.
- Hybrid retrieval.

𝗥𝗔𝗚 𝗮𝗻𝗱 ��𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚:
- Data preparation.
- Data retrieval and generation.
- Reranking.
- MCP.
- LLM Orchestration Frameworks.

𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀:
- AI Agent Design Patterns.
- Multi-Agent systems.
- Memory.
- Human in or on the loop.
- A2A, ACP etc.
- Agent Orchestration Frameworks.

𝗜𝗻��𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲:
- Kubernetes.
- Cloud Services.
- CI/CD.
- Model Routing.
- LLM deployment.

𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻:
- AI Agent instrumentation.
- Observability platforms.
- Evaluation techniques.
- AI Agent Evaluation.

𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆:
- Guardrails.
- Testing LLM based applications.
- Ethical considerations.

𝗙𝗼𝗿𝘄𝗮𝗿𝗱 𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝗲𝗹𝗲𝗺𝗲𝗻𝘁𝘀:
- Voice and Vision Agents.
- Robotics Agents.
- Computer use.
- CLI Agents.
- Automated Prompt Engineering.

credit to @Aurimas_Gr for wrapping it all up under one roadmap 🍻

[Charly Wargnier @DataChaz]

One for vibe-coders to bookmark!

@ycombinator's notes on how to get the most out of vibe coding.

[Deedy @deedydas]

1. Take a screen recording explaining your app
2. Upload it to YouTube
3. "Build me this”

Gemini 2.5’s ability to comprehend video feels straight out of a science fiction novel.

[Charafeddine Mouzouni @theCharafeddine]

To everyone 25–35:
You’ve hit the age where people start giving up on themselves—thinking it’s too late.

Don’t let that energy touch you.
It’s not too late.

A thread:

[Skilled Coder @theskilledcoder]

System Design: Video Streaming Service

[Paweł Huryn @PawelHuryn]

I see abstract AI agent architectures everywhere.

But no one explains how to build them in practice.

Here's a practical guide to doing it with n8n: 🧵

[Sai Nemani @SaiNemani1]

Google LearnAbout by @GoogleLabs is actually AMAZING! This is great!

[SumitM @SumitM_X]

As a senior Java backend developer,

It will be good if you have an understanding of the below 40 topics👇

1. CAP Theorem
2. Consistency Models
3. Distributed System Architectures
4. Socket Programming (TCP/IP and UDP)
5. HTTP and RESTful APIs
6. Remote Procedure Call (RPC) - gRPC, Thrift, RMI
7. Message Queues (Kafka, RabbitMQ, JMS)
8. Java Concurrency (ExecutorService, Future, ForkJoinPool)
9. Thread Safety and Synchronization
10. Java Memory Model
11. Distributed Databases (Cassandra, MongoDB, HBase)
12. Data Sharding and Partitioning
13. Caching Mechanisms (Redis, Memcached, Ehcache)
14. Zookeeper for Distributed Coordination
15. Consensus Algorithms (Paxos, Raft)
16. Distributed Locks (Zookeeper, Redis)
17. Spring Boot and Spring Cloud for Microservices
18. Service Discovery (Consul, Eureka, Kubernetes)
19. API Gateways (Zuul, NGINX, Spring Cloud Gateway)
20. Inter-service Communication (REST, gRPC, Kafka)
21. Circuit Breakers and Retry Patterns (Hystrix, Resilience4j)
22. Load Balancing (NGINX, Kubernetes, Ribbon)
23. Failover Mechanisms
24. Distributed Transactions (2PC, Saga Pattern)
25. Logging and Distributed Tracing (ELK Stack, Jaeger, Zipkin)
26. Monitoring and Metrics (Prometheus, Grafana, Micrometer)
27. Alerting Systems
28. Authentication and Authorization (OAuth, JWT)
29. Encryption (SSL/TLS)
30. Rate Limiting and Throttling
31. Apache Kafka for Distributed Streaming
32. Apache Zookeeper for Coordination
33. In-memory Data Grids (Hazelcast, Infinispan)
34. Akka for Actor-based Concurrency
35. Event-Driven Architecture: Event sourcing and CQRS (Command Query Responsibility Segregation).
36. Cluster Management: Kubernetes for container orchestration.
37. Cloud-Native Development: Using cloud platforms (AWS, GCP, Azure), and serverless computing (e.g., AWS Lambda).
38. Distributed Data Processing: Frameworks like Apache Spark or Apache Flink for large-scale data processing.
39. GraphQL: Alternative to REST for inter-service communication.
40. JVM Tuning for Distributed Systems: Memory management and performance tuning in distributed environments.

[Psyche Wizard @PsycheWizard]

Why the most consistent people always win...

(The discipline loop) 🧵

[Jay Yang @Jayyanginspires]

Sam Altman on how to be successful: